Recent Advances in Electrochemical Biosensors: Applications, Challenges, and Future Scope

Abstract

The electrochemical biosensors are a class of biosensors which convert biological information such as analyte concentration that is a biological recognition element (biochemical receptor) into current or voltage. Electrochemical biosensors depict propitious diagnostic technology which can detect biomarkers in body fluids such as sweat, blood, feces, or urine. Combinations of suitable immobilization techniques with effective transducers give rise to an efficient biosensor. They have been employed in the food industry, medical sciences, defense, studying plant biology, etc. While sensing complex structures and entities, a large data is obtained, and it becomes difficult to manually interpret all the data. Machine learning helps in interpreting large sensing data. In the case of biosensors, the presence of impurity affects the performance of the sensor and machine learning helps in removing signals obtained from the contaminants to obtain a high sensitivity. In this review, we discuss different types of biosensors along with their applications and the benefits of machine learning. This is followed by a discussion on the challenges, missing gaps in the knowledge, and solutions in the field of electrochemical biosensors. This review aims to serve as a valuable resource for scientists and engineers entering the interdisciplinary field of electrochemical biosensors. Furthermore, this review provides insight into the type of electrochemical biosensors, their applications, the importance of machine learning (ML) in biosensing, and challenges and future outlook.

Keywords: amperometric; biosensor; electrochemical; food quality monitoring; machine learning; sensitivity; voltammetric.

Figure 1

(a) Representation of the biosensor having glucose sensor attached on the back of the microneedle; (b) Calibration curves for the concentration between 0–35 mM; (c) Calibration curves of developed biosensors and stand-alone biosensors in ISF, and (d) Calibration curves for developed biosensor for concentration 0–35 mM at 5 s, 30 s, and 100 s of sampling times. Reproduced from Reference [20] with permission. Copyright 2015, Elsevier.

Figure 2

(a) Pseudoreference, working, and auxiliary electrodes; (b) CSEM images of biosensor surface modified with different materials. (c) Diagrammatic representation of the sensors with the modified surface; (d) working of the sensor. Reproduced from Reference [24] with permission. Copyright 2015, Elsevier.

Figure 3

(a) T-shaped pillar array and its optical image; (b) Process of measuring the intensity as a function of frequency; (c) 53 traces corresponding to 53 different pillars as a function of actuation frequency. Actuation mode, (d) single-mode, and (e) multiple modes. Reproduced from Reference [29] with permission. Copyright 2015, Elsevier.

Figure 4

Multiplexed label-free amperometric immunosensor based on SPCE modified by Au-pd NPs and some polymeric matrices for the detection of some biomarkers. Reproduced from Reference [31] with permission. Copyright 2018, Elsevier.

Figure 5

(A) Schematic visualization of the processes on the electrode. (B) Corresponding gel electrophoresis proving the charge of utilized compounds upon experimental pH. Reproduced from Reference [42] with permission. Copyright 2018, Elsevier.

Figure 6

A diagrammatic representation of the photosynthesis inhibition by herbicides. Reproduced from Reference [50] with permission. Copyright 2019, Elsevier.

Figure 7

Diagrammatic representation of the biosensor based on DPV and CV to detect anti-tTG in the human celiac disease. Reproduced from Reference [64] with permission. Copyright 2017, Elsevier.

Figure 8

(A,B) EIS plots. (C) CV study of the biosensor towards AβO. (D) Diagram showing an increase in the surface conductivity due to AβO. Reproduced from Reference [80] with permission. Copyright 2014, Elsevier.

Figure 9

Diagrammatic representation of the tattoo-shaped potentiometric biosensor. (A) The concept of the biosensor. (B) Designing of the tattoo biosensor. (C) The printing process of the biosensor on paper. (D) Successful transfer and removal of the tattoo on human skin. Reproduced from Reference [87] with permission. Copyright 2018, Elsevier.

Figure 10

Electrochemical behaviors (A) of different modified electrode at the scan rate of 50 mVs−1. (B) Corresponding DPV of CBZ at different modified GCE in 0.1 M PBS. (C) DPV of CBZ at Ti2C MXene/Au–Ag NS/GCE in 0.1 M PBS. The linear equation of CBZ (D) at different concentrations ranging from 0.006 to 9.8 μM. RVM models with concentration as input (E) and current as input (F) for estimating CBZ concentration obtained by electrochemical. Comparison of the concentration experimental and RVM predicted values of samples (G). Reproduced from Reference [132] with permission. Copyright 2021, Elsevier.

Figure 11

(a) Workflow and the scenarios of overfitting and underfitting. Reproduced from Reference [137] with permission. Copyright 2019, Elsevier; (b) results of two combined models, i.e., Principal Component Analysis and Support Vector Machine which can be used to distinguish cocaine, oxycodone, tetrahydrocannabinol, and heroin. Reproduced from Reference [139] with permission. Copyright 2018, Elsevier; (c) the prediction of partial least squares discriminant analysis (PLS-DA) model for all external human blood donor samples. Reproduced from Reference [140] with permission. Copyright 2018, Elsevier.

Figure 12

Typical structure of ANN. Reproduced from Reference [149] with permission. Copyright 2018, Elsevier.

Figure 13

Recent reviews/articles published on electrochemical biosensors from the year 2000 to July 2021. Reproduced from Reference [174]. Copyright 2021, https://www.dimensions.ai/; accessed on 12 August 2021.