electrons. This indicates a partial oxidation of surface-terminating Sn species as a result of interactions with the hydroxyl or carbonyl sites. As SnO is a p-type semiconductor, this would increase the resistance due to the associated elimination of hole charge carriers caused by the charge donation when this surface is exposed. By contrast on the (001) surface the change in Sn charges is negligible which is consistent with the weaker interactions between the molecules and this surface. This suggests that the sensing is dominated by the more reactive (101) surface. As already outlined, due to the sparse granular network, the conduction profile is dominated by a series of bottleneck regions of plane interfaces that effectively modulate conduction amplifying the response. The resource-efficient hybrid LIG-SnO VOC sensors presented here demonstrate good sensitivity and stability under ambient conditions. They share the selectivity challenges common to all chemiresistive MOX sensors fabricated using pristine or undecorated MOX particles. However, reported literature methods to improve selectivity in chemiresistive MOX sensors (including codeposition and decoration with noble metals) are resource-intensive and present sustainability challenges . Considering future resource-efficient methods to improve selectivity for our LIG-SnO devices, Jaśkaniec et al. observed that the choice of reflux solvent and temperature determine the morphology of this family of SnO nanoparticles . For example, butanol reflux led to cubic platelets instead of the nanoflowers produced from IPA reflux. Using distinct SnO particle morphologies to create SnO-LIG sensing elements with known cross-selectivity behavior could enable orthogonal sensing approaches based on multi-sensor arrays on a single substrate. Such sensor arrays, integrated with a machine learning layer , could fingerprint the interaction matrix to enable selective identification, and thus accurate concentration estimation for VOCs.