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A facile method is proposed in this work to prepare a carbon paper (CP) with outstanding properties by doping boron to catalyze graphitization at lower temperature. Boron was introduced into CP by impregnating carbon fiber preform into a boron/phenolic resin/ethanol solution, and then CP was obtained after the consecutive procedures of hot pressing, carbonization, and graphitization. Morphology, microstructure, composition, and physical properties of the doped and undoped CPs were thoroughly investigated via various techniques to exam the effects of boron concentration and graphitization temperature. Furthermore, the boron doped CP was fabricated into fuel cell gas diffusion layer (GDL) and membrane electrode assembly (MEA) to evaluate its performance through the single cell performance. The findings indicated CP doping with 2 wt% boron could lower the graphitization temperature by at least 200 °C. The B-doped GDL had a higher power density in comparison with the undoped GDLs made in our lab or from the commercial brands. • The innovative preparation method for carbon paper with high conductivity. • The effect of boron concentration and graphitization temperature on carbon paper. • The single cell performance of B-doped gas diffusion layer. [ABSTRACT FROM AUTHOR]

Brucellosis is a zoonotic disease that affects wild and domestic animals. It is caused by members of the bacterial genus Brucella. Guanylate-binding protein 1 (GBP1) is associated with microbial infections. However, the role of GBP1 during Brucella infection remains unclear. This investigation aimed to identify the association of GBP1 with brucellosis. Results showed that Brucella infection induced GBP1 upregulation in RAW 264.7 murine macrophages. Small interfering GBP1 targeting RNAs were utilized to explore how GBP1 regulates the survival of Brucella intracellularly. Results revealed that GBP1 knockdown promoted Brucella 's survival ability, activated Nod-like receptor (NLR) containing a pyrin domain 3 (NLRP3) and absent in melanoma 2 (AIM2) inflammatory corpuscles, and induced pro-inflammatory cytokines IFN-γ and IL-1β. Furthermore, Brucella stimulated the expression of GBP1 in bone marrow-derived macrophages (BMDMs) and mice. During the inhibition of GBP1 in BMDMs, the intracellular growth of Brucella increased. In comparison, GBP1 downregulation enhanced the accumulation of Brucella -induced reactive oxygen species (ROS) in macrophages. Overall, the data indicate a significant role of GBP1 in regulating brucellosis and suggest the function underlying its suppressive effect on the survival and growth of Brucella intracellularly. • B. melitensis infection increased GBP1 expression in RAW 264.7 murine macrophages. • Knocking down the expression of GBP1 promotes Brucella infection RAW 264.7 murine macrophages. • Knocking down the expression of GBP1 induces the production of inflammatory cytokine in Brucella -infected macrophages. • Knocking down the expression of GBP1 promotes B. abortus persistence in bone marrow-derived macrophages. • Brucella infection increased GBP1 expression in vivo. [ABSTRACT FROM AUTHOR]

Spatial analyses of traffic crashes have drawn much interest due to the nature of the spatial dependence and spatial heterogeneity in the crash data. This study makes the best of Geographically Weighted Random Forest (GW-RF) model to explore the local associations between crash frequency and various influencing factors in the US, including road network attributes, socio-economic characteristics, and land use factors collected from multiple data sources. Special emphasis is put on modeling the spatial heterogeneity in the effects of a factor on crash frequency in different geographical areas in a data-driven way. The GW-RF model outperforms global models (e.g. Random Forest) and conventional geographically weighted regression, demonstrating superior predictive accuracy and elucidating spatial variations. The GW-RF model reveals spatial distinctions in the effects of certain factors on crash frequency. For example, the importance of intersection density varies significantly across regions, with high significance in the southern and northeastern areas. Low-grade road density emerges as influential in specific cities. The findings highlight the significance of different factors in influencing crash frequency across zones. Road network factors, particularly intersection density, exhibit high importance universally, while socioeconomic variables demonstrate moderate effects. Interestingly, land use variables show relatively lower importance. The outcomes could help to allocate resources and implement tailored interventions to reduce the likelihood of crashes. • Geographically weighted modeling with local Random Forest models. • Local associations between crash frequency and various factors in the USA. • Modeling and analyzing nonlinear effects of factors. • Revealing spatially distinctive effects of factors on crash frequency. [ABSTRACT FROM AUTHOR]

