Search

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.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has triggered an ongoing global pandemic, necessitating rapid and accurate diagnostic tools to monitor emerging variants and preparedness for the next outbreak. This study introduces a multidisciplinary approach combining Fourier Transform Infrared (FTIR) microspectroscopy and Machine learning to comprehensively characterize and strain-type SARS-CoV-2 variants. FTIR analysis of pharyngeal swabs from different pandemic waves revealed distinct vibrational profiles, particularly in nucleic acid and protein vibrations. The spectral wavenumber range between 1150 and 1240 cm–1was identified as the classification marker, distinguishing Healthy (noninfected) and infected samples. Machine learning algorithms, with neural networks exhibiting superior performance, successfully classified SARS-CoV-2 variants with a remarkable accuracy of 98.6%. Neural networks were also able to identify and differentiate a small cohort infected with influenza A variants, H1N1 and H3N2, from SARS-CoV-2-infected and Healthy samples. FTIR measurements further show distinct red shifts in vibrational energy and secondary structural alterations in the spike proteins of more transmissible forms of SARS-CoV-2 variants, providing experimental validation of the computational data. This integrated approach presents a promising avenue for rapid and reliable SARS-CoV-2 variant identification, enhancing our understanding of viral evolution and aiding in diagnostic advancements, particularly for an infectious disease with unknown etiology.

In this study, we have systematically investigated the impact of intrinsic device parameters on the performance of beta gallium oxide ( $\beta $ -Ga2O3) nanomembrane metal–semiconductor field effect transistors (NM-MESFETs). The ON- and OFF-state performances of these devices were analyzed using a well-calibrated technology computer-aided design (TCAD) simulator, focusing on breakdown voltage ${(} {V}_{\text {BR}} {)}$ , ON-resistance ( ${R}_{\text {ON}}{)}$ , threshold voltage ${(} {V}_{\text {th}} {)}$ , maximum drain current ${(} {I}_{\text {D},\text {max}} {)}$ , and power figure of merit ${(} \text {PFOM} {)}$ . Two distinct breakdown mechanisms are identified, significantly affecting ${V}_{\text {BR}}$ of these NM-MESFETs. Interestingly, when studying the effect of individual device parameters on ${V}_{\text {BR}}$ , we observed an initial increasing trend up to a critical value, followed by a sharp fall and a subsequent decreasing trend. Through simulations, an empirical model is developed that facilitates the selection of appropriate device parameters to achieve MESFETs with high ${V}_{\text {BR}}$ . In addition, an analytical model of ${V}_{\text {th}}$ of the MESFET in terms of its device parameters is also proposed and validated with simulation results. The performance of the NM devices is also benchmarked against reported Ga2O3 devices.