Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation

Surface Coverage Tuning for Suppressing Over-Oxidation: A Case of Photoelectrochemical Alcohol-to-Aldehyde/Ketone Conversion

Suppressing over-oxidation is a crucial challenge for various chemical intermediate synthesis in heterogeneous catalysis. The distribution of oxidative species and the substrate coverage, governed by the direction of electron transfer, are believed to influence the oxidation extent. In this study, we presented an experimental realization of surface coverage modulation on a photoelectrode using a photo-induced charge activation method. Through the surface coverage modulation, both pre-oxidized alcohol substrates and surface coverage were increased, which not only improved the reaction kinetics but also suppressed the over-oxidation of the generated aldehydes/ketones. As a demonstration, the Faradaic efficiency for the conversion of glycerol to dihydroxyacetone increased from 31.8 % to 46.8 % (with selectivity rising from 47.6 % to 71.3 %), from 73.4 % to 87.8 % for benzyl alcohol to benzyl aldehyde (selectivity increasing from 76.7 % to 92.4 %) and from 4.2 % to 53.6 % for ethylene glycol to glycolaldehyde (selectivity increasing from 6.2 % to 62.7 %). Our findings offer a promising strategy for the production of high-value carbon products in heterogeneous catalysis.

CO Oxidation Catalyzed by Perovskites: The Role of Crystallographic Distortions Highlighted by Systematic Experiments and Artificial Intelligence

The identification of key materials' parameters correlated with the performance can accelerate the development of heterogeneous catalysts and unveil the relevant underlying physical processes. However, the analysis of correlations is often hindered by inconsistent data. Besides, nontrivial, yet unknown relationships may be important, and the intricacy of the various processes may be significant. Here, we tackle these challenges for the CO oxidation catalyzed by perovskites using a combination of rigorous experiments and artificial intelligence. A series of 13 ABO3 (A=La, Pr, Nd, Sm; B=Cr, Mn, Fe, Co) perovskites was synthesized, characterized, and tested in catalysis. To the resulting dataset, we applied the symbolic-regression SISSO approach. We identified an analytical expression correlated with the activity that contains the normalized unit-cell volume, the Pauling electronegativity of the elements A and B, and the ionization energy of the element B. Therefore, the activity is described by crystallographic distortions and by the chemical nature of A and B elements. The generalizability of the identified descriptor is confirmed by the good quality of the predictions for 3 additional ABO3 and 16 chemically more complex AMn(1-x)B'xO3 (A=La, Pr, Nd; B'=Fe, Co, Ni, Cu, Zn) perovskites.

Highly Efficient Continuous Flow Nanocatalyst Platform Constructed with Regenerable Bacterial Cellulose Loaded with Gold Nanoparticles and a Nanoporous Membrane

With the development of society and the growing concern about environmental issues, continuous flow catalytic reactors have gained significant interest due to their resource-efficient advantages over traditional batch devices. In this study, we employed a facile one-step in situ reduction approach to construct highly dispersed gold nanoparticles loaded on regenerable bacterial cellulose nanofiber (BCN) heterogeneous catalysts. These catalysts, in combination with a nanoceramic membrane with a pore size of 1 nm, formed a fully mixed system that was favorable for the efficient continuous flow catalysis of selective reduction reactions of nitrophenol. The reaction system demonstrated remarkable catalytic activity toward nitrophenol reduction reactions at low reductant dosages (<5 equiv), achieving over 95% conversion and 99% selectivity for the aniline product in 10 min under room temperature conditions. Furthermore, continuous flow operations maintained stable catalytic activity with minimal catalyst loss after a 120-h test and were 3 times more time-efficient than batch operations. Additionally, continuous monitoring could be conducted through ultraviolet (UV) spectroscopy. A highly efficient and environmentally friendly strategy was present for designing continuous flow reactions in future applications.

High-Quality Data Enabling Universality of Band Gap Descriptor and Discovery of Photovoltaic Perovskites

Extensive machine-learning-assisted research has been dedicated to predicting band gaps for perovskites, driven by their immense potential in photovoltaics. Yet, the effectiveness is often hampered by the lack of high-quality band gap data sets, particularly for perovskites involving d orbitals. In this work, we consistently calculate a large data set of band gaps with a high level of accuracy, which is rigorously validated by experimental and state-of-the-art GW band gaps. Leveraging this achievement, our machine-learning-derived descriptor exhibits exceptional universality and robustness, proving effectiveness not only for single and double, halide and oxide perovskites regardless of the underlying atomic structures but also for hybrid organic-inorganic perovskites. With this approach, we comprehensively explore up to 15,659 materials, unveiling 14 unreported lead-free perovskites with suitable band gaps for photovoltaics. Notably, MASnBr3, FA2SnGeBr6, MA2AuAuBr6, FA2AuAuBr6, FA2InBiCl6, FA2InBiBr6, and Ba2InBiO6 stand out with direct band gaps, small effective masses, low exciton binding energies, and high stabilities.

