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Distributed electrochemical sensors: recent advances and barriers to market adoption

Abstract

Despite predictions of their widespread application in healthcare and environmental monitoring, electrochemical sensors are yet to be distributed at scale, instead remaining largely confined to R&D labs. This contrasts sharply with the situation for physical sensors, which are now ubiquitous and seamlessly embedded in the mature ecosystem provided by electronics and connectivity protocols. Although chemical sensors could be integrated into the same ecosystem, there are fundamental issues with these sensors in the three key areas of analytical performance, usability, and affordability. Nevertheless, advances are being made in each of these fields, leading to hope that the deployment of automated and user-friendly low-cost electrochemical sensors is on the horizon. Here, we present a brief survey of key challenges and advances in the development of distributed electrochemical sensors for liquid samples, geared towards applications in healthcare and wellbeing, environmental monitoring, and homeland security. As will be seen, in many cases the analytical performance of the sensor is acceptable; it is usability that is the major barrier to commercial viability at this moment. Were this to be overcome, the issue of affordability could be addressed.

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Acknowledgements

The authors would like to acknowledge the financial support from project CTQ2016-77128-R with the associated FPI scholarship BES-2014-067661. This project was financed by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (ERDF).

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Correspondence to Francisco J. Andrade.

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Hoekstra, R., Blondeau, P. & Andrade, F.J. Distributed electrochemical sensors: recent advances and barriers to market adoption. Anal Bioanal Chem 410, 4077–4089 (2018). https://doi.org/10.1007/s00216-018-1104-9

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  • DOI: https://doi.org/10.1007/s00216-018-1104-9

Keywords

  • Electrochemical sensors
  • Autonomous sensing
  • Ultralow-cost diagnostics
  • Distributed sensing networks
  • Remote sensing