journal article·2026-02

Synergistic Effect of High‐Energy (211) Facets and (101) Twin Defects of SnO 2 Nanowires for Ultrasensitive ppb‐Level Formaldehyde Monitoring

Ya‐Ning Wang, Jia Yang, Xue‐Li Hu, Yan‐Yang Li, Lin Zhang, Qing‐Chao Liu, Hong‐Chang Yao

Advanced Functional Materials· Volume 36· Issue 15
Publisher page

Abstract

Abstract SnO 2 gas sensors have attracted significant interest for formaldehyde detection due to their real‐time monitoring capabilities, cost‐effectiveness, and portability. However, low sensitivity, poor selectivity, and limited long‐term stability remain critical challenges for trace‐level formaldehyde detection. Here, the synthesis of SnO 2 nanowires with preferentially exposed high‐energy (211) facets and abundant (101) twin defects via a solvothermal approach is reported. Gas‐sensing evaluations revealed that sensors constructed from these nanowires, particularly those with maximized exposure of (211) facets and (101) twin defects, exhibited an impressive response of 15.2 toward 10 ppm formaldehyde. Notably, these sensors demonstrated exceptional selectivity for formaldehyde against interfering gases, including methanol, toluene, ammonia, formic acid, acetone, and ethanol, with a detection limit as low as 59 ppb and robust long‐term stability. The enhanced formaldehyde sensing performance is attributed to two synergistic effects: i) the increased density of active sites for oxygen adsorption on the high‐energy (211) crystal facets, which possess abundant atomic steps, and ii) the Schottky barrier at the twin boundaries, which broadens the depletion layer and accelerates electron transfer through its intrinsic electric field. These findings highlight the promising strategy of engineering high‐energy crystal facets and twin defects to significantly elevate the gas‐sensing performance of semiconductor nanomaterials.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Gas Sensing Nanomaterials and Sensors · Analytical Chemistry and Sensors · Advanced Chemical Sensor Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Chemistry Zhengzhou University Zhengzhou 450001 China

Funding

Natural Science Foundation of Henan Province

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