Age-related hearing loss (ARHL) is a common neurodegenerative disease. Its molecular mechanisms have not been fully elucidated. In the present study, we obtained differential mRNA expression in the cochlea of 2-month-old miR-29a +/+ mice and miR-29a −/− mice by RNA-seq. Gene ontology (GO) analysis was used to identify molecular functions associated with hearing in miR-29a −/− mice, including being actin binding (GO: 0003779) and immune processes. We focused on the intersection of differential genes, miR-29a target genes and the sensory perception of sound (GO:0007605) genes, with six mRNA at this intersection, and we selected Col1a1 as our target gene. We validated Col1a1 as the direct target of miR-29a by molecular and cellular experiments. Total 6 pathways involved in Col1a1 were identified by through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. We selected the focal adhesion pathway as our target pathway based. Their expression levels in miR-29a −/− mice were verified by qRT-PCR and Western blot. Compared with miR-29a +/+ mice, the expression levels of Col1a1 , Itga4 , Itga2 , Itgb3 , Itgb7 , Pik3r3 and Ptk2 were different in miR-29a −/− mice. Immunofluorescence was used to locate genes in the cochlea. Col1a1 , Itga4 and Itgb3 were differentially expressed in the basilar membranes and stria vascularis and spiral ganglion neurons compared to miR-29a +/+ mice. Pik3r3 and Ptk2 were differentially expressed in the basilar membranes and stria vascularis, but not at the s spiral ganglion neurons compared to miR-29a +/+ mice. Our results show that when miR-29a is knocked out, the Col1a1 mediates the focal adhesion pathway may affect the hearing of miR-29a −/− mice. These findings may provide a new direction for effective treatment of age-related hearing loss. • Age-related hearing loss miR-29a mouse model by RNA-seq analysis. • MiR-29a deletion leads to up-regulation of Col1a1 expression, which results in decreased hearing in mice. • Col1a1 is affecting hearing through the focal adhesion pathway. [ABSTRACT FROM AUTHOR]

• Experimental study on inhibition of gas hydrate formation for CO 2 separation. • Develop a chemical affinity model to predict CO 2 hydrate formation. • Optimal inhibitory compound system is 15 mg/mL glucose + 1.00 mg/ml attapulgite. • Induction time of hydrate formation is extended by approximately 122% • Gas consumption is reduced by 23.72% to improve the inhibitory effect. Natural gas and water can form complex cage crystals in oil and gas pipelines under certain conditions. The natural gas hydrate formed after crystal clustering can block the pipeline and affect the operation efficiency of the pipeline, and even cause equipment damage, bringing economic and security problems to enterprises. In the present study, we experimentally investigated the inhibition of CO 2 formation hydrate using attapulgite (ATP) and glucose (GLC). The kinetic influence on the formation of CO 2 hydrate by the complex pairing of kinetic inhibitor ATP is conducted under the working conditions of 277.15 K and 3.5 MPa. The effect of compound inhibitors on the temperature and pressure conditions, growth process and gas consumption of hydrate formation are revealed. The chemical affinity model of CO 2 hydrate formation under a compound inhibitor system is derived and established based on experimental studies. The results show that the combination of GLC and ATP can inhibit the formation of CO 2 hydrate to varying degrees, prolong the induction time of hydrate, and reduce the consumption of CO 2. The optimized experimental studies demonstrate that the best inhibitory compound system is 15 mg/mL GLC + 1.00 mg/mL ATP. Compared with the pure water system and single 15 mg/mL GLC system, the induction time is extended by 122.61% and 122.23%, while the gas consumption is reduced by 23.72% and 3.41%. The results provide new ideas and methods for the prediction of hydrate formation in the compound inhibitor system for the safe operation of oil and gas pipelines. [ABSTRACT FROM AUTHOR]

The low hydraulic efficiency of the pump as turbine (PAT) poses a major challenge to the runner design. This paper introduces a new strategy for designing and optimizing the performance of PAT runner. The strategy process is mainly divided into four steps, namely blade parametric design, significance evaluation of parameters, construction of optimizing surrogate model and optimization of improved PSO algorithm. Compared with the current design methodologies, not only does this optimization strategy propose the system design and optimization process, but also it breaks down the process into well-defined steps and simplifies them. The hydraulic efficiency of PAT optimized by this optimization strategy reaches 84.76%, which improves 3.93%. A test bench has been built to test the optimizing strategy. The maximum difference of experimental data is 1.5%, which meets the requirements of practical engineering application, and verifies the reliability of the optimization strategy process. The optimizing strategy process can be extended to apply to pump, fan, conventional turbine and other blade fluid models, and it can be further extended to apply to the optimization design of 3D blades with a high specific speed. [Display omitted] [ABSTRACT FROM AUTHOR]

