期刊
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
卷 61, 期 19, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/anie.202117839
关键词
Ion-Pairing; Machine Learning; Polyoxometalate; Polyoxoniobate; SAXS; Solubility
资金
- U.S. Department of Energy, Office of Basic Energy Sciences, Division of Material Sciences and Engineering [DE SC0010802]
- Spanish Ministerio de Ciencia e Innovacion [PID2020-112806RB-I00]
- Severo Ochoa Excellence Accreditation 2020-2023 (MCI/AEI) [CEX2019-000925-S]
- Murdock Charitable Trust [SR-2017297]
- Swedish Research Council [2018-07039]
- Kempe foundation [JCK-2029.1]
- Swedish Research Council [2018-07039] Funding Source: Swedish Research Council
Anomalous solubility behavior of alkali-POMs has been discovered and successfully predicted using computation and simulation, providing guidelines for the discovery of new POMs.
Polyoxometalates (POMs), ranging in size from 1 to 10's of nanometers, resemble building blocks of inorganic materials. Elucidating their complex solubility behavior with alkali-counterions can inform natural and synthetic aqueous processes. In the study of POMs ([Nb24O72H9](15-), Nb-24) we discovered an unusual solubility trend (termed anomalous solubility) of alkali-POMs, in which Nb-24 is most soluble with the smallest (Li+) and largest (Rb/Cs+) alkalis, and least soluble with Na/K+. Via computation, we define a descriptor (sigma-profile) and use an artificial neural network (ANN) to predict all three described alkali-anion solubility trends: amphoteric, normal (Li+>Na+>K+>Rb+>Cs+), and anomalous (Cs+>Rb+>K+>Na+>Li+). Testing predicted amphoteric solubility affirmed the accuracy of the descriptor, provided solution-phase snapshots of alkali-POM interactions, yielded a new POM formulated [Ti6Nb14O54](14-), and provides guidelines to exploit alkali-POM interactions for new POMs discovery.
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