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Self-driving lab automates semiconductor ink synthesis and thin-film characterization

Developing new functional materials often requires testing numerous material combinations. Conventional experiments reach their limits quickly when many variants must be compared. To overcome this bottleneck, the new Energy Materials Acceleration Platform (E-MAP) at KIT automates key lab steps.

Self-driving lab automates semiconductor ink synthesis and thin-film characterization

The Energy Materials Acceleration Platform (E-MAP) at Karlsruhe Institute of Technology (KIT) automates critical lab procedures for semiconductor ink synthesis and thin-film characterization. By employing robot systems to handle tasks such as sample preparation, sample handling, thin-film deposition, and sample analysis, the platform achieves unprecedented precision and reproducibility.

This automation allows researchers to swiftly determine which material compositions and production methods hold the most promise for specific applications. E-MAP operates within a self-contained environment capable of handling sensitive materials under controlled conditions. It generates thin films from solution-based source materials through an automated process facilitated by a microfluidic system.

The platform's modular design permits the incorporation of new experimental techniques and characterization methods, enabling it to adapt to a variety of scientific inquiries. KIT's LTI professor Alexander Colsmann highlights this flexibility, emphasizing the platform's adaptability to diverse research needs.

E-MAP's modular nature also lends itself to collaboration with partners from academia and industry, who can contribute proprietary methodologies and equipment. However, the automation process generates a substantial volume of experimental data. To manage this data effectively, researchers plan to leverage artificial intelligence methods for data analysis, early identification of promising material combinations via virtual simulations, and the control of autonomous or semi-autonomous screening processes.

By integrating synthesis, processing, characterization, and data evaluation, E-MAP streamlines the research process, enabling more efficient planning and execution of experiments based on data insights.

Written by urgent.news from Phys.org's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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