• Performability of dynamic multi-state k -out-of- n : G systems is studied. • A novel and fast analytical modeling method is proposed. • A smart home lighting control system is analyzed. • Correctness is verified using a case study of an oil supply system. • Efficiency is verified using comprehensive empirical and comparative studies. A dynamic multi-state k -out-of- n : G system, denoted by DMS(k, n , G) is a system where the system and its components exhibit multiple performance levels, and the system has different requirements on the number of working components in different states. DMS(k, n , G) abounds in both industrial and military applications. In this paper, a novel and efficient analytical method based on multi-valued decision diagrams (MDDs) is proposed for performability assessment of DMS(k, n , G) with non-identical components. Unlike existing approaches where multiple combination operations between models of lower performance levels are needed for constructing the model of a higher performance level, the proposed MDD generation algorithm constructs the system performability MDD in a top-down manner by considering multiple system state requirements simultaneously. A smart home lighting control system is analyzed to demonstrate the application of the proposed method. A detailed case study of an oil supply system is provided to verify the correctness of the proposed method and illustrate component sensitivity analysis. Complexity analysis and comprehensive empirical studies are performed to demonstrate that the efficiency of the proposed model construction is greatly improved as compared to the existing method, enabling fast model generation and efficient analysis of large-scale DMS(k, n , G). [ABSTRACT FROM AUTHOR]

GntR10 is a transcriptional regulator in Brucella. Nuclear factor-kappa B (NF-κB) is involved in many cellular activities, playing major roles in orchestrating the expression of inflammatory genes and regulating protein function that is essential for pathogenic bacteria during infection. GntR10 deletion was previously found to affect the growth and the virulence of Brucella and expression levels of target genes of GntR10 in mice. However, the mechanisms of affection of NF-κB regulated by Brucella GntR10 are still unclear. Here, GntR10 deletion could regulate the expression of LuxR-type transcriptional activators (VjbR and BlxR) of the quorum sensing system (QSS) and type IV secretion system (T4SS) effectors (BspE and BspF) of Brucella. It could further inhibit the activation of the regulator NF-κB and affect the virulence of Brucella. This research provides new insights into the designing of Brucella vaccines and the screening of drug targets. Transcriptional regulators are predominant bacterial signal transduction factors. The pathogenicity of Brucella is due to its ability to regulate the expression of virulence related genes including quorum sensing system (QSS) and type IV secretion system (T4SS). Transcriptional regulators are designed to regulate gene expression and enact an appropriate adaptive physiological response. Here, we show that Brucella transcriptional regulator GntR10 regulated the expression of QSS and T4SS effectors, which affected the activation of NF-κB. [Display omitted] • 1558 proteins with quantitative information were identified from S2308 and 2308Δ gntR10. • The expression of T4SS virB1 was reduced in 2308Δ gntR10. • Expressions of VjbR and BlxR of QSS were down-regulated. • The activation of regulator NF-κB was inhibited in 2308Δ gntR10. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon confined iron-nickel alloy/iron fluoride doped by oxygen derived from nano rod-like Fe 2 Ni MIL MOF. • Structural transformation demonstrated by spectroscopic analysis was correlated to electrochemical catalytic performance. • C/O-FeNi/FeF 2 exhibited high catalytic activity and stability for OER with faster charge transfer ability. • High activity comes from carbon confined active phase, increased polarity, strong electronic effect and efficient synergism. Carbon confined active phase plays a significant role in the novel catalyst materials development and fabrication. Herein, we demonstrate such a catalyst material in the form of carbon confined iron-nickel alloy/iron fluoride doped by oxygen (C/O-FeNi/FeF 2) derived from the Fe 2 Ni MIL MOF via facile carbonization and fluorination for efficient oxygen evolution reaction (OER) in the water-splitting reaction. The structural transformation from the Fe 2 Ni MIL MOF to the C/O-FeNi/FeF 2 is demonstrated by the spectroscopic analysis and correlated to their electrochemical catalytic performance. Because of some combined merits resulting from the carbon confined active phase, increased polarity, strong electronic effect and efficient synergism of metal and metal fluoride for the facile active phase reconstruction in this hybrid system, this catalyst exhibits many good characteristics for electrochemical measurements in terms of improved conductivity, high intrinsic activity and stability, fast catalytic kinetics, increase surface area and rapid charge transfer ability. Specifically, the C/O-FeNi/FeF 2 catalysts have a low overpotential of ca. 250 mV when loaded on a glass carbon electrode to offer the current density of 10 mA cm−2 in the alkaline electrolyte for OER; and no obvious performance decay is observed in the long-term stability test conducted at different potentials. The current results will be instructive for novel carbon confined catalyst and MOF derived catalyst design and fabrication in the energy catalysis reaction. [ABSTRACT FROM AUTHOR]

