Field-effect transistors (FETs) modified with biological recognition elements represent a powerful class of biosensors capable of detecting biomolecules such as DNA, proteins, and pathogens with high sensitivity and specificity. These devices, often referred to as Bio-FETs, leverage the principles of traditional FETs while incorporating biological components to enable selective interactions with target analytes. Unlike organic FETs (OFETs), which utilize organic semiconductors for electronic applications, Bio-FETs are specifically designed for biomedical sensing. They also differ from conventional photodetectors and sensors by integrating biological layers that facilitate direct molecular recognition without relying solely on optical or physical transduction mechanisms.

The core principle of a Bio-FET involves the modulation of channel conductivity in response to biomolecular binding events occurring at the gate or channel surface. When a target biomolecule interacts with a biorecognition element immobilized on the sensor surface, it induces a change in the local electrostatic environment, leading to a measurable shift in the transistor’s drain current. This electrical readout provides real-time, label-free detection, making Bio-FETs highly attractive for point-of-care diagnostics and continuous monitoring applications.

Surface functionalization is a critical aspect of Bio-FET design, as it determines the device’s sensitivity and selectivity. The most common approach involves immobilizing biorecognition elements such as antibodies, aptamers, or single-stranded DNA probes onto the transistor’s gate dielectric or channel surface. These elements must be carefully chosen to ensure high affinity for the target analyte while minimizing nonspecific binding. Common functionalization strategies include silanization for oxide surfaces, thiol-gold chemistry for gold electrodes, and covalent attachment via carbodiimide crosslinking for carboxyl-terminated surfaces. The density and orientation of these bioreceptors significantly influence sensor performance, with optimal packing densities required to maximize binding efficiency without steric hindrance.

Sensitivity in Bio-FETs is governed by multiple factors, including the Debye length of the sensing environment, the charge density of the target biomolecule, and the transistor’s intrinsic electrical properties. In physiological buffers, the Debye length is typically short (around 1 nm in 150 mM ionic strength solutions), limiting the detection of charged biomolecules to those binding within this screening distance. To overcome this limitation, strategies such as reducing buffer ionic strength or incorporating nanostructured channels have been employed to enhance sensitivity. Nanowire and graphene-based Bio-FETs, for example, exhibit exceptional sensitivity due to their high surface-to-volume ratios and low charge screening effects.

Selectivity is another crucial parameter, as Bio-FETs must distinguish target analytes from complex biological matrices containing interfering species. This is achieved through the specificity of the immobilized biorecognition elements. Antibodies provide high selectivity due to their lock-and-key binding mechanism, while aptamers offer advantages such as thermal stability and ease of synthesis. DNA hybridization-based Bio-FETs are particularly effective for nucleic acid detection, with single-base mismatch discrimination achievable under optimized conditions. Additionally, surface passivation techniques using polyethylene glycol (PEG) or bovine serum albumin (BSA) are commonly employed to minimize nonspecific adsorption.

Applications of Bio-FETs span a wide range of diagnostic and monitoring scenarios. In DNA detection, these devices have been used for identifying genetic mutations, pathogen genomes, and microRNA biomarkers associated with diseases such as cancer. Protein detection applications include the measurement of clinically relevant biomarkers like cardiac troponin for acute myocardial infarction or cytokines for inflammatory disorders. Pathogen detection is another major application, with Bio-FETs demonstrating the ability to detect viruses, bacteria, and other microorganisms at clinically relevant concentrations. The COVID-19 pandemic highlighted the potential of Bio-FETs for rapid viral RNA detection, with some prototypes achieving limits of detection comparable to RT-PCR.

Compared to organic FETs (OFETs), Bio-FETs are distinct in their purpose and operational environment. While OFETs are primarily explored for flexible electronics, displays, and low-cost circuitry, Bio-FETs are tailored for aqueous environments and biological interactions. OFETs rely on organic semiconductors like pentacene or P3HT, whereas Bio-FETs often use inorganic materials such as silicon, graphene, or metal oxides to ensure stability in liquid media. The integration of biological components in Bio-FETs also introduces additional considerations such as biocompatibility and long-term stability, which are not typically concerns for OFETs.

Non-biomedical sensors, such as photodetectors or gas sensors, differ fundamentally in their transduction mechanisms. Photodetectors rely on photon absorption to generate electron-hole pairs, while gas sensors depend on surface reactions with gaseous species. Bio-FETs, in contrast, transduce biochemical interactions directly into electrical signals without intermediate steps. This direct sensing mechanism enables faster response times and simpler system integration compared to optical or electrochemical biosensors.

The future development of Bio-FETs will likely focus on improving integration with portable readout systems, enhancing multiplexing capabilities, and addressing challenges related to long-term stability in real-world samples. Advances in nanomaterials and surface chemistry will further push the limits of detection, enabling earlier disease diagnosis and better monitoring of therapeutic responses. As the field progresses, Bio-FETs are poised to become indispensable tools in modern diagnostics, bridging the gap between laboratory-based assays and real-time, decentralized testing.