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AbstractThere are some connections between aging notions, stochastic orders, and expected utilities. It is known that the DRHR (decreasing reversed hazard rate) aging notion can be characterized via the comparative statics result of risk aversion, and that the location-independent riskier order preserves monotonicity between risk premium and the Arrow–Pratt measure of risk aversion, and that the dispersive order preserves this monotonicity for the larger class of increasing utilities. Here, the aging notions ILR (increasing likelihood ratio), IFR (increasing failure rate), IGLR (increasing generalized likelihood ratio), and IGFR (increasing generalized failure rate) are characterized in terms of expected utilities. Based on these observations, we recover the closure properties of ILR, IFR, and DRHR under convolution, and of IGLR and IGFR under product, and investigate the closure properties of the dispersive order, location-independent riskier order, excess wealth order, the total time on test transform order under convolution, and the star order under product. We have some new findings.

Degradation of polyolefin (PE) plastic by a traditional chemical method requires a high pressure and a high temperature but generates complex products. Here, sulfur vacancy-rich ZnIn2S4and hydroxy-rich ZnIn2S4were rationally fabricated to realize photocatalytic degradation of PE in an aqueous solution under mild conditions. The results reveal that the optimized photocatalyst could degrade PE into CO2and CO, and PE had a weight loss of 84.5% after reaction for 60 h. Systematic experiments confirm that the synergetic effect of hydroxyl groups and S vacancies contributes to improve the photocatalytic degradation properties of plastic wastes. In-depth investigation illustrates that the active radicals attack (h+and •OH) weak spots (C–H and C–C bonds) of the PE chain to form CO2, which is further selectively photoreduced to CO. Multimodule synergistic tandem catalysis can further improve the utilization value of plastic wastes; for example, product CO2/CO in the plastic degradation process can be converted in situ into HCOOH by coupling with electrocatalytic technology.

To enhance the practical consistency and interpretability of deep learning approaches in power flow (PF) calculation, this letter proposes a virtual graph constrained message passing neural network (VGC-MPNN) for PF analysis, which defines a virtual graph from the mathematical expression of variables to enhance the binding force of power flow equations. Different from the existing methods that simply adopt the form of penalty function to learn the physical constraints, the proposed method empowers the mathematical expression into the feedforward process of the neural network to ensure a consistent solution, which performs internal solution logic instead of fitting the labeled output of the Newton-Raphson solver. Numerical analysis shows that the proposed VGC-MPNN could guarantee the physical consistency of original PFEs and improve the sensitivity of physical non-convergence, while the topological adaptability is also proved by considering network variations.

The electrocatalytic nitrate reduction reaction (NO3RR) has recently emerged as a promising technique for readily converting aqueous nitrate (NO3–) pollutants into valuable ammonia (NH3). It is vital to thoroughly understand the mechanism of the reaction to rationally design and construct advanced electrocatalytic systems that can effectively and selectively drive the NO3RR. There are several natural enzymes that incorporate molybdenum (Mo) and that can activate NO3–. Based on this, a cadmium (Cd) single-atom anchored Mo2TiC2Txelectrocatalyst (referred to as CdSA-Mo2TiC2Tx) through the NO3RR to generate NH3was rationally designed and demonstrated. In an H-type electrolysis cell and at a current density of 42.5 mA cm–2, the electrocatalyst had a Faradaic efficiency of >95% and an impressive NH3yield rate of 48.5 mg h–1cm–2. Moreover, the conversion of NO3–to NH3on the CdSA-Mo2TiC2Txsurface was further revealed by operandoattenuated total reflection Fourier-transform infrared spectroscopy and an electrochemical differential mass spectrometer. The electrocatalyst significantly outperformed Mo2TiC2Txas well as reported state-of-the-art catalysts. Density functional theory calculations revealed that CdSA-Mo2TiC2Txdecreased the ability of the d-porbital to hybridize with NH3* intermediates, thereby decreasing the activation energy of the potential-determining step. This work not only highlights the application prospects of heavy metal single-atom catalysts in the NO3RR but also provides examples of bio-inspired electrocatalysts for the synthesis of NH3.

