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Under the synergistic effects of the electronic interaction, the cocktail effect, more interfaces, and micro protocells, the MgH2-3 wt% TiZrFe1.5MnCrV0.5composite exhibits an ultra-fast hydrolysis rate of 289.16 mL/g/min.

The potential of rapeseed-derived peptides (RDPs) in the amelioration of type 2 diabetes mellitus (T2DM) has been hypothesized. However, the mechanisms of the intestinal endocrine hormones activated by RDPs have not been fully understood. This study aimed to explore the amelioration of T2DM and associated hepatic lipid metabolism disorders using RDPs by affecting glucagon-like peptide-1 (GLP-1) secretion. Eight RDPs were prepared by different stepwise enzymatic hydrolysis, wherein RCPP-3 (sequential using alcalase/flavourzyme) and RNPP-1 (sequential using alcalase/trypsin) maintained the normal blood glucose level, significantly increased the body weight (27.17 ± 0.19%) in T2DM mice compared to the positive group (p< 0.05). Western blotting and immunofluorescence experiments indicated that RCPP-3 and RNPP-1 regulated the intestinal endocrine hormones GLP-1 secretion through the calcium-sensing receptor (CaSR). Additionally, the PI3K-Akt pathway was significantly activated by GLP-1, leading to marked improvements in hepatic lipid parameters (TC, TG, LDL-c, and HDL-c) and mitigated fat accumulation (p< 0.05). Notably, the stimulating effect of RCPP-3 on GLP-1 was 10.05% ± 0.71% higher than RNPP-1. G2-R3, a fraction separated from RCPP-3, which contained 14 peptides with the best capacity to stimulate GLP-1 secretion, was identified using HPLC-QTOF-MS/MS. This study suggests the potential of RDPs as novel functional food supplements for ameliorating T2DM.

In the realm of large-scale industrial manufacturing, the precise detection of defective parts stands as a critical imperative. While current unsupervised anomaly detection algorithms exhibit commendable accuracy when applied to clean training datasets, their susceptibility to contaminated training data limits their real-world efficacy. In response to this challenge, this paper proposes a novel Outlier-Probability-Based Feature Adaptation (OPFA) network to realize robust unsupervised anomaly detection on contaminated training data. This method distinguishes itself by maintaining both high accuracy and robustness in the face of contaminated training data, enabling effective learning of discriminative features for anomaly detection. Specifically, the model enhances feature representations through the contraction of normal features and the contrast between normal and outlier features. Our methodology employs an iterative mechanism, featuring three core designs. First, outlier detection evaluates the outlier probabilities of current feature embeddings, providing a basis for subsequent improvements. Second, Gaussian Mixture Model (GMM) is leveraged to model the distributions of normal feature embeddings. Third, the adaptive network refines feature representations based on the GMM models and outlier scores of feature embeddings. Ablation experiments underscore the effectiveness of each component within our model. Furthermore, our approach outperforms other state-of-the-art methods on three benchmark datasets, demonstrating a notable advantage especially in scenarios with contaminated training data.

This paper presents a safe learning control strategy aimed at ensuring the accurate tracking of multiple unmanned aerial vehicles (UAVs) along their predetermined trajectories while also guaranteeing safety under uncertain environments such as trajectory conflict, airflow interference between UAVs, and external disturbances. The proposed control framework employs a high-level learning-based feedback linearization control combined with model predictive control (LB-FBL-MPC), coupled with a low-level safety barrier certificates and control Lyapunov function-based quadratic programs (SC), for nonlinear multiple-UAV systems. The high-level LB-FBL-MPC uses incremental Gaussian processes (IGPs) to learn uncertain disturbances online, and feedback linearization is applied to approximate the linear system. The MPC optimizes the reference trajectory based on the linearized dynamical model to enhance the adaptivity of the system. Furthermore, the low-level SC guarantees the safety and asymptotic stability of the multi-UAV system by using the prediction distribution of the IGPs. Ablation and benchmark comparison experiments demonstrate the efficacy of the proposed tracking control strategy. Note to Practitioners—Controlling multiple unmanned systems to achieve precision and safety in complex environmental disturbances is a challenge. Existing machine learning-based control frameworks are mostly limited by low learning efficiency and poor interpretability, making it difficult to deploy them in practical robot systems. This article introduces a machine learning and control theory combined framework for the safe control of multiple unmanned aerial vehicles. On the one hand, the framework enables UAVs to quickly learn uncertain environmental disturbances without the need for any pre-collected data. The use of a linearized system model greatly reduces computation time and provides a new approach for practical engineering deployment. On the other hand, by designing separate quadratic programming, we prove the stability and safety of the system. Extensive experiments demonstrate that the designed control strategy significantly improves system control performance while ensuring system safety.

