Articles producció científica> Química Física i Inorgànica

Predicting the Solubility of Inorganic Ions Pairs in Water

  • Identification data

    Identifier: imarina:9246577
  • Authors:

    Rahman T
    Petrus E
    Segado M
    Martin NP
    Palys LN
    Rambaran MA
    Ohlin CA
    Bo C
    Nyman M
    Angewandte Chemie (International Ed. Print)
    10.1002/anie.202117839
    Angewandte Chemie (International Ed. Print). 61 (19): e202117839-
  • Others:

    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: 2024-07-20
    Papper version: info:eu-repo/semantics/acceptedVersion
    Link to the original source: https://onlinelibrary.wiley.com/doi/10.1002/anie.202117839
    Licence document URL: http://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
  • Keywords:

    Catalysis,Chemistry,Chemistry (Miscellaneous),Chemistry, Multidisciplinary
    Solubility
    Saxs
    Polyoxoniobate
    Polyoxometalate
    Machine learning
    Ion-pairing
    Hofmeister series
    solvation
    solubility
    saxs
    polyoxoniobate
    polyoxometalate
    metal
    machine learning
    clusters
    calcium
    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
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