Atomically layered stacking (ALS) two-dimensional (2D) materials, owing to their superior electrical properties and flexible tunability in addition to the advantages of traditional 2D materials, have garnered widespread attention in recent years. However, when used as photocatalysts for overall water splitting (OWS), many of them face challenges like low Solar-to-Hydrogen (STH) efficiency and insufficient driving force for photoinduced redox reactions. Here, using Al2X3(X = S, Se) as samples, we demonstrate the potential of the Layerwise Replacement Method (LRM) in reducing the materials' bandgap, improving their light absorption performance, and boosting the STH efficiency. As anticipated, the Al2S2Se-t and Al2TeSe2-m monolayers demonstrate STH efficiencies exceeding 20%, surpassing the performance of the majority of reported photocatalysts. Additionally, under light excitation, the OER reaction on the Al2S2Se-t monolayer is exothermic, while on the Al2TeSe2-m monolayer, the OER energy barrier is reduced to 0.431 eV. Notably, on both materials, the HER energy barriers are approaching 0 eV. The improvement of these properties is primarily ascribed to the modulation of the materials' bandgap viathe LRM, and secondarily to the reconstruction of the vertically intrinsic electric field (IEF). Our work not only offers a fresh perspective for the precise manipulation of atomically layered stacked 2D materials but also provides a rational strategy for designing novel and outstanding photocatalysts.

Silicon-based composites are intensively pursued as one of the most promising anode materials for high-energy lithium-ion batteries (LIBs) owing to their ultrahigh theoretical capacity. However, the extended application of Si-based anode is still retarded by challenge of the poor initial coulombic efficiency (ICE), which will lead to the irreversible capacity loss of the full cell in the first cycle. In recent years, significant achievements have been dedicated to reducing irreversible lithium ion consumption to realize high ICE values. In this review, we summarize the main influence factors of ICE, including electrical conductivity, structural stability, and specific surface area. The major focus of this review is to present the recent progress in rational structural design and scalable prelithiation techniques to boosting the ICE, which is of great significance to promoting the practical applications of silicon-based anodes.

At present, the distribution network investment is faced with complicated operation strategies and various new investment elements. It's difficult to evaluate the impact of different investment measures on the final benefits due to the complicated physical modeling and potential coupling correlation, which contributes to that the traditional model-driven decision-making method may not work. This paper proposes a novel deep transfer learning based surrogate-assisted method to address the challenge. The key idea is to develop a surrogate model to replace the input-output correlation between the certain investment measure and its benefits, which can be further reformulated into mixed-integer linear constraints and embedded into the classical investment decision-making model for the target distribution network. As the model is trained through a designed two-stage adaptive transfer learning algorithm, which can extract the similar rules from other distribution networks, the dilemma of insufficient effective samples is alleviated, while a neural network constrained algorithm is designed to linear the surrogate model for simplifying the optimization. Numerical cases prove the effectiveness of the surrogate model on correlation mining and show the superiority of the surrogate-assisted method in achieving the precision investment for target distribution network.