This article studies the problem of memory event-triggered cooperative adaptive control of heterogeneous nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks based on the multiplayer mixed zero-sum (ZS) game strategy. First, a neural-network-based reinforcement learning scheme is structured to obtain the Nash equilibrium solution of the proposed multiplayer mixed ZS game scheme. Then, a memory-based event-triggered mechanism considering the historical data is proposed. This effectively avoids incorrect triggering information caused by unknown external factors. Moreover, thanks to the idea of switching topology, the mixed ZS game problem under the influence of node-based DoS attacks is solved efficiently. In accordance with the Lyapunov stability theory, it is proved that all signals of heterogeneous MASs are bounded, all heterogeneous followers can track the trajectory of the leader during the no-attack period, the attacked follower can achieve stabilization control during the attack period, and the remaining nonattacked followers can achieve cooperative control during the attack period. Finally, the effectiveness of the designed memory-event-triggered-based mixed ZS game cooperative control strategy is tested by the given simulation results.

AbstractEmotion is an important factor that can lead to the occurrence of aggressive driving. This paper proposes an association rule mining-based method for analysing contributing factors associated with aggressive driving behaviour among online car-hailing drivers. We collected drivers’ emotion data in real time in a natural driving setting. The findings show that 29 of the top 50 association rules for aggressive driving are related to emotions, revealing a strong relationship between driver emotions and aggressive driving behaviour. The emotions of anger, surprised, happy and disgusted are frequently associated with aggressive driving behaviour. Negative emotions combined with other factors (for example, driving at high speeds and high acceleration rates and with no passengers in the vehicle) are more likely to lead to aggressive driving behaviour than negative emotions alone. The results of this study provide practical implications for the supervision and training of car-hailing drivers.

Respiratory syncytial virus (RSV) is a significant cause of acute lower respiratory tract infection (ALRTI) in children under five years of age. Between 2017 and 2021, 396 complete sequences of the RSV Fgene were obtained from 500 RSV-positive throat swabs collected from ten hospitals across nine provinces in China. In addition, 151 sequences from China were sourced from GenBank and GISAID, making a total of 549 RSV Fgene sequences subjected to analysis. Phylogenetic and genetic diversity analyses revealed that the RSV Fgenes circulating in China from 2017 to 2021 have remained relatively conserved, although some amino acids (AAs) have undergone changes. AA mutations with frequencies ≥ 10% were identified at six sites and the p27 region: V384I (site I), N276S (site II), R213S (site Ø), and K124N (p27) for RSV A; F45L (site I), M152I/L172Q/S173 ​L/I185V/K191R (site V), and R202Q/I206M/Q209R (site Ø) for RSV B. Comparing mutational frequencies in RSV-F before and after 2020 revealed minor changes for RSV A, while the K191R, I206M, and Q209R frequencies increased by over 10% in RSV B. Notably, the nirsevimab-resistant mutation, S211N in RSV B, increased in frequency from 0% to 1.15%. Both representative strains aligned with the predicted RSV-F structures of their respective prototypes exhibited similar conformations, with low root-mean-square deviation values. These results could provide foundational data from China for the development of RSV mAbs and vaccines.

