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645def34fb40f6b3ee74071c | 39 | Local concentrations and fluxes in the BPM from the simulations elucidated that alkali cations and (bi)carbonates are the dominant ion and primary charge carrier in the BPM at low current densities, with water-dissociation generated H + and OH -becoming the dominant ions at high current densities once buffering (bi)carbonate anions are consumed by homogeneous reaction. Furthermore, the energy intensity for CO2 desorption as a function of applied current density is also determined, for which the model reveals that the low concentration of (bi)carbonates in simulated seawater leads to mass-transport limitations and consequently high energy intensities for CO2 recovery at high current densities for BPMs immersed in seawater. |
645def34fb40f6b3ee74071c | 40 | Conversely, BPMs in higher concentrations of (bi)carbonates do not become mass-transport limited for CO2 recovery up to 100 mA cm -2 , and can achieve energy intensities competitive with thermal desorption of approximately 100 kJ mol -1 at current densities far exceeding those demonstrated for other EMCC processes. Lastly, analysis of the losses in the system revealed that the greatest opportunities for optimization of these systems are through the management of CO2 bubbles and the improvement of the water-dissociation catalyst. Simulating a BPM with bubblemitigation strategies and a substantially enhanced water dissociation catalyst (consistent with the state-of-the-art BPM in literature) shows that these improvements enable performance for BPM-ED EMCC at 100 mA cm -2 at energy intensities well below 100 kJ mol CO2 -1 . Ultimately, this work provides substantial insight into the mechanistic behavior of reactive carbon species in BPM systems, relevant to many electrochemical systems across the gamut of carbon-capture andconversion technologies, and elucidates the promise of BPMs in performing BPM-ED for carbon capture at current densities exceeding 100 mA cm -2 . |
666cace75101a2ffa89d61c6 | 0 | Tungsten is a prime candidate for plasma-facing components (PFCs) in fusion reactors. Being a refractory metal, W exhibits exceptional high-temperature properties, making it an attractive material for components exposed to the extreme conditions of fusion plasma . Of all metallic elements, W has the highest melting point (3422 ℃), and at elevated temperatures, it exhibits the highest tensile strength among metals . Additionally, W shows exceptional corrosion resistance and is highly resistant to attacks by mineral acids. An important advantage of W is its high threshold energy of sputtering, around 150-200 eV for Deuterium (D) , which helps minimize wall erosion. However, erosion could also occur due to unavoidable impurities, which reduce the threshold value of the D sputtering energy . For example, the threshold energy for the sputtering of W-oxide by D is as low as 65 eV due to the low binding energy of the W-O molecules. Tungsten erosion poses a significant challenge for fusion reactors, as it can impact the lifespan of plasma-facing materials and the overall performance of the reactor. Additionally, the theoretically the effect of chemical sputtering of O on W erosion at temperatures of 1500-2000 K at different O pressures . The introduction of an adequate amount of O to the tungsten surfaces results in the chemical sputtering of WOx molecules, thereby reducing the threshold energy for sputtering with D. W sputtering by D was observed in the presence of 10 -2 Pa of O2 gas at energies below the D-W sputtering threshold . W oxidation has been observed at temperatures below 1500 K, even at room temperature . |
666cace75101a2ffa89d61c6 | 1 | W-based PFCs in ITER and other fusion reactors are actively cooled to room temperature, motivating lab-based experiments performed at ambient temperatures. Because existing studies, to the best of our knowledge, have primarily concentrated on W oxidation at high temperatures, the understanding of the oxidation of tungsten at room temperatures, especially when impinged by nonthermal oxygen atoms, is limited. In this work, we study the creation of surface W oxides at room temperature when irradiated by energetic O atoms (1-30 eV), as well as their properties including the effects on chemical sputtering of W. |
666cace75101a2ffa89d61c6 | 2 | In Section 2, we report on the development and properties of a new Reactive Force Field (ReaxFF) parameter . It should be noted that prior to our study, a classical W-H-O interaction potential, which would utilize their mutual polarization features, had not been generated. Our work introduces a novel ReaxFF parameters to address this gap in the literature. |
666cace75101a2ffa89d61c6 | 3 | Our classical molecular dynamics (CMD) methodology, including preparation of the tungsten surface target, is described in Section 3. In Section 4, we present and analyze the CMD results of the surface evolution of W-O due to cumulative O impacts at various energies. The conclusions of our study are given in Section 5. |
666cace75101a2ffa89d61c6 | 4 | The Reax Force Field, ReaxFF, is a bond order-based computational approach used in molecular dynamics simulations to model the potential energy space of atomic models. In general, standard molecular mechanics force fields are insufficient to describe chemical reactions, because of their inability to simulate bond formation and breaking. However, in ReaxFF, the energy contributions from bonded interactions, such as bonds, angles, torsions, etc., are calculated as functions of the bond order, which is dynamically calculated based on the interatomic distance. ReaxFF parameters also include non-bonded interactions, such as van der Waals forces and Coulombic (electrostatic) interactions. A detailed discussion on the transferability and training of ReaxFF parameters can be found elsewhere . As is the case with empirical force fields, ReaxFF is trained to reproduce physical and chemical space as either observed in experiments or calculated by reliable quantum mechanics (QM) -based methods. The force field training objective is to minimize the error function defined in Eq. 1, |
666cace75101a2ffa89d61c6 | 5 | Here Xi,QM is the reference data (experiment or QM) and Xi,ReaxFF is the corresponding value of ReaxFF and σ is the accuracy parameter (inverse of training weight) for the i-th data. In the singleparameter search optimization scheme, one force field parameter at a time is varied by a small amount to minimize the overall error (Eq. 1) in the training data. This method is further described by van Duin et al.. , and is implemented in the standalone ReaxFF code, which can be made available on request . |
666cace75101a2ffa89d61c6 | 6 | The reference data for force field training (Xi,QM) are collected from the following three sources, a) previously published QM results , b) experimental data , and c) Density Functional Theory (DFT) simulations performed in this work. We performed periodic and molecular DFT simulations to calculate energies, atomic charges, and geometric parameters, including Equation of States (EOS), Heats of Formation (∆Hf), and pair potentials. Molecular DFT (pair potentials, bond scans, and angle scans) simulations were performed using the Amsterdam Density Function (ADF) code within the Amsterdam Modelling Suite (AMS) . We performed spinpolarized calculations with generalized gradient approximation (GGA) based Perdew-Burke-Ernzerhof (PBE) exchange correlation functionals in combination with the TZP base set (triple Z with 2 polarization function) . |
666cace75101a2ffa89d61c6 | 7 | To simulate periodic structures, we performed DFT simulations using the Vienna ab initio software package (VASP) . We used spin-polarized calculations with the generalized gradient approach based on the Perdew-Burke-Ernzerhof (PBE) exchange-correlation pseudopotential with a plane wave cutoff of 520 eV. Grimme's DFT-D3 method with a zero-damping function was used to add a correction term for the dispersion energy. Partial occupancies for each orbital were set using the tetrahedron method with Blöchl corrections of smearing width 0.05 eV. A Gamma-centered 10×10×1 K-point mesh was used for all simulations. |
666cace75101a2ffa89d61c6 | 8 | Table presents a comparison of ReaxFF calculations for tungsten BCC lattice and W-dimer against experiments, first-principles calculations, and two of the widely used bond order-based potentials in the field of fusion materials (Li et al. and Juslin et al.. The valence force field parameters for tungsten (W-W-W) were trained. We followed the training methodology presented by Shin et al. to obtain a good agreement between ReaxFF and DFT for the strain -energy relationship (supporting material (SM), Fig. ). EC = cohesive energy, a = lattice constant (Å), B = bulk modulus (GPa), Cij = stiffness coefficient (GPa), Evf = Vacancy formation energy (eV), Evm = Vacancy migration energy barrier, ESIA = Selfinterstitial formation energy (eV), d-ijk = dumbbell along the ijk direction, t = tetrahedral, o = octahedral. r0 = length of the equilibrium bond in nm. All energies are in eV. |
666cace75101a2ffa89d61c6 | 9 | Fig. shows cases of the comparison between ReaxFF and DFT for structures with W and H atoms. Fig. shows the atomic charges calculated by the EEM method in ReaxFF and Close-range interactions are crucial for simulating high-energy impact. However, DFT often yields erroneous results for small distances . Therefore, the close-range encounters for W-W and W-H atom pairs are trained on the ZBL potential , as shown in Fig. The trained energies encompass adsorption of H on a BCC (100) W surface (top, bridge, and hollow sites), interstitial H at the tetrahedral and octahedral sites, the energy difference between surface adsorbed H and subsurface H atom at the tetrahedral site, and binding of H to a tungsten vacancy site in the lattice. Å) for the reference curve is calculated using the ZBL potential, while PBE-DFT is used for distances > 0.6 Å. The two most stable WO3 lattices are orthorhombic and tetragonal, and Figs. 2d and 2-e show their equations of state (energy vs. volume relationship), respectively. The heats of formation (per atom) for the WO2, WO3 and W18O49 lattices are given in Fig. . Upon irradiation of W with O atoms, the formation of a WOx surface is likely, and the surface energy of the WO3 lattice is thus an important chemical space that we cover by training against the surface energies of the WO3 tetragonal and orthorhombic lattices along with the stable surfaces of W-BCC (Fig. ). In the event of sputtering, stable molecules of WOx are expected to form, and we train the formation energies of stable oligomers of tungsten oxides with respect to the WO3 molecule (Fig. ). |
666cace75101a2ffa89d61c6 | 10 | We conducted classical molecular dynamics simulations using the newly developed ReaxFF potential , described in Section 2, adapted for a mixture of W-H-O materials. Simulations were implemented using the LAMMPS MD simulator running on high performance computing (HPC) infrastructure at UC San Diego and Pennsylvania State University . We started the modelling by creating a 28×28×84 Å 3 mono-crystalline BCC tungsten structure with a lattice constant of 3.1652 Å, containing 4374 atoms. The surface plane of the lattice was [001]. |
666cace75101a2ffa89d61c6 | 11 | The bulk crystal was energy-minimized and slowly thermalized to 300 K under 3D periodic boundary conditions (PBCs), using a Langevin thermostat. Subsequently, the PBCs were removed from the z-direction to create a 2D periodic slab. The system was rethermalized at 300 K and then equilibrated for 0.5 ps to allow slab relaxation and stabilization. Three identical W slabs were irradiated sequentially and cumulatively by 4500 incident oxygen atoms each, with incident energies 1, 10, or 30 eV, respectively, randomly choosing a point of impact. The impact energies were selected to explore the effects of oxide creation with low-and high-energy oxygen coming from the plasma. The energies of the oxygen atoms were below the physical sputtering threshold of ~ 40 eV for W by O . The O atoms were placed in the negative z direction, perpendicular to the top surface of the W slabs every 4 ps for incident energies of 10 and 30 eV and every 3 ps for 1 eV, as shown in the initial setup of the system, Figs. ) and (3-a'). A buffer zone of 5 Å was maintained in the x and y directions of the top surface to avoid overlap of periodic border interactions, resulting in an active deposition area of 23×23= 529 Å 2 . |
