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In this work, we investigated the technical feasibility of ‘on-demand’ production of selected drugs to cover their demand for a time window of 90 days.

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Self-driving laboratories (SDLs), which combine automated experimental hardware with computational experiment planning, have emerged as powerful tools for accelerating materials discovery.

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Crystalline porous organic salts (CPOS) are a subclass of molecular crystals. The low solubility of CPOS and their building blocks limits the choice of crystallisation solvents to water or polar alcohols, hindering the isolation, scale-up, and scope of the porous material.

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Powder X-ray diffraction (PXRD) is a key technique for the structural characterisation of solid-state materials, but compared with tasks such as liquid handling, its end-to-end automation is highly challenging.

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This review proposes the concept of a “frugal twin,” similar to a digital twin, but for physical ex-periments.

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Chemspeed has won ISPE Robotics Application of the Year Award RAYA2023!

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Automated high-throughput platforms and Artificial Intelligence (AI) are already accelerating discovery and optimization in various fields of chemistry and chemical engineering.

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Advances in robotic automation, high-performance computing (HPC), and artificial intelligence (AI) encourage us to conceive of science factories: large, general-purpose computation- and AI-enabled self-driving laboratories (SDLs) with the generality and scale needed both to tackle large discovery problems and to support thousands of scientists.

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The ability to rapidly examine diverse reaction conditions in parallel at micromole or nanomole scales without depleting precious starting materials is of critical importance to methodological development and reaction optimization.

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