Specific Cu Active Sites of CuO Cluster Loading SnO <sub>2</sub> for Sensitive EMC Detection in Lithium-Ion Batteries

Conventional metal oxide sensors often lack sufficient sensitivity due to limited adsorption sites. Loading clusters is an effective surface-modification strategy. In this work, CuO clusters of approximately 3 nm were highly dispersed on high-surface-area SnO<sub>2</sub> nanoboxes. TEM confirmed the cluster sizes and dispersion degrees of the CuO clusters, while Raman and XPS collectively confirmed that CuO modifies the electronic structure of the SnO<sub>2</sub> support and induces the formation of new oxygen vacancies. The optimized Cu/SN-2 sensor exhibited a response of 39.25 toward 10 ppm ethyl methyl carbonate (EMC) at 130 °C, which is 1.92 times higher than that of SnO<sub>2</sub> nanoboxes (20.46 at 140 °C). The diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) indicated that CuO lowers the activation energy for EMC decomposition, producing intermediates like CO<sub>2</sub>, methanol, and ethanol at lower temperatures, thus reducing the sensor operating temperature. The density functional theory (DFT) calculations confirmed that Cu sites act as negatively charged centers, promoting EMC adsorption and enhancing sensitivity. This work offers a simple, low-cost surface modification route to improve gas sensing. Furthermore, as EMC is a common electrolyte in lithium-ion batteries, its leakage poses safety risks. Integrating such EMC sensors into battery safety systems can enable ultraearly leak warnings, helping prevent fires and explosions.

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Publication Details

Journal
ACS Sensors
Published
2025-12-05
DOI
https://doi.org/10.1021/acssensors.5c03151
Primary Topic
Gas Sensing Nanomaterials and Sensors
Type
article
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article

Specific Cu Active Sites of CuO Cluster Loading SnO 2 for Sensitive EMC Detection in Lithium-Ion Batteries

Chaoqi Zhu, Dawen Zeng, Kechen Zhou, Huiyu Su et al.
ACS Sensors
Gas Sensing Nanomaterials and Sensors
article

Specific Cu Active Sites of CuO Cluster Loading SnO 2 for Sensitive EMC Detection in Lithium-Ion Batteries

Chaoqi Zhu, Dawen Zeng, Kechen Zhou, Huiyu Su, Jiahong Tang, Chaofan Ma, Lingfei Luo, Xiaoxia Wang
article en

Abstract

Conventional metal oxide sensors often lack sufficient sensitivity due to limited adsorption sites. Loading clusters is an effective surface-modification strategy. In this work, CuO clusters of approximately 3 nm were highly dispersed on high-surface-area SnO<sub>2</sub> nanoboxes. TEM confirmed the cluster sizes and dispersion degrees of the CuO clusters, while Raman and XPS collectively confirmed that CuO modifies the electronic structure of the SnO<sub>2</sub> support and induces the formation of new oxygen vacancies. The optimized Cu/SN-2 sensor exhibited a response of 39.25 toward 10 ppm ethyl methyl carbonate (EMC) at 130 °C, which is 1.92 times higher than that of SnO<sub>2</sub> nanoboxes (20.46 at 140 °C). The diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) indicated that CuO lowers the activation energy for EMC decomposition, producing intermediates like CO<sub>2</sub>, methanol, and ethanol at lower temperatures, thus reducing the sensor operating temperature. The density functional theory (DFT) calculations confirmed that Cu sites act as negatively charged centers, promoting EMC adsorption and enhancing sensitivity. This work offers a simple, low-cost surface modification route to improve gas sensing. Furthermore, as EMC is a common electrolyte in lithium-ion batteries, its leakage poses safety risks. Integrating such EMC sensors into battery safety systems can enable ultraearly leak warnings, helping prevent fires and explosions.

ACS SensorsVol. 11(1)
Materials Processing (United States) (US), Huazhong University of Science and Technology (CN)
Openalex Percentile: Top 61%
Gas Sensing Nanomaterials and Sensors
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Specific Cu Active Sites of CuO Cluster Loading SnO <sub>2</sub> for Sensitive EMC Detection in Lithium-Ion Batteries — Chaoqi Zhu, Dawen Zeng, et al. · ACS Sensors (2025) | TGRS Research Map | TGRS