Anode materials for Li-ion batteries (LIBs) utilized in electric vehicles, portable electronics, and other devices are mainly graphite (Gr) and its derivatives. However, the limited energy density of Gr-based anodes promotes the exploration of alternative anode materials such as silicon (Si)-based materials because of their abundance in nature and low cost. Specifically, Si can store 10 times more energy than Gr and also has the potential to enhance the energy density of LIBs. Despite the many advantages of Si-based anodes, such as high theoretical capacity and low price, their widespread use is hindered by two major issues: charge-induced volume expansion and unreliable solid electrolyte interphase (SEI) propagation. In this detailed review, we highlight the key issues, current advances, and prospects in the rational design of Si-based electrodes for practical applications. We first explain the fundamental electrochemistry of Si and the importance of Si-based anodes in LIBs. The excessive volume increase, relatively low charge efficiency, and inadequate areal capacity of Si-based anodes are discussed to identify the barriers in enhancing their performance in LIBs. Subsequently, the use of binders (e.g., linear polymer binders, branched polymer binders, cross-linked polymer binders, and conjugated conductive polymer binders), material-based anode composites (such as carbon and its derivatives, metal oxides, and MXenes), and liquid electrolyte construction techniques are highlighted to overcome the identified barriers. Further, tailoring Si-based materials and reshaping their surfaces and interfaces, including improving binders and electrolytes, are shown to be viable approaches to address their drawbacks, such as volume expansion, low charge efficiency, and poor areal capacity. Finally, we highlight that research and development on Si-based anodes are indispensable for their use in commercial applications.Keywords: Lithium-ion battery; Silicon-based anode; Volume expansion; Solid electrolyte interphase propagation; Binders; Composite anode materials.

Metal sulfides (MSs) can efficiently entrap thiophilic components, such as elemental mercury (Hg0), and realize environmental remediation. However, there is still a critical problem challenging the extensive application of MSs in related areas, i.e., how to self-regulate their water (H2O) resistance without complexing the sorbent preparation procedure. This work for the first time developed an in situ acid-etching method that self-engineered the water affinity of MSs through changing the interfacial interaction between MSs and Hg0/H2O. The introduction of abundant, undercoordinated sulfur onto the sorbent surface was the primary reason accounting for the significantly improved H2O resistance. The high surface coverage of undercoordinated sulfur induced the formation of polysulfur chains (Sx2–) that stabilized Hg0via a bridging bond and repelled H2O, attributed to the favorable electron configurations. These properties made the surface of MSs highly hydrophobic and increased the adsorption selectivity toward Hg0over H2O. The MSs exhibited 100% H2O resistance even in the presence of 20% H2O, which is much higher than the H2O concentration under most practical scenarios. From these perspectives, this work for the first time overcame the detrimental effects of H2O on MSs through a self-regulating way that is scalable and negligibly complexes the sorbent preparation pathway. The highly water-resistant and cost-effective MSs as prepared can serve as efficient Hg0removal from industrial flue gas in the future.

Due to the ever-expanding uses of plastics and shortage of rational waste management, plastic wastes have accumulated in the global environment and reached 6,300 million metric tons. Considering the large amount of CO2and microplastics (MPs) emissions throughout the cradle to grave of plastics, there has been a notable surge in interest regarding the utilization of these carbon-rich wastes as feedstocks for the reclamation of valuable chemicals. Here, state-of-the-art technologies of catalytic plastics conversion driven by heat, light, electricity, and enzyme toward various valuable chemicals are summarized. Selective C–H, C–O, and C–C bond transformations in the plastics conversion are highlighted, which will uncover the reaction pathway and optimize the product selectivity. Innovations in catalysts and catalytic systems for circumventing harsh conditions and constructing low-carbon recycling systems are also discussed. Finally, outlooks for the future development of selective plastics-to-chemicals conversion in contributing toward carbon neutrality and zero plastic wastes are proposed.

ABSTRACTIn the current study, the coalescence of multiple argon bubbles at a horizontal plane in liquid steel with different orifice arrangements was numerically investigated using the volume of the fluid model combined with the continuum surface force model. The numerical simulation of pairwise bubbles coalescence was verified with the experimental observation in the air–water system. The arrangements of equilateral triangle, square and rhombic orifices were considered. The effects of bubble size and orifice structure arrangement on the lateral coalescence of multiple argon bubbles were analysed. Argon bubbles coalesce into a bigger one when the initial bubble distance is less than the critical distance of bubble coalescence. The critical distance for bubble coalescence decreases with the increasing bubble size. When the orifice arrangements were arranged in square, rhombic and equilateral triangles, the coalescence of multiple bubbles become easier. In addition, the mechanisms of bubble coalescence and bounce were discussed.

