Sulfur vacancy-mediated self-photocatalysis boosted electrochemiluminescence sensing via molecular oxygen activation for sensitive detection of isocarbophos

Abstract

The luminol-dissolved oxygen electrochemiluminescence (ECL) system is fundamentally limited by the poor redox activity of oxygen, which fails to generate sufficient reactive oxygen species (ROS) for efficient luminol excitation, restricting the performances of the sensors. Herein, a high-performance ECL sensor was fabricated for sensitive detection of isocarbophos (ICP) by utilizing sulfur vacancy engineered ZnIn 2 S 4 (S v -ZIS) as a novel photocatalysis co-reaction accelerator with superior molecular oxygen activation. The introduction of sulfur vacancy not only effectively enhanced visible-light harvesting but also accelerated interfacial electron transfer while maintaining a sufficient thermodynamic driving force for O 2 reduction, generating abundant ROS for the luminol ECL reaction, thus remarkably boosting the ECL signal intensity and luminescence stability of the system. By integrating with the specific target recognition capability of aptamers, the as-fabricated aptasensor achieved highly sensitive and specific detection of organophosphorus pesticide ICP, with a linear range of 0.1 ~ 50 ng/mL, an ultralow limit of detection (LOD) of 32.63 pg/mL at a signal-to-noise (S/N) ratio of 3, and excellent detection stability. This proposed sensing platform can be successfully applied to the quantitative determination of ICP in real samples, providing a reliable strategy for pesticide residue monitoring and a new avenue for sensitive detection of hazardous small molecules in environmental and food analysis.

Supplementary files

Article information

Article type
Paper
Submitted
13 May 2026
Accepted
22 Jun 2026
First published
22 Jun 2026

Analyst, 2026, Accepted Manuscript

Sulfur vacancy-mediated self-photocatalysis boosted electrochemiluminescence sensing via molecular oxygen activation for sensitive detection of isocarbophos

S. Lei, A. Lu, D. Jiang, X. Wang, Z. Chen, X. Du, X. Shan and W. Wang, Analyst, 2026, Accepted Manuscript , DOI: 10.1039/D6AN00561F

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