This paper addresses the long-standing challenge of poor selectivity in MOS sensors towards gases with similar properties, exemplified by the difficulty in distinguishing between H₂ and CO in mixed environments. In this paper, n-SnO₂/p-Co₃O₄ nanocomposites have been prepared as sensing materials by a straightforward pyrolytic bimetallic organic framework method. Optimal hydrogen detection is achieved with a Sn: Co molar ratio of 1:0.15, demonstrating a high response ($S=R_a/R_g=22.75$$S=R_a/R_g=22.75$ for 300 ppm H₂) and exceptional anti-interference capability against CO ($S_{H_2}/S_{CO}=11.67$$S_{H_2}/S_{CO}=11.67$) at 325°C. Furthermore, the incorporation of Extreme Learning Machine (ELM) model for predicting hydrogen concentration significantly reduces the interference effect common in mixed gases, achieving a high prediction accuracy a remarkably low relative error of just 0.6 %. The mechanism behind the enhanced sensitivity and selectivity of the sensor is elucidated in the paper, offering both theoretical insight and practical solution to the issue of selectivity in MOS sensors.

中文翻译：

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SnO2-Co3O4 纳米复合传感器：在混合气体环境中实现超选择性氢气检测


本文解决了 MOS 传感器对具有相似特性的气体选择性差的长期挑战，例如在混合环境中难以区分 H₂ 和 CO。本文采用直接热解双金属有机框架法制备了 n-SnO₂/p-Co₃O₄ 纳米复合材料作为传感材料。当 Sn：Co 摩尔比为 1：0.15 时，可实现最佳氢检测，表现出高响应 （S=Ra/Rg=22.75“>$S=R_a/R_g=22.75$ 对于 300 ppm H₂） 和特殊抗 CO 抗干扰能力 （ ${}_{{\mathrm{S\; 高}}_2}/S_{CO}=11.67$）。 此外，用于预测氢气浓度的极限学习机 （ELM） 模型显著降低了混合气体中常见的干扰效应，实现了高预测精度，相对误差仅为 0.6%。本文阐明了传感器灵敏度和选择性增强背后的机制，为 MOS 传感器的选择性问题提供了理论见解和实际解决方案。