ABSTRACTNanoindentation technique was adopted to study indentation size effects of physical-mechanical and creep performance with various depths (200–2000 nm) in GH901 utilising sharp and spherical tip. Residual impressions of both indenters with pile-up patterns are discussed. Nanohardness, reduced modulus and elastic recovery rates curves versus maximum displacement of two tips are obtained and nanohardness size effects are discussed with different models for Berkovich tip. The data obtained with spherical indentation are analysed separately by establishing stress–strain diagram. The creep results using Berkovich tip indicate that creep strain rate declines while creep stress exponent first decreases and then increases with the increasing depth; the creep stress exponent (n) values, 2.39–7.35, imply the dominant creep deformation mechanism is dislocation control.

Nanoindentation technique was adopted to study indentation size effects of physical-mechanical and creep performance with various depths (200–2000?nm) in GH901 utilising sharp and spherical tip. Residual impressions of both indenters with pile-up patterns are discussed. Nanohardness, reduced modulus and elastic recovery rates curves versus maximum displacement of two tips are obtained and nanohardness size effects are discussed with different models for Berkovich tip. The data obtained with spherical indentation are analysed separately by establishing stress–strain diagram. The creep results using Berkovich tip indicate that creep strain rate declines while creep stress exponent first decreases and then increases with the increasing depth; the creep stress exponent (n) values, 2.39–7.35, imply the dominant creep deformation mechanism is dislocation control.

In the present paper, the fast pulse loading at high temperature (FPLHT) was conducted on Stellite 6 coatings with and without the interlayer, and exerted great effects on the microstructure, distribution of elements, and mechanical property of the two coatings. The evident crack occurred in the coating without the interlayer in the 400-cycle condition, which was induced by the diffusion of the Fe, the formation of reprecipitation carbides, the coarsening grain, and the formation of Laves phase. The mechanisms of the formation of the Fe–Co phase was revealed by transmission electron microscopy, binary phase diagram, and diagrammatic sketch. To specify the functions of fast pulse loading on the diffusion of Fe and the transformation of the microstructure, the Stellite 6 coating without the interlayer aged for 2880 h was employed for comparison with that in the 400-cycle condition. The softened phenomena occurred in the Stellite 6 coating without the interlayer and the Stellite 6 with the interlayer, the former resulting from the diffusion of Fe and the latter resulting from the diffusion of Ni. The Stellite 6 coating with the interlayer exhibited no crack during the FPLHT, and the disparity of deformation between Stellite 6 coatings with and without the interlayer in the 100-cycle condition was less than that in the 400-cycle condition owing to the difference of diffusion speed between Fe and Ni.

The formation of defects over a crystal surface could significantly improve the elemental mercury (Hg0) removal performance of pyrite (FeS2). Herein, the role of surface defects in Hg0immobilization over FeS2(100) was investigated by adopting quantum chemistry. The contribution of surface defects to mercury immobilization over pyrite was mainly manifested in facilitating Hg0adsorption. Compared with a perfect pyrite surface, a defective pyrite surface exhibited a higher affinity to Hg0, whose adsorption energy was up to −53.03 kJ/mol. In addition, surface defects could induce the dissociation of HCl, a common component in coal combustion flue gas, which provided active chlorine (Cl*) for subsequent Hg0oxidation. The Langmuir–Hinshelwood mechanism is responsible for Hg0oxidation over the defective FeS2(100) surface. First, Hg0and HCl were simultaneously adsorbed over the defective surface, and the presence of defects could promote Hg0adsorption and HCl dissociation. Subsequently, adsorbed Hg0would combine with the adjacent active Cl* site, whose energy barrier was 92.60 kJ/mol. Eventually, HgCl could spontaneously react with another Cl* site to form a HgCl2molecule. Thus, the formation of a HgCl intermediate was the rate-determining step for Hg0oxidation over a defective pyrite surface. This work reveals the role of defects in Hg0immobilization over a pyrite surface, which provides a scientific basis for the design and modification of natural mineral sulfide sorbents.

Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run. In addition to the incomplete consideration of influencing factors, the prediction time scale of existing studies is rough. Therefore, this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network (ATENet) based on structural health monitoring (SHM) data. An autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions, and the recurrent neural network is applied to understanding the temporal correlation from the time series. Then, the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 h. As a case study, the proposed model is formulated on the SHM data collected from a representative underwater shield tunnel. The robustness study is carried out to verify the reliability and the prediction capability of the proposed model. Finally, the ATENet model is compared with some typical models, and the results indicate that it has the best performance. ATENet model is of great value to predict the real-time evolution trend of tunnel structure.

AimsEccentric reductions may become concentric through femoral head ‘docking’ (FHD) following closed reduction (CR) for developmental dysplasia of the hip (DDH). However, changes regarding position and morphology through FHD are not well understood. We aimed to assess these changes using serial MRI.MethodsWe reviewed 103 patients with DDH successfully treated by CR and spica casting in a single institution between January 2016 and December 2020. MRI was routinely performed immediately after CR and at the end of each cast. Using MRI, we described the labrum-acetabular cartilage complex (LACC) morphology, and measured the femoral head to triradiate cartilage distance (FTD) on the midcoronal section. A total of 13 hips with initial complete reduction (i.e. FTD < 1 mm) and ten hips with incomplete MRI follow-up were excluded. A total of 86 patients (92 hips) with a FTD > 1 mm were included in the analysis.ResultsAt the end of the first cast period, 73 hips (79.3%) had a FTD < 1 mm. Multiple regression analysis showed that FTD (p = 0.011) and immobilization duration (p = 0.028) were associated with complete reduction. At the end of the second cast period, all 92 hips achieved complete reduction. The LACC on initial MRI was inverted in 69 hips (75.0%), partly inverted in 16 hips (17.4%), and everted in seven hips (7.6%). The LACC became everted-congruent in 45 hips (48.9%) and 92 hips (100%) at the end of the first and second cast period, respectively. However, a residual inverted labrum was present in 50/85 hips (58.8%) with an initial inverted or partly inverted LACC.ConclusionAn eccentric reduction can become concentric after complete reduction and LACC remodelling following CR for DDH. Varying immobilization durations were required for achieving complete reduction. A residual inverted labrum was present in more than half of all hips after LACC remodelling.Cite this article: Bone Joint J2023;105-B(2):140–147.

In recent years, single-atom catalysts (SACs) have attracted tremendous research interests in energy conversion and storage due to their maximum atom utilization efficiency, remarkable catalytic activity, and superior reaction selectivity. p-block elements demonstrate specific advantages as the active single-atom sites in SACs owing to their unique physical and chemical properties. In this review, we comprehensively summarize recent progress of p-block SACs, including different types of support materials for p-block SACs and their electrocatalytic applications, such as electrocatalytic oxygen reduction, electrocatalytic hydrogen evolution, and electrocatalytic CO2reduction. Current challenges and future prospects for the development of p-block SACs are also provided.

Orthopedic implants account for 99% of orthopedic surgeries, however, orthopedic implant-related infection is one of the most serious complications owing to the potential for limb-threatening sequelae and mortality. Current antibiotic treatments still lack the capacity to target bone infection sites, thereby resulting in unsatisfactory therapeutic effects. Here, the bone infection site targeting efficacy of D6 and UBI29-41peptides was investigated, and bone-and-bacteria dual-targeted nanoparticles (NPs) with D6 and UBI29-41peptides were first fabricated to target bone infection site and control the release of vancomycin in bone infection site. The results of this study demonstrated that the bone-and-bacteria dual-targeted mesoporous silica NPs exhibit excellent bone and bacteria targeting efficacy, excellent biocompatibility and effective antibacterial properties in vitro. Furthermore, in a rat model of orthopedic implant-related infection with methicillin-resistant Staphylococcus aureus, the growth of bacteria was evidently inhibited without cytotoxicity, thus realizing the early treatment of implant-related infection. Hence, the bone-and-bacteria dual-targeted molecule-modified NPs may target bacteria-infected bone sites and act as ideal candidates for the therapy of orthopedic implant-related infections.