666cace75101a2ffa89d61c6 | 12 | The total number of time steps for each O impact was 3000 for 1eV energy and 4000 for 10 and 30 eV impact energies. Each O impact caused a collisional cascade on the picosecond time scale, and the surface processes were allowed to evolve without forced exchange of energy with the environment. After incident O atom thermalization due to the collisional cascade, the entire system underwent a cooling to 300 K by applying the Langevin thermostat for 0.5 ps, which dissipated any excess heat resulting from an impact and emulated the natural or forced cooling process occurring over much longer timescales. The cooling step was imperative due to the disparity in time scales between molecular dynamics simulations (ranging from femtoseconds to nanoseconds) and real experiments, where the irradiation flux is usually many orders of magnitude smaller, allowing cooling to a desired average temperature of the slab. The simulation process produced the oxygen irradiation fluence of 8.5×10 20 atoms/m 2 after 4500 atom impacts. The deposition flux was 2.83×10 32 atoms/m 2 sec for the 1 eV O impact energy case and 2.1×10 32 atoms/m 2 sec for the 10 and 30 eV incident energy cases. |
666cace75101a2ffa89d61c6 | 13 | We conducted an additional series of simulations to explore the instantaneous surface processes while irradiating tungsten oxide with oxygen. The objective was to examine the sputtering of W and O and reflection dynamics upon single O impact on preoxidated W slabs during the irradiation process. To achieve this, we employed two types of slabs derived from previous accumulation simulations: one with 200 O deposits for all incident energies (low oxygen irradiation) and the other with 500, 900, and 1200 O deposits resulting from cumulative O impacts of 1, 10 and 30 eV on W slabs, respectively (high oxygen saturation). We designate the three low oxygen irradiation scenarios as "A-deps" and the three high oxygen saturation scenarios as "B-deps". The A-deps (Fig. ) and B-deps (Fig. ) slabs. For the A-deps slabs, it is evident that most of the impacting O atoms form an ad layer over the W surface, penetrating less than 2 below the surface. However, at the end of the accumulation (Fig. ), the penetrated O atoms are distributed up to 5, 10, and 13 Å the W for impact energies 1,10 and 30 eV, respectively. Fig. shows the increase in the thickness of the ad layer during irradiation by O, with increasing the number of deposited O atoms. |
666cace75101a2ffa89d61c6 | 14 | For a more comprehensive analysis, we divided the W slab into bins in the negative z direction, each containing roughly 200 oxygen deposits (equivalent to approximately 3 to 3.5 bin thickness). To understand the dynamics of O accumulation, we studied the nO/nW ratio along the thickness of the slab + ad layer, shown in Figure . Bins of 200 atoms, as previously illustrated in Figure S-2, were utilized. We found that the nO/nW ratios for the 1, 10 and 30 eV cases were larger than 3 in the higher top of the ad layers, indicating a high buildup of O on the surface of W at the beginning of the accumulation process. However, in the B-A-B region, the ratio was 1.7:1. We also observed an increase in the nO/nW ratio throughout the W and ad-layers as a function of the z coordinate. This ratio reaches 3 at the upper part of the ad layers, typical for WO3. The B-A-B regions exhibit lower nO/nW ratios. The higher bins (Bin 5 for 30eV, Bin 4 for 10eV, and Bin 3 for 1eV in Fig. ) exhibit the highest nO/nW ratios compared to the lower bins. Within the ad layer, as we get closer to the original surface, this ratio decreases, but for all ad layer bins, the ratio is always ≥ 3:1. Detailed results are illustrated in Table -1 of SM and Fig. . Additionally, the nO/nW ratio on the B-A-B layer decreases to 2:1 in the bins close to the original top surface of W and decreases further as we go deeper into the bulk of W. Obtaining nO/nW ratios of ~2 near the bulk W and ~3 in the ad layers aligns well with the existing literature . Oxygen atoms move downward while W atoms migrate to higher ad layers until the ratio of the top layers turns to be ~3:1. The process of O deposition on W involves a series of sequential substeps, as reported in the literature . Initially, O impacts at 1 eV lack the penetrative power to overcome the surface barrier of W. However, the adsorbed oxygens bond to the top layer of W and amorphize it, changing the surface potential barrier, and thus opening the doors for incoming oxygen atoms to penetrate below the original W surface, as seen in Fig. . It is noteworthy that we applied the NVE ensemble constraints to all three O-saturated W systems, 100 picoseconds, to observe the stability of the nO/nW ratios. These ratios remained stable over time. WO3 is recognized as the most stable form of tungsten oxide in the literature , |
666cace75101a2ffa89d61c6 | 15 | One of the factors that causes upward diffusion of W from the bulk to the ad layers is the polarization of the atoms arising from the difference in electronegativities of W (2. We can observe a dipolar characteristic between the ad layer and the W slab. Also, the binaveraged charges of W atoms on the ad layers vary between -0.2 and 1.5, and these keep decreasing as we approach the bulk, falling to almost zero. On the other hand, the oxygen charges in the adlayers hover around -0. Therefore, as mentioned before, we concluded that the nO/nW ratio for the adlayer of the three studied W slabs is about 3:1, as illustrated in Fig. . Furthermore, we also observed that the average charge ratio |qW/qO| for the adlayers in the three W slabs is also |
666cace75101a2ffa89d61c6 | 16 | For all three impact energies, 1, 10 and 30 eV, chemical sputtering of W in the form of sputtered WOx molecules was observed. It is interesting to point out that in all cases, no individual sputtered W atoms were observed; W was always chemically sputtered in the form of W-O compounds, having lower bounding energy to the system than W. In particular, no W was subjected to physical sputtering, consistent with the findings of the literature that the threshold for physical W sputtering of O is 40 eV . Single sputtered O atoms and O2 molecules were observed. During the irradiation of the W surface by 4500 O atoms of 30 eV, 326 W atoms were sputtered. For the 10 eV O impact case, a total of 72 W atoms were sputtered. In the 1 eV O shots case, 18 W atoms were sputtered. W sputtering for the 10 eV cases commenced after 1000 O impacts (i.e. after deposition of approximately 800 O atoms). After reaching saturation, 3 tungsten atoms are sputtered on average for every 100 O impacts, indicating a tungsten sputtering yield of 0.03 from tungsten oxide ad-layers. On the other hand, after impacts of 1800 O atoms with 30 eV the sputtering of W started after 7.2 ns, with about 15 W atoms sputtered per every 100 O impacts, i.e., the tungsten sputtering yield increases to 0.15. The predominant chemically sputtered species for all three W slabs is WO4, single O atoms, and O2 molecules. |
666cace75101a2ffa89d61c6 | 17 | Chemically sputtered WO2 and WO3 are also observed, but with a smaller frequency than WO4. When these particles were obtained in the vacuum environment, several molecular decays were observed. Initially, ejected WO4 caught a single sputtered O atom and was converted into WO5, which then decomposed into WO3 and two single oxygen atoms. Subsequently, WO3 underwent decomposition into WO2 and a single oxygen atom. Throughout the simulation, single oxygen atoms and WO4 continued to form various WOx compounds , such as WO2, WO3, WO4, and WO5, as well as individual O atoms and O2 molecules. We observed a notable increase of the sputtered WOx molecules after reaching saturation of the oxide ad-layers, determined by an impact O energy. The number of WOx particles sputtered at 30 eV significantly exceeded that at 1eV impact. |
666cace75101a2ffa89d61c6 | 18 | We probed the A-deps and B-deps samples created by impacts of 1, 10, and 30 eV by noncumulative O impacts after creating oxidized ad layers by cumulative O irradiation at the respective impact energies. These energies corresponded to the initial O impacts that formed the W-O samples that we utilized. As shown before, we obtained sputtering of W in the form of WOx molecules in the cumulative simulations of O upon saturation. To reduce statistical errors, we applied one thousand individual (non-cumulative) impacts at various points of ad layer surfaces. |
666cace75101a2ffa89d61c6 | 19 | The main goal in this computation was to obtain instantaneous values of the surface parameters, for example, to differentiate between O reflection and sputtering processes, which were not distinguished in the cumulative simulations. We observed only sputtering yields of O atoms. The process of formation and uptake of WOx that carries chemical sputtering of tungsten is too slow to be detected on a scale of single impact cascade of a few picoseconds. On the other hand, the applied impact energies of O are below the threshold for physical sputtering of tungsten. Thus, the sputtering of tungsten was not seen in the noncumulative irradiation of oxidized tungsten layers. |
666cace75101a2ffa89d61c6 | 20 | Table presents the data on reflection, retention, and sputtering for both A-deps and B-deps scenarios following the impact of O atoms. Additionally, it shows the maximal depths reached by 90% of reflected O atoms and the depths from which 90% of sputtered atoms were ejected. No sputtering of O was observed for the 1 eV impact energy. Moreover, the O retention probability (which complements reflection probability to 1) was notably high for 10 and 30 eV impacts. In contrast, for the 1 eV scenario, the retention probability was approximately 50% for the low-O oxide layer, but only 12% for O in a saturated oxide layer. Details of the sputtering and reflection depths for impacted oxygens are illustrated in SI, Figs. S-5 and S-6. For the 10 eV impacts, 90% of the sputtered O atoms were ejected from less than 1Å below the top of the ad layer, that is, from more than 4 Å above the original W surface of the A deps. This implies that the O atoms sputtered from that surface were predominantly ejected from the ad layer. A similar conclusion can be drawn for sputtering of O in the case of B-deps of the 10 eV impact, where O atoms are sputtered from less than about 6 Å below the top ad layer's surface, well above the B-A-B region. Conversely, for the A-deps scenario, for 30 eV with 200 O deposits, most of the sputtered O was ejected from less |
6166466af718df11a8dcc2f8 | 0 | Within this context, utilising biowaste as a feedstock is particularly promising as: (i) the carbon loop can be closed through the biomass cycle, 3 (ii) biowaste is often composed of complex molecules containing oxygen and nitrogen-based functional groups, 4,5 and (iii) a reduction of the 1.3 billion tons of biowaste per year, which are globally estimated at present, is urgently needed. The utilisation of biowaste for chemical production takes place either in the single stream specialised facilities, or in biorefineries. Biorefineries can potentially handle crude biowaste streams ranging from food waste to lignocellulosic waste that come from a variety of sectors, such as agricultural, industrial, forestry, and municipal. |
