Featured articles
Statistically driven automated method for catalytic glucose conversion optimisation
A statistically driven, automated approach to optimize glucose transformations to platform chemicals, methyl lactate and levulinic acid, is reported.
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery.
Automation and Machine Learning for Accelerated Polymer Characterization and Development: Past, Potential, and a Path Forward
Automation and machine learning techniques are poised to dramatically accelerate the development of new materials while simultaneously increasing our understanding of the physics and chemistry that underlie the formation of such materials.
Exploration of the polymorphic solid-state landscape of an amide-linked organic cage using computation and automation
Organic cages can possess complex, functionalised internal cavities that make them promising candidates for synthetic enzyme mimics.