In the past decade, machine learning has emerged as a powerful tool to predict reaction outcomes.
Covalent organic frameworks (COFs) are versatile materials platforms for precise function integration owing to their high crystallinity, large surface areas, tunable characteristics and diverse and predictable structures. However, the dominant solvothermal method for COF synthesis requires harsh conditions, including high temperatures, toxic organic solvents, sealed and pressurized reactors, and extended reaction times that often exceed several days.
Reversible addition–fragmentation chain transfer(RAFT) polymerization has undergone transformative growth since itsinception in 1998, emerging as a powerful and versatile tool for precision polymer synthesis.
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