Ultrawide-band-gap aluminum nitride (AlN) boasts high breakdown field strength, superior thermal conductivity, and exceptional stability, making it ideal for deep ultraviolet optoelectronics, radio frequency, and power devices. To date, the epitaxial growth of high-quality doped AlN via MOCVD has primarily been on AlN or SiC single-crystal substrates to reduce dislocation densities. However, the limited size and high cost of these single-crystal substrates necessitate the exploration of alternative substrates to enhance commercial viability. This study demonstrates and analyzes the epitaxial growth modes and conductivity modulation mechanisms of AlN:Si on a cost-effective sapphire substrate. By adjusting MOCVD epitaxial parameters: growth temperature and V/III ratio, we controlled the impact of compensating defects (CNand VAl) on conductivity, achieving conductivity enhancements of 26 and 41%, respectively. Our research validates the feasibility of obtaining AlN:Si with enhanced electrical performance and crystal quality on sapphire substrates. It represents a significant step toward the development of high-power, high-efficiency AlN electronic devices.

This article addresses the distributed zero-sum differential game adaptive control problem of six-rotor unmanned aerial vehicles under denial-of-service attacks based on the self-triggered mechanism. First, a fuzzy-logic-system-based identifier-critic framework is structured to obtain the alternative approximate solution with fewer learning parameters. Then, a novel optimal value function, considering the optimal control signal, worst disturbance signal, steady-state, and dynamic performances, is designed. Subsequently, an improved self-triggered strategy, featuring negative feedback adjustment between the threshold and system consensus error, is proposed to reduce the communication resource loss and decrease the influence of introducing the self-triggered mechanism on the system performance. Unlike the event-triggered strategy, the next trigger moment of the self-triggered strategy is determined by the current information, eliminating the need for continuous monitoring of the trigger state, which is more convenient for the physical implementation. Moreover, the connectivity-broken denial-of-service attacks on the information transmission process among unmanned aerial vehicles are considered. Next, through the transformation between stabilization control and cooperative control, the difficulty of realizing cooperative control caused by the temporarily disrupted topological relationships due to denial-of-service attacks is solved effectively. Using Lyapunov stability theory, it is proved that all signals of six-rotor unmanned aerial vehicles are bounded, and the consensus control performance is achieved. Finally, the rationality of the designed zero-sum differential game adaptive control scheme is verified by some simulation results.

Several lines of evidence indicate the involvement of neuroinflammatory processes in the pathophysiology of schizophrenia (SCZ). Microglia are brain resident immune cells responding toward invading pathogens and injury-related products, and additionally, have a critical role in improving neurogenesis and synaptic functions. Aberrant activation of microglia in SCZ is one of the leading hypotheses for disease pathogenesis, but due to the lack of proper human cell models, the role of microglia in SCZ is not well studied. We used monozygotic twins discordant for SCZ and healthy individuals to generate human induced pluripotent stem cell-derived microglia to assess the transcriptional and functional differences in microglia between healthy controls, affected twins and unaffected twins. The microglia from affected twins had increased expression of several common inflammation-related genes compared to healthy individuals. Microglia from affected twins had also reduced response to interleukin 1 beta (IL1β) treatment, but no significant differences in migration or phagocytotic activity. Ingenuity Pathway Analysis (IPA) showed abnormalities related to extracellular matrix signaling. RNA sequencing predicted downregulation of extracellular matrix structure constituent Gene Ontology (GO) terms and hepatic fibrosis pathway activation that were shared by microglia of both affected and unaffected twins, but the upregulation of major histocompatibility complex (MHC) class II receptors was observed only in affected twin microglia. Also, the microglia of affected twins had heterogeneous response to clozapine, minocycline, and sulforaphane treatments. Overall, despite the increased expression of inflammatory genes, we observed no clear functional signs of hyperactivation in microglia from patients with SCZ. We conclude that microglia of the patients with SCZ have gene expression aberrations related to inflammation response and extracellular matrix without contributing to increased microglial activation.

