A High‐Entropy Strategy for Chemoresistive Ethanol Sensors With Remarkably Rapid and Selective Response
Year: 2026
Type: article
Abstract: ABSTRACT Chemoresistive gas sensors based on metal oxide semiconductors provide a stable and low‐cost platform for gas monitoring. However, single‐metal oxides exhibit limited sensitivity and selectivity due to insufficient active sites and weak catalytic activity. Although metal doping and noble‐metal decoration provide partial improvements, these strategies remain constrained by dopant instability, restricted compositional diversity, and high material costs of noble metals. High‐entropy materials offer an attractive platform for tuning structure and reactivity through large configurational entropy, which induces lattice distortion, diverse electronic coordination, and defect‐rich environments. Here, we present an ethanol (C 2 H 5 OH) gas sensor based on high‐entropy oxide (HEO) nanostructures composed of In, Sn, Fe, Zn, and W. The HEO‐based sensor exhibits a higher response to ethanol than low‐ and medium‐entropy oxides and maintains fast kinetics, stable cycling, and reliable operation under humid conditions. Mechanistic analysis reveals that entropy‐driven shifts in the d ‐band structure, the enrichment of oxygen vacancies, and increased chemisorbed oxygen strengthen the surface reaction pathway. These findings establish configurational entropy as an effective strategy for chemoresistive gas sensors with high reactivity and robust long‐term performance.
Source: Advanced Science
DOI: 10.1002/advs.75724
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,730
Funders: National Research Foundation, Seoul National University, Ministry of Education, Rural Development Administration, National Research Foundation of Korea, National Research Council of Science and Technology, Ministry of Science and ICT, South Korea, Institute of Engineering Research, Seoul National University
OpenAlex ID: W7161799242
