However, such optimization routes typically require critical raw materials (e.g., Pt, Rh, Pd, Ti, W) , exotic fabrication conditions, and an increase of the complexity of the particle formation process without a significant reduction in device operating temperature. This yields sensor platforms that require significant resources for fabrication and operation, which need to be reduced further if they are to be used extensively as part of the Internet of Things. A comparison of typical sensor fabrication and operation conditions is presented in Table . Semiconductor MOX nanoparticle investigations have typically favored n-type particles, in part due to their greater enhancement in sensing performance with particle size reduction than ptype MOX . However, p-type materials have recently been demonstrated to have lower optimal operating temperatures than n-type MOX; this suggests their suitability for room temperature VOC sensors , which would dramatically reduce their energy consumption and allow for human wearable sensors. Further, p-type responsive sensors are invaluable for machine-learning-enabled enose sensor arrays, providing a discrete and opposite response polarity assisting in the fingerprinting of analytes .