摘要
Defect-engineered ZIF-67/BC composite achieves BET surface area of 747.0 m 2 /g. • Sensor sensitivity is 4.56-fold higher than conventional paper-based counterparts. • High selectivity for C 2 H 4 with stable response across 10-80% RH humidity range. • Colorimetric sensors enable full-cycle visual monitoring of fruit ripening stages. The development of highly sensitive ethylene (C 2 H 4 ) colorimetric sensors is often hindered by the low gas adsorption capacity of conventional substrates. Herein, we report a highly sensitive colorimetric sensor based on a defect-engineered ZIF-67/bacterial cellulose (Def-ZIF-67/BC) composite. Nitrogen adsorption analysis revealed that the integration of ZIF-67 and subsequent defect induction elevated the BET surface area from 83.9 m 2 /g (pristine BC) to a remarkable 747.0 m 2 /g, providing an 18.3-fold increase in adsorption capacity compared to paper-based substrates. The interconnected 3D nanofibrillar network of the Def-ZIF-67/BC composite provides a robust platform for efficient signal transduction. The sensor utilizes PdCl 2 -catalyzed Wacker oxidation of C 2 H 4 . Water-induced defect engineering enhanced the apparent ethylene-triggered colorimetric response, while PdCl 2 was essential for signal generation. Through systematic optimization of the catalyst-to-dye ratio, the TB and MR + BG sensing elements yielded maximum Euclidean distance values of 137 and 140, respectively, upon exposure to 50 ppm C 2 H 4 . This synergistic combination of physical adsorption enrichment and chemical signal amplification results in a 4.56-fold enhancement in sensitivity over traditional paper-based substrates, demonstrating the efficacy of defect engineering in trace gas detection. Performance evaluations confirmed that the sensor is highly selective toward C 2 H 4 and remains stable under fluctuating humidity levels due to the structural advantages of the BC substrate. Monitoring of climacteric fruits demonstrated the ability of the sensor to distinguish unripe, ripe, and overripe stages through clear colorimetric transitions. This work provides a robust and cost-effective solution for non-destructive quality monitoring in intelligent food supply chains. Download: Download high-res image (427KB) Download: Download full-size image
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