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dc.contributor.authorKumar, Sushil
dc.contributor.authorIgnacz, Gergo
dc.contributor.authorSzekely, Gyorgy
dc.date.accessioned2021-10-10T06:30:44Z
dc.date.available2021-10-10T06:30:44Z
dc.date.issued2021
dc.identifier.citationKUMAR, S., Ignacz, G., & Szekely, G. (2021). Synthesis of covalent organic frameworks using sustainable solvents and machine learning. Green Chemistry. doi:10.1039/d1gc02796d
dc.identifier.doi10.1039/D1GC02796D
dc.identifier.urihttp://hdl.handle.net/10754/672461
dc.description.abstractCovalent organic frameworks (COFs) have attracted considerable interest owing to their structural predesign ability, controllable chemistry, long-range periodicity, and pore interior functionalization ability. The most widely adopted solvothermal synthesis of COFs requires the use of toxic organic solvents. In line with the 5th principle of green chemistry and the United Nations’ 12th sustainable development goal, we aim to mitigate the adverse effect of solvents on COF synthesis. Here we have investigated twelve green solvents for the sustainable synthesis of five series of COFs using the solvothermal approach. Crystallinity and porosity were used to assess the quality of the obtained COFs. In addition, the suitability of the solvents in the synthesis of crystalline and porous COFs was investigated and color-coded for the final green assessment. In particular, γ–butyrolactone (for TpPa, TpBD, and TpAzo); para–cymene (TpAnq); and PolarClean (TpTab) were found to be excellent green solvents to produce high-quality COFs. For the first time, we successfully used quantitative structure property relationships in combination with machine learning approaches to predict both the surface area and crystallinity of the COFs by utilizing the structure of the solvents and COF building blocks
dc.description.sponsorshipThis work was supported by King Abdullah University of Science and Technology (KAUST). The postdoctoral (SK) and PhD (GI) fellowships from the Advanced Membranes and Porous Materials Center at KAUST are gratefully acknowledged.
dc.language.isoen
dc.publisherRoyal Society of Chemistry
dc.relation.urlhttps://doi.org/10.1039/D1GC02796D
dc.rightsThis article is open access licensed under a Creative Commons Attribution 3.0 Unported Licence.
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/
dc.titleSynthesis of covalent organic frameworks using sustainable solvents and machine learning
dc.typeArticle
dc.contributor.departmentAdvanced Membranes and Porous Materials Research Center
dc.contributor.departmentChemical Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalGreen Chemistry
dc.rights.embargodate2022-10-08
dc.eprint.versionPost-print
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
pubs.publication-statusAccepted
kaust.personKumar, Sushil
kaust.personIgnacz, Gergo
kaust.personSzekely, Gyorgy
refterms.dateFOA2021-10-10T06:30:44Z
kaust.acknowledged.supportUnitAdvanced Membranes and Porous Materials Center at KAUST


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