Predicting the Solubility of Inorganic Ions Pairs in Water

Identificador:  imarina:9246577

Handle: https://hdl.handle.net/20.500.11797/imarina9246577

Autores:  Rahman, Tasnim; Petrus, Enric; Segado, Mireia; Martin, Nicolas P; Palys, Lauren N; Rambaran, Mark A; Ohlin, C Andre; Bo, Carles; Nyman, May

Resumen:
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.