A Modular Programmable Inorganic Cluster Discovery Robot for the Discovery and Synthesis of Polyoxometalates

Abstract

<p>The exploration of complex multi-component chemical reactions leading to new clusters, where discovery requires both molecular self-assembly and crystallization, is a major challenge. This is because the systematic approach required for an experimental search is limited when the number of parameters in a chemical space becomes too large, restricting both exploration, and reproducibility. Herein, we present a synthetic strategy to systematically search a very large set of potential reactions, using an inexpensive, high-throughput platform; modular in terms of both hardware and software, and capable of running multiple reactions with in-line analysis; for the automation of inorganic and materials chemistry. The platform has been used to explore several inorganic chemical spaces to discover new, and reproduce known, tungsten-based, mixed transition-metal polyoxometalate clusters, giving a digital code allowing the easy repeat synthesis of the clusters. Among the many species identified in this work, most significantly is the discovery of a novel, purely inorganic W<sub>24</sub>Fe<sup>III</sup>-superoxide cluster formed under ambient conditions. The Modular Wheel Platform (MWP) was then employed to undertake two chemical space explorations producing compounds [1-4]: (C<sub>2</sub>H<sub>8</sub>N)<sub>10</sub>Na<sub>2</sub>[H<sub>6</sub>Fe(O<sub>2</sub>)W<sub>24</sub>O<sub>82</sub>(H<sub>2</sub>O)<sub>25</sub>] (1, {W<sub>24</sub>Fe}), (C<sub>2</sub>H<sub>8</sub>N)<sub>72</sub>Na<sub>16</sub>[H<sub>16</sub>Co<sub>8</sub>W<sub>200</sub>O<sub>660</sub>(H<sub>2</sub>O)<sub>40</sub>] (2, {W<sub>200</sub>Co<sub>8</sub>}), (C<sub>2</sub>H<sub>8</sub>N)<sub>72</sub>Na<sub>16</sub>[H<sub>16</sub>Ni<sub>8</sub>W<sub>200 </sub>O<sub>660-</sub>(H<sub>2</sub>O)<sub>40</sub>] (3, {W<sub>200</sub>Ni<sub>8</sub>}) and (C<sub>2</sub>H<sub>8</sub>N)<sub>14</sub>[H<sub>26</sub>W<sub>34</sub>V<sub>4</sub>O<sub>130</sub>] (4, {W<sub>34</sub>V<sub>4</sub>}), along with many other known species, for example simple Keggin clusters and 1D {W<sub>11</sub>M<sup>2+</sup>} chains. <b></b></p>

Robotic Stepwise Synthesis of Hetero-Multinuclear Metal Oxo Clusters as Single-Molecule Magnets

“…The system itself uses in house software, written in Python to control hardware via Arduino to run the operations of the synthesis. 11 The MWP-assisted screening was attempted using the following stock solutions: acetonitrile, acetonitrile solution of I (8.32 mM), acetonitrile solution of AOTf (A = Li, Na, K, Ag; 50 mM), and diethyl ether (poor solvent) (Figure 3). The volume of each solution was programmed as follows: The concentration of I in the synthetic solution was set to 10, 20, and 30 mg/mL, and the equivalents of AOTf with respect to I was set to 1, 2, 3, 4, 5, and 6, resulting in 72 reaction conditions (Table S1).…”

“…The platform was published in 2020. 11 Full details of the platform can be found in the Supporting Information of that previous work, including the bill of materials and instructions for the control software and construction of the platform. For this work, a summary of the platform is included here.…”

“…10 More recently, a modular system for the combinatorial exploration of inorganic chemical space has been developed in the Cronin group at the University of Glasgow named the Modular Wheel Platform (MWP). 11 This system has proven to be a powerful tool for automated screening of reaction conditions, leading to the discovery of gigantic and novel POMs. As this system was used for one-step, one-pot condensation reactions of metal sources, we envisioned expanding this methodology for stepwise synthesis using precursors, which should reduce time and effort for discovering new heteromultinuclear metal clusters.…”

Chemputation and the Standardization of Chemical Informatics

“…Even achieving satisfactory reaction conditions is nontrivial and can take weeks of experimentation for a human researcher. 20,23,129 Most recent developments in reaction automation have been applied to facilitate optimal parameters discovery, however the vast majority of the published platforms are bespoke systems, capable of performing single reaction optimization in flow. 130 These platforms, despite demonstrating proof-of-concept results, are typically limited to a specific task, while creating a universal, fully automated framework remains challenging (Figure 8).…”

Automated pH Adjustment Driven by Robotic Workflows and Active Machine Learning

“…Technical design of the bespoke robotic platform was based on previous studies. 35,36 We utilized FLab, a Python-based library, for facilitating communication between the motors, pumps, the pH electrode and the implementation of the ML based optimization algorithm. 37 See SI (Figure S1) for more detailed images and information on the robotic platform.…”