Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery.

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.

Synthesis of Layered Lead-Free Perovskite Nanocrystals with Precise Size and Shape Control and Their Photocatalytic Activity.

Halide perovskites have attracted enormous attention due to their potential applications in optoelectronics and photocatalysis. However, concerns over their instability, toxicity, and unsatisfactory efficiency have necessitated the development of lead-free all-inorganic halide perovskites. A major challenge in designing efficient halide perovskites for practical applications is the lack of effective methods for producing nanocrystals with precise size and shape control. In this work, a layered perovskite, Cs4ZnSb2Cl12 (CZS), is found from calculations to exhibit size- and facet-dependent optoelectronic properties in the nanoscale, and thus, a colloidal method is used to synthesize the CZS nanoparticles with size-tunable morphologies: zero- (nanodots), one- (nanowires and nanorods), two- (nanoplates), and three-dimensional (nanopolyhedra). The growth kinetics of the CZS nanostructures, along with the effects of surface ligands, reaction temperature, and time were investigated. The optoelectronic properties of the nanocrystals varied with size due to quantum confinement effects and with shape due to anisotropy within the crystals and the exposure of specific facets. These properties could be modulated to enhance the visible-light photocatalytic performance for toluene oxidation. In particular, the 9.7 nm CZS nanoplates displayed a toluene to benzaldehyde conversion rate of 1893 µmol g-1 h-1 (95% selectivity), 500 times higher than the bulk synthesized CZS, and comparable with the reported photocatalysts. This study demonstrates the integration of theoretical calculations and synthesis, revealing an approach to the design and fabrication of novel, high-performance colloidal perovskite nanocrystals for optoelectronic and photocatalytic applications.

Rational Atom Substitution to Obtain Efficient, Lead-Free Photocatalytic Perovskites Assisted by Machine Learning and DFT Calculations.

Inorganic lead-free halide perovskites, devoid of toxic or rare elements, have garnered considerable attention as photocatalysts for pollution control, CO2 reduction and hydrogen production. In the extensive perovskite design space, factors like substitution or doping level profoundly impact their performance. To address this complexity, a synergistic combination of machine learning models and theoretical calculations were used to efficiently screen substitution elements that enhanced the photoactivity of substituted Cs2 AgBiBr6 perovskites. Machine learning models determined the importance of d10 orbitals, highlighting how substituent electron configuration affects electronic structure of Cs2 AgBiBr6 . Conspicuously, d10 -configured Zn2+ boosted the photoactivity of Cs2 AgBiBr6 . Experimental verification validated these model results, revealing a 13-fold increase in photocatalytic toluene conversion compared to the unsubstituted counterpart. This enhancement resulted from the small charge carrier effective mass, as well as the creation of shallow trap states, shifting the conduction band minimum, introducing electron-deficient Br, and altering the distance between the B-site cations d band centre and the halide anions p band centre, a parameter tuneable through d10 configuration substituents. This study exemplifies the application of computational modelling in photocatalyst design and elucidating structure-property relationships. It underscores the potential of synergistic integration of calculations, modelling, and experimental analysis across various applications.

Developing sustainable, high-performance perovskites in photocatalysis: design strategies and applications.

Solar energy is attractive because it is free, renewable, abundant and sustainable. Photocatalysis is one of the feasible routes to utilize solar energy for the degradation of pollutants and the production of fuel. Perovskites and their derivatives have received substantial attention in both photocatalytic wastewater treatment and energy production because of their highly tailorable structural and physicochemical properties. This review illustrates the basic principles of photocatalytic reactions and the application of these principles to the design of robust and sustainable perovskite photocatalysts. It details the structures of the perovskites and the physics and chemistry behind photocatalytic reactions and describes the advantages and limitations of popular strategies for the design of photoactive perovskites. This is followed by examples of how these strategies are applied to enhance the photocatalytic efficiency of oxide, halide and oxyhalide perovskites, with a focus on materials with potential for practical application, that is, not containing scarce or toxic elements. It is expected that this overview of the development of photocatalysts and deeper understanding of photocatalytic principles will accelerate the exploitation of efficient perovskite photocatalysts and bring about effective solutions to the energy and environmental crisis.

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture.

Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods-data collection, featurization, model generation, and model evaluation-and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature-property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.

Hierarchically Porous WO3/CdWO4 Fiber-in-Tube Nanostructures Featuring Readily Accessible Active Sites and Enhanced Photocatalytic Effectiveness for Antibiotic Degradation in Water.

The intentional design and construction of photocatalysts containing heterojunctions with readily accessible active sites will improve their ability to degrade pollutants. Herein, hierarchically porous WO3/CdWO4 fiber-in-tube nanostructures with three accessible surfaces (surface of core fiber and inner and outer surfaces of the porous tube shell) were fabricated by an electrospinning method. This WO3/CdWO4 heterostructure, assembled by interconnected nanoparticles, displays good photocatalytic degradation of ciprofloxacin (CIP, 93.4%) and tetracycline (TC, 81.6%) after 90 min of simulated sunlight irradiation, much higher than the pristine WO3 (<75.3% for CIP and <53.6% for TC) or CdWO4 materials (<58.9% for CIP and <39.5% for TC). The WO3/CdWO4 fiber-in-tube promotes the separation of photoinduced electrons and holes and also provides readily accessible reaction sites for photocatalytic degradation. The dominant active species determined by trapping active species and electron paramagnetic resonance were hydroxyl radicals followed by photogenerated holes and superoxide anions. The WO3/CdWO4 materials formed a Z-scheme heterojunction that generated superoxide anion and hydroxyl radicals, leading to degradation of antibiotics (CIP and TC) via photocatalysis in aqueous solution.

Use of metamodels for rapid discovery of narrow bandgap oxide photocatalysts.

New photocatalysts are traditionally identified through trial-and-error methods. Machine learning has shown considerable promise for improving the efficiency of photocatalyst discovery from a large potential pool. Here, we describe a multi-step, target-driven consensus method using a stacking meta-learning algorithm that robustly predicts bandgaps and H2 evolution activities of photocatalysts. Trained on small datasets, these models can rapidly screen a large space (>10 million materials) to identify promising, non-toxic compounds as candidate water splitting photocatalysts. Two effective compounds and two controls possessing optimal bandgap values (∼2 eV) but not photoactivity as predicted by the models were synthesized. Their experimentally measured bandgaps and H2 evolution activities were consistent with the predictions. Conspicuously, the two compounds with strong photoactivities under UV and visible light are promising visible-light-driven water splitting photocatalysts. This study demonstrates the power of machine learning and the potential of big data to accelerate discovery of next-generation photocatalysts.

Photovoltaic Effect of a Ferroelectric-Luminescent Heterostructure under Infrared Light Illumination.

In this report, a ferroelectric-luminescent heterostructure is designed to convert infrared light into electric power. We use BiFeO3 (BFO) as the ferroelectric layer and Y2O3:Yb,Tm (YOT) as the upconversion layer. Different from conventional ferroelectric materials, this heterostructure exhibits switchable and stable photovoltaic effects under 980 nm illumination, whose energy is much lower than the band gap of BFO. The energy transfer mechanism in this heterostructure is therefore studied carefully. It is found that a highly efficient nonradiative energy transfer process from YOT to BFO plays a critical role in achieving the below-band-gap photon-excited photovoltaic effects in this heterostructure. Our results also indicate that by introducing asymmetric electrodes, both the photovoltage and photocurrent are further enhanced when the built-in field and the depolarization field are aligned. The construction of ferroelectric-luminescent heterostructure is consequently proposed as a promising route to enhance the photovoltaic effects of ferroelectric materials by extending the absorption of the solar spectrum.

Above-Band Gap Photoinduced Stabilization of Engineered Ferroelectric Domains.

The effect of above-band gap photons on the domains of the BiFeO3 (BFO) thin film was investigated via piezoresponse force microscopy and Kelvin probe force microscopy. It is found that under above-band gap illumination, the relaxation time of the polarization state was significantly extended, while the effective polarizing voltage for the pristine domains was reduced. We propose that this photoinduced domain stabilization can be attributed to the interaction between photogenerated surface charges and domains. Importantly, a similar phenomenon is observed in other ferroelectric (FE) materials with an internal electric field once they are illuminated by above-band gap light, indicating that this photoinduced stabilization is potentially universal rather than specific to BFO. Thus, this study will not only contribute to the knowledge of photovoltaic (PV) phenomena but also provide a new route to promote the stability of PV and FE materials.