An automatic chamber (AC) is an alternative method to the eddy covariance (EC) technique for estimating of CO2fluxes. However, few direct comparisons between two techniques have been performed on nature alpine meadows. In order to determine diurnal pattern of net ecosystem exchange (NEE) of CO2in summer on alpine meadows, and test the NEE measurement performance of AC under site conditions, we conducted simultaneous measurements of NEE using both EC and AC methods. The NEEACcan be estimated with the information from the initial slope of the time series of CO2mixing ratio. We found that the application of linear regression, when photosynthesis processes were considered, could lead to serious deviations, even if closure times were short. The linear regression, however, was adequate for estimating CO2fluxes when photosynthesis processes were not involved in, at least for short chamber closing times. Using appropriate fitting method, the fluxes of AC were relatively close to that of EC, but presented a daytime underestimation and nighttime overestimation. With the subsequent friction velocity (u*) filtering (

Electromagnetic-wave absorbing (EMA) materials that have efficient absorption performances, great mechanical properties and chemical stability are rare and yet essential for communication security and protection. Herein, flaky interstitial-carbon-doped FeCoNiCu high entropy alloys (HEAs) as novel EMA materials were successfully prepared by high-energy ball-milling method. Interstitial-carbon doping as a modulating approach impacted the phase forming, morphology and electromagnetic properties of FeCoNiCu HEAs. Impedance matching was significantly optimized via tuning interstitial carbon contents. The carbon-doped FeCoNiCu HEAs with appropriate carbon contents delivered superior EMA performance compared with other HEAs EMA materials. Strong reflection loss as low as -61.1 dB in the Ku band, broad effective absorption bandwidth of 5.1 GHz was achieved for FeCoNiCuC 0.04. Moreover, the carbon-doped FeCoNiCu HEAs exhibited excellent mechanical hardness and chemical stability. This work not only suggests that interstitial-carbon doping is an available approach to tuning electromagnetic properties of HEAs, but also presents carbon-doped FeCoNiCu HEAs as promising EMA materials for civilian and military due to the efficient absorption, broad bandwidth, great durability and stability. [Display omitted] [ABSTRACT FROM AUTHOR]

Atmospheric mercury (Hg) pollution has attracted global attention as a result of its great harm to the ecosystem. By virtue of abundant surface Hg-philic sulfur sites, metal sulfides have been regarded as a promising Hg adsorbent. The Hg adsorption performance of metal sulfides is mainly determined by the distribution of surface active sites; thus, it is crucial to understand the interactions between the Hg atom and different surface sites. In this review, the immobilization mechanism of the Hg atom over metal sulfides was systematically summarized. First, the roles of surface active sites, i.e., metal and sulfur sites, played in Hg0adsorption were systematically elaborated, to explain the excellent performance of metal sulfides. Second, the improvement on the Hg0adsorption ability of engineered metal sulfides was attributed to the reasonable regulation of surface unsaturation and the introduction of alien active sites. Eventually, the effects of flue gas components on mineral sulfides were illuminated from the perspective of the introduction/consumption of surface active sites. The objective of this review was to interpret the detailed Hg0immobilization mechanism and to provide guidance for developing metal-sulfide-based sorbents, and the future prospects and challenges related to the Hg0adsorption mechanism by metal sulfides were discussed.

