Scalable chemical vapor deposited graphene field-effect transistors for bio/chemical assay

Abstract

The adsorption of chemical species on the surface of graphene alters the concentration of charge carries by either increasing or decreasing it depending on the nature of the adsorbed chemical species and inducing noticeable changes in the material's electronic properties. This remarkable feature enables graphene-based sensors to detect a wide range of biomolecules, chemicals, and gas/vapors. A lot of progress has been made in this field and technologies based on reduced graphene oxide flakes have been well reviewed. Graphene grown by chemical vapor deposition has proven to be the most prominent route to large-scale production of devices. However, until now, a comprehensive review on scalable, macro-sized, chemical vapor-deposited, graphene-based field-effect transistor sensors was missing. Here we review the present status, current challenges, and future prospects for the bio/chemical sensing applications of scalable, chemical vapor-deposited, graphene field-effect transistors. We explain and examine the fabrication techniques, sensing mechanisms, and various assay strategies. We also discuss the sensing performance of graphene based on quantum mechanics simulations and theoretical calculations.

Technological advancements in bio‐recognition using liquid crystals: Techniques, applications, and performance

“…for the detection of various analytes using a suitable transduction mechanism having optical, [28] thermal, electrochemical [60] or field-effect transistor techniques. [61] Contrary to the conventional techniques for biomolecular detection such as ELISA,, optical assays, Phage and ribosome display, etc. [62,63] LC-based sensing technique is label-free, portable, cost-effective, requires lesser detection time and does not require any sophisticated equipment and hence has the potential to replace the traditional sensing techniques.…”

Direct Laser Writing of the Porous Graphene Foam for Multiplexed Electrochemical Sweat Sensors

“…A wearable glove-embedded sensor was developed for the noninvasive and selective determination of biomarkers in therapeutic drugs and sweat samples. , As a result, diverse wearable electrochemical sensors have been developed to monitor the metabolites and electrolytes in the sweat on-body (e.g., Glu, ,− lactate, , ion, ,− UA, , and Tyr − sensors). As the enzyme-based wearable sweat sensors are often expensive with sensitivity susceptible to temperature and pH, − enzyme-free sweat sensors relying on diverse sensing materials (e.g., graphene, − laser-induced graphene (LIG), , and PEDOT/PSS hydrogel) have been developed with excellent stability in harsh environment. − Compared with graphene prepared by the complicated fabrication process, , the 3D porous graphene comes from the low-cost, rapid, and scalable direct laser writing, which also exhibits fast electron mobility, high current density, and ultra-large surface area. ,, Benefiting from large surface areas and rich surface defects induced during the laser scribing process, pristine LIG-based devices have been widely explored for the detection of small molecules. ,− However, the resulting electrochemical sensors based on the porous graphene still show limited peak response and are greatly affected by background current. Besides the biomarkers, the electrolytes in sweat can inform the body’s hydration state to alert dehydration (for avoiding impaired endurance and increased carbohydrate reliance in athletes) or hyponatremia (or low plasma sodium from overconsumption of water or sports drinks).…”

Interfacial Ammonia Selectivity, Atmospheric Passivation, and Molecular Identification in Graphene-Nanopored Activated Carbon Molecular-Sieve Gas Sensors

“…Also, the ability to characterize adsorbate molecules on the graphene channel is highly desired and is currently the focus of graphene-based electric nose (e-nose) sensors. ,− For e-nose sensing, integrating machine learning algorithms for pattern recognition based on changes in gas-adsorption-induced electronic responses of a single graphene sensor as recently demonstrated remains the most promising approach due to its ability to characterize a huge variety of gases in principle. However, since gas-specific electronic responses improve the performance of e-nose machine learning algorithms, the nonspecificity of gas-adsorption-induced electronic responses such as doping concentration and mobility changes remains a significant limitation to the aforementioned approach compared to gas-specific functionalized sensor arrays …”