Polypropylene random copolymer (PPR) is a two-phase polymer composed of a crystallizable propylene−propylene−propylene (PPP) phase and an ethylene−propylene (EP) rubbery phase evolved through phase separation. Traditionally, it aims to solve the poor nucleation efficiency of β-nucleating agent (β-NA) toward PPR to improve the impact toughness by synthesizing chemical compounds highly selective for β-modification, with the correlation between phase separation and crystallization behaviors inadequately elucidated. In this work, phase separation behaviors and correlated crystallization of β-nucleated PPR were investigated and their interplay on impact strength were researched. The structures and temperature-dependent phase separation behaviors of neat PPR were resolved via 13C nuclear magnetic resonance spectroscopy (NMR), rheological analysis, and scanning electron microscopy (SEM). The solubility at different final heating temperatures (Tf) of β-NA in PPR was observed via polarized optical microscopy (POM), and the effect of solubility on phase separation behaviors was illustrated via SEM and dynamic time sweep in linear rheology. The crystallization behaviors of β-nucleated PPR composites cooling from different Tfwere then analyzed via in situ two-dimensional wide-angle X-ray scattering (2D-WAXS). It was found that both a higher Tfand the dissolve of β-NAs favored the phase separation process, witnessed by a size increase of EP rubbery phases, but decreased relative β-crystal content (Kβ). A high degree of phase separation with a Kβof ∼0.38 improved low temperature toughness, while a high Kβwith small rubbery particle sizes benefited room temperature toughening. This work illustrates phase separation behaviors and the correlated crystallization of β-nucleated PPR composites and provides a new perspective on toughening PPR using β-NAs.

Foxes are susceptible to SARS-CoV-2 in laboratory settings, and there have also been reports of natural infections of both SARS-CoV and SARS-CoV-2 in foxes. In this study, we assessed the binding capacities of fox ACE2 to important sarbecoviruses, including SARS-CoV, SARS-CoV-2, and animal-origin SARS-CoV-2 related viruses. Our findings demonstrated that fox ACE2 exhibits broad binding capabilities to receptor-binding domains (RBDs) of sarbecoviruses. We further determined the cryo-EM structures of fox ACE2 complexed with RBDs of SARS-CoV, SARS-CoV-2 prototype (PT), and Omicron BF.7. Through structural analysis, we identified that the K417 mutation can weaken the ability of SARS-CoV-2 sub-variants to bind to fox ACE2, thereby reducing the susceptibility of foxes to SARS-CoV-2 sub-variants. In addition, the Y498 residue in the SARS-CoV RBD plays a crucial role in forming a vital cation-π interaction with K353 in the fox ACE2 receptor. This interaction is the primary determinant for the higher affinity of the SARS-CoV RBD compared to that of the SARS-CoV-2 PT RBD. These results indicate that foxes serve as potential hosts for numerous sarbecoviruses, highlighting the critical importance of surveillance efforts.

• The dehydrogenation process of MgH 2 can be regarded as the dissociation of Mg-H and desorption of H 2 from MgH 2 surface. • The appearance of vacancy defects on the (110) surface significantly reduces the energy barrier required for H dissociation to 0.070 Ha. • Vacancy defects accelerate the transition of electrons from H− s orbit to Mg2+ 3s orbit, resulting in a decrement of the Mg H bond strength. The vacancy defect exhibits a remarkable improvement in the dehydriding property of MgH 2 @Ni-CNTs. However, the corresponding mechanism is still not fully understood. Herein, the impact of vacancy defects on the dehydrogenation properties of MgH 2 @Ni-CNTs was studied by DFT simulation, and the corresponding models were constructed based on MS. The dehydrogenation process of MgH 2 can be regarded as the dissociation of Mg-H and desorption of H 2 from the MgH 2 surface. In view of the whole dehydrogenation, the dissociation of H− is the rate-determining step, which is the main reason for restricting the dehydrogenation kinetics. Compared with vacancy vacancy-defective MgH 2 (001) surface, the appearance of vacancy defects on the (110) surface substantially reduces the energy barrier required for H dissociation to 0.070 Ha. The reason is that vacancy defects accelerate the transition of electrons from the H− s orbit to the Mg2+ 3s orbit, resulting in a decrement of the Mg-H bond strength, which makes H atoms more easily dissociated from the MgH 2 (110) surface. Therefore, the existence of vacancy defects improves the dehydriding kinetic of MgH 2. Most importantly, this research offers crucial directions for developing hydrogen storage materials as well as a potential fix for the slow dehydrogenation kinetics of nano-confined MgH 2. [Display omitted] [ABSTRACT FROM AUTHOR]

