Thermal Shock Synthesis for Loading Sub-2 nm Ru Nanoclusters on Titanium Nitride as a Remarkable Electrocatalyst toward Hydrogen Evolution Reaction.

Heterogeneous catalysts embracing metal entities on suitable supports are profound in catalyzing various chemical reactions, and substantial synthetic endeavors in metal-support interaction modulation are made to enhance catalytic performance. Here, it is reported the loading of sub-2 nm Ru nanocrystals (NCs) on titanium nitride support (HTS-Ru-NCs/TiN) via a special Ru-Ti interaction using the high-temperature shock (HTS) method. Direct dechlorination of the adsorbed RuCl3, ultrafast nucleation process, and short coalescence duration at ultrahigh temperatures contribute to the immobilization of Ru NCs on TiN support via producing the Ru-Ti interfacial perimeter. HTS-Ru-NCs/TiN shows remarkable activity toward hydrogen evolution reaction (HER) in alkaline solution, yielding ultralow overpotentials of 16.3 and 86.6 mV to achieve 10 and 100 mA cm-2, respectively. The alkaline and anion exchange membrane water electrolyzers assembled using HTS-Ru-NCs/TiN yield 1.0 A cm-2 at 1.65 and 1.67 V, respectively, which validate its applicability in the hydrogen production industry. Theoretical simulations reveal the favorable formation of Ru─O and Ti─H bonds at the interfacial perimeters between Ru NCs and TiN, which accelerates the prerequisite water dissociation kinetics for enhanced HER activity. This exemplified work motivates the design of specific interfacial perimeters via the HTS strategy to improve the performance of diverse catalysis.

Molecular Dynamics Simulation and Experimental Study of Mechanical Properties of Graphene-Cement Composites.

To investigate the mechanical properties of graphene (G) and calcium silicate hydrate (C-S-H) composites in different directions, molecular dynamics (MD) simulations and experiments were used, and the effects of temperature, loading rate, and graphene defects were also investigated. The experimental results show that the addition of graphene can improve the flexural, compressive, and tensile strength of the composite. The results of molecular dynamics simulation show that the addition of graphene in x and z directions can enhance the tensile strength of G/C-S-H in three directions, while the addition of graphene in y direction can reduce the tensile strength of G/C-S-H. At the same time, the tensile strength of G/C-S-H decreases with the increase in temperature and increases with the increase in loading rate. Meanwhile, the mechanical properties of G/C-S-H can be improved using a certain concentration of monatomic vacancy defects, diatomic vacancy defects, and S-W defects.

Mitogenome-based phylogeny of mosquitoes (Diptera: Culicidae).

Mosquitoes are of great medical significance as vectors of many deadly diseases. Mitogenomes have been widely used in phylogenetic studies, but mitogenome knowledge within the family Culicidae is limited, and Culicidae phylogeny is far from resolved. In this study, we surveyed the mitogenomes of 149 Culicidae species, including 7 newly sequenced species. Comparative analysis of 149 mosquito mitogenomes shows gene composition and order to be identical to that of an ancestral insect, and the AT bias, length variation, and codon usage are all consistent with that of other reported Dipteran mitogenomes. Phylogenetic analyses based on the DNA sequences of the 13 protein-coding genes from the 149 species robustly support the monophyly of the subfamily Anophelinae and the tribes Aedini, Culicini, Mansoniini, Sabethini, and Toxorhynchitini. To resolve ambiguous relationships between clades within the subfamily Culicinae, we performed topological tests and show that Aedini is a sister to Culicini and that Uranotaeniini is a sister to (Mansoniini + (Toxorhynchitini + Sabethini)). In addition, we estimated divergence times using a Bayesian relaxation clock based on the sequence data and 3 fossil calibration points. The results show mosquitoes diverged during the Early Jurassic with massive Culicinae radiations during the Cretaceous, coincident with the emergence of angiosperms and the burst of mammals and birds. Overall, this study, which uses the largest number of Culicidae mitogenomes sequenced to date, comprehensively reveals the mitogenome characteristics and mitogenome-based phylogeny and divergence times of Culicidae, providing information for further studies on the mitogenome, phylogeny, evolution, and taxonomic revision of Culicidae.

