Electrochemical Methods in the Cloud: FreiStat, an IoT-Enabled Embedded Potentiostat

Embedded potentiostatsenable electrochemical measurements in theInternet-of-Things (IoT) or other decentralized applications, suchas remote environmental monitoring, electrochemical energy systems,and biomedical point-of-care applications. We report on Freiburg'sPotentiostat (FreiStat) based on the AD5941 potentiostat circuit fromAnalog Devices, together with custom firmware, as the key to preciseand advanced electrochemical methods. We demonstrated its analyticalperformance by various cyclic voltammetry measurements, advanced techniquessuch as differential pulse voltammetry, and a lactate biosensor measurementwith currents in the nA range and a resolution of 54 pA. The FreiStatyielded analytical results comparable to benchtop devices and outperformedcurrent commercial embedded potentiostats at significantly lower cost,smaller size, and lower power consumption. Decentralized corrosionanalysis by a Tafel plot using the IBM Cloud showed its applicabilityin a typical IoT scenario. The developed open-source software frameworkfacilitates the integration of electrochemical instrumentation intoapplications utilizing machine learning and other artificial intelligence.Together with the affordable and highly capable embedded potentiostat,our approach can leverage analytical chemistry toward increasinglyimportant, more widespread and decentralized applications.

Automated summary

Embedded potentiostats enable precise and advanced electrochemical measurements in decentralized applications such as IoT, remote environmental monitoring, and biomedical point-of-care. The FreiStat, based on Analog Devices' AD5941 potentiostat circuit with custom firmware, demonstrated comparable analytical performance to benchtop devices at a significantly lower cost, smaller size, and lower power consumption. Its applicability in a typical IoT scenario was shown through decentralized corrosion analysis using the IBM Cloud. The development of an open-source software framework facilitates the integration of electrochemical instrumentation into applications utilizing machine learning and other artificial intelligence, enabling analytical chemistry to be applied in increasingly important, widespread, and decentralized applications.