Marijuana is listed as a Schedule I substance under the American Controlled Substances Act of 1970. As more U.S. states and countries beyond the U.S. seek legalization, demands grow for identifying individuals driving under the influence (DUI) of marijuana. Currently no roadside DUI test exists for determining marijuana impairment, thus the merit lies in detecting the primary and the most sought psychoactive compound tetrahydrocannabinol (THC) in marijuana. Salivary THC levels are correlated to blood THC levels making it a non-invasive medium for rapid THC testing. Affinity biosensing is leveraged for THC biomarker detection through the chemical reaction between target THC and THC specific antibody to a measure signal output related to the concentration of the targeted biomarker. Here, we propose a novel, rapid, electrochemical biosensor for the detection of THC in saliva as a marijuana roadside DUI test with a lower detection limit of 100 pg/ml and a dynamic range of 100 pg/ml – 100 ng/ml in human saliva. The developed biosensor is the first of its kind to utilize affinity-based detection through impedimetric measurements with a rapid detection time of less than a minute. Fourier transform infrared spectroscopy analysis confirmed the successful immobilization of the THC immobilization assay on the biosensing platform. Zeta potential studies provided information regarding the stability and the electrochemical behavior of THC immunoassay in varying salivary pH buffers. We have demonstrated stable, dose dependent biosensing in varying salivary pH’s. A binary classification system demonstrating a high general performance (AUC = 0.95) was employed to predict the presence of THC in human saliva. The biosensor on integration with low-power electronics and a portable saliva swab serves as a roadside DUI hand-held platform for rapid identification of THC in saliva samples obtained from human subjects.

Antimicrobial use in livestock has emerged as a pressing global issue because of the rise of antimicrobial-resistant bacteria. Regulatory authorities across the globe have taken steps to discourage the misuse of these antibiotics by banning or limiting the use of medically important antibiotics in food animals. However, to ensure that food animals are not being administered antibiotics inappropriately, there is a need for a reliable, raid-response biosensor that can detect the presence of these antibiotic residuals in meat products. We have developed an affinity-based electrochemical biosensor for the label-free detection of ceftiofur residues in meat samples. The sensor uses a self-assembled immunoassay to target the ceftiofur biomarker by employing electrochemical impedance spectroscopy to probe the interfacial capacitive changes as ceftiofur binds to the sensor surface. We have demonstrated a platform that can detect ceftiofur within 15 min of introducing the sample at concentrations down to 0.01 ng/mL in 1× phosphate-buffered saline and 10 ng/mL in 220 mg ground turkey meat samples.

Sustainable agriculture is the answer to the rapid rise in food demand which is straining our soil, leading to desertification, food insecurity, and ecosystem imbalance. Sustainable agriculture revolves around having real-time soil health information to allow farmers to make the correct decisions. We present an ion-selective electrode (ISE) electrochemical soil nitrate sensor that utilizes electrochemical impedance spectroscopy (EIS) for direct real-time continuous soil nitrate measurement without any soil pretreatment. The sensor functionality, performance, and in-soil dynamics have been reported. The ion-selective electrode (ISE) is applied by drop casting onto the working electrode. The study was conducted on three different soil textures (clay, sandy loam, and loamy clay) to cover the range of the soil texture triangle. The non-linear regression models showed a nitrate-dependent response with R² > 0.97 for the various soil textures in the nitrate range of 5–512 ppm. The validation of the sensor showed an error rate of less than 20% between the measured nitrate and reference nitrate for multiple different soil textures, including ones that were not used in the calibration of the sensor. A 7-day-long in situ soil study showed the capability of the sensor to measure soil nitrate in a temporally dynamic manner with an error rate of less than 20%.

C-reactive protein (CRP) is considered to be an important biomarker associated with many diseases. During any physiological inflammation, the level of CRP reaches its peak at 48 h, whereas its half-life is around 19 h. Hence, the detection of low-level CRP is an important task for the prognostic management of diseases like cancer, stress, metabolic disorders, cardiovascular diseases, and so on. There are various techniques available in the market to detect low-level CRP like ELISA, Western blot, etc. An electrochemical biosensor is one of the important miniaturized platforms which provides sensitivity along with ease of operation. The most important element of an electrochemical biosensor platform is the electrode which, upon functionalization with a probe, captures the selective antibody–antigen interaction and produces a digital signal in the form of potential/current. Optimization of the electrode design can increase the sensitivity of the sensor by 5–10-fold. Herein, we come up with a new sensor design called the spiral electrochemical notification coupled electrode (SENCE) where the working electrode (WE) is concentric in nature, which shows better response than the market-available standard screen-printed electrode. The sensor is thoroughly characterized using a standard Ferro/Ferri couple. The sensing performance of the fabricated platform is also characterized by the detection of standard H₂O₂ using a diffusion-driven technique, and a low detection limit of 15 µM was achieved. Furthermore, we utilized the platform to detect a low level (100 ng/mL) of CRP in synthetic sweat. The manuscript provides emphasis on the design of a sensor that can offer good sensitivity in electrochemical biosensing applications.

Climate change directly affects all aspects of the environment and accounts for changes in our surroundings seen or unseen. With the growing need for accountability in the agricultural commercial and regulatory spheres, there is a widely accepted consensus that there needs to be quantitative information on the different environmental entities to understand and solve challenges posed towards food production and agricultural activities. The main purpose of the creation of this article is to propagate an era of quantitative metrics to better assess environmental health which can adversely impact human health. This review assesses 3 different environmental vectors prone to pollution and in high contact with human entities. This review also discusses different electrochemical sensing platforms used as biosensors for the detection of a wide myriad of pollutants with an adverse effect on human health. Furthermore, the breakdown of each section includes a survey of the evolution and design of various sensor platforms that are aimed towards a comprehensive monitoring platform for pollutant screening with an emphasis on agro-safety and environmental health. Detailed pesticide screening case-studies are also explored for a better understanding of the current electrochemical biosensors that exist for the sole purpose of environmental monitoring.

Interleukin-31 has been reported to be involved with chronic skin conditions like atopic dermatitis (AD). This work focuses on the development of a portable IL-31 detection system that works with passive sweat over the physiologically relevant range-150–620 pg ml⁻¹. Four simulated flaring profiles were used to benchmark the IL-31 rise and fall detection capabilities of the sensor. These temporal profiles were generated according to the SCORAD range for severity of AD and were spanned across different dosing regimens. The sensing platform displays good sensitivity with a limit of detection of 50 pg ml⁻¹ and dynamic range of 50–750 pg ml⁻¹ for the flaring profiles in synthetic and human sweat, and with coupled portable electronics. Furthermore, in order to create a robust and predictive system, a machine learning algorithm was incorporated to create a flare prediction system. This algorithm shows high accuracy for the test data sets and provides the proof-of-concept for the use of ml coupled electrochemical systems for chronic diseases like AD.