Algorithm-Driven Robotic Discovery of Polyoxometalate-Scaffolding Metal-Organic Frameworks.

The experimental exploration of the chemical space of crystalline materials, especially metal-organic frameworks (MOFs), requires multiparameter control of a large set of reactions, which is unavoidably time-consuming and labor-intensive when performed manually. To accelerate the rate of material discovery while maintaining high reproducibility, we developed a machine learning algorithm integrated with a robotic synthesis platform for closed-loop exploration of the chemical space for polyoxometalate-scaffolding metal-organic frameworks (POMOFs). The eXtreme Gradient Boosting (XGBoost) model was optimized by using updating data obtained from the uncertainty feedback experiments and a multiclass classification extension based on the POMOF classification from their chemical constitution. The digital signatures for the robotic synthesis of POMOFs were represented by the universal chemical description language (χDL) to precisely record the synthetic steps and enhance the reproducibility. Nine novel POMOFs including one with mixed ligands derived from individual ligands through the imidization reaction of POM amine derivatives with various aldehydes have been discovered with a good repeatability. In addition, chemical space maps were plotted based on the XGBoost models whose F1 scores are above 0.8. Furthermore, the electrochemical properties of the synthesized POMOFs indicate superior electron transfer compared to the molecular POMs and the direct effect of the ratio of Zn, the type of ligands used, and the topology structures in POMOFs for modulating electron transfer abilities.

Magnetic Hyperthermia Enhancement in Iron-based Materials Driven by Carbon Support Interactions.

Magnetic hyperthermia (MH) shows great potential in clinical applications because of its very localized action and minimal side effects. Because of their high saturation magnetization values, reduced forms of iron are promising candidates for MH. However, they must be protected in order to overcome their toxicity and instability (i. e., oxidation) under biological conditions. In this work, a novel methodology for the protection of iron nanoparticles through confinement within graphitic carbon layers after thermal treatment of preformed nanoparticles supported on carbon is reported. We demonstrate that the size and composition of the nascent confined iron nanoparticles, as well as the thickness of their protective carbon layer can be controlled by selecting the nature of the carbon support. Our findings reveal that a higher nanoparticle-carbon interaction, mediated by the presence of oxygen-containing groups, induces the formation of small and well-protected α-Fe-based nanoparticles that exhibit promising results towards MH based on their enhanced specific absorption rate values.

Boosting Oxygen Reduction Reaction Selectivity in Metal Nanoparticles with Polyoxometalates.

The lack of selectivity toward the oxygen reduction reaction (ORR) in metal nanoparticles can be linked to the generation of intermediates. This constitutes a crucial constraint on the performance of specific electrochemical devices, such as fuel cells and metal-air batteries. To boost selectivity of metal nanoparticles, a novel methodology that harnesses the unique electrocatalytic properties of polyoxometalates (POM) to scavenge undesired intermediates of the ORR (such as HO2 -) promoting selectivity is proposed. It involves the covalent functionalization of metal nanoparticle's surface with an electrochemically active capping layer containing a new sulfur-functionalized vanadium-based POM (AuNP@POM). To demonstrate this approach, preformed thiolate Au(111) nanoparticles with a relatively poor ORR selectivity are chosen. The dispersion of AuNP@POM on the surface of carbon nanofibers (CNF) enhances oxygen diffusion, and therefore the ORR activity. The resulting electrocatalyst (AuNP@POM/CNF) exhibits superior stability against impurities like methanol and a higher pH tolerance range compared to the standard commercial Pt/C. The work demonstrates for the first time, the use of a POM-based electrochemically active capping layer to switch on the selectivity of poorly selective gold nanoparticles, offering a promising avenue for the preparation of electrocatalyst materials with improved selectivity, performance, and stability for ORR-based devices.