6166466af718df11a8dcc2f8 | 1 | The raw streams are complex and diverse in composition, but usually are a mixture of one or more of the following feedstocks 5 : (i) biopolymers such as cellulose, hemicellulose, starch, chitin etc., (ii) mono or disaccharides such as glucose, sucrose, fructose, xylose, (iii) proteins, and (iv) extracts or secondary metabolites, such as triglycerides, terpenes, phenolics, tannins, carotenoids, sterols and flavonoids. Pretreatment technologies are required to fractionate the raw biowaste streams into these feedstocks, and further transformations can convert these feedstocks into value added platform chemicals for integration in chemical supply chains (details of this classification and specifics of the case study are given in Supporting Information, Figure ). Thus, there exists a complex network of options, when varied composition raw materials could be pretreated using different processing technologies and these would yield a variation of feedstocks streams. In the end, this mix of processing steps and technologies must remain economically and environmentally competitive. To enumerate the economic and the environmental performance and to identify most suitable target chemicals, a systematic evaluation of reaction pathway options is needed at an early stage of new process development. The evaluation is commonly based on a reaction network, 12 which may represent quite a significant part of chemical space. Different small-scale case studies have been conducted in literature, relying on networks originating from a feedstock or a platform chemical. For instance, the pathway evaluation for renewables by Bao et al. starts with cellulose (feedstock), Voll and Marquardt focus on itaconic acid (platform chemical) and Jacob et al. on limonene (feedstock). 14 However, these approaches do not yet account for the isolation of feedstocks from the biowaste streams in the first place, although for complete life cycle assessment of new technologies this is essential. Therefore, there is a need to expand reaction networks to include data on: (i) crude biowaste streams from which feedstocks can be obtained, and (ii) pretreatment methods to achieve this conversion. |
6166466af718df11a8dcc2f8 | 2 | In the context of gathering crude biowaste stream data, regional dependencies of biowaste sources hinders a one-size-fits-all solution, and requires in-depth local studies of availabilities and chemical compositions that reflect obtainable feedstocks. 6,8 Geographical Information Systems have been utilised to locate agricultural biowaste resources, e.g. locations of palm oil or sugarcane mills as well as plantations, and domestic waste has been studied, for instance by sampling selected households. 17 Alternatively, data provided by governmental agencies and literature can be used to gain a rough overview on the waste ecosystem and waste stream compositions at specific locations. In cases where quantitative data is not available the same sources could also be used to judge the practical viability of utilizing biowaste streams based on qualitative criteria. |
6166466af718df11a8dcc2f8 | 3 | Once a set of regionally available biowaste streams is identified and characterized, a similar analysis should be performed for pretreatment processes that can isolate feedstocks. There are a variety of pretreatment methods identified in literature, which differ with regards to the required raw materials, biowaste source, process conditions, processing steps, yields and recoveries of high-quality feedstocks. With this data, exergy analysis is a common method to characterize processes, as it links both to economic and environmental considerations, taking into account thermodynamic inefficiencies and losses. Furthermore, it can connect the pretreatment analysis to the subsequent reaction network analysis, where exergy has previously been applied as selection criteria. To evaluate the exergy requirement of biowaste pretreatment, previous literature has simulated processes in ASPEN Plus to extract enthalpy and entropy information and then compute exergetic profiles. For early-stage and large-scale evaluation of different treatments an automation of process modelling or heuristics for exergy profile estimations should be envisioned. |
6166466af718df11a8dcc2f8 | 4 | With identification and characterization of both biowaste streams and pretreatment processes for a given region, a pretreatment network can be assembled. A general example of such a network integrated with a conventional reaction network is shown in Figure . Realizing such a network will require large-scale extraction and parsing of data from a variety of different sources that may or may not have the desired information. It is, therefore, prudent as a first step to conduct a case study of a chosen region to better understand data requirements and viability. For this study, the region of Singapore has been chosen, including its neighbours Malaysia and Indonesia. The primary aims are, to: a) Identify and characterize key biowaste streams/sources in the region. This will ideally involve quantified availabilities, qualitative judgements based on other criteria, and quantified chemical compositions, if data is available. b) Select the most attractive biowaste stream based on its characterization data for further pretreatment. c) Identify and simulate a pretreatment process for isolating feedstocks from the biowaste stream in Aspen PLUS. For this aim availability of data such as raw materials, process conditions, processing steps, feedstock recoveries and purities is crucial. d) Conduct an exergy analysis on the chosen pretreatment process, highlighting inefficiencies, areas of improvement and trends. |
6166466af718df11a8dcc2f8 | 5 | It is important to note that the goal of this work is to build a foundation for future development of a comprehensive biowaste pretreatment network and its integration with reaction networks. The workflows addressing aims (a) to (d) will need to be replicated (with modifications where needed) across a larger range of regions, biowaste streams and pretreatment methods. Regardless, we hope that this study will stimulate a discussion in the wider community on the identification of key biowaste-derived molecular building blocks and the data requirements for enumeration of environmental impacts of circular chemical routes. |
6166466af718df11a8dcc2f8 | 6 | This section outlines the methodology for addressing aims (a) and (b). have been proposed to characterize the identified biowaste streams (aim (a)), along with key questions to answer which are outlined in Table . Based on the five criteria, the waste streams can be evaluated and assessed, to identify the most attractive source for further pretreatment, aim (b). In general, the selected waste stream is required to be available at large quantities (1) and with relatively stable supply from year to year (2). While upgrading a waste stream from use as an energy source to material recovery (upgrade within the waste hierarchy) is advisable, focusing on underutilized waste |
6166466af718df11a8dcc2f8 | 7 | The reader is referred to Sections 3.1 and 3.2 for results covering the identification and characterization of key biowaste sources in and around Singapore using the five criteria, respectively. Section 3.3 presents a summary, at the end of which oil palm empty fruit bunch (EFB), a type of lignocellulosic biowaste abundant in Malaysia and Indonesia, was selected for further pretreatment. |
6166466af718df11a8dcc2f8 | 8 | Having selected EFB, a compatible pretreatment method needs to be identified in accordance with aim (c). There exists a variety of processes for waste treatment, as mentioned in the introduction. While physical treatment (e.g. milling, freezing, extrusion) is commonly used for size reduction, chemical treatment (e.g. with acids, alkaline, or by organosolv process), physico-chemical (e.g. steam explosion, ultrasonic, liquid hot water), biological treatment (e.g. enzymatic, fungal, bacterial), or combined methods can fractionate and extract diverse sets of molecules. The ideal pre-treatment process is cheap, energy-efficient, suitable for different material types/compositions, and aids recovery of all feedstocks of interest at high qualities. Comprehensive advantages and disadvantages for treatment of lignocellulosic materials are provided in ref. 18. |
6166466af718df11a8dcc2f8 | 9 | Solvent choice, temperature, pressure, solvent concentration, acid catalyst, acid concentration, residence time, and solid-liquid ratio are some of the important process parameters/conditions that affect feedstock recoveries, purities as well as material requirements. To define them, we turn to experimental studies in literature that have examined EFB sourced from Indonesia and Malaysia. For instance, recently examined the ethanol organosolv process for EFB biowaste sourced from Indonesia and determined optimal process parameters for cellulose and lignin recovery. However, degradation products were not quantified. These process parameters have been summarized below in Table . Feedstock recoveries, purities and other performance metrics specified by the study are summarized in Table . There are many other process parameters and unit operations reliant on processing steps defined in the following section. |
6166466af718df11a8dcc2f8 | 10 | The ethanol organosolv process was simulated using ASPEN Plus. The Non-Random Two Liquid (NRTL) model was chosen to handle non-idealities and the Redlich-Kwong-Soave (RKS) equation of state (EoS) was applied to account for high operating pressures. EFB was specified as per its estimated chemical composition expressed in terms of feedstocks (cellulose, lignin, hemicellulose) specified in Table in Section 3.3 in the results. The National Renewable Energy Laboratory (NREL) database and technical reports were utilised to obtain thermodynamic properties of these feedstock biopolymers. For lignin and cellulose it was assumed that the molecular formula is unchanged after dissolution. Hemicellulose is hydrolysed to xylose due to its thermochemical sensitivity. Furfural degradation products and oligomers of each biopolymer are also expected but are not integrated due to the lack of data. |
6166466af718df11a8dcc2f8 | 11 | The main processing steps of an organosolv process have been examined and simulated in more detail in prior literature and patents. For example, present an in-depth study on the ethanol organosolv process for wood chips considering fractionation of all products, additional washing stages, solvent recovery, heat integration and furfural separation. presented an even more detailed ASPEN Plus process simulation of the ethanol organosolv process for wood chips, in the wider context of bioethanol production, including a boiler for utilities. Therefore, processing steps for the simulation were based on ref 42, key process parameters defined in Table , and performance variables defined in Table . All utilities employed during the simulation follow heuristics given by refs 46 and 47. Table and Figure to 7 outline the main stages and steps of the organosolv process. Further references for individual stages are given in Table . |
6166466af718df11a8dcc2f8 | 12 | Stage 0 EFB drying and comminution Wet EFB [BIOMASS] with moisture content 67 wt% at ambient conditions is dried to 7 wt% in dryer D- an organic phase which can be decanted. In DECANTER, 60% of low molecular weight lignin was simulated to enter the organic phase [LMWLIG] with a purity of 70 wt%. |
6166466af718df11a8dcc2f8 | 13 | With the simulation complete, the methodology for exergy analysis in accordance with aim 28 simulated a lactic acid biorefinery using sugarcane bagasse as the substrate, and steam explosion as the pretreatment process. Although the organosolv process was not explored, unlike in other studies, a boiler unit with fuel combustion for hot utilities was included. An exergy analysis revealed the lowest exergy efficiency of all studies, less than 50 %, with most exergy destruction traced to the boiler unit. |
6166466af718df11a8dcc2f8 | 14 | To the best of our knowledge, there are no exergetic studies of an ethanol organosolv process for EFB from Malaysia or Indonesia that have considered all main processing steps, including the boiler unit. However, the fundamental workflow across these works is similar. To describe the exergy of a chemical process, we can differentiate between exergy related to matter flow and exergy which is not related to matter flow. The latter is thermal exergy (𝐸𝑥 ! ) as a result of thermal energy transfer and work here described as shaft work (𝐸𝑥 " ! ). |
6166466af718df11a8dcc2f8 | 15 | and potential exergies (𝐸𝑥 % ). However, kinetic and potential exergies may be neglected in the case of chemical reactions due to data shortage on the exact process layout, 14 rendering: We define the reference environment with mean concentrations of reference compounds in the surrounding environment specified by Szargut et al. Note that the reference state for all elements is not the actual natural environment, but a state that represents the most devaluated form of the elements. Standard chemical exergies for common and simple components, e.g. carbon dioxide, ethanol, nitrogen, have already been calculated in previous literature using Eq. ( ) and can be taken from tabulated works. For complex materials such as biomass, Δ𝐺 , ° is difficult to obtain. ) refers to the exergy destruction as a result of solution mixing and is analogous to the Gibb's energy change of mixing. In the case of ideal mixing, as is the case for the solids in the system, 𝛾 * is 1. For mixtures of gases, the molar fraction in the gaseous phase, 𝑦 0,/ and the fugacity coefficient, ϕ 1 are used instead of 𝑥 0,/ and γ 0 respectively. |