Abstract: Developing efficient materials and techniques to remove elemental mercury (Hg0) from anthropogenic sources is the key to fulfilling the Minamata Convention and freeing us from a Hg-polluted world. Functionalized substrate-supported metal chalcogenides have been found to be a group of effective sorbents for Hg0capture from industrial sources in the past decade. However, there is still no work bridging previous achievements and future developments in related areas. This review thus categorized supported metal chalcogenides into six groups and systematically explored the preparation methods, properties, performance, and design logics of supported metal chalcogenides for Hg0capture from anthropogenic sources. The supporting effects increase the exposure of metal chalcogenides, promote the diffusion of Hg0, combine different active components, improve the resistance to flue gas interference, and serve the recycling of both spent sorbents and mercury resources. Based on systematical discussions, future developmental prospects of supported metal chalcogenides are proposed, aiming to further refine the design logic of materials, develop more effective synthesis methods, and standardize the performance evaluation systems. The main purpose of this work is to guide future research on supported metal chalcogenides for Hg0removal from anthropogenic sources in the post-Minamata era. Graphical Abstract:

Lined rock caverns (LRC) to store high-pressure hydrogen is a creative way to increase the utilization of renewable energy to satisfy enormous energy demands for society. The stability and permeability of LRC will determine the success or failure of hydrogen storage. Therefore, numerical simulations can be used to evaluate the damage to supporting structures and the stability of surrounding rock mass. However, the birth of new materials and new structures has changed the previous structural form of LRC for underground energy storage. This paper provided reinforced ultra-high performance concrete (UHPC) as a supporting structure in LRC. Considering the first hydrogen storage LRC project in China, the mechanical behavior of UHPC is introduced in detail, and a quasi-three-dimensional finite element model is given. The numerical results of this model were evaluated considering the corresponding specifications for underground engineering, and the allowable upper limit pressure of LRC was suggested. To construct shallow-buried hydrogen storage LRC, reinforced UHPC could be a reliable support method and will be constructed.

Hierarchical self-assembly with long-range order above centimeters widely exists in nature. Mimicking similar structures to promote reaction kinetics of electrochemical energy devices is of immense interest, yet remains challenging. Here, we report a bottom-up self-assembly approach to constructing ordered mesoporous nanofibers with a structure resembling vascular bundles via electrospinning. The synthesis involves self-assembling polystyrene (PS) homopolymer, amphiphilic diblock copolymer, and precursors into supramolecular micelles. Elongational dynamics of viscoelastic micelle solution together with fast solvent evaporation during electrospinning cause simultaneous close packing and uniaxial stretching of micelles, consequently producing polymer nanofibers consisting of oriented micelles. The method is versatile for the fabrication of large-scale ordered mesoporous nanofibers with adjustable pore diameter and various compositions such as carbon, SiO2, TiO2and WO3. The aligned longitudinal mesopores connected side-by-side by tiny pores offer highly exposed active sites and expedite electron/ion transport. The assembled electrodes deliver outstanding performance for lithium metal batteries.This work reports a bottom-up self-assembly strategy to synthesize single-oriented ordered mesoporous nanofibers with structure mimicking vascular bundles based on electrospinning supramolecular micelle solution, showing great potential in electrochemical energy applications.

