Despite recent significant advancements in gas sensor array technology, accurately identifying gases in mixed environments remains challenging. This difficulty is primarily due to the rapid and competing processes of gas molecules attaching to (adsorption) and detaching from (desorption) the sensor. In this study, we present a simple method to fabricate a 2 × 4 SMO-based gas sensor array, coupled with a sparse recurrent neural network (SRNN) that employs weight regularization. The recurrent layers of the SRNN process nonlinear information and capture temporal dependencies in the sensor data, while the regularization technique simplifies the model, making it both efficient and easier to interpret. Additionally, we introduce a novel feature: the dynamics of current, labeled as ΔI. This feature enables the SRNN model to efficiently detect the adsorption and desorption of gas molecules. We demonstrate that our model can distinguish between three intuitively indistinguishable datasets of gas species (NO2, HCHO, and a mixture) with up to 92 % accuracy. By utilizing the fast and competitive adsorption/desorption information of gas molecules, our model can be applied to various gas combination environments, unlike conventional gas sensing data measured over longer periods. By integrating the sensor array with the advanced SRNN model, we pave the way for sophisticated e-nose systems, with potential applications in advanced gas sensing technologies, such as disease diagnosis through exhaled breath analysis and the detection of toxic species in mixed gas environments.

中文翻译：

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增强 e-nose 系统中的混合气体鉴别能力：利用 SMO 阵列传感器瞬态电流波动的稀疏递归神经网络


尽管气体传感器阵列技术最近取得了重大进展，但在混合环境中准确识别气体仍然具有挑战性。这种困难主要是由于气体分子附着（吸附）和分离（解吸）传感器的快速竞争过程。在这项研究中，我们提出了一种简单的方法来制造基于 2 × 4 SMO 的气体传感器阵列，并耦合采用权重正则化的稀疏递归神经网络 （SRNN）。SRNN 的递归层处理非线性信息并捕获传感器数据中的时间依赖关系，而正则化技术简化了模型，使其既高效又易于解释。此外，我们还引入了一个新特征：电流动力学，标记为 ΔI。这一特征使 SRNN 模型能够有效地检测气体分子的吸附和解吸。我们证明，我们的模型可以区分三种直观上无法区分的气体种类数据集（NO2、HCHO 和混合物），准确率高达 92%。通过利用气体分子的快速和有竞争力的吸附/解吸信息，我们的模型可以应用于各种气体组合环境，这与在较长时间内测量的传统气体传感数据不同。通过将传感器阵列与先进的 SRNN 模型集成，我们为复杂的电子鼻系统铺平了道路，在先进的气体传感技术中具有潜在应用，例如通过呼气分析进行疾病诊断和混合气体环境中有毒物质的检测。