Highly sensitive detection of multiple proteins from single cells by MoS2-FET biosensors

Alzheimer's Disease Biomarker Detection Using Field Effect Transistor-Based Biosensor

Alzheimer's disease (AD) is closely related to neurodegeneration, leading to dementia and cognitive impairment, especially in people aged > 65 years old. The detection of biomarkers plays a pivotal role in the diagnosis and treatment of AD, particularly at the onset stage. Field-effect transistor (FET)-based sensors are emerging devices that have drawn considerable attention due to their crucial ability to recognize various biomarkers at ultra-low concentrations. Thus, FET is broadly manipulated for AD biomarker detection. In this review, an overview of typical FET features and their operational mechanisms is described in detail. In addition, a summary of AD biomarker detection and the applicability of FET biosensors in this research field are outlined and discussed. Furthermore, the trends and future prospects of FET devices in AD diagnostic applications are also discussed.

Investigating on sensing mechanism of MoS2-FET biosensors in response to proteins

Field-effect transistor (FET) biosensors based on two-dimensional materials have gained extensive attention due to their high sensitivity, label-free detection capability, and fast response. Molybdenum disulfide (MoS2), with tunable bandgap, high surface-to-volume ratio, and smooth surface without dangling bonds, is a promising material for FET biosensors. Previous reports have demonstrated the fabrication of MoS2-FET biosensors and their high sensitivity detection of proteins. However, most prior research has focused on the realization of MoS2-FETs for detecting different kinds of proteins or molecules, while comprehensive analysis of the sensing mechanism and dominant device factors of MoS2-FETs in response to proteins is yet to investigate. In this study, we first fabricated MoS2-FET biosensor and detected different types of proteins (immunoglobulin G (IgG),β-actin, and prostate-specific antigen (PSA)). Secondly, we built the model of the device and analyzed the sensing mechanism of MoS2-FETs in response to proteins. Experimental and modeling results showed that the induced doping effect and gating effect caused by the target protein binding to the device surface were the major influential factors. Specifically, the channel doping concentration and gate voltage (Vg) offset exhibited monotonic change as the concentration of the protein solution increases. For example, the channel doping concentration increased up to ∼37.9% and theVgoffset was ∼-1.3 V with 10-7μgμl-1IgG. The change was less affected by the device size. We also investigated the effects of proteins with opposite acid-base properties (β-actin and PSA) to IgG on the device sensing mechanism.β-actin and PSA exhibited behavior opposite to that of IgG. Additionally, we studied the response behavior of MoS2-FETs with different dimensions and dielectric materials (channel length, MoS2thickness, dielectric layer thickness, dielectric layer material) to proteins. The underlying mechanisms were discussed in details. This study provides valuable guidelines for the design and application of MoS2-FET biosensors.

Machine Learning-Assisted Large-Area Preparation of MoS2 Materials

Molybdenum disulfide (MoS2) is a layered transition metal-sulfur compound semiconductor that shows promising prospects for applications in optoelectronics and integrated circuits because of its low preparation cost, good stability and excellent physicochemical, biological and mechanical properties. MoS2 with high quality, large size and outstanding performance can be prepared via chemical vapor deposition (CVD). However, its preparation process is complex, and the area of MoS2 obtained is difficult to control. Machine learning (ML), as a powerful tool, has been widely applied in materials science. Based on this, in this paper, a ML Gaussian regression model was constructed to explore the growth mechanism of MoS2 material prepared with the CVD method. The parameters of the regression model were evaluated by combining the four indicators of goodness of fit (r2), mean squared error (MSE), Pearson correlation coefficient (p) and p-value (p_val) of Pearson's correlation coefficient. After comprehensive comparison, it was found that the performance of the model was optimal when the number of iterations was 15. Additionally, feature importance analysis was conducted on the growth parameters using the established model. The results showed that the carrier gas flow rate (Fr), molybdenum sulfur ratio (R) and reaction temperature (T) had a crucial impact on the CVD growth of MoS2 materials. The optimal model was used to predict the size of molybdenum disulfide synthesis under 185,900 experimental conditions in the simulation dataset so as to select the optimal range for the synthesis of large-size molybdenum disulfide. Furthermore, the model prediction results were verified through literature and experimental results. It was found that the relative error between the prediction results and the literature and experimental results was small. These findings provide an effective solution to the preparation of MoS2 materials with a reduction in the time and cost of trial and error.

Review on two-dimensional material-based field-effect transistor biosensors: accomplishments, mechanisms, and perspectives

Field-effect transistor (FET) is regarded as the most promising candidate for the next-generation biosensor, benefiting from the advantages of label-free, easy operation, low cost, easy integration, and direct detection of biomarkers in liquid environments. With the burgeoning advances in nanotechnology and biotechnology, researchers are trying to improve the sensitivity of FET biosensors and broaden their application scenarios from multiple strategies. In order to enable researchers to understand and apply FET biosensors deeply, focusing on the multidisciplinary technical details, the iteration and evolution of FET biosensors are reviewed from exploring the sensing mechanism in detecting biomolecules (research direction 1), the response signal type (research direction 2), the sensing performance optimization (research direction 3), and the integration strategy (research direction 4). Aiming at each research direction, forward perspectives and dialectical evaluations are summarized to enlighten rewarding investigations.

