Chemoresistive metal oxide gas sensors typically exhibit predictable resistance changes upon exposure to oxidizing or reducing gases, depending on their semiconductor type. However, anomalous concentration-dependent inversion of response has been reported, often attributed to intrinsic material properties. In this study, we investigate such inversion phenomena in Cr2O3-based sensors doped with Ti(IV), W(VI), and Ni(II), alongside Ag-decorated WO3 sensors. Initial observations suggested inversion of response to low concentrations of O2, consistent with prior hypotheses involving carrier-type transitions. However, systematic experiments revealed that this inversion was not intrinsic but rather caused by trace water contamination in the analyte gas stream. Only through cryogenic drying of the analyte gases was the expected sensor behavior restored. Mass spectrometry confirmed water as the sole contaminant responsible. Further tests demonstrated that water induces a rapid resistance increase in p-type Cr2O3 sensors, mimicking an n-type response. These findings highlight the critical influence of humidity on sensor behavior and suggest that some previously reported inversion phenomena may stem from undetected water interference. This work underscores the necessity of rigorous gas stream purification in low-concentration gas sensing studies.
Aggregation-induced emission luminogens (AIEgens) exhibit significant application potential in materials science due to their unique photophysical properties. However, systematically elucidating their structure-property relationships remains challenging due to the high dispersion of data, the complex correlations of features, and the limited interpretability of traditional machine learning models. Herein, we constructed a data-driven and interpretable deep learning model (referred to as GATM) that integrates multisource data from the literature, including molecular structures, photophysical parameters, and solvent environments. By integrating graph neural networks with machine learning algorithms, this multimodal predictive framework successfully deciphers the intricate relationships between molecular structural features, solvent environments, and photophysical properties. The visualization of solvent-solute interaction mechanisms was achieved through multilevel attention capture and feature quantification analysis utilizing the graph attention network (GAT). Furthermore, the GAT also provided deep insights into the influence of key structural features-such as atomic type and hybridization state-on the luminescence mechanisms of AIEgens. The results demonstrate that GATM achieves high predictive accuracy (mean R2 > 0.90) for key parameters of AIEgens, including fluorescence lifetime, quantum yield, and maximum absorption/emission wavelengths. Subsequent molecular synthesis experiments further validated the model's predictive accuracy. Furthermore, the synthesized molecules underwent organic pesticide detection and discrimination experiments, achieving a low detection limit (0.4 nM) and 100% discrimination accuracy. This intelligent prediction platform provides a novel paradigm for the rational design of new AIEgens and paves the way for future inverse design research for other functional materials.
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