Methanol is a plant-associated volatile organic compound linked to growth, stress, senescence, and postharvest physiological changes, making its detection relevant to agricultural monitoring. In this work, we enhanced a micro-proton exchange membrane fuel cell (micro-PEMFC) methanol sensor through two complementary strategies: doping of the proton exchange membrane and systematic evaluation of Monel mesh electrode architecture by increasing mesh density and modifying the electrode–membrane interfacial distribution. Sulfonated poly(ether ether ketone) (SPEEK) and phosphotungstic acid-doped SPEEK (HPW/SPEEK) membranes were evaluated using Monel 400 electrodes with mesh counts of 50, 100, and 200 mesh/in. HPW incorporation markedly improved the sensor response by enhancing proton transport, while increasing mesh density provided a more densely distributed electrode–membrane interface without substantially reducing vapor-accessible open area. Consequently, the best-performing HPW/SPEEK–200-mesh configuration produced an OCP response of 81.35 ± 1.46 mV at 500 parts per million (PPM) methanol, substantially higher than that of the 50-mesh/SPEEK configuration. The best-performing sensor exhibited logarithmic responses over 1–500 PPM, with OCP and amperometric sensitivities of 5.6759 mV/ln(PPM) and 0.7090 µA/ln(PPM), respectively, and corresponding limits of detection of 1.8 and 0.80 PPM. The amperometric response exhibited a response time of approximately 1.5 s, with OCP and amperometric repeatability relative standard deviations of 0.43% and 1.65%, respectively, while Bland–Altman analysis showed 95% limits of agreement from 1.46 to 2.81 mV at 200 PPM and −3.63 to 2.90 mV at 500 PPM, with all paired measurements falling within these limits, demonstrating encouraging consistency between the two independently fabricated sensors. These results demonstrate that coordinated membrane modification and evaluation of electrode architecture provide an effective route for improving micro-PEMFC methanol sensing and support its potential for postharvest and broader agricultural monitoring.
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Carrillo et al. (2026) studied this question.
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