Microplotter-Printed On-Chip Combinatorial Library of Ink-Derived Multiple Metal Oxides as an “Electronic Olfaction” Unit

Abstract

Information about the surrounding atmosphere at a real timescale significantly relies on available gas sensors to be efficiently combined into multisensor arrays as electronic olfaction units. However, the array’s performance is challenged by the ability to provide orthogonal responses from the employed sensors at a reasonable cost. This issue becomes more demanded when the arrays are designed under an on-chip paradigm to meet a number of emerging calls either in the internet-of-things industry or in situ noninvasive diagnostics of human breath, to name a few, for small-sized low-powered detectors. The recent advances in additive manufacturing provide a solid top-down background to develop such chip-based gas-analytical systems under low-cost technology protocols. Here, we employ hydrolytically active heteroligand complexes of metals as ink components for microplotter patterning a multioxide combinatorial library of chemiresistive type at a single chip equipped with multiple electrodes. To primarily test the performance of such a multisensor array, various semiconducting oxides of the p- and n-conductance origins based on pristine and mixed nanocrystalline MnO x , TiO2, ZrO2, CeO2, ZnO, Cr2O3, Co3O4, and SnO2 thin films, of up to 70 nm thick, have been printed over hundred μm areas and their micronanostructure and fabrication conditions are thoroughly assessed. The developed multioxide library is shown to deliver at a range of operating temperatures, up to 400 °C, highly sensitive and highly selective vector signals to different, but chemically akin, alcohol vapors (methanol, ethanol, isopropanol, and n-butanol) as examples at low ppm concentrations when mixed with air. The suggested approach provides us a promising way to achieve cost-effective and well-performed electronic olfaction devices matured from the diverse chemiresistive responses of the printed nanocrystalline oxides.

Fabrication of Conductive and Gas-Sensing Microstructures Using Focused Deposition of Copper Nanoparticles Synthesized by Spark Discharge

“…This type of conductivity is indirectly confirmed by an increase in the resistance of the unsintered line in the presence of a gas impurity (carbon monoxide and ammonia). It is known that the resistance increase in a concentrated gas environment occurs due to the formation of surface-active radicals exhibited by the dissociation of gas impurity molecules at heated oxide surfaces [10]. This leads to the formation of free electrons.…”

“…This leads to the formation of free electrons. The excessive free electrons appearing because of the surface reactions recombine with holes at p-type oxides reduces a free carrier concentration and thus increasing the resistance of the oxide [10].…”

“…Thus, various methods of direct printing with nanoparticles have been developed in recent decades. Among them are the following: inkjet [7], aerosol jet [8], screen [9], microplotter [10] and electrohydrodynamic [11] printing, focusing of aerosol nanoparticles using aerodynamic lenses [12], electrostatic masks [13], micro-nozzles [14], and others. However, one can note the technology of "dry" aerosol jet printing (AJP) [15,16].…”

Quantum of selectivity testing: detection of isomers and close homologs using an AZO based e-nose without a prior training

“…32 Unlike many other techniques requiring bulky apparatus, e-nose is often made following the lab-on-chip paradigm. 31,33 At this standpoint, e-nose functionally occupies a niche between single-unit sensors, 2 possessing good sensitivity, fast response, reasonable stability and low power consumption, and GC-MS devices with good sensitivity and selectivity, able to decompose complex gas-mixtures, still having high power consumption, high price and low portability. However, the e-nose is required to be "trained" for the task of recognition of a smell or analyte prior to its real application, i.e.…”

“…[27][28][29][30] Such a gas analytical system has been proven suitable for the discrimination of close homologs. 31 Using MOFs, the e-nose approach enhanced with machine learning yields enantioselective detection and discrimination of chiral molecules. 32 Unlike many other techniques requiring bulky apparatus, an e-nose is oen made following the lab-onchip paradigm.…”