The implementation of low-cost sensitive and selective gas sensors for monitoring fruit ripening and quality strongly depends on their long-term stability. Gas sensor drift undermines the long-term reliability of low-cost sensing platforms, particularly in precision agriculture. We present a real-time drift compensation framework based on a lightweight Temporal Convolutional Neural Network (TCNN) combined with a Hadamard spectral transform. The model operates causally on incoming sensor data, achieving a mean absolute error below 1 mV on long-term recordings (equivalent to <1 particle per million (ppm) gas concentration). Through quantization, we compress the model by over 70%, without sacrificing accuracy. Demonstrated on a combustion-type gas sensor system (dubbed GMOS) for ethylene monitoring, our approach enables continuous, drift-corrected operation without the need for recalibration or dependence on cloud-based services, offering a generalizable solution for embedded environmental sensing—in food transportation containers, cold storage facilities, de-greening rooms and directly in the field.

This is the fourth part of a study presenting a miniature, combustion-type gas sensor (dubbed GMOS) based on a novel thermal sensor (dubbed TMOS). The TMOS is a micromachined CMOS-SOI transistor, which acts as the sensing element and is integrated with a catalytic reaction plate, where ignition of the gas takes place. The GMOS measures the temperature change due to a combustion exothermic reaction. The controlling parameters of the sensor are the ignition temperature applied to the catalytic layer and the increased temperature of the hotplate due to the released power of the combustion reaction. The solid-state device applies electrical parameters, which are related to the thermal parameters. The heating is applied by Joule heating with a resistor underneath the catalytic layer while the signal is monitored by the change in voltage of the TMOS sensor. Voltage, like temperature, is an intensive parameter, and one always measures changes in such parameters relative to a reference point. The reference point for both parameters (temperature and voltage) is the blind sensor, without any catalytic layer and hence where no reaction takes place. The present paper focuses on the study of the effect of humidity upon performance. In real life, the sensors are exposed to environmental parameters, where humidity plays a significant role. Humidity is high in storage rooms of fruits and vegetables, in refrigerators, in silos, in fields as well as in homes and cars. This study is significant and innovative since it extends our understanding of the performance of the GMOS, as well as pellistor sensors in general, in the presence of humidity. The three main challenges in simulating the performance are (i) how to define the operating temperature based on the input parameters of the heater voltage in the presence of humidity; (ii) how to measure the dynamics of the temperature increase during cyclic operation at a given duty cycle; and (iii) how to model the correlation between the operating temperature and the sensing response in the presence of humidity. Due to the complexity of the 3D analysis of packaged GMOS, and the many aspects of humidity simultanoesuly affecting performane, advanced simulation software is applied, incorporating computational fluid dynamics (CFD). The simulation and experimental data of this study show that the GMOS sensor can operate in the presence of high humidity.

The importance and challenges of ethylene detection based on combustion-type low-cost commercial sensors for agricultural and industrial applications are well-established. This work summarizes the significant progress in ethylene detection based on an innovative Gas Metal Oxide Semiconductor (GMOS) sensor and a new catalytic composition of metallic nanoparticles. The paper presents a study on ethylene and ethanol sensing using a miniature catalytic sensor fabricated by Complementary Metal Oxide Semiconductor–Silicon-on-Insulator–Micro-Electro-Mechanical System (CMOS-SOI-MEMS) technology. The GMOS performance with bimetallic palladium–platinum (Pd-Pt) and monometallic palladium (Pd) and platinum (Pt) catalysts is compared. The synergetic effect of the Pd-Pt catalyst is observed, which is expressed in the shift of combustion reaction ignition to lower catalyst temperatures as well as increased sensitivity compared to monometallic components. The optimal catalysts and their temperature regimes for low and high ethylene concentrations are chosen, resulting in lower power consumption by the sensor.

The long-term reliability of catalytic gas sensors is strongly influenced by changes in the chemical state and cleanliness of the catalyst surface. In this work, we investigate the surface composition and stability of the platinum (Pt) nanoparticle catalytic layer in Gas Metal-Oxide-Semiconductor (GMOS) sensors under varying environmental conditions. Using X-ray Photoelectron Spectroscopy (XPS) and High-Resolution (HR) XPS, we compared fresh, aged samples, thermally treated samples, and samples stored with or without a mechanical filter. The results show that prolonged ambient storage leads to the accumulation of adventitious carbon and nitrogen-containing species, as well as partial oxidation of platinum, which reduces the number of active metallic Pt sites. Thermal treatment at 300 °C for 30 min restores metallic Pt exposure by removing surface contaminants and narrowing the Pt 4f peaks. However, recontamination occurs during subsequent storage, with significant differences depending on surface protection. Sensors equipped with a mechanical filter exhibited obvious Pt metallic peaks in HR-XPS analysis, with lower carbon and nitrogen levels, compared to unprotected samples. These findings demonstrate that while heating refreshes catalytic activity, long-term stability requires complementary filtration to prevent re-adsorption of airborne species. The combined approach of heating and filtration is thus essential to ensure reliable performance of GMOS sensors for indoor and outdoor air quality monitoring.