6166466af718df11a8dcc2f8 | 16 | Additional terms based on the state and phase, e.g. evaporation enthalpy, and deviations from ideality, are captured by the NRTL-RK property package in APSEN Plus. Thus, physical exergy values are directly extracted from the software. Based on the different types of exergy covered, all relevant stream data (flow, heat, and work) were exported, including 𝑒𝑥 %! values. |
6166466af718df11a8dcc2f8 | 17 | The chemical exergy for each stream was calculated using Eq. ( ). The required mass and molar fractions of components as well as the activity/fugacity were obtained from the ASPEN Plus database and simulation and the standard chemical exergies of compounds were retrieved using tabulated values and the correlation function of Shieh et al. Metrics such as the exergy destruction, exergy loss, cumulative exergy demand and exergetic efficiency can indicate the resource intensity and efficiency of a process. Eq. ( ) provides the exergy balance, where indices 𝑖𝑛 indicate incoming exergies and 𝑜𝑢𝑡 outgoing exergies. The Right-Hand-Side of Eq. ( ) is described as term 𝐼 and records the amount of exergy destruction within the system which accounts for entropy generation 𝑆 2-/ . |
6166466af718df11a8dcc2f8 | 18 | The exergy loss describes the exergy leaving the system as a waste stream, ∑𝐸𝑥 (,345,6 , and the cumulative exergy demand, 𝐶𝐸𝑥 7 , is the exergy which is used as input to the system, see Eq. ( ). The exergetic efficiency, η -8 see Eq. ( ), is the ratio of useful exergy outlet over exergy inlet. |
6166466af718df11a8dcc2f8 | 19 | All exergy calculations were done on an Excel worksheet with exported stream data from the simulation as well as literature values. It is important that for future integration of waste materials into reaction networks, predictive methods based on early-stage data or automated simulations are developed and full datasets and models for any published itineraries are available. |
6166466af718df11a8dcc2f8 | 20 | As per aim (a), biowaste sources in the region of Singapore, Malaysia and Indonesia need to first be identified. Outlined by the statistics from National Environment Agency (NEA) in Singapore, 60 nearly 7.2 million tons of solid waste were generated in Singapore in 2019. Waste types generated were, in million tons (MT): |
6166466af718df11a8dcc2f8 | 21 | Relevant to production of value-added chemicals are biological waste sources containing complex and highly functionalized feedstocks. Main sources of such molecules are lignocellulosic waste (paper/cardboard, wood, and horticultural) and food waste. Wood waste include crates, boxes, wooden planks used in construction, furniture and pallets, and horticultural waste includes tree trunks, branches, plant parts, trimmings. Food waste comprises domestic waste (households and residential complexes) as well as non-domestic (for instance restaurants, hotels, shopping malls). Other waste types, such as ferrous metal or construction debris, either lack the desired molecular structures, or are generated in rather small quantities. |
6166466af718df11a8dcc2f8 | 22 | Another significant area of opportunity lies with Singapore's neighbours, Malaysia and Indonesia, which have large agricultural activities and an abundance of lignocellulosic waste from these. Notably, both countries together account for 85% of global production for crude palm oil, which originates from the oil palm crop. 61 More than 80% of the oil palm is disposed as biowaste, making it a promising source. 62 Due to the close proximity and large availabilities, research efforts aiming to valorize oil palm biowaste have also gained traction in Singapore. |
6166466af718df11a8dcc2f8 | 23 | Having identified some of the prominent waste streams in the region, we can now apply the five criteria outlined in Table in Section 2.1 to characterize and evaluate them, as per aim (a). A total of 2.6 million tons of biowaste is generated in Singapore per annum with an overall recycling rate of 45 %, see values for 2019 in Table . Note, that recycled material in the NEA statistics also accounts for cogeneration of steam and energy for chemical industries and for co-digestion with water sludge for biogas production. Material used in Waste to Energy (WtE) |
6166466af718df11a8dcc2f8 | 24 | However, the segregation of feedstocks from food waste (criteria (4)) poses significant challenges and remains the main reason for low food recycling rates. Food waste requires three steps of segregation: firstly, a separation of food waste from mixed waste streams, secondly, a separation into food waste fractions (e.g. seafood shell waste, coffee grounds etc.), and thirdly, the isolation of feedstocks (pretreatment). 50% of the total food waste in Singapore comes from the domestic sector, where commonly the Central Refuse Chute system with only one waste disposal chute is used, making the first level of segregation at source problematic. Much of the non-domestic food waste is also not segregated at source and together with the domestic waste utilised for energy recovery in a Waste-to-Energy plant. |
6166466af718df11a8dcc2f8 | 25 | Some food manufacturers manage to segregate the first two levels so that food waste fractions, such as spent grain from breweries (75,000 t/a 64 ), bread waste and okara (soya bean curd residue from tofu production) (11,000 t/a 65 ), can be sold to recyclers, and converted into low-value animal feed. Important feedstocks from okara include cellulose, hemicellulose and lignin (explained in the next section); proteins which can lead to amino acid platform chemicals; and isoflavones (daidzein, genistein, glycitein). 66 Spent grain can lead to not only cellulose, hemicellulose, lignin, and proteins but also triglyceride feedstocks which can yield fatty acids and glycerol platform chemicals; phytosterols; and phenolic acids (ferulic acid, p-coumaric acid). Other potential single-stream sources include used cooking oil from restaurants/fast food chains (20,000 t/a 70 ) which mostly contain triglycerides; fruit waste (20,000 t/a 71 ) such as orange peels from juicing facilities which can yield terpenes such as limonene , shown to lead to pharmaceuticals such as paracetamol 72 ; spent coffee grounds (6,300 t/a 64 ) which contain cellulose, hemicellulose, lignin and proteins; and seafood shell waste (30,000 t/a 73 ) from seafood restaurant kitchens which contains chitin feedstocks that can be converted into nitrogen-rich chemicals (N-containing furan derivatives like 3acetamido-5-acetylfuran), pyrroles, amine/amide alcohols, amino acids, and organic acids. If chitin is deacetylated into chitosan, other platform chemicals such as 5hydroxymethylfurfural and lactic acid can be generated. 74 Thus, given the large diversity of derivable feedstocks and potential platform chemicals, food waste also fulfills criteria (5). |
6166466af718df11a8dcc2f8 | 26 | Numerous start-ups, companies and research efforts 71,75-77 are beginning to explore other avenues from these waste sources such as value-added food products, composite materials, and packaging, but chemicals remains a relatively unexplored area. Nevertheless, ease of segregation at large scale and easy perishability are still the major hurdle for food waste utilisation, and most of the identified waste sources are mixed and incinerated at present. |
6166466af718df11a8dcc2f8 | 27 | Singapore generates 1.8 million tons of lignocellulosic waste per annum from which 56% were recycled in 2019 and which were relatively stable over the last four years, satisfying both criteria ( ) and ( ). This is true for both wood and horticultural waste, but paper/cardboard waste has experienced a slight decline (instability) with declining demand due to digitization. |
6166466af718df11a8dcc2f8 | 28 | The feedstocks in the lignocellulosic waste are represented by three types of biopolymerscellulose, hemicellulose, and lignin, which are each complex and can lead to numerous valueadded platform chemicals. Cellulose can be used for production of intermediates such as sorbitol, furans, alcohols by hydrolysis via glucose monomeric units. 78 Hemicellulose on the other hand contains repeating C5 sugars, such as xylose, which can be used to produce xylitol, furfural, or ethanol (through fermentation). Value-added intermediates from both cellulose and hemicellulose include levulinic acid, glutamic acid, glucaric acid, itaconic acid, succinic acid, glycerol and 3-hydroxybutyrolactone. For the production of aromatic value-added molecules such as vanillin, catechol and cinnamic acid, lignin is most suited as it is the most abundant aromatic biopolymer. 4 Therewith criteria ( ) is also fulfilled. |
6166466af718df11a8dcc2f8 | 29 | To have a better understanding of the utilisation of lignocellulosic materials in Singapore, it is worthwhile to illustrate the ecosystem of waste usage. Figure outlines the uses of lignocellulosic materials including percentages, based on the 2019 data. Paper and cardboard material from recycling bins partly separated at a central material recovery facility (MRF) from other recycling material (e.g. glass or plastics) and then further processed into pulp. Stained paper/cardboard or material above the facility occupancy is sent to WtE together with paper from the central chute. Wood can be upcycled into new products (e.g. furniture products), composted, or shredded into woodchips and used together with chips from horticultural waste to generate steam and electricity for industrial activities. The non-woody portion of the horticultural waste is composted. The use of the mixed wood chips for value-added chemicals is seen as problematic, as wood from consumer goods introduces significant impurities to the stream such as resigns and adhesives, bringing about challenges for criteria (4). Despite the possibilities of further substituting WtE treatment of lignocellulosic waste by material usage, the recycling system is well-established and economically viable and thus is failing criteria (3). As shown above, the combined availability of dry oil palm biowaste in Indonesia and Malaysia is more than 70 times higher than total biowaste available domestically in Singapore (191 million tons against 2.6 million tons), performing well in criteria (1) and allowing for economies of scale. Looking at specific oil palm biowaste streams, MF and PKS are already well utilized as boiler fuel in oil palm mills as they have a low moisture content, thus falling short in criteria (3). OPT and OPF while underutilized and highest in availability, 80 are dispersed across the plantation, requiring extensive labor to collect, potentially impacting supply stability and criteria (2). 4,83 EFB, on the other hand, is an unavoidable waste stream from the milling process at palm oil factories, easy to collect, already segregated, and with no widely established further use (mainly mulch, compost and landfill material). EFB contains the feedstock biopolymers lignin, cellulose and hemicellulose, and its availability is estimated at 26 million tons/year, which is almost ten times higher than the total available biowaste quantity in Singapore, and is expected to stay stable or increase with projected growing global palm oil demand. 87 Thus, EFB fulfills all waste criteria. Note that within the context of deforestation and clearing of peatlands for the expansion of plantations, it is important to utilize EFB solely from strictly controlled sustainable farms. |
6166466af718df11a8dcc2f8 | 30 | As an extension of criteria (5), it is important to understand the chemical composition of EFB in more detail and its feedstock makeup. This poses a challenge, as composition depends on maturity (age) of the oil palm, soil conditions, weather, species, growth conditions and duration of storage. As an approximation, the composition of EFB has been calculated by averaging the results of six studies as shown in Table . |
6166466af718df11a8dcc2f8 | 31 | Results have been obtained by applying the methodology specified in Section 0 for exergy analysis. The simulation of the EFB treatment resulted in following product recoveries, purities, and yields (Table ). Values are in accordance with the simulation of Mondylaksita et al. on which input parameters had been based. The low yields are due to the high moisture content of EFB and cellulose is produced at a higher rate as it is the dominant component in EFB. |
