Autor segons l'article: Rahman T; Petrus E; Segado M; Martin NP; Palys LN; Rambaran MA; Ohlin CA; Bo C; Nyman M
Departament: Química Física i Inorgànica
Autor/s de la URV: Bo Jané, Carles / Petrus Pérez, Enric
Paraules clau: Solubility Saxs Polyoxoniobate Polyoxometalate Machine learning Ion-pairing Hofmeister series solvation solubility saxs polyoxoniobate polyoxometalate metal machine learning clusters calcium
Resum: 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.
Àrees temàtiques: 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
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: carles.bo@urv.cat enric.petrus@estudiants.urv.cat
Identificador de l'autor: 0000-0001-9581-2922
Data d'alta del registre: 2024-09-07
Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
Enllaç font original: https://onlinelibrary.wiley.com/doi/10.1002/anie.202117839
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Angewandte Chemie (International Ed. Print). 61 (19): e202117839-
Referència de l'ítem segons les normes 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
DOI de l'article: 10.1002/anie.202117839
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2022
Tipus de publicació: Journal Publications
Repositori URV