Utilising Commercially Fabricated Printed Circuit Boards as an Electrochemical Biosensing Platform.
Uroš ZupančičJoshua RainbowPedro EstrelaDespina MoschouPublished in: Micromachines (2021)
Printed circuit boards (PCBs) offer a promising platform for the development of electronics-assisted biomedical diagnostic sensors and microsystems. The long-standing industrial basis offers distinctive advantages for cost-effective, reproducible, and easily integrated sample-in-answer-out diagnostic microsystems. Nonetheless, the commercial techniques used in the fabrication of PCBs produce various contaminants potentially degrading severely their stability and repeatability in electrochemical sensing applications. Herein, we analyse for the first time such critical technological considerations, allowing the exploitation of commercial PCB platforms as reliable electrochemical sensing platforms. The presented electrochemical and physical characterisation data reveal clear evidence of both organic and inorganic sensing electrode surface contaminants, which can be removed using various pre-cleaning techniques. We demonstrate that, following such pre-treatment rules, PCB-based electrodes can be reliably fabricated for sensitive electrochemical biosensors. Herein, we demonstrate the applicability of the methodology both for labelled protein (procalcitonin) and label-free nucleic acid (E. coli-specific DNA) biomarker quantification, with observed limits of detection (LoD) of 2 pM and 110 pM, respectively. The proposed optimisation of surface pre-treatment is critical in the development of robust and sensitive PCB-based electrochemical sensors for both clinical and environmental diagnostics and monitoring applications.
Keyphrases
- label free
- gold nanoparticles
- nucleic acid
- low cost
- molecularly imprinted
- heavy metals
- air pollution
- particulate matter
- ionic liquid
- escherichia coli
- drinking water
- physical activity
- genome wide
- mental health
- polycyclic aromatic hydrocarbons
- big data
- single molecule
- dna methylation
- machine learning
- mass spectrometry
- binding protein
- artificial intelligence
- climate change
- deep learning
- smoking cessation
- quantum dots
- tissue engineering
- single cell
- amino acid
- circulating tumor cells