The Integration of Field Effect Transistors to Microfluidic Devices

Devices that integrate field effect transistors into microfluidic channels are becoming increasingly promising in the medical, environmental, and food realms, among other applications. The uniqueness of this type of sensor lies in its ability to reduce the background signals existing in the measurements, which interfere in obtaining good limits of detection for the target analyte. This and other advantages intensify the development of selective new sensors and biosensors with coupling configuration. This review work focused on the main advances in the fabrication and application of field effect transistors integrated into microfluidic devices as a way of identifying the potentialities that exist in these systems when used in chemical and biochemical analyses. The emergence of research on integrated sensors is not a recent study, although more recently the progress of these devices is more accentuated. Among the studies that used integrated sensors with electrical and microfluidic parts, those that investigated protein binding interactions seem to be the ones that expanded the most due, among other things, to the possibility of obtaining several physicochemical parameters involved in protein-protein interactions. Studies in this area have a great possibility of advancing innovations in sensors with electrical and microfluidic interfaces in new designs and applications.

Fabrication and Functionalisation of Nanocarbon-Based Field-Effect Transistor Biosensors

Nanocarbon-based field-effect transistor (NC-FET) biosensors are at the forefront of future diagnostic technology. By integrating biological molecules with electrically conducting carbon-based platforms, high sensitivity real-time multiplexed sensing is possible. Combined with their small footprint, portability, ease of use, and label-free sensing mechanisms, NC-FETs are prime candidates for the rapidly expanding areas of point-of-care testing, environmental monitoring and biosensing as a whole. In this review we provide an overview of the basic operational mechanisms behind NC-FETs, synthesis and fabrication of FET devices, and developments in functionalisation strategies for biosensing applications.

A Hybrid Microfluidic Electronic Sensing Platform for Life Science Applications

This paper presents a novel hybrid microfluidic electronic sensing platform, featuring an electronic sensor incorporated with a microfluidic structure for life science applications. This sensor with a large sensing area of 0.7 mm² is implemented through a foundry process called Open-Gate Junction FET (OG-JFET). The proposed OG-JFET sensor with a back gate enables the charge by directly introducing the biological and chemical samples on the top of the device. This paper puts forward the design and implementation of a PDMS microfluidic structure integrated with an OG-JFET chip to direct the samples toward the sensing site. At the same time, the sensor’s gain is controlled with a back gate electrical voltage. Herein, we demonstrate and discuss the functionality and applicability of the proposed sensing platform using a chemical solution with different pH values. Additionally, we introduce a mathematical model to describe the charge sensitivity of the OG-JFET sensor. Based on the results, the maximum value of transconductance gain of the sensor is ~1 mA/V at Vgs = 0, which is decreased to ~0.42 mA/V at Vgs = 1, all in Vds = 5. Furthermore, the variation of the back-gate voltage from 1.0 V to 0.0 V increases the sensitivity from ~40 mV/pH to ~55 mV/pH. As per the experimental and simulation results and discussions in this paper, the proposed hybrid microfluidic OG-JFET sensor is a reliable and high-precision measurement platform for various life science and industrial applications.

Wide-Linear Range Cholesterol Detection Using Fe2O3 Nanoparticles Decorated ZnO Nanorods Based Electrolyte-Gated Transistor

Electrolyte-gated transistor (EGT)-based biosensors are created with nanomaterials to harness the advantages of miniaturization and excellent sensing performance. A cholesterol EGT biosensor based on iron oxide (Fe2O3) nanoparticles decorated ZnO nanorods is proposed here. ZnO nanorods are directly grown on the seeded channel using a hydrothermal method, keeping in mind the stability of nanorods on the channel during biosensor measurements in an electrolyte. Most importantly, ZnO nanorods can be effectively grown and modified with Fe2O3 nanoparticles to enhance stability, surface roughness, and performance. The cholesterol oxidase (ChOx) enzyme is immobilized over Fe2O3 nanoparticles decorated ZnO nanorods for cholesterol detection. With cholesterol addition in buffer solution, the electro-oxidation of cholesterol on enzyme immobilized surface led to increased the biosensor’s current response. The cholesterol EGT biosensor detected cholesterol in wide-linear range (i.e., 0.1 to 60.0 mM) with high sensitivity (37.34 µA/mMcm2) compared to conventional electrochemical sensors. Furthermore, we obtained excellent selectivity, fabrication reproducibility, long-term storage stability, and practical applicability in real serum samples. The demonstrated EGT biosensor can be extended with changing enzymes or nanomaterials or hybrid nanomaterials for specific analyte detection.