A series of aromatic polyimides were synthesized from three Cardo-based diamines, BAF, DDA, BAP, with different building blocks (ether linkage, dioxane linkage and C–C linkage), and commercially available dianhydride 6FDA. All polyimides were readily soluble in conventional aprotic and protic solvents, which guaranted the fabrication of self-standing membranes for gas separation applications. The Cardo-based polyimides also exhibited excellent thermal stability, with weight loss temperatures (T 5 %) of over 538 °C, and good mechanical properties, with tensile strength in the range of 41–64 MPa. Compared to 6FDA-BAF, the substitution of dioxane linkage for ether linkage resulted in increased chain rigidity, looser polymer chain packing, and larger fractional free volume. Consequently, as displayed by single gas permeation tests, 6FDA-DDA showed higher permeability coefficients without significantly sacrificing selectivity. In addition, the CO 2 permeability of 6FDA-BAP was around 2 times as high as that of 6FDA-BAF, while the CO 2 /CH 4 selectivity increased by 11%. This can be explained by the lower rotational mobility and the relatively higher imide content caused by the absence of the flexible ether linkage and dioxane linkage in polymer backbone. In short, fundamental insights into structure-property relationships of the Cardo-based polyimides observed in this study provided guidance for the molecular structure design of the gas separation membranes. [Display omitted] • Three Cardo-based polyimides containing different building blocks were successfully synthesized. • The effect of rigidity and microstructure on gas separation performance of the Cardo-based polyimides was elucidate. • The substitution of dioxane for ether linkage led to the increased chain rigidity and the looser chain packing. • The polyimide containing C–C linkage showed better combination of permeability and gas-pair selectivity. [ABSTRACT FROM AUTHOR]

The gas diffusion substrate (GDS) is essential in the proton exchange membrane fuel cells. Its fabrication techniques affect the performance significantly and are worthy of investigation. In this study, a manufacturing process of the GDS is proposed to understand the formation process of GDS and promote its structure and performance more pertinently. Different states during the preparation process, raw carbon paper, pre-curing, curing, carbonation, and graphitization, are characterized and measured. Experimental and numerical methods are employed to determine the relationships between microstructure, transport, and mechanical performance variation with the fabricating processes. The results show that its porosity, average pore size, and effective diffusivity decrease first and increase after curing. These parameters after graphitization are lower than that of the carbon paper (CP). The electrical resistivity increases dramatically while pre-curing and decreases gradually after curing, carbonation, and graphitization, and it is much reduced after graphitization. Moreover, mechanical measurement results show that both the picks of tensile strength and flexural modulus occur after curing. Its tensile strength shows little change after graphitization compared to the initial paper's. In contrast, the flexural modulus is improved significantly. • Microstructural characteristics of GDS during fabrication are investigated. • Transport properties and pore-scale simulations for GDS are performed. • Precuring, curing, carbonation, and graphitization on properties are studied. [ABSTRACT FROM AUTHOR]

Herein, a copper (II) ions - doped thin film composite (TFC-Cu) membrane with ridge-valley morphology was prepared by interfacial polymerization (IP) via incorporating copper (II) ions in the aqueous phase. The complexation of copper (II) ions with N atoms on m-phenylenediamine (MPD) affects the diffusion of MPD from the aqueous to the oil phase, leading to instability of the IP process and consequently the formation of ridge-valley morphology on the membrane surface. The ridge-valley morphology increases the contact area between the membrane and the solvent, which lays the foundation for increased solvent permeability. Moreover, the hydrophilicity and permeability of the composite membrane can be modulated by adjusting the content of copper (II) ions in the aqueous phase. The methanol permeation performance of the preferred TFC-Cu 100 membrane was 13.27 L m−2 h−1 bar−1, an improvement of 65.46% over the unmodified TFC membrane. Meanwhile, the TFC-Cu 100 membrane also showed a high retention level (98.82%) of RDB dye. In addition, the TFC-Cu 100 membrane maintained good long-term organic solvent nanofiltration (OSN) performance after 10 days of immersion in N, N-dimethylformamide (DMF) at 80 °C. This work offers attractive prospects for the modification of the IP process using other inorganic salts to optimize the OSN performance of the membrane. [Display omitted] • A copper (II) ions-doped TFC-Cu membrane with ridge-valley morphology was prepared by inorganic salt-modified IP. • Copper (II) ions-doped membrane showed the markedly enhanced solvent flux compared with TFC membrane. • The surface morphology and permeability can be modulated by controlling the copper (II) ions content. • The copper (II) ions-doped membranes have good long-term organic solvent nanofiltration performance in DMF. • The combination of copper (II) ions and the separation layer strategy provide a way to improve the OSN performance of TFC-Cu membranes. [ABSTRACT FROM AUTHOR]

