High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials. Issue 44 (21st September 2020)

• Record Type: Journal Article
• Authors: Clayson, Ivan G.; Hewitt, Daniel; Hutereau, Martin; Pope, Tom; Slater, Ben
• Is Part Of: Advanced materials. Volume 32:Issue 44(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-21
• Subjects: high throughput methods -- high throughput synthesis -- machine learning -- metal–organic frameworks -- porous materials -- zeolites; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adma.202002780
• Languages: English
• ISSNs: 0935-9648

Abstract

Abstract: Porous materials are widely employed in a large range of applications, in particular, for storage, separation, and catalysis of fine chemicals. Synthesis, characterization, and pre‐ and post‐synthetic computer simulations are mostly carried out in a piecemeal and ad hoc manner. Whilst high throughput approaches have been used for more than 30 years in the porous material fields, routine integration of experimental and computational processes is only now becoming more established. Herein, important developments are highlighted and emerging challenges for the community identified, including the need to work toward more integrated workflows. Abstract : High throughput synthetic, computational, and analytic/machine learning approaches applied to porous materials are reviewed. The benefits of a virtuous circle, where there is feedback between each strand of research, are highlighted.