Model-derived conversion formula for real-time gas monitoring based on chemiresistive sensors

Abstract

Chemiresistive gas sensors transduce gas adsorption into changes in the electrical resistance across a pair of electrodes connected by a sensitive layer of material. This type of sensor is used due to its simple operation, high sensitivity, low cost, and convenience for scaled-up manufacturing of microsized devices. The conversion of the electrical resistance to a corresponding gas concentration is often performed through calibration procedures using empirical formulas, overlooking part of the physical phenomena involved in the process, both on the sorption kinetics and on the transduction. Consequently, a direct evaluation of gas concentration is plagued by the response delays and slow recovery intrinsic to these processes. In contrast to this approach, here we first propose a physical model, based on gas-modulated potential barriers, and considering the out-of-equilibrium dynamic response. Based on this model, we derive an original conversion formula able to dynamically convert the resistance changes into a corresponding gas concentration thus eliminating the main drawback related to slow response and recovery. This new strategy is demonstrated for real-time NO2 gas sensing, using chemiresistors based on oxidized PbS nanocrystals. In addition, the broader application of the proposed model and strategy is demonstrated for NH3 sensing, based on polypyrrole/gold junctions.

Figures (3)

• Figure 1: (a) A scheme of the sample/die and the geometries of IDEs used for coating with the PbS-NCs. (b) SEM-image showing the morphology of the sensing layer of PbS-NCs. In the inset, is the high-resolution image (HR-TEM) of a single PbS-NC. (c) XRD spectra of PbS-NCs, after thermal-treatment at 220 ° C for 30 minutes in vacuum (in red) and after subsequent thermal-treatment at 220 ° C for 30 minutes in ambient-air (in blue).
• Figure 2: (a) Normalized resistance variation of sensors sv and sa when different concentrations of NO_2-gas are mixed (in intervals of 30-minutes) to a constant flow (2-liters/minute) of synthetic-air with constant relative humidity (44%). The red lines corresponding to sample sv and the blue lines corresponding to sample sa. The continuous lines corresponds to the experimental data, and the dashed lines are fittings to the data using the model, with the parameters in Table \ref{['tab.ModParam']} substituted in (\ref{['eq:transd']}) and (\ref{['eq:sorp']}). (b) Validation of the DS-strategy: In purple colour, the comparison between the continuous and the dashed lines allows to confirm the validity of relation (\ref{['eq:dconva']}) when applied to the resistance data shown in panel (a). In panels (c) and (d), the corresponding surface occupancy functions of each sensor during the test, $\theta_{sv}(t)$ and $\theta_{sa}(t)$, after solving (\ref{['eq:sorp']}).
• Figure 3: Configuration of a sensing device formed by nanocrystals. (a) Side view representation of intergranular potential barriers ($\phi_{B}$) that are formed between adjacent nanocrystals (NCs). (b) Top view configuration of a device based on a random network of nanocrystals connecting the two electrodes, where the conductive path is formed by random percolation chains (white arrows representing the charge path). The electron-depletion layer on the surface of n-type particles (or a hole-accumulation layer for p-type particles) is represented in white around the NCs.