[Display omitted] • Fe and Ni sites were selectively separated by melamine-assisted MOF pyrolysis. • The fluorination process selectively forms FeF 2 rather than NiF 2. • Theoretical and spectral techniques confirmed FeF 2 transformation to active FeOOH. • Enhanced electronic interactions and adsorption energy underscore the impact of fluorination. • Regulation of Fe sites and their synergistic effect with Ni improved catalytic performance. Given its initially low activity and difficulty in active phase formation, Fe site regulation is rarely touched in FeNi-based oxygen evolution reaction (OER) catalysts. Herein, the Fe sites regulation and activity deciphering are demonstrated by selective phase transformation taking nitrogen-doped carbon confined FeNi nanoparticles derived from Fe 2 Ni-MIL-MOF pyrolysis as an example; driven by the reduction atmosphere and Kirkendall effect assisted by melamine pyrolysis, some Fe and Ni sites were separated to form metallic Fe and FeNi alloy and the subsequent fluorination process induced the ionic compound FeF 2 formation rather than NiF 2 , offering a good platform to evaluate the influence of active Fe phase for OER catalysis. Theoretical calculations combined with ex-situ and in-situ spectral characterization techniques proved the complete convention of FeF 2 into a high-valent and reactive intermediate state of FeOOH. Considering the stable metallic state of Ni, the largely improved catalytic performance could be attributed to the Fe site regulation and the subsequent synergistic catalysis effect. Compared to the traditional iron-based catalyst, theoretically, the electronic structure of FeF 2 was more conducive to the active FeOOH phase formation and significantly lowered the reaction energy barrier for OER. By addressing the aforementioned problems, low overpotential and good stability for OER catalysts were realized. The current work showed valid proof of the FeNi-based catalysts regulation via Fe sites for energy-relevant catalysis reactions. [ABSTRACT FROM AUTHOR]

Adopting drought-tolerant (DT) cultivars is an effective strategy to sustain maize (Zea mays L.) production under water shortage. Optimizing plant density is an important management practice for improving maize yield. In a two-year field trial, the response of yield, actual evapotranspiration (ET c act), and water productivity (WP) to plant density (6, 7.5, 9 plants m−2) was assessed under irrigated and rainfed conditions using a DT (ZD958) and a drought-susceptible (DS, ZY309) maize cultivar, and additionally, the comparison of soil water depletion will be conducted among soils growing different DT maize varieties. Under rainfed, average yield, ET c act , and WP were 24.7%, 8.6% and 14.8% greater in ZD958 than ZY309, respectively. When density increased from 6 to 9 plants m−2, for ZD958 and ZY309 ET c act remained relatively constant, whereas their yield and WP first increased and then decreased and ultimately reached their maximum at 7.5 plants m−2. Under irrigation, increasing density (6–9 plants m−2) significantly increased yield and WP for ZD958, but for ZY309, yield and WP were not significantly impacted. Yield across seasons did not differ between cultivars at 6 and 7.5 plants m−2, and ZD958 had a 10.2% yield advantage over ZY309 at 9 plants m−2. The findings imply that DT cultivar showed greater high density tolerance than DS cultivar and thus higher optimal density under irrigation. Under rainfed, both cultivars had similar density tolerance and optimum density, whereas DT cultivar had stronger drought tolerance than DS cultivar, which could explain DT cultivar's greater yield and WP. This study indicate that DT cultivar showed higher and more stable yields than DS cultivar across rainfed and irrigated conditions when grown at optimal densities. Thus, sustainable maize production could be achieved by adopting DT cultivars and optimizing density for different conditions in the study region. • Drought-tolerant cultivar had greater crowding tolerance than drought-susceptible cultivar under irrigation. • Crowding tolerance did not differ between drought-tolerant and drought- susceptible cultivars under rainfed. • Higher tolerance of drought-tolerant cultivar accounts for its higher yield and water productivity under rainfed. • Higher optimum density found in drought-tolerant vs. susceptible cultivar under irrigation but not under rainfed. • Drought-tolerant cultivar extracted more soil water than drought-susceptible cultivar under rainfed. [ABSTRACT FROM AUTHOR]

Metal halide perovskite single crystal (SC) has attracted wide attentions in the field of photoelectric detection. All-inorganic perovskite SCs are much more stable than the hybrid counterparts, but its rapid fabrications usually end up low quality. We proposed a modified solvent-evaporation method that can rapidly obtain smooth and transparent high-quality CsPbBr 3 single crystal with a surface roughness of only 5 nm in a few hours. In this method, the lattice phase of the SC and the growth rate is precisely controlled by the solution temperature, which is raised to the narrow range above the cubic-to-tetragonal phase transition temperature. This precisely controlled solvent temperature allows the CsPbBr 3 SC growing immediately into cubic phase, and limits the volatilization rate of solvent, thus inhibiting the defect density. The SCs grown this way are of high crystallinity, as the half-height width of the (100) and (200) peaks of XRD rocking curve is 0.0154° and 0.0176°, respectively. Characterization of photoelectric properties shows great potential for applications in X-ray detection and photodetection. • CsPbBr 3 single crystals were grown by a modified evaporative solvent method. • The crystal quality is very high crystallinity as the half-height width of the rocking curve as low as 0.0176°. • The effect of growth temperature on crystal quality is investigated. • The growth time of large size (3 mm) CsPbBr 3 single crystals is reduced to 10 h. [ABSTRACT FROM AUTHOR]

