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Predicting the Solubility of Inorganic Ion Pairs in Water

Rahman, Tasnim ; Petrus, Enric ; Segado, Mireia ; Martin, Nicolas P. ; Palys, Lauren N. ; Rambaran, Mark A. LU ; Ohlin, C. Andre ; Bo, Carles and Nyman, May (2022) In Angewandte Chemie - International Edition 61(19).
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:... (More)

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.

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author
; ; ; ; ; ; ; and
publishing date
type
Contribution to journal
publication status
published
keywords
Ion-Pairing, Machine Learning, Polyoxometalate, Polyoxoniobate, SAXS, Solubility
in
Angewandte Chemie - International Edition
volume
61
issue
19
article number
e202117839
pages
8 pages
publisher
John Wiley & Sons Inc.
external identifiers
  • scopus:85125208000
  • pmid:35148455
ISSN
1433-7851
DOI
10.1002/anie.202117839
language
English
LU publication?
no
additional info
Publisher Copyright: © 2022 Wiley-VCH GmbH.
id
85d2e253-4402-431b-bcae-23e8a37e04c9
date added to LUP
2023-02-15 16:52:22
date last changed
2024-04-04 16:39:55
@article{85d2e253-4402-431b-bcae-23e8a37e04c9,
  abstract     = {{<p>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 ([Nb<sub>24</sub>O<sub>72</sub>H<sub>9</sub>]<sup>15−</sup>, Nb<sub>24</sub>) we discovered an unusual solubility trend (termed anomalous solubility) of alkali-POMs, in which Nb<sub>24</sub> is most soluble with the smallest (Li<sup>+</sup>) and largest (Rb/Cs<sup>+</sup>) alkalis, and least soluble with Na/K<sup>+</sup>. 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<sup>+</sup>&gt;Na<sup>+</sup>&gt;K<sup>+</sup>&gt;Rb<sup>+</sup>&gt;Cs<sup>+</sup>), and anomalous (Cs<sup>+</sup>&gt;Rb<sup>+</sup>&gt;K<sup>+</sup>&gt;Na<sup>+</sup>&gt;Li<sup>+</sup>). Testing predicted amphoteric solubility affirmed the accuracy of the descriptor, provided solution-phase snapshots of alkali–POM interactions, yielded a new POM formulated [Ti<sub>6</sub>Nb<sub>14</sub>O<sub>54</sub>]<sup>14−</sup>, and provides guidelines to exploit alkali–POM interactions for new POMs discovery.</p>}},
  author       = {{Rahman, Tasnim and Petrus, Enric and Segado, Mireia and Martin, Nicolas P. and Palys, Lauren N. and Rambaran, Mark A. and Ohlin, C. Andre and Bo, Carles and Nyman, May}},
  issn         = {{1433-7851}},
  keywords     = {{Ion-Pairing; Machine Learning; Polyoxometalate; Polyoxoniobate; SAXS; Solubility}},
  language     = {{eng}},
  month        = {{05}},
  number       = {{19}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Angewandte Chemie - International Edition}},
  title        = {{Predicting the Solubility of Inorganic Ion Pairs in Water}},
  url          = {{http://dx.doi.org/10.1002/anie.202117839}},
  doi          = {{10.1002/anie.202117839}},
  volume       = {{61}},
  year         = {{2022}},
}