Self-validating photoelectrochemical/photoelectrochromic visual sensing platform for ciprofloxacin precise detection in milk.

Background: In the process of food production, ciprofloxacin (CIP), a highly prescribed fluoroquinolone antibiotic, is often excessively used to reduce the risk of bacterial infection. However, this overuse can cause severe harm to human health, including allergic responses, gastrointestinal complications, and potential renal dysfunction. The development of a robust and precise detection method for CIP is crucial, given the interconnection between food security and human health. Compared to the single-mode detection methods currently in use, dual-mode detection provides enhanced accuracy in detecting results due to its inherent self-validation and self-correction capabilities.

Results: Herein, a photoelectrochemical and photoelectrochromic self-validated dual-mode sensing platform was developed to detect CIP in milk by laser etching method, signal generation (SG) region, signal output (SO) region and conductive channel was integrated on the same fluoide-doped tin oxide electrode and TiC/ZnO composite was modified in the electron SG region, and Prussian blue (PB) was electrodeposited in the SO region. By irradiating the SG region, photogenerated electrons are generated and injected into the SO region through the conductive pathway, resulting in the reduction of the PB to Prussian white (PW). Because the binding of CIP to its specifically recognized aptamers hinders electron transfer, a "Signal-Off" response mechanism can be used for simultaneous quantitative detection of CIP using photocurrent or color changes, which presents a great advantage in the detection process.

Significance: By integrating different detection mechanisms within a single linear range, the constructed dual-mode sensor has a wide detection range and low detection limit in milk samples. Additionally, it shows good selectivity in anti-interference experiments, providing a new idea for the development of visual analysis and detection platforms for food safety.