Synthesis of Ordered Mesoporous Molecular Sieve-Supported Cobalt Catalyst via Organometallic Complexation for Propane Non-Oxidative Dehydrogenation

Co-based catalysts have shown great promise for propane dehydrogenation (PDH) reactions due to their merits of environmental friendliness and low cost. In this study, ordered mesoporous molecular sieve-supported CoOx species (CoOx/Al-SBA-15 catalyst) were prepared by one-step organometallic complexation. The catalysts show worm-like morphology with regular straight-through mesoporous pores and high external specific surface area. These typical features can substantially enhance the dispersion of CoOx species and mass transfer of reactants and products. Compared with the conventional impregnation method, the 10CSOC (10 wt.% Co/Al-SBA-15 prepared by the organometallic complexation method) sample presents a smaller CoOx size and higher Co2+/Co3+ ratio. When applied to PDH reaction, the 10CSOC delivers higher propane conversion and propylene selectivity. Under the optimal conditions (625 °C and 4500 h-1), 10CSOC achieves high propane conversion (43%) and propylene selectivity (83%). This is attributed to the smaller and better dispersion of CoOx nanoparticles, more suitable acid properties, and higher content of Co2+ species. This work paves the way for the rational design of high-performance catalysts for industrially important reactions.

Computational Discovery of Intermolecular Singlet Fission Materials Using Many-Body Perturbation Theory

Intermolecular singlet fission (SF) is the conversion of a photogenerated singlet exciton into two triplet excitons residing on different molecules. SF has the potential to enhance the conversion efficiency of solar cells by harvesting two charge carriers from one high-energy photon, whose surplus energy would otherwise be lost to heat. The development of commercial SF-augmented modules is hindered by the limited selection of molecular crystals that exhibit intermolecular SF in the solid state. Computational exploration may accelerate the discovery of new SF materials. The GW approximation and Bethe-Salpeter equation (GW+BSE) within the framework of many-body perturbation theory is the current state-of-the-art method for calculating the excited-state properties of molecular crystals with periodic boundary conditions. In this Review, we discuss the usage of GW+BSE to assess candidate SF materials as well as its combination with low-cost physical or machine learned models in materials discovery workflows. We demonstrate three successful strategies for the discovery of new SF materials: (i) functionalization of known materials to tune their properties, (ii) finding potential polymorphs with improved crystal packing, and (iii) exploring new classes of materials. In addition, three new candidate SF materials are proposed here, which have not been published previously.

Materials Genes of CO2 Hydrogenation on Supported Cobalt Catalysts: An Artificial Intelligence Approach Integrating Theoretical and Experimental Data

Designing materials for catalysis is challenging because the performance is governed by an intricate interplay of various multiscale phenomena, such as the chemical reactions on surfaces and the materials' restructuring during the catalytic process. In the case of supported catalysts, the role of the support material can be also crucial. Here, we address this intricacy challenge by a symbolic-regression artificial intelligence (AI) approach. We identify the key physicochemical parameters correlated with the measured performance, out of many offered candidate parameters characterizing the materials, reaction environment, and possibly relevant underlying phenomena. Importantly, these parameters are obtained by both experiments and ab initio simulations. The identified key parameters might be called "materials genes", in analogy to genes in biology: they correlate with the property or function of interest, but the explicit physical relationship is not (necessarily) known. To demonstrate the approach, we investigate the CO2 hydrogenation catalyzed by cobalt nanoparticles supported on silica. Crucially, the silica support is modified with the additive metals magnesium, calcium, titanium, aluminum, or zirconium, which results in six materials with significantly different performances. These systems mimic hydrothermal vents, which might have produced the first organic molecules on Earth. The key parameters correlated with the CH3OH selectivity reflect the reducibility of cobalt species, the adsorption strength of reaction intermediates, and the chemical nature of the additive metal. By using an AI model trained on basic elemental properties of the additive metals (e.g., ionization potential) as physicochemical parameters, new additives are suggested. The predicted CH3OH selectivity of cobalt catalysts supported on silica modified with vanadium and zinc is confirmed by new experiments.

Tuning catalysis by surface-deposition of elements on oxidation catalysts via atomic layer deposition

This study on surface-modifications of bulk oxidation catalysts with sub-monolayers of POx, BOx and MnOxvia atomic layer deposition demonstrates this method to be a powerful tool for tuning the performance in selective oxidations of light alkanes.