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.
Materials science is undergoing a revolution, generating valuable new materials such as flexible solar panels, biomaterials and printable tissues, new catalysts, polymers, and porous materials with ...unprecedented properties. However, the number of potentially accessible materials is immense. Artificial evolutionary methods such as genetic algorithms, which explore large, complex search spaces very efficiently, can be applied to the identification and optimization of novel materials more rapidly than by physical experiments alone. Machine learning models can augment experimental measurements of materials fitness to accelerate identification of useful and novel materials in vast materials composition or property spaces. This review discusses the problems of large materials spaces, the types of evolutionary algorithms employed to identify or optimize materials, and how materials can be represented mathematically as genomes, describes fitness landscapes and mutation operators commonly employed in materials evolution, and provides a comprehensive summary of published research on the use of evolutionary methods to generate new catalysts, phosphors, and a range of other materials. The review identifies the potential for evolutionary methods to revolutionize a wide range of manufacturing, medical, and materials based industries.
Strong light-matter interaction leads to the formation of hybrid polariton states and alters the photophysical dynamics of organic materials and biological systems without modifying their chemical ...structure. Here, we experimentally investigated a well-known photosynthetic protein, light harvesting 2 complexes (LH2) from purple bacteria under strong coupling with the light mode of a Fabry-Perot optical microcavity. Using femtosecond pump probe spectroscopy, we analyzed the polariton dynamics of the strongly coupled system and observed a significant prolongation of the excited state lifetime compared with the bare exciton, which can be explained in terms of the exciton reservoir model. Our findings indicate the potential of tuning the dynamic of the whole photosynthetic unit, which contains several light harvesting complexes and reaction centers, with the help of strong exciton-photon coupling, and opening the discussion about possible design strategies of artificial photosynthetic devices.
One favorable situation for spins to enter the long-sought quantum spin liquid (QSL) state is when they sit on a kagome lattice. No consensus has been reached in theory regarding the true ground ...state of this promising platform. The experimental efforts, relying mostly on one archetypal material ZnCu_{3}(OH)_{6}Cl_{2}, have also led to diverse possibilities. Apart from subtle interactions in the Hamiltonian, there is the additional degree of complexity associated with disorder in the real material ZnCu_{3}(OH)_{6}Cl_{2} that haunts most experimental probes. Here we resort to heat transport measurement, a cleaner probe in which instead of contributing directly, the disorder only impacts the signal from the kagome spins. For ZnCu_{3}(OH)_{6}Cl_{2}, we observed no contribution by any spin excitation nor obvious field-induced change to the thermal conductivity. These results impose strong constraints on various scenarios about the ground state of this kagome compound: while certain quantum paramagnetic states other than a QSL may serve as natural candidates, a QSL state, gapless or gapped, must be dramatically modified by the disorder so that the kagome spin excitations are localized.
To establish an ideal microenvironment for regenerating maxillofacial defects, recent research interests have concentrated on developing scaffolds with intricate configurations and manipulating the ...stiffness of extracellular matrix toward osteogenesis. Herein, we propose to infuse a degradable RGD-functionalized alginate matrix (RAM) with osteoid-like stiffness, as an artificial extracellular matrix, to a rigid 3D-printed hydroxyapatite scaffold for maxillofacial regeneration. The 3D-printed hydroxyapatite scaffold was produced by microextrusion technology and showed good dimensional stability with consistent microporous detail. RAM was crosslinked by calcium sulfate to manipulate the stiffness, and its degradation was accelerated by partial oxidation using sodium periodate. The results revealed that viability of bone marrow stem cells was significantly improved on the RAM and was promoted on the oxidized RAM. In addition, the migration and osteogenic differentiation of bone marrow stem cells were promoted on the RAM with osteoid-like stiffness, specifically on the oxidized RAM. The in vivo evidence revealed that nonoxidized RAM with osteoid-like stiffness upregulated osteogenic genes but prevented ingrowth of newly formed bone, leading to limited regeneration. Oxidized RAM with osteoid-like stiffness facilitated collagen synthesis, angiogenesis, and osteogenesis and induced robust bone formation, thereby significantly promoting maxillofacial regeneration. Overall, this study supported that in the stabilized microenvironment, oxidized RAM with osteoid-like stiffness offered requisite mechanical cues for osteogenesis and an appropriate degradation profile to facilitate bone formation. Combining the 3D-printed hydroxyapatite scaffold and oxidized RAM with osteoid-like stiffness may be an advantageous approach for maxillofacial regeneration.
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.
Background and purpose
Chronic obstructive pulmonary disease (COPD) is frequently associated with various comorbidities. However, the proportion of COPD patients with dementia has not been adequately ...examined. This retrospective cohort study investigated the association between COPD and dementia by using a nationwide population‐based database in Taiwan.
Methods
Data were retrieved from the Taiwanese National Health Insurance Research Database and analyzed using multivariate Cox proportional hazards regression models to assess the effects of COPD on the risk of dementia after adjusting for demographic characteristics and comorbidities.
Results
The COPD cohort exhibited a higher prevalence of diabetes, hypertension, coronary artery disease, head injury and depression at baseline than did the non‐COPD cohort (P < 0.0001). After adjusting for covariates, the COPD patients exhibited a 1.27‐fold higher risk of developing dementia (hazard ratio 1.27, 95% confidence interval 1.20–1.36). The incidence rate was higher in patients with frequent acute exacerbations than in the non‐COPD patients regardless of whether a hospital admission or emergency room visit was required (hazard ratio 196.8 vs. 41.7, 95% confidence intervals 145.9–265.5 and 22.3–78.0).
Conclusion
This study shows that COPD is associated with a subsequent higher risk of dementia after adjusting for comorbidities. Specifically, the association between COPD and dementia is greater in patients with more frequent acute exacerbation events of COPD.
Preventing biological contamination (biofouling) is key to successful development of novel surface and nanoparticle-based technologies in the manufacturing industry and biomedicine. Protein ...adsorption is a crucial mediator of the interactions at the bio - nano -materials interface but is not well understood. Although general, empirical rules have been developed to guide the design of protein-resistant surface coatings, they are still largely qualitative. Herein we demonstrate that this knowledge gap can be addressed by using machine learning approaches to extract quantitative relationships between the material surface chemistry and the protein adsorption characteristics. We illustrate how robust linear and non-linear models can be constructed to accurately predict the percentage of protein adsorbed onto these surfaces using lysozyme or fibrinogen as prototype common contaminants. Our computational models could recapitulate the adsorption of proteins on functionalised surfaces in a test set with an r
of 0.82 and standard error of prediction of 13%. Using the same data set that enabled the development of the Whitesides rules, we discovered an extension to the original rules. We describe a workflow that can be applied to large, consistently obtained data sets covering a broad range of surface functional groups and protein types.
Age‐related hearing loss (ARHL) is the most common cause of hearing loss in the world. The development of ARHL in each individual is multifactorial, involving both intrinsic and extrinsic factors. ...This review highlights several of the key findings in the ARHL literature and discusses future directions.
Level of Evidence
NA.
PDF
