The inadequate use of fertilizer leads to an imbalance of nitrogen in soil, which presents significant challenges to sustainable agriculture. To address this issue, a novel soil nitrogen sensor using reduced graphene oxide (rGO)-based field-effect transistor (FET) is proposed. In soil, nitrogen is present in the form of nitrate, nitrite, and ammonium ion; however, as nitrite content is exceptionally low, the detection of nitrite is not possible. Most of the research focuses on nitrate detection, but simultaneous detection of nitrate and ammonium ions is highly significant and challenging. The proposed concept enables a single FET device to detect both ammonium and nitrate ions at different gate potentials. The sensor demonstrates a very high response of 1050% for 3.5-ppm nitrate ion with a sensitivity of 0.9 µA/ppm and 860% for 3.5-ppm ammonium ion with a sensitivity of 0.45 µA/ppm at an optimized V_gs of 3.9 and 0.8 V, respectively. Moreover, the sensor exhibits promising attributes, including high selectivity and rapid response (35 s for NO₃^- ions and 41 s for NH₄⁺ ions). This facilitates real-time monitoring of soil nitrogen levels for precision agriculture applications.

Real-time pH and oxygen concentration sensing is critical for monitoring tissue damage and organ health; however, there is no report to date in such context of a single device that can simultaneously detect both pH and oxygen changes. This paper presents the development of a single optical sensor device that can simultaneously and reversibly respond to changes in both pH and oxygen concentration. The proposed optical sensor integrates both pH- and oxygen-sensitive probes, and is optimized to achieve minimal cross-sensitivity during simultaneous measurements. This approach enables high accuracy in early detection of chemical correlates of tissue or organ damage, improving screening and efficacy in organ transplants.

Breath biomarker detection has been a significant non-invasive approach for disease diagnosis. This method has significant potential for early diagnosis and accurate analysis of diseases. Emission from breath contains several volatile organic compounds. Among them, ammonia is a very commonly found VOC and mainly responsible for chronic kidney diseases. There exist several strategies to detect ammonia, however they demonstrate severe limitations such as cross-sensitivity and poor selectivity. This work demonstrates the synergistic effect of sensor functionalization and application of machine learning for selective detection of ammonia in the environment. The sensor exhibits high degree of selectivity towards ammonia owing to enormous hydroxyl groups contributed through curcumin. At 500 ppm ammonia, the sensor demonstrates 274% response and very high selectivity among seven volatile organic compounds. The machine learning models were trained with the help of sensor transients. Random Forest and CNN models were applied to predict the presence of ammonia in a mixture. Random Forest achieved 96.25% accuracy compared to 89% accuracy of CNN. Hence, Random Forest algorithms applied to curcumin functionalized reduced graphene oxide sensors can detect ammonia vapors with very high efficiency among a mixture of gases.

Graphene is the most fascinating material due to its exceptional electrical and mechanical properties. The reduced form of graphene oxide (GO) is best suitable for sensor applications. There are numerous methods available for reducing GO by synthetic reducing agents that are not environment-friendly and cost-effective. Therefore, in this research work, natural lemon juice is used as a potential reducing agent. It creates defects on the GO surface by altering various functional groups. These are clearly visible in Raman and FTIR results and help in the sensing of the lead (Pb²⁺) and cadmium (Cd²⁺) ions. Field effect transistor (FET) based active structure is used to amplify its sensitivity and discriminate the lead and cadmium ions by tuning the gate voltage. The optimized gate voltages for lead (Pb²⁺) and cadmium (Cd²⁺) ions are -1.6 V and 0.8 V, respectively, at which the sensor shows the maximum response. This green synthesis approach of sensor fabrication highlights sustainable and cost-effective solutions in the field of reduced graphene-based FET sensors.

This study introduces a high-performance flexible temperature sensor prepared using laser-induced graphene (LIG) doped with nickel oxide (NiO) nanoparticles (NPs). Unlike conventional LIG surface doping methods, we developed a nickel oxide-doped LIG flexible temperature sensor by introducing NiO NPs into a polyimide (PI) precursor solution cured into a film followed by ultraviolet (UV) laser treatment. This approach achieves a more stable and uniform doping process while further improving the sensing performance of LIG-based temperature sensors. Over a prospective temperature detection range (30-100 °C), the sensitivity of the NiO-doped LIG temperature sensor is significantly improved from -0.064% °C-¹ to -0.079% °C-¹, an improvement of 19.3%, compared to that of the intrinsic LIG temperature sensor, while maintaining high linearity (R2 = 0.999) as well as excellent temperature stability and reliability. This research not only enhances the performance of flexible temperature sensors based on LIG but also paves new pathways for its industrial production in various application fields.

