Author, as appears in the article.: Rahman T; Petrus E; Segado M; Martin NP; Palys LN; Rambaran MA; Ohlin CA; Bo C; Nyman M
Department: Química Física i Inorgànica
URV's Author/s: Bo Jané, Carles / Petrus Pérez, Enric
Keywords: Solubility Saxs Polyoxoniobate Polyoxometalate Machine learning Ion-pairing Hofmeister series solvation solubility saxs polyoxoniobate polyoxometalate metal machine learning clusters calcium
Abstract: 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−, Nb24) we discovered an unusual solubility trend (termed anomalous solubility) of alkali-POMs, in which Nb24 is most soluble with the smallest (Li+) and largest (Rb/Cs+) alkalis, and least soluble with Na/K+. Via computation, we define a descriptor (σ-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.
Thematic Areas: Química Medicina ii Medicina i Materiais Interdisciplinar General medicine General chemistry Farmacia Engenharias ii Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Chemistry, multidisciplinary Chemistry (miscellaneous) Chemistry (all) Chemistry Catalysis Astronomia / física
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: carles.bo@urv.cat enric.petrus@estudiants.urv.cat
Author identifier: 0000-0001-9581-2922
Record's date: 2024-09-07
Papper version: info:eu-repo/semantics/acceptedVersion
Link to the original source: https://onlinelibrary.wiley.com/doi/10.1002/anie.202117839
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Angewandte Chemie (International Ed. Print). 61 (19): e202117839-
APA: Rahman T; Petrus E; Segado M; Martin NP; Palys LN; Rambaran MA; Ohlin CA; Bo C; Nyman M (2022). Predicting the Solubility of Inorganic Ions Pairs in Water. Angewandte Chemie (International Ed. Print), 61(19), e202117839-. DOI: 10.1002/anie.202117839
Article's DOI: 10.1002/anie.202117839
Entity: Universitat Rovira i Virgili
Journal publication year: 2022
Publication Type: Journal Publications
Repositori URV