The polymer materials in nuclear facilities are exposed to high temperatures, radiation, and high loads. Their excellent heat resistance, radiation resistance, and shielding properties are important for the safe operation of nuclear facilities. In this study, 1,2-bis(hydroxymethyl)carborane (CBOH) was first synthesized in large quantities through water extraction and then introduced into an epoxy resin cross-linked network. The incorporation of a carborane structure significantly improved the heat resistance of epoxy. Under air atmosphere, the T5%(the temperature at 5 wt % degradation), T10%(the temperature at 10 wt % degradation), and char yield at 800 °C of the materials were 315.67 °C (16.51 °C increased), 326.29 °C (12.96 °C increased), and 33.22% (33.22% increased), respectively. The maximum decomposition temperature of the material increased from 550 to 750 °C, and 7.05 wt % of the carbon constituent was protected after thermal degradation at 800 °C. The glass transition temperature (Tg) of the materials greatly increased up to over 300 °C after introducing the carborane structure into the networks. The radiation resistance of the material was enhanced due to the in situ capture of oxygen radicals by the carborane structure. Moreover, the incorporation of carborane structure also increased the content of H and B in materials, improving their neutron-shielding capability. This work confers multifunctionality to epoxy resin and enhances its various properties, ensuring the safety of epoxy resin in nuclear facilities.

The rise in urban traffic challenges has spurred the expansion of subterranean transportation systems within metropolitan areas. In densely populated regions, surface settlement has emerged as a pivotal factor in tunnel design. To ensure effective planning, it is imperative to furnish comprehensive insights into soil profiles, pre-existing structures, and precise modeling. Notably, the ground movements during urban tunnel excavation, sensitive to settlement levels and surface structure vulnerabilities, contrast with those in deeper settings like mine tunnels. The complexities introduced by shifting soil profiles, geotechnical attributes, intricate soil behaviors, surface conditions, and the presence of structures along the tunnel amplify the challenges, necessitating pinpoint accurate modeling to resolve issues. To this end, this study delves into a pertinent segment of a subway project through the lens of a case study. Employing PLAXIS2D software for modeling and monitoring, the research entails a meticulous examination of numerical modeling results. A specific section from the project garners focus, chosen due to its settlement measurement of 9.5 cm, which exceeded allowable limits. The influence of geotechnical parameters on surface settlement is probed, revealing Eand koparameters as the most influential. Employing a back-analysis approach, optimal values for these parameters are identified. Furthermore, a comparison ensues between transverse surface settlement curves derived from numerical modeling and actual measurements. Remarkably, this analysis underscores an exceptional alignment between measured and predicted values from numerical simulations.

