Predicting the Solubility of Inorganic Ion Pairs in Water

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 ([Nb₂₄ O₇₂ H₉ ]^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 [Ti₆ Nb₁₄ O₅₄ ]^14- , and provides guidelines to exploit alkali-POM interactions for new POMs discovery.