6166466af718df11a8dcc2f8 | 32 | It is important to note that although energetic analysis is important, it can be unreliable as it is based on the First Law of thermodynamics and is always conserved, disregarding entropy generation and internal irreversible losses as prescribed by the Second Law of Thermodynamics. Furthermore, it does not account for external losses embodied by waste streams. Exergy analysis is a useful alternative that can overcome these limitations, reflecting the quality or the fraction of energy that is utilizable and accounting for any losses. Exergy destruction, exergy loss, cumulative exergy demand and exergy efficiency are all potential metrics that can indicate the resource intensity and efficiency of a process, reflecting the environmental impact of a process more holistically than energy. |
6166466af718df11a8dcc2f8 | 33 | The cumulative exergy demand is made up from all incoming streams. Exergy is then either attributed to the products stream, the waste stream (external exergy loss), thermal outlets, or internal exergy destruction. Figure outlines that only a small fraction of exergy is incorporated in the products here, and that most exergy is introduced to the process by methane combustion, rather than the biomass source. We will discuss reasons and hotspots for the limited exergy performance of the process in the following sections. precipitator. Note that the ethanol stream reflects fresh solvent required after taking into account solvent recycling which is not shown. |
6166466af718df11a8dcc2f8 | 34 | The cumulative exergy demand per kg wet biomass is 80.9 MJ, which is 11 times more than the original exergy contained in the wet biomass feed. In our study, 67% of the cumulative energy demand is due to methane, 11% each caused by ethanol and by cooling water and biomass input makes up 9% of the cumulative energy demand. The high liquid loads, which increase steam requirements, were identified to be the main cause for the high cumulative energy demand of methane and cooling water. In particular, nearly 84% of methane demand is split evenly between steam utilities required for the reboiler and E-1, the evaporator following it and 80 % of cooling water utilities is required to condense large vapor loads entering the condenser of the column. While the cumulative exergy demand of ethanol currently accounts for 11%, it would account for 48% without solvent recovery. Future strategies should primarily focus on the reduction of fuel demand, e.g. by reducing liquid loads, and continue to decrease solvent demand. |
6166466af718df11a8dcc2f8 | 35 | The destroyed exergy per kg of wet biomass is 35.6 MJ/kg and the lost exergy per biomass during the process is 21.2 MJ/kg. The overall exergetic efficiency was calculated to be 30%, which is significantly lower than previous studies that report efficiencies above 80% for the organosolv process on sugarcane bagasse and oil palm fronds. A breakdown of exergy destruction over all simulated process parts, see Figure , elucidates that the majority of exergy destruction (74.43%) is due to the boiler unit. Methane chemical exergy is destroyed during fuel combustion, generating entropy due to phase changes and high temperature gradients between heat exchanger and boiler feedwater. The approximate magnitude of boiler contribution to exergy destruction is in agreement with the work on lignocellulosic treatment by steam explosion by Previously mentioned works reporting an exergetic efficiency of over 80%, have not included the boiler unit for utilities, which explains large deviations. We emphasize the relevance of the boiler utilities to prevent an inflated exergetic efficiency and reduced exergetic destruction. With regards to the exergy loss, we find that nearly 32% are attributed to E-1, as 180 tons of water per hour are evaporated and lost in this unit. Another 31%, 9% and 2% of exergy loss are due to wasted solvent and wasted water in WASH-1, WASH-2 and WASH-3. However, there is a potential to address the above through better process integration, e.g. recycling SOLVENT3 to the digester or recycling between WASH-1 and WASH-2. If all waste streams are utilized and considered products, the exergy efficiency of the process can potentially increase to 56%. |
6166466af718df11a8dcc2f8 | 36 | Therefore, a case study on Singapore and its neighbours was proposed to illustrate data requirements and workflows, structured around four aims. We first identify key biowaste sources, and characterize them using five criteria: availability, supply stability, underutilization, segregation/purity and derivable key feedstocks. We find that Singapore's neighbours have a significant amount of suitable empty fruit bunch (EFB) biowaste; easy to collect and stable in supply, possible to segregate, at present neither economically utilised nor properly managed from an environmental perspective, and able to yield useful feedstock such as cellulose, lignin and hemicellulose sugars. In the interest of extracting these feedstocks, an ethanol organosolv pretreatment process is then simulated in Aspen Plus using process conditions, parameters and steps from literature. An exergy profile is obtained from stream data which highlights opportunities for process improvement via metrics such as the cumulative exergy demand, exergy efficiency, exergy destruction and exergy loss. |
6166466af718df11a8dcc2f8 | 37 | The benefit of this study mainly lies in setting up future work, which should focus on scaling the methods outlined here across a wider range of regions, biowaste streams and pretreatment methods, leading to a pretreatment network. Exergy can be used as a common basis to integrate the pretreatment method with conventional reaction networks, allowing |
630b693358843b6bbaa09a41 | 0 | Atomically precise clusters are valuable building blocks for material science. Clusters are used to build stimuli-responsive materials, or hierarchical (porous) structures. Clusters can be precursors or intermediates in nanocrystal synthesis. Composites with improved strength are produced by incorporating zirconium oxo clusters in polymers. Zirconium and hafnium oxo clusters are used as catalysts to make or break amide bonds, and they are are suitable as inorganic extreme-ultraviolet photoresists. Subtle differences in cluster chemistry between Zr and Hf are exploited in the industrial separation of Zr from Hf. The whole field of group 4 MOFs is based on group 4 metal oxo clusters that act as secondary building units (or nodes) in the MOF framework. Apart from the typical Ti8, Zr6, and Hf6 oxo clusters used in MOFs, many discrete group 4 oxo clusters exist with different nuclearities. Discrete group 4 oxo clusters are conceptually close to colloidal oxide nanocrystals and share some features with them. For example, both have an inorganic core, usually capped by an organic ligand shell. Discrete oxo clusters are usually synthesized with short and rigid ligands. The resulting clusters crystallize from the reaction mixture, allowing for convenient purification and structural characterization by single crystal X-ray diffraction (XRD). For example, Zr6-carboxylate clusters have the general formula: Zr 6 O 4 (OH) 4 (OOCR) 12 . |
630b693358843b6bbaa09a41 | 1 | The Zr atoms are arranged as an octahedron and are connected through µ 3 -oxygen bridges, with the largest Zr -Zr distance being 0.5 nm. All zirconium atoms are eight-coordinate and the zirconium-oxygen arrangement is almost identical to bulk, cubic zirconia. Therefore, these clusters can be regarded as the lower limit of zirconia nanocrystals. In contrast to larger nanocrystals, oxo clusters are atomically precise (polydispersity = 0), and their ligand shell is less densely packed. (ligand density = 2.4 ligands/nm 2 , compared to 3-4 ligands/nm 2 for nanocrystals). When capped by ligands with low sterical hindrance, the Zr6 cluster dimerizes to form a Zr12 cluster that has two Zr6 subunits, connected by four inter-cluster bridging carboxylate ligands, 2 see Figure . Apart from seven intra-cluster bridging ligands, there are also three chelating ligands per Zr6 subunit. There exist two types of Zr6 monomor structures, either with all bridging ligands or with three chelating and nine bridging ligands, see Figure . Due to their rigid ligands, the typical oxo clusters have a low solubility, rendering them poorly suitable for subsequent processing. Ligands typically used for providing colloidal solubility to nanocrystals (e.g, oleic acid), have not been explored on clusters. In general, the surface chemistry of oxo clusters has been less studied than that of oxide nanocrystals. Only carboxylate for carboxylate ligand exchange has been explored. At room temperature, the Zr6 and Zr12 clusters do not interconvert and the bridging ligands appear inaccessible for exchange. |
630b693358843b6bbaa09a41 | 2 | Figure : Simplified model to show the difference between dimeric and monomeric oxo clusters. For the dimeric species three different type of ligands exist, chelating, bridging and inter-cluster bridging. The latter connect two metal oxo octaeders to form the dimer. For the monomeric species no inter-cluster ligand can found. These clusters either have only bridging ligands or a combination of bridging and chelating ligands. Note that for simplicity no hydrogens are showns and ligands are shortened to the acetate form. Also the oxygen atoms on the cluster core were omitted. |
630b693358843b6bbaa09a41 | 3 | Since longer carboxylate ligands do not allow for crystallization, the question becomes how to purify and structurally characterize such objects. Recently, a purification method based on resins with amine groups was developed for oxo clusters with mixed (but still short) ligand shells. About 20 % free acid persisted in the final product. Regarding characterization, the oxo clusters in MOFs have been studied by X-ray total scattering and Pair Distribution Function analysis (PDF). Structural changes upon heating have been observed, usually by a model-free analysis. On the other hand, X-ray PDF has been often used to study the structure of nanocrystals. In this case, the organic ligands have usually been neglected in the structural models because the organic ligands scatter quite little compared to the inorganic core. In the case of larger clusters (Bi38, Cd35, Au144, Cd37) PDF could confirm the structure of the core, again by largely neglecting the organic ligands. However, to our knowledge, PDF has not yet been applied to the small discrete oxo clusters capped with organic ligands, as discussed here. |
630b693358843b6bbaa09a41 | 4 | Here, we establish standardized conditions for the synthesis of a range of carboxylate capped zirconium oxo clusters. We develop PDF modeling tools to structurally characterize the oxo clusters. We find that excellent refinements are only obtained when the structural models include the contribution of the organic ligands. PDF analysis can clearly distinguish a Zr6 from a Zr12 cluster and dismiss other possible cluster structures such as Zr4. The organic ligand shell is then comprehensively characterized by FTIR, NMR and TGA. The molecular formula is finally confirmed by ESI-HR-MS. Using our characterization tools, we evaluate carboxylate for carboxylate ligand exchange and we find that the ligand is structure directing, determining whether a Zr6 or a Zr12 cluster is formed. These results are further generalized to hafnium oxo clusters. Finally, we test the notion that the fatty acid capped metal oxo clusters are the smallest conceivable nanocrystal prototypes. We compare the catalytic activity of Zr12-oleate clusters with oleic acid capped zirconium oxide nanocrystals, and find superior activity for the oxo clusters. |
