Reduced Graphene Oxide Electrolyte-Gated Transistor Immunosensor with Highly Selective Multiparametric Detection of Anti-Drug Antibodies

Applications of Carbon-Based Multivariable Chemical Sensors for Analyte Recognition

Over recent decades, carbon-based chemical sensor technologies have advanced significantly. Nevertheless, significant opportunities persist for enhancing analyte recognition capabilities, particularly in complex environments. Conventional monovariable sensors exhibit inherent limitations, such as susceptibility to interference from coexisting analytes, which results in response overlap. Although sensor arrays, through modification of multiple sensing materials, offer a potential solution for analyte recognition, their practical applications are constrained by intricate material modification processes. In this context, multivariable chemical sensors have emerged as a promising alternative, enabling the generation of multiple outputs to construct a comprehensive sensing space for analyte recognition, while utilizing a single sensing material. Among various carbon-based materials, carbon nanotubes (CNTs) and graphene have emerged as ideal candidates for constructing high-performance chemical sensors, owing to their well-established batch fabrication processes, superior electrical properties, and outstanding sensing capabilities. This review examines the progress of carbon-based multivariable chemical sensors, focusing on CNTs/graphene as sensing materials and field-effect transistors as transducers for analyte recognition. The discussion encompasses fundamental aspects of these sensors, including sensing materials, sensor architectures, performance metrics, pattern recognition algorithms, and multivariable sensing mechanism. Furthermore, the review highlights innovative multivariable extraction schemes and their practical applications when integrated with advanced pattern recognition algorithms.

Harnessing Small-Molecule Analyte Detection in Complex Media: Combining Molecularly Imprinted Polymers, Electrolytic Transistors, and Machine Learning

Small-molecule analyte detection is key for improving quality of life, particularly in health monitoring through the early detection of diseases. However, detecting specific markers in complex multicomponent media using devices compatible with point-of-care (PoC) technologies is still a major challenge. Here, we introduce a novel approach that combines molecularly imprinted polymers (MIPs), electrolyte-gated transistors (EGTs) based on 2D materials, and machine learning (ML) to detect hippuric acid (HA) in artificial urine, being a critical marker for toluene intoxication, parasitic infections, and kidney and bowel inflammation. Reduced graphene oxide (rGO) was used as the sensory material and molecularly imprinted polymer (MIP) as supramolecular receptors. Employing supervised ML techniques based on symbolic regression and compressive sensing enabled us to comprehensively analyze the EGT transfer curves, eliminating the need for arbitrary signal selection and allowing a multivariate analysis during HA detection. The resulting device displayed simultaneously low operating voltages (<0.5 V), rapid response times (≤10 s), operation across a wide range of HA concentrations (from 0.05 to 200 nmol L-1), and a low limit of detection (LoD) of 39 pmol L-1. Thanks to the ML multivariate analysis, we achieved a 2.5-fold increase in the device sensitivity (1.007 μA/nmol L-1) with respect to the human data analysis (0.388 μA/nmol L-1). Our method represents a major advance in PoC technologies, by enabling the accurate determination of small-molecule markers in complex media via the combination of ML analysis, supramolecular analyte recognition, and electrolytic transistors.

Dual-compartment-gate organic transistors for monitoring biogenic amines from food

According to the Food and Agriculture Organization of the United Nations (FAO) more than 14% of the world's food production is lost every year before reaching retail, and another 17% is lost during the retail stage. The use of the expiration date as the main estimator of the life-end of food products creates unjustified food waste. Sensors capable of quantifying the effective food freshness and quality could substantially reduce food waste and enable more effective management of the food chain. We propose an electrolyte-gated organic transistor (EGOT) that responds to the release of biogenic amines, like diamines and tyramine, generated by the degradation of protein-rich food. The EGOT sensor features a polymeric poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) gate electrode fabricated in the shape of a miniaturized beaker containing an aqueous solution in the inner side (to be exposed to food) and capacitively coupled through a hydrogel to the transistor channel on the outside (not in contact with food). The hydrogen bonds formed by the water-dissolved amines with PEDOT:PSS modulate the EGOT channel across a wide range of amine concentrations. We demonstrate that our sensor can detect different amines by the combinatorial analysis of the response from different channel materials, PEDOT:PSS and the other DPP-DTT, with a limit of detection as low as 100 pM.

Simultaneous Detection of 4-Nitroquinoline-1-oxide and Nitroimidazole Drug Using a Perovskite-BaZrO3@Nb4N5-Nanocomposite-Modified Disposable Carbon Electrode

Pharmaceutical ingredients in water have become a serious threat to living bodies and lead to assorted ecological predicaments. In this study, we have established an electrochemical probe for the simultaneous detection of synthetic pharmaceutical ingredients, including 4-nitroquinoline-N-Oxide (NQN) and ornidazole (ODZ), in both human and environmental samples. This study establishes the detection of NQN and ODZ using a screen-printed carbon electrode (SPCE) modified by highly conducting Nb4N5 incorporated with BaZrO3 perovskite. The electrochemical characteristics of the modified SPCEs were explored by different techniques including electrochemical impedance spectroscopy, differential pulse voltammetry, and cyclic voltammetry. The structural and morphological parameters of the synthesized BaZrO3@Nb4N5 probe were confirmed by X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, and Brunauer-Emmett-Teller techniques. The modified probe shows excellent sensing characteristics, such as a very low detection limit up to 16.9 nM for NQN and 8.6 nM for ODZ with a wide linear range varying from 0.02 to 84.5 μM and 0.02 to 99.12 μM, respectively. The analytical performance of the modified SPCEs was successfully verified by incorporating the probe for detecting NQN and ODZ in real samples together with wastewater and human urine. Finally, the applicability of our proposed method was validated through a standard HPLC technique.

Charge Carrier Density in Organic Semiconductors Modulates the Effective Capacitance: A Unified View of Electrolyte Gated Organic Transistors

A framework for electrolyte-gated organic transistors (EGOTs) that unifies the view of interfacial capacitive coupling of electrolyte-gated organic field-effect transistors (EGOFETs) with the volumetric capacitive coupling in organic electrochemical transistors (OECTs) is proposed. The EGOT effective capacitance arises from in-series capacitances of the electrolyte/gate electrode and electrolyte/channel interfaces, and the chemical capacitance of the organic semiconductor channel whose weight with respect to the interfacial capacitance is modulated by the charge carrier density, hence by the gate voltage. The expression for chemical capacitance is derived from the DOS of the organic semiconductor, which it is assumed to exhibit exponential energy disorder in the HOMO-LUMO gap. The analytical expression of the EGOT current is assessed on experimental data and shown to accurately predict the shape of the whole transfer curve of an EGOT thus allowing to extract accurate values for the switch-on voltage and the interfacial transconductance, without assumptions on specific response regime and, in OECT, without invoking the volumetric capacitance. Interestingly, the EGOT model recovers EGOFET and OECT as limit cases and, in the latter case, explicitly represents the volumetric capacitance in terms of the energy disorder and the bandgap of the organic semiconductor.