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.
Self-driving laboratories (SDLs), which combine automated experimental hardware with computational experiment planning, have emerged as powerful tools for accelerating materials discovery.
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.
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.
This review proposes the concept of a “frugal twin,” similar to a digital twin, but for physical ex-periments.
Chemspeed has won ISPE Robotics Application of the Year Award RAYA2023!
Automated high-throughput platforms and Artificial Intelligence (AI) are already accelerating discovery and optimization in various fields of chemistry and chemical engineering.
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.
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.