Polymer-based shielding materials have garnered attention due to their lightweightness, ease of fabrication, and tailorable performance. However, they tend to creep and deform when the materials are exposed to external heat, which may cause a catastrophic disaster due to mechanical failure. In this study, a hybrid loading of graphite (Gt) and boron nitride (BN) fillers was used to improve the thermal dissipation, neutron shielding, and tribological properties of ultrahigh molecular weight polyethylene (UHMWPE), which demonstrate potential applications in nuclear sectors among others. The results revealed that the thermal conductivity of UHMWPE/BN 30 wt %/Gt 40 wt % composite (i.e., BN30Gt40) reached as high as 8.43 W/mK, which was 295% higher than UHMWPE/BN 30 wt %, 201% higher than UHMWPE/Gt 40 wt %, and 1700% higher than pure UHMWPE, respectively. Simulation analysis using Geant4 suggested that UHMWPE/BN/Gt composites exhibited excellent neutron-shielding performance due to the presence of abundant hydrogen and boron elements. In addition, the composites demonstrated excellent friction and wear performance due to the combined effects of constructing transfer film on the surface of the friction pair and timely removal of frictional heat that was generated at the sliding interface.

•Population-scale genomics (N = 937 939) allowed assembly of a large FVL/PTGM double-heterozygous cohort while mitigating ascertainment bias.•FVL/PTGM DH appeared to be more common than FVL homozygosity and conferred a similar risk of venous thrombosis.

Roughness was one of the most visual manifestations of the surface quality of metal parts. It affected the performance and life of the parts. Accurate and efficient roughness grade detection technology was of great significance to smart manufacturing. Traditional machine shops often used roughness comparison sample blocks and stylus profilers to check roughness. However, there were disadvantages such as slow detection speed and high influence by human factors. As a non-destructive testing technique, optical imaging gad already demonstrated to be an effective roughness inspection method. In this paper, a roughness detection approach based on image multi-features was proposed, using part surface images as the research object. First, gray level co-occurrence matrix (GLCM), Gabor transform, and local binary patterns (LBP) were used for the extraction of image texture features. After using principal components analysis to reduce the dimensionality of texture features, multiple texture features were concatenated to form a multi-feature vector. Finally, the multi-feature vectors were input into the Gaussian radial basis kernel support vector machine to classify the part surface images and thus completed the detection of roughness grade.

In this work, the total-ionizing-dose (TID) effects on graphene field effect transistors (GFETs) were investigated using 10 keV X-ray irradiation under various gate biases in irradiation environment. For reliability applications, the Dirac voltage (VDirac) shifted negatively during irradiation as the hole mobility (μh) and electron mobility (μe) declined under the positive gate and zero bias. Under negative gate bias, the Dirac voltage (VDirac) moved in a positive direction, reducing hole mobility (μh) and electron mobility (μe). Thus, we can conclude that the positive gate bias is the worst bias of GFETs by contrasting the experimental outcomes under various biases. During the recovery time of 9 h and 24 h after irradiation, and it became clear that the Dirac voltage (VDirac) shifted in a positive direction. Notably, the emergence of trap charges caused by irradiation, and the accumulation of trap charges can be used to explain these phenomena. The recovery time outcome data indicate that radiation damage was caused by the trap charge created during irradiation. Therefore, this work assists in the implementation of GFETs in challenging environments.

This paper introduces an innovative approach to promote sustainable electrification in Ethiopia through the strategic development of minigrid clusters. In collaboration with Ethiopian authorities, technical and economic barriers obstructing the large-scale deployment of minigrid clusters are systematically identified. A novel Multi-Tier Framework (MTF)-based energy access matrix is presented, facilitating the creation of hybrid load profiles prioritizing affordability and reliability. The most suitable multi-criteria optimization strategy tailored to the Ethiopian context is explored, emphasizing the need for scalable and adaptable solutions. The proposed approach is rigorously validated through case studies in three villages within the Southern Nations, Nationalities, and Peoples' Region (SNNP). The research findings reveal the remarkable techno-economic advantages of minigrid clusters when compared to single minigrids, establishing the practicality and significance of the methodology. Sensitivity analysis is conducted to assess the impact of varying techno-economic scenarios on optimal sizing, providing essential insights for decision-makers. As a result, this paper significantly contributes to the upcoming deployment of four planned large-scale minigrids in the SNNP region, funded by the African Development Bank. Valuable guidance for stakeholders and decision-makers involved in minigrid cluster development in Ethiopia is offered, underscoring the critical role of such systems in achieving sustainable energy access and fostering socioeconomic progress.

