PhD candidate David Macedo, MOBIUS Director Professor Conor Hogan, and MOBIUS Postdoctoral Researchers Dr Samridhi Bajaj and Dr Helmini Jayarathne, and colleagues, have developed a free, open-source program that enables more accurate analysis of electrochemical sensor data than typical existing methods.

Electrochemical sensors are often hampered by difficulties in data analysis. For example, cyclic voltammetry – a common technique – often yields signals that are overlapping and hard to separate, leading to inaccurate results.

David and the team developed an algorithm to automatically perform signal fitting using semi-derivatives and signal deconvolution, enabling more accurate measurements of current.

The team demonstrated the algorithm’s accuracy and effectiveness using a combination of simulated and experimental data, reported recently in Analytical Chemistry.

“We designed the program to perform this task without the user needing any prior knowledge of the electrochemical parameters that more complicated modelling methods require,” explained David. “It means our approach is easier to use but still highly accurate.”

“Our method could help enable new sensor designs and improve analytic performance for existing sensors,” said David.

“An easy-to-use executable program and its Python source code have been made available for free on GitHub, so we welcome researchers to incorporate our method into their own projects, and contribute to its development.”

Find the free, open-source program on GitHub.

Read the article in Analytical Chemistry.