Predicting the Solubility of Inorganic Ion Pairs in Water
(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
- Rahman, Tasnim ; Petrus, Enric ; Segado, Mireia ; Martin, Nicolas P. ; Palys, Lauren N. ; Rambaran, Mark A. LU ; Ohlin, C. Andre ; Bo, Carles and Nyman, May
- publishing date
- 2022-05-02
- 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
-
- pmid:35148455
- scopus:85125208000
- 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
- 2024-07-27 04:50:30
@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>>Na<sup>+</sup>>K<sup>+</sup>>Rb<sup>+</sup>>Cs<sup>+</sup>), and anomalous (Cs<sup>+</sup>>Rb<sup>+</sup>>K<sup>+</sup>>Na<sup>+</sup>>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}}, }