While the majority of the reports on toluene gas sensors are on rigid electrodes and based on composite materials, doping with additional noble metals, or a high temperature detection method, this work is the first demonstration of the vanadium carbide (V₂C) MXene based flexible and room-temperature (RT) toluene gas sensor. The V₂C MXene is synthesized by an HF etching route. The field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) images exhibit a typical accordion-like multilayered structure of the V₂C MXene, where the Fourier-transform infrared spectroscopy (FTIR), Raman, and X-ray photoelectron spectroscopy (XPS) data further ensured its successful growth. The V₂C (band gap of 3.9 eV) based flexible gas sensor employing a polyester substrate, displays good reproducibility, quick response/recovery time (14 s/34 s), long-term stability, good cross-selectivity, and a low detection limit of 47.85 ppb over the linear region of 5-200 ppm toluene at RT (27 ± 1 °C). The effect of relative humidity (RH) toward RT toluene gas sensing has also been investigated here. This sensor shows an excellent response of 775% at 200 ppm toluene, with brilliant selectivity toward toluene over six other hazardous gases. The sensor’s plentiful surface functional groups (−F, −OH, −O) and superior electrical characteristics are responsible for its enhanced performance. In light of this, the flexible and RT toluene gas sensor based on the V₂C MXene can be a smart way to fabricate the next-generation toluene gas sensors.

According to the World Health Organization (WHO), mercury is one of the top ten toxic groups of substances that can pose the greatest threat to human life. Very minor contamination with mercury can adversely impact the nervous, digestive, and immune systems of the lungs, kidneys, skin, and eyes, resulting in severe health problems, including death. Conventional detection techniques are incredibly complex, costly, and lack portability. This article describes a highly sensitive, selective, and stable field effect transistor (FET)-based sensor for efficiently detecting mercury ions in water. Glutathione-reduced graphene oxide (glu-rGO) is chosen as the sensing material. The operating gate voltage of the device is optimized to -4.98 V to achieve maximum response. At a gate voltage of -4.98 V, the device's sensitivity is evaluated as 1.04μA/ppb for 1.2 ppb mercury compared to 0.51μ A/ppb at zero gate voltage. The device is tested against six common heavy metal ions and is found to be highly selective toward mercury. Therefore, the glu-rGO-based FET device is promising for future portable, economical, and user-friendly mercury ion detector systems.

Uncontrolled release of various harmful gases from automobiles and chemical industries demands accurate methods for gas classification and detection. In this context, this article proposes an effective method to classify and detect four gases - ammonia, formaldehyde, toluene, and acetone using a single field-effect transistor (FET)-based gas sensor. The gate voltage of the FET sensor played a pivotal role in this classification mechanism L-ascorbic acid functionalized graphene oxide (GO) was used as the sensing material of the FET device. Initially, various features of the fabricated FET sensor (i.e., % of response, response time, and recovery time) were captured by varying the applied gate voltage. Furthermore, classification algorithms such as decision tree (DT), support vector machine (SVM), gradient boosting (GB), and random forest (RF) were trained to automatically predict the target gases. An accuracy of 73% was achieved for all three classifiers other than the SVM classifier. The use of machine learning algorithms was fruitful to accurately detect four gases at different gate voltages when any unknown one among the four was exposed to the single gate-tuned sensor. Moreover, it also saved the system's power consumption as a single sensor was behaving like several sensors.

A precise measurement of soil potassium (K) concentration is crucial for enhancing agricultural productivity and promoting sustainable land management. The efficiency of real-time soil quality monitoring is hampered by the time-consuming laboratory analysis that is commonly associated with conventional methods. The present research introduces an innovative approach utilizing a field-effect transistor (FET) structure coated with reduced graphene oxide-decorated valinomycin (rGO-v) for the detection of potassium ions in soil samples. The sensor exploits the distinctive electrical properties of reduced graphene oxide (rGO) and the specific affinity of valinomycin for potassium ions. To construct the device, we applied rGO-v onto an FET substrate. The conductance of the FET can be modified by the interaction between valinomycin and potassium ions, enabling the detection of potassium ions. Some of the advantages of this technology are its high sensitivity, fast response time, and potential for miniaturization. In addition, the device is tuned to demonstrate an enhanced sensitivity of 0.98 μA/(kg/ha) below the threshold voltage. The sensor exhibits a response time of 40 s and demonstrates exceptional stability in the face of unfavorable conditions, specifically humidity. Therefore, valinomycin-decorated reduced graphene oxide, when subjected to appropriate gate bias, demonstrates promising results as a versatile, cost-effective, and easy-to-use potassium ion sensor.