Herein, we demonstrated that a novel derivative of zeolitic imidazolate framework (ZIF) obtained by direct fluorination (ZIF-67) is an efficient electrocatalyst for Oxygen Evolution Reaction (OER). The obtained catalyst showed the main phase of tetragonal CoF 2 from the metal center and the nitrogen-doped carbon from the ligands of ZIF-67. The polyhedral morphology was well kept but with a more rough surface consisting of assembled nanoparticles. Due to the good conductivity and high polarity in the system, this electrocatalyst showed an overpotential of 294 mV required to yield 10 mA cm−2 with good stability. [Display omitted] • ZIF-67 derived cobalt fluoride is active for oxygen evolution reaction. • The structure of N-doped carbon can increase the conductivity of the catalyst. • The obtained CoF 2 from the metal center has high polarity. • Extending the application of the novel derivative by direct fluorination of MOF-based materials. [ABSTRACT FROM AUTHOR]

Support engineering modulated Pt by hierarchical MoSe 2 @mesoporous hollow carbon support well addressed the challenges in low intrinsic activity and easy poisoning problems during methanol electrolysis. [Display omitted] • Electrochemical energy conversion from methanol to hydrogen via methanol electrolysis. • Support engineering modulated Pt by hierarchical MoSe 2 @mesoporous hollow carbon support. • Strong interaction and coupling ability between Pt and MoSe 2 regulated the active centers. • Weakened binding strength of CO ad and H ad intermediates during catalysis. • High electrochemical properties due to enhanced oxophilicity and poisoning tolerance ability. Hydrogen generation from methanol via methanol-assisted water splitting is a promising electrochemical energy conversion technology but still meets challenges in addressing the low intrinsic activity and easy poisoning problems of the conventional Pt catalysts. Herein, the support engineering modulated Pt by hierarchical MoSe 2 @mesoporous hollow carbon support was proposed to integrate some structural and catalytic advantages for hydrogen evolution reaction (HER) and methanol oxidation reaction (MOR). Supporting engineering by layered MoSe 2 nanosheets confined in mesoporous hollow carbon spheres (MHCSs) is adopted to uniformly anchor Pt nanoparticles (Pt/MoSe 2 @MHCS), which effectively enhances the conductivity and prevents the layer-by-layer stacking of MoSe 2 ; Strong interaction and coupling ability between Pt and MoSe 2 and the resultant electronic structure regulation of the active centers could weaken the binding strength of CO* and H* intermediates during catalysis and strengthen the oxophilicity leading to improved MOR and HER kinetics. Specifically, Pt/MoSe 2 @MHCS shows a significantly improved MOR current density of 81.1 mA cm−2, about 3.0 times that of the benchmark Pt/C catalyst. Meanwhile, it also exhibits the HER performance of 28 mV to achieve a kinetic current density of 10 mA cm−2 in methanol-contained electrolytes. When employed for overall methanol electrolysis, a lowered input voltage of 1.11 V can be obtained to drive the current density (10 mA cm−2) compared to that for water electrolysis (0.62 V vs. 1.73 V). The electrochemical properties of high activity, rapid catalytic kinetics, and good stability were also demonstrated due to the enhanced oxophilicity and high poisoning tolerance ability resulting from the MoSe 2 /carbon substrate-induced electron redistribution of Pt sites. This work reports a novel platform to address the catalysis challenges in hydrogen production via the methanol electrolysis technique. [ABSTRACT FROM AUTHOR]