630b693358843b6bbaa09a41 | 5 | Zirconium propoxide (70 w% in propanol) or zirconium butoxide (80 w% in butanol) reacts with an excess of acetic acid (8 equivalents) to form the Zr12-acetate cluster, which is a dimer of Zr6. The reaction is performed in dichloromethane (DCM) solvent to obtain higher quality crystals. Both acetic acid and DCM are co-crystallized with the cluster. The balanced chemical equation is shown in Scheme 1. We further washed the crystalline powder with a solution of acetic acid in DCM to remove reaction by-products and other zirconium species. Although the powder can be handled in air, it is recommended for long term storage to keep the clusters in a glovebox or desiccator, since they react slowly with atmospheric water and become partially insoluble. Scheme 1: Balanced chemical equation for the synthesis of Zr12-acetate clusters from zirconium propoxide (or butoxide) and acetic acid. The final product has co-crystallized acetic acid and dichloromethane (the latter is not shown). Also the structures of the other carboxylic acids used here for cluster synthesis are shown. |
630b693358843b6bbaa09a41 | 6 | Zr12-propionate is similarly synthesized and purified as Zr12-acetate. We extended the synthetic strategy to longer carboxylic acid ligands (see Scheme 1), but the resulting clusters cannot be crystallized anymore and therefore other purification techniques are required. The Zr12-butanoate is washed overnight in acetonitrile, while Zr12-hexanoate, Zr6-methylbutyrate and Zr6-methylheptanoate clusters can be precipitated with acetonitrile and isolated by centrifugation. Dissolution in DCM and repeating the precipitation cycle twice, delivers the purified clusters. This type of purification is typical for colloidal nanocrystals, and thus reinforces the idea of fatty acid capped clusters as atomically precise models for nanocrystals. For the clusters with even longer ligands (octyl, decyl, dodecyl and oleyl chains), the first precipitation is performed with acetonitrile, while acetone is the preferred anti-solvent for the two consecutive steps to precipitate the clusters, and separate it from, e.g., the ester co-product. The physical appearance of the clusters ranges from semi-crystalline solids (acetate, propionate, methylbutanoate) to waxy solids (butanoate, hexanoate, decanoate, dodecanoate) or viscous oils (octanoate, oleate, methylheptanoate), see Figure . |
630b693358843b6bbaa09a41 | 7 | While the Zr12-acetate and Zr12-propionate clusters are known in literature, the others are not. To prove their structure, we analyzed the clusters with X-ray total scattering and Pair Distribution Function (PDF) analysis. Total scattering takes into account both Bragg and diffuse scattering. The real space PDF is ideal to study nanomaterials, local effects and amorphous materials. We measured the experimental PDF of all the above clusters and ordered them according to increasing carbon content, see Figure . Focusing first on the PDF of acetate capped clusters and comparing with the structure from single crystal XRD, we easily recognize the Zr-O distance at 2.2 Å and the Zr-Zr distances within one Zr6 cluster at 3.5 Å and 5 Å. We label them as intra-Zr6 distances. The longer range Zr-Zr distances are specific to the Zr12 dimer and are present at 5.7 Å, 8.4 Å, and 11.5 Å. They are labeled as inter-Zr6 distances. When the chain length increases, the same features remain present in the PDF, except for the case of methylbutanoate and methylheptanoate where the inter-Zr6 distances are missing. This suggests that both the Zr6-methylbutanoate and Zr6methylheptanoate clusters are monomers while the others are dimers. This is in agreement with our earlier hypothesis that branching of the carboxylic acid at the alpha position is the deciding factor between monomer and dimer. 2 With increasing chain length, we also observe the growth of a peak at 1.5 Å, which we assign to C-C distances in the ligand chain. |
630b693358843b6bbaa09a41 | 8 | Apart from this model-free analysis, we sought to quantitatively fit the PDFs. We focus again first on Zr12-acetate to develop the method because a single crystal XRD structure is available for this cluster. To demonstrate the importance of the organic ligands to the PDF, we theoretically calculated the PDF of Zr12-acetate, including either (i) no carbon atoms, (ii) only the carbonyl carbon atoms, or (iii) all carbon atoms, see Figure . The car-Figure : Experimental PDF of zirconium oxo clusters with different capping ligands. The C-C and Zr-Zr are assigned. We make a distinction between the Zr-Zr distances within one Zr6 cluster and the Zr-Zr distances that are characteristic for the dimer. bon atoms contribute significantly to the PDF, especially between 4 Å and 8 Å. This result stands in stark contrast to the regular practice of ignoring the ligands in the PDF refinement of nanocrystals and larger clusters. The ligand effect is further demonstrated by fitting the Zr12-acetate PDF with various models, see Figure . For each model, we refined thermal motion parameters while keeping the structure (i.e., the atomic positions) unchanged. In a first attempt, similar to traditional approaches, we only used the Zr6 cluster core as a model. |
630b693358843b6bbaa09a41 | 9 | The model could capture the basic Zr-O and intra-Zr6 Zr-Zr distances but gave overall a poor agreement and a significant misfit in the Zr-O distance. Using the dimer structure, the inter-Zr6 Zr-Zr distances are well described. By including the oxygen atoms from the coordinated acetates, a significant reduction in Rw is achieved and the Zr-O peak is now accurately fitted. By including the carbon atoms from the coordinated acetate, we obtain a decent fit, with an Rw = 0.14. The crystal structure of Zr12-acetate contains also hydrogen bonded ligands and co-crystallized DCM molecules. When the hydrogen bonded ligands are explicitly included, only a marginally better fit is obtained, see Figure . Alternatively, we added an exponentially decaying sinusoidal function as a second phase, resulting in a slightly (but significantly) better fit with Rw value 0.11, see Figure . This second phase describes disordered but still structured intensity and has previously been used to model solvent restructuring, or a regular array of high/low density regions. While it is gratifying to obtain an excellent fit for the data, we sought to determine whether we could distinguish the Zr12-acetate structure from other reported clusters structures (determined by single crystals XRD). We thus attempted to fit the experimental PDF of Zr12-acetate clusters with the reported structures of Zr4-methacrylate, Zr6-methacrylate, Zr6-acetate, Zr6-isobutyrate, Zr6-tert-butanoate and Zr12-propionate, see Figure . For these refinements, we removed the excess carbons in the structures to arrive at models with acetate ligands. |
630b693358843b6bbaa09a41 | 10 | The model based on Zr4-methacrylate had a very poor agreement (Rw = 0.66). The Zr6 structure models also delivered poor fits (Rw = 0.24-0.31). The second best agreement was obtained for the Zr12-propionate model (Rw = 0.18). While this model is structurally very similar to the Zr12-acetate model, the Zr-Zr distances are slightly larger in the propionate model (Table ). We conclude that PDF is able to pick up the correct structure from a series of possible clusters structures, based on the goodness of fit. |
630b693358843b6bbaa09a41 | 11 | For Zr12-butanoate and Zr12-octanoate, we find that the Zr12-acetate structure model gives a satisfactory fit (Figure ), which is clearly better than the refinement with a Zr6acetate structural model (Figure ). The latter structure was reported after crystallization from aqueous solution. In contrast, the Zr6-methylbutanoate cluster, is best described by the Zr6 model and gives a worse fit for the Zr12 model. The Zr12-oleate cluster was best described by the Zr12-propionate structure model with the slightly larger Zr-Zr distances. |
630b693358843b6bbaa09a41 | 12 | Again, the Zr6 model delivered a worse fit (Figure ). We can thus distinguish the Zr6 structures from the Zr12 structures, based on the refinement. However, we notice that the Figure : PDF refinement of the Zr12-acetate cluster with various models, derived from the reported crystal structure. The best fit is obtained when including the oxygen and carbon atoms from the carboxylate ligand. The refined parameters are given in Table . |
630b693358843b6bbaa09a41 | 13 | The short-range C-C distances (at 1.5 and 2.6 Å) are not fitted in the refinement. However, the sinusoidal function seems to describe the long range distances quite well, presumably because the ligand chain is disordered. This hypothesis is confirmed by the increase in the relative amplitude of the sine wave from Zr12-acetate to Zr12-oleate (Table ). The improvement in Rw upon including the sinusoid is small for acetate (0.03) while large for oleate (0.09) (see Figure ). A more effective way of dealing with the long ligands is measuring the free ligand explicitly as a background and subtracting its scattering from the data in reciprocal space. For Zr12-oleate, this leads to a significant improvement of the fit. The short range C-C distances disappeared from the PDF (Figure ) and the sine wave is completely obsolete (Table ). The goodness of fit is now comparable to the case of the clusters with shorter chains. Note that the presence of longs ligands reduces the signal-to-noise ratio during the data collection (due to a lower fraction of oxo clusters in the X-ray beam). We conclude that, after initial training on the acetate clusters, our PDF analysis is able to confirm the structure of these previously unreported clusters. Our synthetic procedures indeed lead to the formation of soluble Zr6 oxo clusters, forming monomers or dimers depending on the sterical hindrance of the ligand. We further emphasize that good refinements can only be achieved by including the binding group of the ligand explicitly in the model (even after background subtraction). This is much more important for the small clusters discussed here, than for larger clusters or nanocrystals. For the latter two, there is a larger core of strongly scattering atoms, and relatively fewer, weakly scattering ligands. However, small nanocrystals have typically worse goodness of fits compared to larger ones and it might be worth to consider including ligands explicitly to obtain better refinements. Figure : PDF refinement for Zr12-butanoate and Zr12-octanoate using the Zr12-acetate structure model. PDF refinement of Zr6-methylbutanoate using the Zr6-acetate structure model. Finally, the PDF refinement of Zr12-oleate using the Zr12-propionate structure model, with or without background correction in reciprocal space. If applied in the refinement, the exponentially dampening sine wave is shown (orange dotted lines). The refined parameters are given in Table . |
630b693358843b6bbaa09a41 | 14 | For PDF modelling we need to include the carboxylate head group, but PDF is in general not very sensitive to the organic fraction. Detailed information on the presence of uncoordinated ligands or coordination modes is hard to obtain from PDF. We turn now to FTIR, TGA and NMR. In Figure , we show the FTIR spectra of selected oxo clusters (the complete FTIR spectra of all clusters are presented in Figure ). The CH 2 stretches (2800-3000 cm -1 ) gain intensity with increasing chain length. The small, sharp peak around 3450 cm and the broad peak between 3500 and 3000 cm -1 were assigned to the OH moieties onto the cluster, which are involved in hydrogen bonding. While all these samples were extensively purified by multiple precipitation/re-dissolution cycles, it is clear that there is still protonated carboxylic acid in the sample (observed at 1710 cm -1 ), presumably hydrogen bonded to the cluster. Such hydrogen bonding is also observed for the co-crystallized acetic acid in the single-crystal structure of Zr12-acetate, see Figure . Our efforts to purify the clusters further and remove the hydrogen bonded acid were not successful or even compromised the integrity of the cluster core, see supporting information. Since FTIR is not quantitative, we used TGA to quantify the amount of excess carboxylic acid. We find about 1.1 -3.7 excess ligand molecules per Zr6 monomer, see supporting information. There is no clear trend among the different clusters. Only the Zr12-acetate stands out with 5.5 excess ligands per monomer. |
630b693358843b6bbaa09a41 | 15 | Figure : FTIR spectra of Zr12-acetate, -propionate, -hexanoate, -oleate and Zr6methylheptanoate. The weak band at 1750 cm -1 in the spectrum of Zr12-acetate is assigned to a small amount of acetic acid that is not involved in hydrogen bonding of any kind. An ester impurity is ruled out because of the absence of any signal around 4 ppm in NMR, (see Figure ). |