The hydrogen storage potential and cost-effectiveness of MgH2have rendered it a desirable material for hydrogen storage. However, its application has been hindered by limitations in thermal stability and dehydrogenation kinetics. In this study, the carbon nanotube-Pd bifunctional catalyst was prepared by using hydrothermal and calcination technology, significantly improving the kinetics of Mg/MgH2during hydrogen adsorption/desorption. The activation energy for the Mg@ CNTs-Pd was reduced to 42.73 kJ/mol, as determined by fitting the Avrami–Erofeev model with the Arrhenius equation. Additionally, the activation energy during dehydrogenation was found to be 73.98 kJ/mol based on the Johnson–Mehl–Avrami–Kolmogorov equation and the Arrhenius equation. It is worth noting that the dynamic enhancement persists even after 10 cycles. This groundbreaking research has paved the way for potential applications of MgH2as a hydrogen storage material, providing it with broader development opportunities.

AlCoCrFeNi2.1eutectic high-entropy alloy (EHEA), with its distinctive heterogeneous structure, not only overcomes the drawback of insufficient strength of FCC single-phase high-entropy alloys (HEAs), but also overcomes the drawback of insufficient ductility of BCC single-phase HEAs, making it a promising candidate for engineering application. In addition, EHEAs possessing excellent castability are considered to be suitable for additive manufacturing (AM). The AlCoCrFeNi2.1EHEA prepared by direct energy deposition (DED) exhibits a dual-phase structure consisting of FCC(L12) and BCC(B2) phases. With the increase of deposition height, its morphology transforms from columnar and dendrite structures to lamellar structure. Furthermore, the tensile strength and yield strength of the DED-ed EHEA samples increase, while the ductility decreases with the increase of deposition height. Especially, the middle region of the DED-ed samples achieves a good strength-ductility balance. The lamellar structure composed of soft FCC(L12) phase and hard BCC(B2) phase produces a hetero deformation induced (HDI) hardening, which improves the strength of the samples and promotes plastic deformation. The FCC(L12)/BCC(B2) phase interfaces not only hinder the movement of a large number of dislocations and cause dislocation pile-up, but also allow some dislocations to slip across, which helps to plastic co-deformation of two phases in the EHEA samples. This work provides useful guidance for the performance improvement of EHEAs using laser additive manufacturing.

[Display omitted] • Versatile Bi-decorated ZnSnO 3 composite are synthesized via a solvothermal method. • Bi/ZnSnO 3 composite exhibits superior photocatalytic performance for N 2 fixation and RhB degradation. • The optimized Bi/ZnSnO 3 composite generates NH 3 at a rate of 988 μmol/L g-1h−1 under simulated sunlight. • Successful loading of Bi nanoparticles on ZnSnO 3 and Schottky barrier formation improve charge separation and photocatalytic activity. A composite of cheap metallic Bi-decorated ZnSnO 3 was synthesized via a solvothermal method and applied for photocatalytic N 2 fixation for the first time. The results demonstrated that the Bi/ZnSnO 3 composite with 1.5 % Bi exhibited superior photocatalytic performance. with the optimal sample producing NH 3 at a rate of 988 μmol/L g-1h−1 under simulated sunlight. This is 5 times faster than that of pure ZnSnO 3. Additionally, the Bi/ZnSnO 3 composite showed significantly better photocatalytic performance than pure ZnSnO 3 in RhB degradation. Structural analyses confirmed successful loading of Bi nanoparticles on the surface of ZnSnO 3. Furthermore, diffuse reflectance spectroscopy analysis revealed that the surface plasmon resonance effect of metal Bi effectively improved the optical absorption performance. However, the sensitization of metal Bi had a limited contribution to the high photocatalytic activity of the Bi/ZnSnO 3. Electrochemical analyses indicated that the Schottky barrier formed between Bi and ZnSnO 3 captured photo-generated electrons and promoted the charge separation, which was considered responsible for the increased photoactivity of Bi/ZnSnO 3.This work provides a new application of Bi/ZnSnO 3 and may offer new ideas for the application of ZnSnO 3 materials in the field of photocatalysis, which are crucial for addressing environmental pollution and energy conversion challenges. [ABSTRACT FROM AUTHOR]

