\u201cOur platform can test hundreds of formulations in days rather than months using traditional trial-and-error methods,\u201d Lin said. \u201cFor example, it can test the cyclic voltammetry \u2013 which measures the current that develops in an electrochemical cell \u2013 of 96 different electrolyte formulations in just three hours, while conventional testing would require a full day of continuous work to do the same job. That\u2019s eight times faster.\u201d<\/p>\n
The team\u2019s results appear in\u00a0<\/span>Science Advances<\/a>.<\/p>\n The early stages of this two-year project, supported by the Ralph O\u2019Connor Sustainable Energy Institute (ROSEI), focused on designing microelectrode bundles, establishing automated protocols, and validating the reliability of the system.\u00a0<\/span><\/strong>Once the working platform was created, the researchers began to integrate machine learning capabilities to help predict zinc metal battery electrochemical performance, discover new electrolytes for high-performance batteries and uncover property-performance correlations.<\/p>\n The result is an affordable and customizable high-throughput experimentation platform that dramatically accelerates electrochemical research.<\/p>\n \u201cTechniques that rapidly test large numbers of samples in parallel\u2014known as \u2018high-throughput methods\u2019\u2014 have revolutionized fields like pharmaceutical chemistry and materials science, but their implementation in electrochemistry has been limited due to technical challenges and cost barriers,\u201d Lin said. \u201cOur platform avoids those technical issues and uses off-the-shelf components with custom 3D-printed parts. This makes it very affordable so research labs that don\u2019t have enormous budgets can use it.\u201d<\/p>\n The automated platform that could speed up the search for better materials for renewable energy storage<\/p>\n<\/div>\n The automation aspect of the system is a major achievement because it solves a common problem with traditional high-throughput systems \u2013 the challenge of reproducing exact conditions for multiple tests happening at the same time. When humans operate these systems, small unintentional differences in technique can lead to significantly different results. The group\u2019s system being automated eliminates this issue, ensuring consistency.<\/p>\n \u201cBecause is it consistent, our new platform generates more reliable data,\u201d Lin said. \u201cThis standardization leads to more robust scientific conclusions and accelerates real progress rather than spending time troubleshooting inconsistent results.\u201d<\/p>\n Lin and the team plan to expand their platform\u2019s use to other areas, including as fuel cells, electrolyzers, and CO\u2082 reduction. He believes that any field relying on high throughput experimentation can benefit.<\/p>\n \u201cThe possible combinations of electrolyte components are virtually infinite, and traditionally we\u2019ve explored this space through intuition and trial-and-error,\u201d Lin said. \u201cBy combining high-throughput experimentation with automation, we\u2019ve created a systematic approach to navigate this vast chemical space efficiently, leading to discoveries that might otherwise have been missed.\u201d<\/p>\n Lin works in the laboratory of\u00a0<\/span>Yayuan Liu<\/a>, a Russell Croft Faculty Scholar and assistant professor of chemical and biomolecular engineering who is also an associate researcher with ROSEI.<\/p>\n <\/p>\n![\"\"](\"https:\/\/energyinstitute.jhu.edu\/wp-content\/uploads\/2025\/04\/Main-image-2-300x226.png\")
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