A promising approach to steady-state fusion: High-temperature superconducting strong-field stellarator with precise omnigenity.

The stellarator has inherent advantages over the tokamak in achieving steady-state operation, especially due to its absence of disruptions and lack of need for current drive and the associated recirculating power. In recent years, there have been remarkable advances in the field of stellarator optimization, where precisely quasi-symmetric and precisely quasi-isodynamic magnetic configurations have been achieved with coils, allowing the neoclassical transport and energetic particle losses of stellarators to be reduced to levels comparable to those of tokamaks. At the same time, the development of high-temperature superconducting magnet technology will potentially double the magnetic field strength of stellarators. While these strong fields are expected to introduce new challenges, and while turbulent transport remains a common challenge for both stellarators and tokamaks, the combination of these physical and technological advances results in the expectation that stellarators will become a competitive approach to tokamaks for realizing steady-state fusion.

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.

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.

The Influence of Annealing on the Microstructural and Textural Evolution of Cold-Rolled Er Metal.

The microstructural and textural evolution of 60% cold-rolling-deformation Er metal (purity ≥ 99.7%) during annealing were investigated by electron-backscattered diffraction (EBSD) and X-ray diffraction (XRD). The research results showed that the texture of the (0001) plane orientation was strengthened, but there was no apparent enhancement of the (011¯0) and (1¯21¯0) plane orientations with increasing the annealing temperature. The recrystallization frequency and grain sizes gradually stabilized after the annealing duration of more than 1 h at 740 °C; the annealing duration and the recrystallization frequency were fitted to the equation: y=1 − exp (−0.3269x0.2506). HAGBs were predominant, and the distribution of grain sizes was the most uniform after annealing at 740 °C × 1 h, which was the optimal annealing process of the Er metal with 60% cold-rolling deformation. However, the recrystallization was transferred to the substructure due to grain boundary migration and twining under an excessive annealing temperature and duration.

Effect of Lanthanum Addition on Formation Behaviors of Inclusions in Q355B Weathering Steel.

The effect of lanthanum addition on the formation behaviors of inclusions in Q355B weathering steel was investigated by laboratory experiments and thermodynamic calculations. The results demonstrate that the main inclusions in weathering steel without La addition are large-sized irregular Al2O3 and MnS, with an average size of about 5.35 µm. As La content increases from 0.0075 to 0.0184 wt.%, the dominant inclusions transform from MnS, LaAlO3, and Al2O3-LaAlO3 into MnS, La2O3, and LaAlO3-La2O3. Meanwhile, the average size of inclusions significantly decreases from 3.4 to 2.48 µm and the distribution is more dispersive. When the La content increases to 0.0425 wt.%, the original MnS and Al2O3 inclusions are completely modified into La2O2S and La2O3 but the inclusions demonstrate serious agglomeration and growth. The thermodynamic calculations indicate that Al2O3 and various lanthanum-containing inclusions are formed in the liquid phase. As the La content in molten steel increases from 0 to 0.0425 wt.%, the Al2O3 inclusion is inclined to be modified into lanthanum oxide and lanthanum oxysulfide and the modification process is Al2O3 → LaAlO3 → La2O3 → La2O2S, which is very consistent with the experimental observations.

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.

Research of High-Purity Lanthanum Prepared by Zone Refining.

In this paper, the purification of lanthanum was studied by using zone-refining technology. The equilibrium distribution coefficient of impurities was calculated using a liquidus slope method to reveal impurities' distribution properties. Meanwhile, the analysis of impurities' concentration distribution for Fe and Si has been investigated based on the SPIM model. The calculated findings based on SPIM were found to be in good agreement with the experimental results. In addition, the influence of the zone-refining rate and the number of passes on the purification of lanthanum were studied. It was found that after ten times of zone refining with a zone-refining rate of 5 mm/min, the contents of Fe and Si impurities in metal decreased to 4 and 2 ppm, respectively.

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.

