Self-driving labs (SDLs) combine robotic automation with artificial intelligence (AI) to allow autonomous, high-throughput experimentation. However, robot manipulation in most SDL workflows operates in an open-loop manner, lacking real-time error detection and error correction. This can reduce reliability and overall efficiency.
Diverse collections of well-defined glycans are needed to investigate the molecular mechanisms by which these biomolecules mediate biological and disease processes. Several automation approaches have been introduced to accelerate the enzymatic synthesis of complex glycans. These methodologies have, however, provided only relatively simple oligosaccharides due to limitations of glycosyl transferase selectivity.
The design and discovery of porous materials have become a central theme in materials science, driven by their applications in gas storage, separation, carbon capture, and catalysis. Rapid advances in synthetic chemistry, particularly in metal–organic frameworks, porous organic cages, and conjugated microporous polymers, have enabled the generation of increasingly large and diverse material libraries.
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