630b693358843b6bbaa09a41 | 16 | Returning to the FTIR spectrum, the bands in the region 1500-1600 cm -1 are assigned to the asymmetric stretch of the carboxylate, ν as (COO). While the symmetric stretch, ν s (COO), is expected around 1400-1450 cm -1 , there is significant overlap with CH 2 and CH 3 deformation bands which are expected around 1440-1460. Focusing here on the qualitative differences, we first draw the attention to the spectrum of Zr6-methylheptanoate. In contrast to the IR spectrum of Zr6-pivalate, ν as (COO) is split in two bands (1580 and 1550 cm -1 ).While the Zr6-pivalate has all bridging carboxylates, the band splitting for Zr6methylheptanoate indicates that the part of the methylheptanoate ligands are in the chelating mode. When comparing the pattern of the ν as (COO) bands in Zr6-methylheptanoate with those of Zr12-oleate, we find striking differences. For Zr6-methylheptanoate, the most intense band is at 1580 cm -1 while for Zr12-oleate, the most intense band is at 1532 cm -1 . |
630b693358843b6bbaa09a41 | 17 | In addition, Zr12-oleate has a band with the highest wavenumber, 1597 cm -1 . Typically the wavelength difference between ν as (COO) and ν s (COO) is used as a diagnostic tool to distinguish between chelating (∆ν < 110 cm -1 ), bridging (∆ν = 140 -190 cm -1 ) or mondentate (∆ν = 200-320 cm -1 ) binding modes. This is an empirical rule-of-thumb and in case of multiple bands it is difficult to assign the paired ν s (COO). With these limitations in mind, we tentatively calculate ∆ν = 190 cm -1 for the band at 1597 cm -1 , and assign it to the ligands that bridge the two clusters. More importantly, the same ν as (COO) pattern of Zr12oleate is retrieved for all the Zr12 clusters (see also Figure ). Furthermore, the ν as (COO) pattern of Zr6-methylheptanoate is quasi identical to that of Zr6-methylbutanoate. This is an exciting observation since it provides a very fast method to distinguish between the Zr6 and Zr12 clusters. |
630b693358843b6bbaa09a41 | 18 | The 1 H NMR spectra of selected oxo clusters are shown in Figure . For Zr12-propionate, hexanoate and oleate, we recognize three different resonances for the alpha CH 2 moiety, indicating three different distinguishable carboxylate environments (see also Figure for the other Zr12 clusters). On the other hand, Zr 6 -methylheptanoate has only two alpha CH resonances (see Figure for Zr6-methylbutanoate). It is striking that the NMR pattern seems to roughly agree with the ν as (COO) pattern in FTIR. Also for the other clusters, For Zr12-acetate, we find four methyl resonances. Diffusion ordered spectroscopy (DOSY), |
630b693358843b6bbaa09a41 | 19 | shows that three resonances (1.7-2 ppm) have the same diffusion coefficient, while the fourth (2.1 ppm) diffuse more quickly, see Figure . The resonance at 2.1 ppm thus corresponds to the excess acetic acid, as also determined by FTIR and TGA. The measured diffusion coefficient (D = 1106 µm/s) is lower than the reference of free acetic acid (D = 1851 µm/s), and we conclude that the excess acetic acid is in fast exchange between two states: (i) free and (ii) hydrogen bonded to the cluster. The signals between 5 and 7 ppm in all clusters spectra are assigned to the exchangeable protons on the cluster core itself since the resonances disappear upon addition of D 2 O, see Figure . Apart from Zr12-acetate and -propionate, all the spectra show significant line broadening, similar to nanocrystal-bound ligands. The longer the ligands, the broader the alpha CH 2 resonance, but the sharper the methyl resonance becomes. Homogeneous line broadening depends on the rotational mobility and it is expect that the methyl group has increased mobility in a longer ligand. The alpha resonance is closest to the surface. Its mobility is not enhanced in a longer ligand and instead, the total rotational mobility of the cluster decreases as it becomes a bigger object. In contrast to nanocrystal-bound ligands, we find little contribution of heterogeneous broadening to the resonances, see Table . |
630b693358843b6bbaa09a41 | 20 | We sought to use electrospray ionization high resolution mass spectrometry (ESI-HR-MS) to obtain the mass of the clusters. Unfortunately, our carboxylate capped clusters show limited solubility in typical ESI-HR-MS solvents such as methanol and acetonitrile. Zr12acetate clusters dissolve in methanol but no clusters were detected in ESI-HR-MS and NMR analysis provides evidence for cluster degradation in methanol, see Figure -17. The limited solubility of Zr12-acetate did not allow for any further HR-MS analysis. On the other end, the Zr12-oleate clusters were too non-polar. The other clusters could be dissolved in tetrahydrofuran (THF) and were analyzed with ESI-HR-MS after adding acetonitrile as co-solvent (full spectra and assignments in figures S18-25). The Zr6 clusters give reasonably clean spectra where the molecular ion can be recognized as Zr 6 O 4 (OH) 3 (L) + 12 (the cluster minus an OH -group). The example of Zr6-methylbutaonate is shown in Figure . The Z12 clusters appear less stable and the ion [Zr 6 O 4 (OH) 7 (L) 9 ] 2 H + was observed in most cases (see Figure ). This ion is the proton adduct of the Zr12 dimer after substituting six carboxylates for six hydroxides. The Zr12 cluster features indeed 6 chelating ligands, which are most labile according to literature. It is thus plausible that the such an exchange takes place, further catalyzed by a coordinating solvent like THF. Interestingly, more fragmentation was seen for longer ligands and the monomeric form is even dominant for Zr12-decanoate and Zr12-dodecanoate. Despite the limitations, MS thus confirms the successful formation of oxo cluster with long carboxylate ligands. It is however not the correct technique to distinguish the dimer from the monomer structure. |
630b693358843b6bbaa09a41 | 21 | Ligand exchange where one carboxylate is replaced by another carboxylate is an interesting way to change the cluster structure and/or functionality. Pucherberger et al. previously studied the exchange between methacrylate and propionate/acetate on zirconium oxo clusters by NMR. They inferred that, at room temperature, the monomer and dimer cannot be converted in one another and that the inter-cluster bridging ligands are unavailable for exchange. Here, we re-evaluate the ligand exchange process under more forcing conditions and apply our characterization toolbox to elucidate the cluster structure. Starting from the Zr12-acetate cluster, we stir it with 1.5 equivalents of new ligand (a carboxylic acid from Scheme 1) at 70 • C before we evaporate all volatile species. Since acetic acid has a reasonably low boiling point, it is readily removed from the reaction mixture, forcing the ligand exchange to proceed to completion (confirmed by the absence of acetate signals in the C NMR, see Figure ). The clusters are then purified to remove the excess of high boiling ligand. Model-free PDF analysis of the clusters shows that the dimer is obtained for butanoate, hexanoate, octanoate, decanoate, dodecanoate and oleate, see Figure . The Zr6 monomer is retrieved for methylbutanoate or methylheptanoate, showing that the dimer can be broken up under appropriate conditions and with the right structure-directing ligand. |
630b693358843b6bbaa09a41 | 22 | Also the IR and NMR spectra look identical to the ones from the clusters that are directly synthesized from zirconium propoxide, see Figure and S29. We also treated the Zr6methylbutanoate cluster twice with 3 equivalents of hexanoic acid. Methylbutanoic acid does not evaporate as easily as acetic acid and a higher excess was needed. The resulting cluster has the typical IR and NMR signatures of a Zr12 cluster, see Figures and. Also |
630b693358843b6bbaa09a41 | 23 | To generalize our methodology to hafnium oxo clusters, we reacted hafnium butoxide with acetic acid, methylbutanoic acid, or oleic acid. After purification, the resulting clusters were characterized by PDF, FTIR, TGA, NMR and HR-MS. The acquisition and refinement of PDF data are more straightforward for the hafnium oxo clusters since hafnium has a higher scattering cross section, see Figure . All pairs that have a least one hafnium atom are enhanced in intensity, and all other pairs (e.g., C-C) loose relative intensity. Using a structure model of Hf12-acetate, we can readily describe the PDF data of the Hf12acetate and Hf12-oleate clusters. The goodness of fit is even better than for the respective Zr12 clusters that were refined in an equivalent way, see Figure . In the absence of a Hf6 structure model, we constructed a Hf6 cluster model from the Hf12 structure. This model accurately described the PDF data, confirming that the Hf6-methylbutanoate clusters is a monomer. The hafnium oxo clusters thus follow the same structure-directing rules as the zirconium oxo clusters. Also the FTIR spectra (Figure ) and NMR spectra (Figure ) |
630b693358843b6bbaa09a41 | 24 | Finally, we take the perspective that these fatty acid capped oxo clusters are the smallest conceivable nanocrystal prototypes. While technically not a crystal anymore, such a cluster is the lower limit of scaling down a ligand capped metal oxide nanocrystal. Hence, it has a maximized surface-to-volume ratio. Maximizing surface area is particularly important in heterogeneous catalysis and we thus hypothesized that oxo clusters (diameter = 0.5 nm) |
630b693358843b6bbaa09a41 | 25 | would have superior catalytic performance over metal oxide nanocrystals (diameter = 2-10 nm). To test this, we take the previously reported esterification of oleic acid as catalytic model system. We synthesized 3 nm tetragonal zirconia nanocrystals, and functionalized their surface with oleate ligands. Zr12-oleate clusters are the atomically precise, smallest conceivable prototype of such nanocrystals. In addition, the catalytic model system using oleic acid as catalytic substrate, avoiding competition between ligand and substrate for the surface sites. To compare the efficiency of the two catalysts, we added in both cases 10 mol% Zr to the reaction mixture. We monitored the reactions for the first 5 hours by taking aliquots and quantifying the ester product via NMR, see Figure . The oxo clusters are clearly more catalytically active than the nanocrystals, confirming our hypothesis. We do not necessarily ascribe this effect to a special intrinsic activity of the cluster. The cluster has simply every zirconium atom available at its surface. In nanocrystals, most of the zirconium atoms is buried inside the nanocrystal core and unavailable for catalysis. Our experiment is a fair comparison since the catalyst cost scales with the amount of zirconium in the flask. These results thus show that fatty acid capped oxo clusters are an exciting materials class and they can possess advantages over their larger relatives; oxide nanocrystals. One can thus consider (oxo) clusters for many more catalytic reactions that are currently performed with nanocrystal catalysts. As atomically precise nanocrys-tal prototypes, the oxo clusters are also better suited for mechanistic studies compared to nanocrystals which feature polydispersity and a more irregular surface structure. |
630b693358843b6bbaa09a41 | 26 | We have synthesized fatty acid capped zirconium and hafnium oxo clusters with a library of linear and branched carboxylic acids. To prove the structure of the oxo clusters, we developed a method based on X-ray PDF analysis. In contrast to traditional PDF analysis of larger clusters and nanocrystals, we showed that the structure models need to include the carboxylate binding groups to obtain excellent refinements. PDF analysis is then able to distinguish Zr4 from Zr6 clusters, and cluster monomers from cluster dimers. Given the larger scattering power of hafnium, the refinements were even more straightforward for the hafnium oxo clusters. We were thus able to prove the structure of our fatty acid capped oxo clusters, despite the fact that they cannot be crystallized. Given the lack of crystallization, we developed purification protocols akin to the ones for colloidal nanocrystals, reinforcing the notion that these fatty acid capped oxo clusters are atomically precise nanocrystal prototypes, of the smallest conceivable scale. We further comprehensively characterized the oxo clusters by FTIR, NMR, TGA and ESI-HR-MS. We find that the cationic cluster cores are charge balanced by chelating and bridging carboxylate ligands and that a small excess of carboxylic acid is hydrogen bonded to the cluster core. Using our characterization tools, we studied carboxylate for carboxylate ligand exchange and found that the Zr6/Hf6 monomer and the Zr12/Hf12 dimer structure can be converted into one another, depending on the structure of the final ligand. Regarding the fatty acid capped metal oxo clusters as smallest conceivable nanocrystal prototypes, we compared their performance as catalyst with larger oxide nanocrystals. We found that, for the same amount of zirconium added, the oxo clusters are much more catalytically active than oxide nanocrystals, due to their higher surface area. |