• A holistic water quality assessment was conducted in the Erdao Songhua River Basin. • The IWPI showed "Good" water quality with spatiotemporal variations in studied basin. • PI, DO, COD cr , and BOD 5 are the most important parameters in this basin. • The potential risks of point source and non-point source pollution in middle-downstream should be of concern. The Erdao Songhua River provides drinking water for 16.82 million people; therefore, ensuring that the water quality adequate is of particular concern. In this study, an improved water pollution index (IWPI) and multiple statistical methods were employed to assess the overall water pollution situation and investigate spatiotemporal variations of seven physicochemical parameters such as the permanganate index (PI), dissolved oxygen (DO), chemical oxygen demand (COD cr), five-day biochemical oxygen demand (BOD 5), total nitrogen (TN), total phosphorus (TP) and ammonia nitrogen (NH 3 -N), which were collected monthly at 20 sites within the mainstream and major tributaries of the Erdao Songhua River Basin (ESRB) from 2015 to 2020. Stepwise regression analysis was conducted to build a minimum improved water pollution index (IWPImin) model consisted of four key elements (PI, DO, COD cr , and BOD 5) proposed from seven parameters. The results demonstrated water quality within the ESRB was considered to be "Good" with the mean IWPI values <40. However, the water quality deteriorated from the upstream to the downstream within the basin, manifested as the mean value of IWPI of the downstream is 1.7 times of upstream. Seasonally, an improving trend of water quality was observed during the monitoring period and the mean IWPI value decreased by 23%. Furthermore, Seasonal variation in the IWPI value was evident, and the best water quality was found in winter (lowest IWPI value of 14.1) and the worst in summer (highest IWPI value of 21.7). The proposed IWPImin model uses the selected four crucial parameters and the weights of those parameters has exhibited excellent performance in the water quality assessment, with the highest coefficient of determination (R2) and lowest Root Mean Square Error (RMSE) values of 0.996 and 0.51, respectively, which can be used to optimize water quality assessment strategies at a lower cost. For future management, the water quality of middle and downstream should be carefully inspected, and strictly control the effects of point source and non-point source pollution in the ESRB. [ABSTRACT FROM AUTHOR]

• Provides a protocol for transferring hydrological knowledge between basins. • Proposes a TL-Transformer model for flood simulation and forecasting in data-sparse basins. • Explores the impact of data quality on transfer learning effectiveness. • Explains the manifestation of hydrological similarity in transfer learning through topographic index. • Compares the performance of models including physical and neural network models. There exists a substantial disparity in the distribution of streamflow gauge and basin characteristic information, with a majority of flood observations being recorded from a limited number of well-monitored locations. Transferring hydrological knowledge from data-rich to data-sparse basins has been a persistent issue. While artificial intelligence methods, such as Long-Short-Term Memory Neural Networks (LSTMs) have been attempted for simulating across different basins, the problem of gradient vanishing still inevitably lead to difficulties in responding to new basin datasets. A novel solution is presented in the form of the Transfer Learning Framework based on Transformer (TL-Transformer), which can accurately predict flooding in data-sparse basins (targets) by using models from data-rich basins (sources) without requiring extensive basin attributes at the target location. The framework was demonstrated in the middle reaches of the Yellow River, and performance was evaluated using the Nash Sutcliffe efficiency coefficient, root mean square error, and bias. The results show that TL-Transformer outperforms other models, including TOPMODEL, MLP, TL-MLP, LSTM, TL-LSTM and Transformer in all target basin stations. Compared with those models that without transfer learning, TL-Transformer showed significantly improved performance, where NSE increased by 0.2 and reached above 0.75, RMSE and bias were also controlled within 60 and 20, respectively. Furthermore, pre-training on basins with hydrological similarities increases the benefits of Transfer Learning, and we explained this response phenomenon through the topographic index. These results suggest that deep learning can tap into the commonalities in hydrological data across basins to improve the accuracy of flood forecast applications in areas with limited observations. [ABSTRACT FROM AUTHOR]

Atomic layer deposition (ALD) plays a pivotal role in raising the long-term stability of perovskite quantum dot (PQD). The deposition temperature of excellent alumina film prepared by ALD is generally higher than 200 °C. In such a temperature environment, the lattice morphology of PQD is seriously damaged. In this work, to effectively protect PQD, the optimum temperature for depositing alumina on the surface of CsPbBr 3 PQD is investigated. The CsPbBr 3 PQD is deposited on a transparent glass substrate. Subsequently, an alumina layer is deposited on the surface of PQD via ALD at a deposition temperature of 40–120 °C. In our experiment, the significant degradation of PQD is observed when the deposition temperature of ALD is set to 80 °C and above. Lowering the deposition temperature can effectively improve the optical performance of PQD. The samples with alumina deposited at 75 °C show excellent stability after undergoing aging tests, and therefore 75 °C is considered the best ALD temperature. Besides, white light-emitting diode (W-LED) is produced by a method that a stack structure consisting of CsPbBr 3 coated via ALD at 75 °C and CdSe QD is excited using blue Micro-LED, and the color gamut of W-LED accounts for the wide area of 94.00% NTSC (1953) and 86.20% Rec.2020 displayed wide color gamut features. The experiment provides practical guidance for selecting the optimal deposition temperature for ALD-packaged PQD-based devices. • An optimum temperature for depositing alumina on CsPbBr 3 via ALD is investigated. • Low temperature ALD technology can effectively passivate the surface defects of CsPbBr 3. • Samples have preferable environmental stability after coating with Al 2 O 3. [ABSTRACT FROM AUTHOR]

