Organic thin-film transistors (OTFTs) are miniaturized devices based upon the electronic responses of organic semiconductors. In comparison to their conventional inorganic counterparts, organic semiconductors are cheaper, can undergo reversible doping processes and may have electronic properties chiefly modulated by molecular engineering approaches. More recently, OTFTs have been designed as gas sensor devices, displaying remarkable performance for the detection of important target analytes, such as ammonia, nitrogen dioxide, hydrogen sulfide and volatile organic compounds (VOCs). The present manuscript provides a comprehensive review on the working principle of OTFTs for gas sensing, with concise descriptions of devices’ architectures and parameter extraction based upon a constant charge carrier mobility model. Then, it moves on with methods of device fabrication and physicochemical descriptions of the main organic semiconductors recently applied to gas sensors (i.e., since 2015 but emphasizing even more recent results). Finally, it describes the achievements of OTFTs in the detection of important gas pollutants alongside an outlook toward the future of this exciting technology.

Articulate the most diverse and sophisticated technologies, such as Remote Sensing, Big Data, Cloud Computing, Internet of Things, 3D Printing, among others, is part of universe 4.0, whether industrial or agricultural. Focusing on agricultural context, this paper proposes a low-cost 4.0 device to perform the monitoring and control of certain environmental variables for the detection of aflatoxins in peanut crops. Aflatoxins are toxic metabolite of fungi genus Aspergillus that can cause toxic and carcinogenic effects in humans and animals. The device developed was able to monitor temperature and humidity variations helping the aflatoxins identification. The equipment portability allows its use in silos with encapsulation via Additive Manufacturing, besides the aflatoxin prediction from Machine Learning algorithms.

When present in specific amounts in the air, the colorless, odorless, and tasteless gases monoxide and carbon dioxide may either replace oxygen in red blood cells (CO) or increase the respiratory rate causing cardiac arrhythmias (CO2), leading to death. Commercial sensors take around 8 h to detect levels of CO (50 PPM), causing moderate poisoning. SnO2 presents controlled interactions with the atmosphere using conductance and vacancy adjustments to capture electrical properties. However, the selectivity of gas detection by SnO2 can still be improved, thus also increasing the application possibilities. The present study aimed to optimize the sensing of CO and CO2 in SnO2 using palladium functionalization. The vapor-liquid-solid method synthesized a network of SnO2 nanobelts decorated with palladium nanoparticles. The sensitivity of the sensors for CO and CO2 were evaluated, characterizing parameters such as response time, a wide range of CO and CO2 concentrations, and temperature. In the seventh measurement cycle, the sensor response for different concentrations of gases in consecutive cycles showed a sensitivity of up to 125% for CO in 60 s. Furthermore, we observed increased sensor sensitivity with material doping with nanoparticles from 130 ppm to 1360 ppm in 30 seconds to CO. Conclusion: The results provide a better understanding of the sensitivity of SnO2 in palladium-decorated nanoparticles, offering insights for detecting low CO concentrations quickly. The behavior of these doped nanosensors showed us the importance of considering them as a practical possibility for detecting these gases of importance to human health.

The change in the color of the vegetables peel during the ripening process is the main criterion used by the consumer to define the fruit ripeness degree and for the producer to determine the best time of harvest. This relationship between bark coloration and different maturation stages allows the producer to establish harvest planning and extend shelf life.  Students and faculty of the Biosystems Engineering course at São Paulo State University (UNESP), Tupã Campus, designed and developed a low-cost prototype of a fruit sorting belt, specifically for cherry group tomatoes. In the future, improvement in machinery with the insertion of new devices such as cameras, embedded system, combines sensor technology 3.0 with machine learning 4.0.

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