Simulation of Lanthanum Purification Using a Finite Element Method.

The zone refining technology is considered to be one of the most effective means of purifying lanthanum. However, it is tough to obtain the temperature distribution of the molten region through experimental methods. In this study, finite element analysis was used to establish the zone refining simulation model, and the impurity distribution of lanthanum after purification was investigated experimentally. Good agreement between the simulated and experimental results was obtained. The effects of the current and the frequency on the temperature distribution and the width of the region were studied using the simulation model. Through the zone refining experiment, the impurity distributions under different widths of molten region were revealed. Finally, the influence of molten region width on the limiting distribution was calculated by solving the limiting distribution equation.

Effect of Cold Rolling and Annealing on the Microstructure and Texture of Erbium Metal.

Erbium metal with purity ≥ 99% was cold rolled to 30%, 40%, 50%, and 60% deformations and the Er metal of 60% deformation was annealed at different temperatures for 1 h. The effect of cold rolling deformation and annealing on the microstructure and texture evolution of Er metal was investigated by XRD, EBSD, Microhardness tester, and OM. P is the orientation index, which is used to judge the preferred orientation. The research results showed that grains were broken and refined gradually with increasing deformation, the average grain size was 3.37 µm, and the orientation distribution was uniform for 60% deformation; deformation twins appeared in the grain when the deformation was less than 40%, which contributed to the generation of (0001) plane orientation. Comparing with the initial state, the (011-0) plane orientation gradually weakened and the (111-0) plane orientation had a trend of further strengthening with the increasing deformation; the (1-21-0) plane orientation remained unchanged, but there was a gradual weakening trend when the deformation was greater than 50%. For 60% deformation of Er metal, the deformed microstructure was replaced by fine equiaxed grains with the increasing annealing temperature, and the high-performance Er metal with fine and uniform equiaxed grains can be obtained under annealing at 740 °C for 1 h.

Electroencephalogram Image under Complex Domain Analysis Algorithm to Analyze Neurological Status Epilepticus and Poor Prognostic Factors of Children.

This study was to adopt the electroencephalogram (EEG) image to analyze the neurological status epilepticus (SE) and adverse prognostic factors of children using the complex domain analysis algorithm, aiming at providing a theoretical basis for the clinical treatment of children with SE. 24-hour EEG was adopted to diagnose 197 children with SE. The patients were divided into an experimental group (100 cases) and a control group (97 cases) using a random number table method. The EEGs of children in the experimental group were analyzed using the compound domain analysis algorithm, and those in the control group were diagnosed by a professional doctor. The indicators of children in two groups were compared to analyze the effect of the compound domain analysis algorithm in diagnosing diseases through EEG. The prognostic scores of 197 children were scored one month after they were diagnosed, treated, and discharged, and the adverse prognostic factors were analyzed. As a result, EEG can accurately and effectively analyze the brain diseases in children. The sensitivity and specificity of the complex domain analysis algorithm for the detection of epilepsy EEG were much higher than those of the EEG automatic detection algorithm based on time-domain waveform similarity and the EEG automatic detection algorithm based on convolutional neural network (CNN), and the average running time was opposite, showing obvious difference (P < 0.05).The average accuracy, sensitivity, and specificity of children in the experimental group were 96.11%, 97.10%, and 95.19%, respectively; and those in the control group were 88.83%, 90.14%, and 87.82%, respectively, so there was an obvious difference in accuracy between two groups (P < 0.05). There were 57 cases with good prognosis and 140 cases with poor prognosis; there were 70 males with good prognosis and 19 poor prognoses and 69 women with good prognosis and 19 poor prognoses. Among 121 patients with infections, 84 cases had good prognosis and 37 cases had poor prognosis; 39 cases of irregular medication had good prognosis in 31 cases and a poor prognosis in 8 cases; and 37 cases had no obvious cause, including 25 cases with good prognosis and 12 cases with poor prognosis. In short, the EEG diagnosis and treatment effect of the compound domain analysis algorithm were better than those of professional doctors; the gender of the patient had no effect on the poor prognosis, and the pathogenic factors had an impact on the poor prognosis of the patient.