630b693358843b6bbaa09a41 | 27 | Z12-acetate synthesis. A 20 mL vial was equipped with a septum and cycled three times between argon and vacuum. Zirconium propoxide (2.25 mL, 5 mmol, 1 eq.) was added to the vial, together with dry DCM (5.463 mL). Under stirring, distilled acetic acid (2.287 mL, 40 mmol, 8 eq.) was injected, reaching a total reaction volume of 10 mL and thus a zirconium concentration of 0.5 M. After 12 hours at room temperature (19-24°C), the crystalline powder is isolated by filtration and further washed with 50 mL of a DCM:acetic acid mixture (4:1). Finally, the white powder is dried overnight under high vacuum. The powder is stored in a dessicator. to maintain a Zr concentration of 0.5 M. After stirring 24 hours at 30 °C the clusters were purified. The butanoate capped cluster was purified by evaporating the solvent using the Schlenk line after which a white waxy solid is obtained. Acetonitrile (15 mL) was added and the clusters were macerated overnight. After centrifugation 4 minutes at 5000 rcf the supernatant is discarded and the clusters are dried on the Schlenk line for 24h yielding a white solid. For hexanoate and octanoate clusters the solution was divided over 2 large centrifuge tubes (± 5 mL reaction mixture in each) and acetonitrile (10 mL) was added to each centrifuge tube in order to precipitate the clusters. Via centrifugation (5000 rcf, 4 min), the cluster (viscous oil) was separated from the supernatant and both oils were redissolved in DCM (1 mL). To each tube 6 mL acetonitrile was added to precipitate the cluster. This last step was repeated twice and finally, the clusters were dried overnight under vacuum. Zr12-hexanoate was obtained as a waxy solid and Zr12-octanoate was obtained as a viscous liquid. Zr12-butanoate was obtained with a yield of 49 %. Zr12-hexanoate was obtained as a waxy solid with a yield of 71 %.Zr12-octanoate was obtained as a viscous oil with a yield of 52.3 %. |
630b693358843b6bbaa09a41 | 28 | Zr12-decanoate, dodecanoate and oleate. For decanoic and dodecanoic acid, zirconium propoxide (2.25 mL, 5 mmol, 1 eq.) was mixed with 8 equivalents acid and diluted with DCM to 0.33 M and 0.25 M respectively in Zr. Decanoic and dodecanoic acid are solids and therefore the concentration had to be lowered in order to dissolve these acids. For oleic acid (which is a liquid), the volume is already 15 mL without DCM. Therefore, no DCM is added and a 40 mL flask is used. After stirring 24 hours at 30°C, the DCM was evaporated using the schlenk line for both Zr12-decanoate and Zr12-dodecanoate, while the oleate sample can be purified as such. The solution for each cluster was divided over 2 centrifuge tubes and ACN (10 mL per tube) was added to precipitate the clusters. Via centrifugation (5000 rcf, 4 min), the cluster precipitate was separated from the supernatant and redissolved in DCM (1 mL). To this 6 mL acetone was added to precipitate out the cluster. This last step was repeated twice and finally, the clusters were dried under vacuum. Zr12-decanoate and Zr12-dodecanoate were obtained as waxy solids with a yield of 80 % and 93 % respectively. |
630b693358843b6bbaa09a41 | 29 | Zr6-methylbutanoate and methylheptanoate. Zirconium propoxide (2.25 mL, 5 mmol, 1 eq.) was mixed with 8 equivalents acid and diluted with DCM to 0.5 M in Zr. After stirring 48 hours at 30°C, the solution was dried under vacuum and dissolved again in 1 mL DCM. Afterwards, the solution is divided over 2 centrifuge tubes and acetonitrile (4 mL per tube) was added to precipitate the clusters. Via centrifugation (5000 rcf, 5 min), the cluster precipitate was separated from the supernatant and redissolved in DCM (1 mL each). To both tubes acetonitrile (double the volume) was added to precipitate the cluster. This last step was repeated twice and finally, the clusters were dried overnight under vacuum. Zr6methylbutanoate was obtained as a white solid with a yield of 93 %. Zr6-methylheptanoate was obtained as a viscous oil with a yield of 55 %. |
630b693358843b6bbaa09a41 | 30 | Zr12-acetate ligand exchange The Zr12-acetate cluster (200 mg, 1.4 mmol acetate, 1 eq.) was weighed into a 20 mL vial. The incoming carboxylic acid (1.5 equivalents) was added together with 1 mL of DCM and stirred for 60 min, after which a clear solution is obtained. Subsequently, the solution was further stirred under vacuum at 70 °C for 1 hour. |
630b693358843b6bbaa09a41 | 31 | Zr6-methylbutanoate ligand exchange monomer. The Zr6-methylbutanoate cluster (200 mg, 1.27 mmol methylbutanoate, 1 eq.) was weighed into a 20 mL vial. 2 mL DCM together with 3 equivalents of hexanoic acid were added and the solution was stirred for 1 hour at room temperature. Afterwards, the solution is dried under vacuum for 1 hour at 70°C. One purification cycle was performed by dissolving the sample in 0.5 mL DCM and precipitating it by adding 2 mL ACN. This turbid solution is centrifuged at 5000 rcf for 4 minutes. The whole process was repeated once to ensure full exchange. After the second exchange step the sample was purified twice as described above. Zr12-hexanoate was obtained as a viscous liquid with a yield of 56.5%. |
630b693358843b6bbaa09a41 | 32 | Hf12-acetate, oleate and methylbutanoate. Hafnium n-butoxide (1.9 mL, mmol, 1 eq.) was mixed with 8 equivalents of acetic, oleic or methylbutyric acid. Prior to mixing dry DCM was added to the hafnium precursor in order to set [Hf] = 0.5 M in the whole reaction mixture. The Hf12-acetate and Hf12-oleate were reacted for 24h at 30°C. For the Hf12-oleate no dry DCM was added. The Hf6-methylbutanoate synthesis was kept at 30°C for 48h. Purification was done according to the zirconium clusters for the respective acids. |
630b693358843b6bbaa09a41 | 33 | Hf12-acetate ligand exchange.The Hf12-acetate cluster (200 mg, 1.26 mmol acetate, 1 eq.) was weighed into a 20 mL vial. 1.5 equivalents (1.89 mmol) of the new carboxylic acid, 597 µL oleic acid or 301 µL methylheptanoic acid ,was added together with 1 mL of DCM and stirred for 60 min, after which a clear solution is obtained. Subsequently, the solution was further stirred under vacuum at 70 °C for 1 hour. The sample transforms from a heterogeneous mixture to a viscous liquid. Purification was performed as described above for the respective cluster. The exchanged clusters were obtained as a viscous oil with a yield of 70 % for the oleate exchanged cluster and 37 % for the methylheptanoic acid exchanged clusters. |
630b693358843b6bbaa09a41 | 34 | Nanocrystal synthesis. The zirconia nanocrystals were synthesized according to Garnweitner et al. Briefly, Zr(OiPr) 4 (6.6g, 17 mmol) was mixed with benzylalcohol (60 mL, 580 mmol) in a Parr bomb inside a nitrogen filled glovebox. Afterwards, the autoclave was heated for 48h at 210°C. After reaction a white powder was obtained via centrifugation. This was washed with diethyl ether. The white solid is dispersed in 40 mL toluene and divided over 4 centrifuge tubes (10 mL each). To each centrifuge tube oleic acid was added (600 µL, 1.9 mmol) to functionalize the nanocrystal surface and the centrifuge tubes were stirred and sonicated for 30 minutes. This procedure resulted in a transparent solution of nanocrystals, with a few insolubles. The insolubles were removed by centrifugation and the soluble nanocrystals in the supernatant were collected and purified via precipitation/redissolving cycles; the nanocrystals in 5 mL toluene (in each tube) are precipitated by adding 15 mL acetone to each tube. The nanocrystals were obtained as a white solid with a yield of 43 %. Analysis of synchrotron X-ray total scattering data. Raw 2D data were corrected for geometrical effects and polarization, then azimuthally integrated to produce 1D scattering intensities versus the magnitude of the momentum transfer Q (where Q = 4πsinθ/λ for elastic scattering) using pyFAI and xpdtools. The program xPDFsuite with PDFgetX3 was used to perform the background subtraction, further corrections, and normalization to obtain the reduced total scattering structure function F(Q), and Fourier transformation to obtain the pair distribution function (PDF), G(r). For data reduction, the following parameters were used after proper background subtraction: Qmin = 0.8 Å-1 , Qmax = 22 Å-1 , Rpoly = 0.9 Å. Modeling and fitting were carried out using Diffpy-CMI. The exponentially dampening sine-wave contribution was calculated according to the following equation. |
6698e118c9c6a5c07a97d651 | 0 | Recently, in 2023, we proposed the q-BWF function as a generalization of the Breit-Wigner-Fano (BWF) line shape, to obtain an asymmetric form of the q-Gaussian function, to be applied to the decomposition of Raman spectra. The BWF line shape is a modified Lorentzian function, which is used to consider the asymmetry due to Fano resonance . In fact, , contemplated the BWF function as suitable to describe asymmetries in the case of the Raman spectroscopy of carbonaceous material. The generalization we proposed in 2023 is using, in the BWF line, a q-Gaussian line shape instead of a Lorentzian profile. A q-Gaussian is a function based on the Tsallis q-form of the exponential function ; this generalized exponential is characterized by a q-parameter and when q is equal to 2, we have the Lorentzian function. If q is close to 1, we have a Gaussian function (this is the reason why the Tsallis function is also known as "q-Gaussian"). For values of q between 1 and 2, we have a bell-shaped function with power-law wings ranging from Gaussian to Lorentzian tails. As shown on many occasions, the q-Gaussian is suitable for fitting Raman spectra . |
6698e118c9c6a5c07a97d651 | 1 | The q-BWF functions have been applied to the study of the Raman spectra of molybdenite, that is, the molybdenum disulfide, MoS2. Due to good result we obtained in that case, we start trying to apply q-BWFs to other cases, using them to highlight asymmetries and a behavior of the line shape different from the usual Lorentzian or Gaussian profile. For Molybdenite, we used the Raman spectra from RRUFF database . Here we investigate glasses. The spectra that we find in RRUFF are those of CaO-Al2O3-SiO2 systems with Rare Earth Element (REE) oxides. |
6698e118c9c6a5c07a97d651 | 2 | Cairns et al., 2007, prepared rare earth element (REE) standards, according to the method described in . "The finished glass contains SiO2, Rare Earth (RE) oxide [REO] (generally RE2O3), CaO and Al2O3. The gels were made in a similar manner to that described in . The gel starting materials were prepared by mixing the required weights of standard solutions of aluminium, calcium and rare earth nitrates to produce a final weight of either 5g or 10g. Ethanol was then added to the nitrate mixture. This was to ensure the miscibility of tetraethyl orthosilicate …, used for the silica component, which was added next. Concentrated ammonia … was then added to form a gelatinous precipitate of hydroxides. The mixture was then covered and left for at least 16 hours to ensure the complete hydrolysis of the TEOS. The gels were then slowly dried," … etc. . |
6698e118c9c6a5c07a97d651 | 3 | The rare earth elements (REE) set consists of 17 chemical elements, that is the fifteen lanthanides, plus scandium and yttrium. "Compounds known as rare earth oxides (REO) are readily formed from rare earth elements as they are typically very reactive with oxygen in the ambient atmosphere" (Mo-Sci, 2023). Industries regarding "catalysts, glassmaking, lighting, and metallurgy, have been using rare earth elements for a long time. Such industries, when combined, account for 59% of the total worldwide consumption" (Mo-Sci, 2023). Newer application areas are concerning battery alloys and ceramics. For glass production, the addition of REOs changes the properties of glasses. "The ability to change the fluorescent properties of the glass is one of the most important uses of REO in glass" (Mo-Sci, 2023). |
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