Room-temperature rapid oxygen monitoring system in high humidity hydrogen gas environment towards water electrolysis application

• Published in: Sensors and actuators. B, Chemical Vol. 422; p. 136693
• Main Authors: Kwon, Yeongjae, Lee, Kichul, Kang, Mingu, Kim, Cheolmin, Ha, Ji-Hwan, Han, Hyeonseok, Yang, Seungki, Yang, Daejong, Seo, Jung Hwan, Park, Inkyu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2025
• Subjects: Glancing angle deposition; Machine learning algorithm; Metal oxide; Photoactivated gas sensor; Water electrolysis system; Metal oxide; Water electrolysis system; Photoactivated gas sensor; Machine learning algorithm; Glancing angle deposition
• ISSN: 0925-4005
• DOI: 10.1016/j.snb.2024.136693

Water electrolysis is one of the key processes for carbon-free hydrogen (H2) generation, yet it inherently presents the risk of oxygen (O2) permeation, a significant concern that must be addressed. Contrary to external atmospheric conditions, the environment within a water electrolysis system is characterized exclusively by high-purity H2, little O2, and moisture (H2O). However, there has been limited research on sensors capable of detecting O2 crossover in such a pure H2 atmosphere under high humidity conditions. In this study, we developed an electrolysis O2 monitoring (E-OM) sensor based on a semiconducting metal oxide (SMO) that can operate at a room-temperature with such high humidity through photoactivation with 365 nm LED light. A porous In2O3 nanofilm, prepared by glancing angle deposition (GLAD), was used as the gas sensing material, and copper nanoparticles (Cu NPs) were decorated on the In2O3 surface through electron beam evaporation to accelerate the O2 adsorption. Remarkably, the E-OM sensor showed a humidity-independent response to O2 gas in the H2 atmosphere. Meanwhile, since the response of the E-OM sensor itself was not fast enough for water electrolysis system application, convolutional neural network (CNN)-based signal processing in the time domain was applied. As a result, our E-OM sensor system could predict O2 concentrations from 0 to 2 vol% in an H2 environment with a relative humidity (RH) of 30–90 %, and the detection time was under 5 s. This development could enable safe, rapid, and cost-effective monitoring of O2 crossover in H2 production infrastructure. •First-time demonstration on O2 detection behavior of a semiconductor metal oxide (SMO) gas sensor in pure H2.•Nanoparticle catalyst study for O2 dissociation energy barrier in SMO gas sensor.•Humidity-independent response of photoactivated SMO gas sensor for room temperature O2 detection in H2 environment.•Highly accurate and fast calibration of O2 concentration via convolutional neural network(CNN)-based signal processing.