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.

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.

Comparison of the clinical characteristics and mortalities of severe COVID-19 patients between pre- and post-menopause women and age-matched men.

The mortality rate of young female COVID-19 patients is reported to be lower than that of young males but no significant difference in mortality was found between female and male COVID-19 patients aged over 65 years, and the underlying mechanism is unknown. We retrospectively analyzed clinical characteristics and outcomes of severely ill pre- and post-menopausal COVID-19 patients and compared with age-matched males. Of the 459 patients included, 141 aged ≤55, among whom 19 died (16 males vs. 3 females, p<0.005). While for patients >55 years (n=318), 115 died (47 females vs. 68 males, p=0.149). In patients ≤55 years old, the levels of NLR, median LDH, median c-reactive protein and procalcitonin were significantly higher while the median lymphocyte count and LCR were lower in male than in female (all p<0.0001). In patients over 55, these biochemical parameters were far away from related normal/reference values in the vast majority of these patients in both genders which were in contrast to that seen in the young group. It is concluded that the mortality of severely ill pre-menopausal but not post-menopausal COVID-19 female patients is lower than age-matched male. Our findings support the notion that estrogen plays a beneficial role in combating COVID-19.

circ_VMA21 protects WI-38 cells against LPS-induced apoptotic and inflammatory injury by acting on the miR-409-3p/KLF4 axis.

Pneumonia is a common inflammatory lung disease. Circular RNA (circRNA) vacuolar ATPase assembly factor (circ_VMA21) has been reported to mitigate the inflammatory injury in WI-38 cells. This study was to investigate the functional mechanism of circ_VMA21. Cell model was established by lipopolysaccharide (LPS) treatment in WI-38 cells. Cell cycle and apoptosis were analyzed by flow cytometry. Cell proliferation was assessed by colony formation and MTT assays. The expression quantification of circ_VMA21, microRNA-409-3p (miR-409-3p) and Kruppel-like transcription factor 4 (KLF4) was performed by qRT-PCR method. The expression of relative protein was detected by Western blot. Inflammatory response was evaluated by ELISA. The target binding was validated by dual-luciferase reporter assay. Cellular analysis indicated that LPS repressed cell cycle and proliferation but induced apoptosis. circ_VMA21 was downregulated in pneumonia samples and LPS-treated WI-38 cells. Functionally, circ_VMA21 assuaged the LPS-induced apoptotic and inflammatory damages. In addition, circ_VMA21 directly targeted miR-409-3p and its function was dependent on the sponge effect on miR-409-3p. Also, KLF4 was a target of miR-409-3p and miR-409-3p inhibitor attenuated LPS-induced cell injury by upregulating KLF4. Moreover, KLF4 was upregulated by circ_VMA21/miR-409-3p axis. These findings suggested that circ_VMA21 relieved the LPS-induced apoptotic and inflammatory injury by the regulation of miR-409-3p/KLF4 axis.

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.

Trace-Level Fluorination of Mesoporous TiO2 Improves Photocatalytic and Pb(II) Adsorbent Performances.

Fluorination is an effective way of tuning the physicochemical property and activity of TiO2 nanocrystallites, which usually requires a considerable amount of hydrofluoric acid (or NH4F) for a typical F/Ti molar ratio, RF, of 0.5-69.0 during synthesis. This has consequential environmental issues due to the high toxicity and hazard of the reactants. In the present work, an environmentally benign fluorination approach is demonstrated that uses only a trace amount of sodium fluoride with an RF of 10-6 during synthesis. While it maintained the desirable high surface area (102.4 m2/g), the trace-level fluorination enabled significant enhancements on photocatalytic activities (e.g., a 56% increase on hydrogen evolution rate) and heavy metal Pb(II) removal (31%) of the mesoporous TiO2. This can be attributed to enriched Ti3+ and localized spatial charge separation due to fluorination as proved by X-ray photoelectron spectroscopy (XPS), electron paramagnetic resonance spectroscopy (EPR), and density functional theory (DFT) analyses.