p-Type CuInS ₂ Nanoplates as Bipolar and Quasi-Orthogonal Sensing Material for Room-Temperature Detection of H ₂ S/NO ₂ Gas Mixtures

Traditional gas-sensing research has largely focused on achieving high selectivity for single-gas detection, routinely dismissing materials that exhibit cross-sensitivity. Here, we challenge this paradigm by employing p-type bimetal sulfide CuInS 2 nanoplates that intrinsically exhibit distinct bipolar responses toward reducing H 2 S and oxidizing NO 2 . Rather than suppressing this cross-sensitivity, we convert it into discriminative information by integrating vacancy-amplified bipolar signal divergence, kinetic fingerprints, and a multilayer perceptron (MLP) model within a hierarchical quasi-orthogonal sensing framework. In this framework, the signed bipolar response expands the sensing distributions into oppositely directed quadrants, while additional weakly correlated kinetic descriptors further unfold the sensing manifold into a three-dimensional (3D) feature space. As a result, complex NO 2 /H 2 S mixtures can be accurately discriminated using a single sensing device, enabling array-level recognition without increasing the sensor number. Specifically, expanding the sensing manifold from 2D (response magnitude combined with either kinetic descriptor) to 3D (incorporating all three descriptors) improves the classification accuracy from 95 to 100% across a broad concentration domain (0.4–8 ppm), because the additional quasi-orthogonal dimension resolves residual overlaps remaining in lower-dimensional feature projections. These results establish a strategy for intelligent gas sensing by shifting the material-design paradigm from eliminating cross-sensitivity to deliberately engineering information-rich nanoscale sensing interfaces for data-driven discrimination.