PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 3, 2026ACS Applied Electronic Materials1 citationsOpen Access

High-Precision Multigas Detection Based on Pd–Au Bimetallic Decorated ZnO Gas Sensors and PSO Feature Optimization

View Full Paper
MJMingzhi JiaoXuzhou Medical CollegeLDLei DuanHenan University of TechnologyJZJiade ZhangChina University of Mining and Technology

Key Points

  • The aim is to improve multigas detection sensitivity and selectivity using ZnO gas sensors with bimetallic decoration.
  • Development of ZnO gas sensors decorated with palladium and gold.
  • Analysis of surface morphology using electron microscopy.
  • Surface composition examined through X-ray photoelectron spectroscopy.
  • Gas sensing capabilities assessed for hydrogen, ethylene, and acetylene.
  • Utilization of particle swarm optimization for feature selection with support vector machine classification.
  • Pd/Au decoration significantly increases sensor response, achieving a 178-fold sensitivity for 160 ppm hydrogen.
  • Recognition accuracy of 96.07% across various gas samples shows superior performance of the developed sensors.

Abstract

To address the demands for efficient detection of combustible gas, such as hydrogen (H2), ethylene (C2H4), and acetylene (C2H2), in complex gas environments relevant to environmental monitoring, industrial safety, and smart homes, we developed zinc oxide (ZnO) gas sensors featuring bimetallic surface decoration with palladium (Pd) and gold (Au). Electron microscopy reveals the surface morphology of the sensing film and Pd/Au codecoration conformation, while X-ray photoelectron spectroscopy supports that codecoration modulates the distribution of surface oxygen species on ZnO. Gas sensing measurements demonstrate that Pd/Au decoration enhances both sensitivity and selectivity toward H2, C2H4, and C2H2. Specifically, a sensor decorated with 0.6 nm Pd and 2.3 nm Au exhibits a 178-fold increase in response to 160 ppm H2. Furthermore, by combining particle swarm optimization algorithm for feature selection with a support vector machine classifier, a recognition accuracy of 96.07% is achieved across seven different pure and mixture gas samples. This study presents a high-accuracy ZnO-based gas sensing platform optimized through both material engineering and machine learning algorithm, providing a reliable solution for real-world applications in industrial safety and environmental monitoring.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Jiao et al. (2026) studied this question.

synapsesocial.com/papers/69cf5ebc5a333a821460d459https://doi.org/10.1021/acsaelm.6c00259
Ask AI
Helpful
Bookmark
Share
View Full Paper