Shape-selective synthesis and oxygen storage behavior of ceria nanopolyhedra, nanorods, and nanocubes.

Single-crystalline and uniform nanopolyhedra, nanorods, and nanocubes of cubic CeO2 were selectively prepared by a hydrothermal method at temperatures in the range of 100-180 degrees C under different NaOH concentrations, using Ce(NO3)3 as the cerium source. According to high-resolution transmission electron microscopy, they have different exposed crystal planes: {111} and {100} for polyhedra, {110} and {100} for rods, and {100} for cubes. During the synthesis, the formation of hexagonal Ce(OH)3 intermediate species and their transformation into CeO2 at elevated temperature, together with the base concentration, have been demonstrated as the key factors responsible for the shape evolution. Oxygen storage capacity (OSC) measurements at 400 degrees C revealed that the oxygen storage takes place both at the surface and in the bulk for the as-obtained CeO2 nanorods and nanocubes, but is restricted at the surface for the nanopolyhedra just like the bulk one, because the {100}/{110}-dominated surface structures are more reactive for CO oxidation than the {111}-dominated one. This result suggests that high OSC materials might be designed and obtained by shape-selective synthetic strategy.

Spontaneous organization of uniform CeO2 nanoflowers by 3D oriented attachment in hot surfactant solutions monitored with an in situ electrical conductance technique.

Uniform CeO(2) nanoflowers were synthesized by rapid thermolysis of (NH(4))(2)Ce(NO(3))(6) in oleic acid (OA)/oleylamine (OM), by a unique 3D oriented-attachment mechanism. CeO(2) nanoflowers with controlled shape (cubic, four-petaled, and starlike) and tunable size (10-40 nm) were obtained by adjusting the reaction conditions including solvent composition, precursor concentration, reaction temperature, and reaction time. The nanoflower growth mechanism was investigated by in situ electrical conductance measurements, transmission electron microscopy, and UV/Vis spectroscopy. The CeO(2) nanoflowers are likely formed in two major steps, that is, initial formation of ceria cluster particles capped with various ligands (e.g., OA, OM, and NO(3) (-)) via hydrolysis of (NH(4))(2)Ce(NO(3))(6) at temperatures in the range 140-220 degrees C, and subsequent spontaneous organization of the primary particles into nanoflowers by 3D oriented attachment, due to a rapid decrease in surface ligand coverage caused by sudden decomposition of the precursor at temperatures above 220 degrees C in a strong redox reaction. After calcination at 400 degrees C for 4 h the 33.8 nm CeO(2) nanoflowers have a specific surface area as large as 156 m(2) g(-1) with high porosity, and they are highly active for conversion of CO to CO(2) in the low temperature range of 200-400 degrees C. The present approach has also been extended to the preparation of other transition metal oxide (CoO, NiO, and CuO(x)) nanoflowers.

High-quality sodium rare-earth fluoride nanocrystals: controlled synthesis and optical properties.

We report a general synthesis of high-quality cubic (alpha-phase) and hexagonal (beta-phase) NaREF4 (RE: Pr to Lu, Y) nanocrystals (nanopolyhedra, nanorods, nanoplates, and nanospheres) and NaYF(4):Yb,Er/Tm nanocrystals (nanopolyhedra and nanoplates) via the co-thermolysis of Na(CF3COO) and RE(CF3COO)3 in oleic acid/oleylamine/1-octadecene. By tuning the ratio of Na/RE, solvent composition, reaction temperature and time, we can manipulate phase, shape, and size of the nanocrystals. On the basis of its alpha --> beta phase transition behavior, along the rare-earth series, NaREF4 can be divided into three groups (I: Pr and Nd; II: Sm to Tb; III: Dy to Lu, Y). The whole controlled-synthesis mechanism can be explained from the point of view of free energy. Photoluminescent measurements indicate that the value of I610/I590 and the overall emission intensity of the NaEuF4 nanocrystals are highly correlative with the symmetries of the Eu3+ ions in both the lattice and the surface.