Laser-Induced Electrochemical Biosensor Modified with Graphene-Based Ink for Label-Free Detection of Alpha-Fetoprotein and 17β-Estradiol

Author:

Tabassum Ridma1,Sarkar Pritu Parna1ORCID,Jalal Ahmed Hasnain2ORCID,Ashraf Ali3ORCID,Islam Nazmul2

Affiliation:

1. Graduate Research Assistant, The University of Texas Rio Grande Valley, Edinburg, TX 78539, USA

2. Department of Electrical & Computer Engineering, The University of Texas Rio Grande Valley, Edinburg, TX 78539, USA

3. Department of Mechanical Engineering, The University of Texas Rio Grande Valley, Edinburg, TX 78539, USA

Abstract

In this research, a novel electrochemical biosensor is proposed based on inducing graphene formation on polyimide substrate via laser engraving. Graphene polyaniline (G-PANI) conductive ink was synthesized by planetary mixing and applied to the working zone of the developed sensor to effectively enhance the electrical signals. The laser-induced graphene (LIG) sensor was used to detect alpha-fetoprotein (AFP) and 17β-Estradiol (E2) in the phosphate buffer saline (PBS) buffer and human serum. The electrochemical performance of the biosensor in determining these biomarkers was investigated by differential pulse voltammetry (DPV) and chronoamperometry (CA). In a buffer environment, alpha-fetoprotein (AFP) and 17β-Estradiol detection range were 4–400 ng/mL and 20–400 pg/mL respectively. The experimental results showed a limit of detection (LOD) of 1.15 ng/mL and 0.96 pg/mL for AFP and estrogen, respectively, with an excellent linear range (R2 = 0.98 and 0.99). In addition, the designed sensor was able to detect these two types of biomarkers in human serum successfully. The proposed sensor exhibited excellent reproducibility, repeatability, and good stability (relative standard deviation, RSD = 0.96%, 1.12%, 2.92%, respectively). The electrochemical biosensor proposed herein is easy to prepare and can be successfully used for low-cost, rapid detection of AFP and E2. This approach provides a promising platform for clinical detection and is advantageous to healthcare applications.

Funder

National Science Foundation

Publisher

MDPI AG

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