WAFFLE – an automated platform for enhancing the performance of electrochemical biosensors

Dobrea, Alexandra and Blake, Rowan and Macdonald, Daniel and McKenzie, Cormack and Altmann, Yoann and Corrigan, Damion K. and Jimenez, Melanie (2026) WAFFLE – an automated platform for enhancing the performance of electrochemical biosensors. Lab on a Chip. ISSN 1473-0197 (https://doi.org/10.1039/d5lc00988j)

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Abstract

Electrochemical biosensors and microfluidics have an inherently synergistic relationship which can allow unparalleled levels of signal enhancement, automation and scalability. In spite of this, the full advantages of fluidic automation remain underexplored with most works automating some but not all biosensor fabrication steps. In this work, we present for the first time the Wee Ally for Flow Functionalisation of Low-cost Electrodes (WAFFLE) – an automated platform designed specifically for researchers to standardise the fabrication of electrochemical biosensors and enhance their performance, and a novel data analysis scheme based on the Markov chain Monte Carlo (MCMC) method for increasing the robustness of data fitting. We first discuss the design of the WAFFLE which features a modular construction, off-the-shelf components (ESP32 microcontroller, Bartels mp-6 μ-pump and memetis μ-valves), an easy-to-manufacture fluidic cartridge, and web interface that can be accessed from any Wi-Fi enabled device. The entire platform can be manufactured for approximately £1 k, less than the cost of a single standard syringe pump. We showcase the sensing benefits of the WAFFLE using two electrochemical immunoassays of high clinical relevance for interleukin-6 (IL-6) and cardiac troponin I (TnI), and one aptamer-based impedimetric assay for cortisol. As well as unilaterally enhancing the sensitivity of those sensors and decreasing sensor variability, the WAFFLE also highlighted some key insights into the assembly of the bioactive surface layer under flow. Finally, we demonstrate how MCMC-integration into impedance fitting algorithms can resolve the issue of local minima trapping.

ORCID iDs

Dobrea, Alexandra, Blake, Rowan, Macdonald, Daniel ORCID logoORCID: https://orcid.org/0000-0002-9250-434X, McKenzie, Cormack, Altmann, Yoann, Corrigan, Damion K. ORCID logoORCID: https://orcid.org/0000-0002-4647-7483 and Jimenez, Melanie;