The cross-sectioned sample was deposited on a MEMS heating chip, lying flat on the chip and perpendicular to the ion beam. TOF-SIMS analysis was performed using Ga primary ion beams at an accelerating voltage of 30 kV and a current of 0.23 nA. The lateral resolution of an image under these conditions was defined as better than 290 nm, following the approach of measuring the distance across an abrupt step in the Na + signal accounting for an intensity change from 20% to 80% of the maximum intensity (see SI, Fig. ). This estimated spatial resolution incorporates considerations of the ion yield, signal-to-noise ratio, and mass resolution (see SI, Fig. ) and represents a highly conservative estimate as it assumes a sharp, edge-on experimental interface. Moreover, experimental features at distances less than 290 nm may be resolvable as a result of simultaneous changes in other signal channels. In a typical in situ TOF-SIMS heating experiment, the ion beam dynamically traverses the surface of a cross-sectioned specimen (see SI, Experimental Section). Here, isochronal heating was applied from 50 to 850 °C (at a heating rate of 1 °C/second), producing a Z-direction (direction of successive two-dimensional scans) that directly tracks the temperature. Each frame's acquisition time was 1.4 seconds, allowing for real-time observation of chemical dynamics. A list of peaks found in the mass spectrum (a 'peak list') was generated at every probe position, resulting in a hyperspectral data-cube at each temperature that contains information about both chemical elements and compounds. Figure shows four spatial maps and their associated spectral factors from a TOF-SIMS result at a temperature frame of 250 °C, extracted through non-negative matrix factorization (NMF). The region-of-interest is cropped from the larger area used for data collection (yellow dashed line in Fig. ), offering a simpler, multi-layered structure for close analysis. NMF, a blind source separation technique rooted in machine learning, can effectively disentangle multiple contributing signals (sources) within a spectrum image . NMF is one of several multivariate statistical approaches which aims to linearly decompose a data set into a simplified representation subject to particular assumptions or constraints. NMF operates under the physical assumptions that the data are non- negative and that the entire spectrum image can be separated into a few spectral factors and their corresponding maps. The number of components used in NMF was determined using established procedures for principal component analysis (PCA) and NMF decomposition (see SI, Experimental Section and Fig. ). These components contain essential information for comprehensive representation of the dataset while excluding factors that describe noise. In this instance, we found that eight non-negative components adequately described the dataset. In Figure , we show only the four main components with spectral factors primarily presenting 11 B + , Na + , 52 Cr + , and 56 Fe + peaks while the next four components show the peaks for 10 B + , Cr + , Cr + , and 55 Fe + (Fig. ).