Shape memory materials with controllable property for harsh condition like high temperature are strongly required. Here, three novel shape memory polyimides (SMPIs) have been realized by bearing various side-chain groups (-H, –CH 3 or –OCH 3) to study their relationship between structures and shape memory performances. These PIs possess excellent shape memory behaviors in both fixity ratio (R f > 99.1%) and recovery ratio (R r > 86.8%). The rheological information revealed different creep and stress relaxation accompanied with the different SMPIs. Meanwhile, fractional free volume (FFV) and simulating of geometry optimized molecule chain via Materials Studio were referred to reveal the inner relationship between shape memory performance and polymer structure. This work provides a design metric to guiding significance to the structure design of shape memory polyimides. • Three shape memory polyimides with various chain side groups are manufactured. • The SMPIs possess excellent shape memory behaviors. • Rheological properties and molecular simulation are discussed. [ABSTRACT FROM AUTHOR]

• Urea oxidation catalyzed by NiSe 2 largely improved by a facile Fe doping strategy. • Doping amount of Fe would greatly impact the catalytic ability. • NiSe 2 with 1.68 at.% Fe-doping exhibited the best catalytic performance. • Current density at 1.54 V was 4.4 times higher than that of pristine NiSe 2 electrode. • The cell voltage of 1.45 V to reach 10 mA cm−2 was 170 mV lower than water electrolysis. The non-precious Ni-based catalysts are promising in the electrooxidation of urea while the low intrinsic activity still needs to be increased for practical application. Herein, we demonstrated the urea oxidation catalyzed by NiSe 2 nanorod could be greatly improved by Fe doping strategy. The Fe-doping effect was probed by density functional theory which revealed that doping could enhance the interface electric field inducing the charge redistribution near the Ni atom, and enhancing the adsorption capacity of Ni to urea molecules. As a result, the balanced interaction of Fe/Ni synergism and the Ni active site exposure can be realized by tuning the Fe doped amount in the system, which would greatly impact the catalytic ability and intrinsic performance. The NiSe 2 with 1.68 at.% iron doping demonstrated the highest catalytic performance for urea oxidation, displaying the largest surface-active area, highly efficient kinetics, fast charge transfer rate, and outstanding stability. The current density of 125.8 mA cm−2 for urea oxidation catalyzed by Fe doped NiSe 2 electrode was obtained at 1.54 V vs. RHE when supported on the glassy carbon electrode, almost 4.4 times that of the pristine NiSe 2 electrode. When constructed in the two-electrode system combined with the commercial Pt/C cathode, the cell voltage used for urea electrolysis was 1.45 V to provide the specific current density of 10 mA cm−2, approximately 170 mV lower than that of pure water electrolysis, indicating the potential application of hydrogen generation for urea-assisted water splitting. The urea oxidation catalyzed by NiSe 2 nanorod can be largely improved by a facile Fe doping strategy, and the doping amount of Fe would greatly impact the catalytic ability and intrinsic performance. [Display omitted] [ABSTRACT FROM AUTHOR]

• Provides a protocol to optimize hyperparameters of LSTM. • Proposes a PSO-LSTM model for flood simulation and forecast. • Study the effect of hyperparameter changes on LSTM results. • Explores the optimal hyperparameter combination of LSTM at different lead times in flood forecast, and achieve the best simulation results by optimizing model hyperparameters. • Compares the performance of the models including physical and neural network models. Flood forecasting is an essential non-engineering measure for flood prevention and disaster reduction. Many models have been developed to study the complex and highly random rainfall-runoff process. In recent years, artificial intelligence methods, such as the artificial neural network (ANN), have attempted to construct rainfall-runoff models. The more advanced deep learning methods of long short-term memory (LSTM) network have been proved to better predict hydrological time series. However, the selection of LSTM hyperparameters in the past mostly relied on the experience of the staff, which often led to failure to achieve the best performance. The aim of this study is to develop a method to improve flood forecast accuracy and lead time. A deep learning neural network model based on LSTM networks and particle swarm optimization (PSO) is proposed in this paper. The PSO algorithm was used to optimize the LSTM hyperparameter to improve the ability to learn data sequence features. The model focuses on the Jingle Watershed in the Fenhe River and the Lushi Watershed in the Luohe River and was used to predict flood processes using rainfall and runoff observation data from stations in the watersheds. We evaluated the performance of the model with the Nash Sutcliffe efficiency coefficient, root mean square error, and bias. The results show that the PSO-LSTM model outperforms the M-EIES, ANN, PSO-ANN, and LSTM at all stations in the watersheds. The PSO-LSTM model improves the flood forecasting accuracy at different lead times, especially for those exceeding 6 h, and has higher prediction accuracy and stability. The PSO-LSTM model could be used to improve accuracy in short-term flood forecast applications. [ABSTRACT FROM AUTHOR]