The permeability of the highly selective blood–brain barrier (BBB) to anticancer drugs and the difficulties in defining deep tumor boundaries often reduce the effectiveness of glioma treatment. Thus, exploring the combination of multiple treatment modalities under the guidance of second-generation near-infrared (NIR-II) window fluorescence (FL) imaging is considered a strategic approach in glioma theranostics. Herein, a hybrid X-ray-activated nanoprodrug was developed to precisely visualize the structural features of glioma microvasculature and delineate the boundary of glioma for synergistic chemo-radiotherapy. The nanoprodrug comprised down-converted nanoparticle (DCNP) coated with X-ray sensitive poly(Se-Se/DOX-co-acrylic acid) and targeted Angiopep-2 peptide (DCNP@P(Se-DOX)@ANG). Because of its ultrasmall size and the presence of DOX, the nanoprodrug could easily cross BBB to precisely monitor and localize glioblastoma via intracranial NIR-II FL imaging and synergistically administer antiglioblastoma chemo-radiotherapy through specific X-ray-induced DOX release and radiosensitization. This study provides a novel and effective strategy for glioblastoma imaging and chemo-radiotherapy.

• Hydrolysis performance of Mg is significantly improved by FLG-loaded ultra-fine, highly dispersed Ni/Sc 2 O 3 nanocatalyst. • The Mg-4wt% Ni/Sc 2 O 3 @FLG sample releases 833 mL g–1 of H 2 in 20 s at 30 °C with mHGR of 5942 mL g–1 min–1. • Mg is able to produce 690 mL g–1 of H 2 in 30 s with minor catalyst addition of 0.2 wt%. • The mechanism of rapid hydrolysis of Mg catalyzed by Ni/Sc 2 O 3 @FLG composite is well elucidated. Magnesium is one of the most promising candidates of light metal materials for hydrogen production by hydrolysis due to its efficient and economical properties. Various modification methods have been investigated to improve the hydrolytic properties of Mg. However, the direction of the design of efficient catalysts is unclear and needs to be guided by a richer catalytic mechanism of hydrolysis. In this work, a simple approach was used to synthesize Few Layer Graphene (FLG)-loaded ultra-fine highly dispersed Ni/Sc 2 O 3 nanocatalyst, which achieves impressive catalytic hydrolysis results. Here, the addition of 4 wt% Ni/Sc 2 O 3 @FLG catalyst allows Mg to produce 833 mL g–1 of H 2 in 20 s at 30 °C. There is an initial hydrogen release rate as high as 5942 mL g–1 min–1, a final hydrogen yield of 859 mL g–1 (99.13%), and almost complete conversion of Mg to Mg(OH) 2. Furthermore, surprisingly, even with only 0.2 wt% catalysts added, Mg still has an initial hydrogen generation rate of 3627 mL g–1 min–1, which is over 20 times faster than that of Mg. It also produces 690 mL g–1 of H 2 in 30 s at 30 °C. Hydrolysis kinetic curves and microscopic morphology tests show that FLG could shape and hold Mg into thin sheets, giving them an ultra-high hydrolysis rate and conversion rate. The formation of micro-galvanic cells between Ni and Mg accelerates the electrochemical corrosion of Mg and greatly enhances electron transfer during hydrolysis. This work provides a new strategy for the preparation of efficient nanocatalysts, which is expected to make "Mg-efficient catalyst" the most ideal light metal-based material for hydrogen production by hydrolysis. [Display omitted] [ABSTRACT FROM AUTHOR]