BEGIN:VCALENDAR VERSION:2.0 PRODID:-//TYPO3/NONSGML Calendarize//EN BEGIN:VEVENT UID:calendarize-versatile-automated-electroanalysis-via-the-electrolab-tow ards-high-throughput-integrated-electrochemical-characterization DTSTAMP:20260820T093246Z DTSTART:20260921T141500Z DTEND:20260921T160000Z SUMMARY:“Versatile Automated Electroanalysis Using the Electrolab: Towar ds High-Throughput\, Integrated Electrochemical Characterisation” DESCRIPTION:An abundance of high-quality data is key to enabling successfu l data-driven approaches for the exploration and optimisation of chemical systems using modern machine learning and artificial intelligence tools. P articularly for electrochemical energy applications – such as the discov ery of materials for batteries\, the characterisation of heterogeneous and homogeneous electrocatalysts\, and the study of fundamental interfacial p rocesses – there is a pressing need to explore vast ranges of chemical v ariables\, such as solvent and electrolyte types\, reactant concentrations \, pH\, and electrochemical parameters such as scan rates\, electrodes\, e lectrochemical windows and molecular motifs. To meet these needs\, our gro up has recently introduced a range of tools under the umbrella of The Elec trolab\, an automated electrochemical platform that combines hardware\, Py thon-based software\, automated robotic systems for electrolyte dispensing \, and custom-designed electrochemical chips\, which together enable chara cterisation campaigns with minimal supervision. From simple but tedious ex periments\, such as determining diffusion coefficients\, to systematic mea surements of homogeneous electrocatalysis for alcohol oxidations\, to comp lex surface characterisation experiments evaluating the redox capacity and dynamics of materials for redox-targeted flow batteries\, our methods syn ergistically create new opportunities in electrochemistry. I will discuss prospects for a variety of materials and interface characterisation proble ms. X-ALT-DESC;FMTTYPE=text/html:

An abundance of high-quality data is key t o enabling successful data-driven approaches for the exploration and optim isation of chemical systems using modern machine learning and artificial i ntelligence tools. Particularly for electrochemical energy applications – such as the discovery of materials for batteries\, the characterisation o f heterogeneous and homogeneous electrocatalysts\, and the study of fundam ental interfacial processes – there is a pressing need to explore vast r anges of chemical variables\, such as solvent and electrolyte types\, reac tant concentrations\, pH\, and electrochemical parameters such as scan rat es\, electrodes\, electrochemical windows and molecular motifs. To meet th ese needs\, our group has recently introduced a range of tools under the u mbrella of The Electrolab\, an automated electrochemical platform that com bines hardware\, Python-based software\, automated robotic systems for ele ctrolyte dispensing\, and custom-designed electrochemical chips\, which to gether enable characterisation campaigns with minimal supervision. From si mple but tedious experiments\, such as determining diffusion coefficients\ , to systematic measurements of homogeneous electrocatalysis for alcohol o xidations\, to complex surface characterisation experiments evaluating the redox capacity and dynamics of materials for redox-targeted flow batterie s\, our methods synergistically create new opportunities in electrochemist ry. I will discuss prospects for a variety of materials and interface char acterisation problems.

LOCATION:University of Oldenburg\, Wechloy campus\, W03 1-156 END:VEVENT END:VCALENDAR