Southern corn rust (SCR) is a destructive maize disease caused by Puccinia polysoraUnderw. To investigate the mechanism of SCR resistance in maize, a highly resistant inbred line, L119A, and a highly susceptible line, Lx9801, were subjected to gene mapping and transcriptome analysis. Bulked-segregant analysis coupled with whole-genome sequencing revealed several quantitative trait loci (QTL) on chromosomes 1, 6, 8, and 10. A set of 25 genes, including two coiled-coil nucleotide-binding site leucine-rich repeat (CC-NBS-LRR) genes, were identified as candidate genes for a major-effect QTL on chromosome 10. To investigate the mechanism of SCR resistance in L119A, RNA-seq of P. polysora-inoculated and non-inoculated plants of L119A and Lx9801 was performed. Unexpectedly, the number of differentially expressed genes in inoculated versus non-inoculated L119A plants was about 10 times that of Lx9801, with only 29 common genes identified in both lines, suggesting extensive gene expression changes in the highly resistant but not in the susceptible line. Based on the transcriptome analysis, one of the CC-NBS-LRR candidate genes was confirmed to be upregulated in L119A relative to Lx9801 independently of P. polysorainoculation. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses indicated that transcription factors, as well as genes involved in defense responses and metabolic processes, were dominantly enriched, with the phenylpropanoid biosynthesis pathway most specifically activated. Consistently, accumulation of phenylpropanoid-derived lignin, especially S lignin, was drastically increased in L119A after P. polysorainoculation, but remained unchanged in Lx9801, suggesting a critical role of lignin in SCR resistance. A regulatory network of defense activation and metabolic change in SCR-resistant maize upon P. polysorainfection is described.

With the rapid development of clean energy and the deepening of the interaction between supply and demand, power grid investment upgrading measures involve many new elements, such as clean energy installation and distribution automation. Traditional investment decision-making models are difficult to establish and solve. In view of this, this paper analyzes the investment benefit mechanism directly from the perspective of investment input–output relationship, and designs an interactive auxiliary investment decision-making system based on correlation rule mining. The system constructs an investment benefit mapping model from power grid investment measures to benefit output by means of deep transfer learning, and provides three objective functions, which consider the optimal economy, performance improvement and comprehensive index optimization, thus assisting decision makers to formulate investment alternatives according to different investment needs. A case demonstrates the decision-making process based on an actual power grid, and verifies the practicability and effectiveness of the system.

Efficient and stable electromagnetic-wave (EMW) absorption materials have attracted great attention in the field of reducing microwave pollution. Herein, FeCoNiCuTixhigh-entropy alloys (HEAs) as electromagnetic-wave absorbing materials were prepared by a high-energy ball-milling method. The as-milled HEA powders presented a flaky shape with a high aspect ratio. Impedance matching was efficiently optimized by severe lattice distortion, which was caused by Ti incorporation. The introduced plentiful defects in FeCoNiCuTixHEAs provided abundant polarization sites for dielectric loss. By tuning Ti contents, FeCoNiCuTi0.2HEAs delivered excellent EMW absorption performances. The maximal reflection loss (RLmax) values reached −47.8 dB at 10.86 GHz as thin as 2.16 mm, and the widest bandwidth was 4.76 GHz (5.97–10.73 GHz). Furthermore, the introduction of Ti enhanced corrosion resistance via increasing the charge transfer resistance of a passivated film. Those characteristics of FeCoNiCuTixHEAs made these materials a practical gigahertz-range EMW absorber. Additionally, our findings provided a facile and tunable strategy for designing EMW absorbing materials, which was aimed at lightweight, highly efficient absorption, and resistance to harsh environments.

Metal selenides have been demonstrated as promising Hg0remediators, while their inadequate adsorption rate primarily impedes their application feasibility. Based on the critical role of coordinatively unsaturated selenide ligands in immobilizing Hg0, this work proposed a novel strategy to enhance the Hg0adsorption rate of metal selenides by magnitudes by purposefully adjusting the selenide saturation. Copper iron diselenide (CuFeSe2), in which the surface reconstruction tended to occur at ambient temperature, was adopted as the concentrator of unsaturated selenides. The adsorption rate of CuFeSe2reached as high as 900.71 μg·g–1·min–1, far exceeding those of the previously reported metal selenides by at least 1 magnitude. The excellent resistance of CuFeSe2to flue gas interference and temperature fluctuation warrants its applicability in real-world conditions. The theoretical investigations and mechanistic interpretations based on density functional theory (DFT) calculation further confirmed the indispensable role of unsaturated selenides in Hg0adsorption. This work aims not only to develop a Hg0remediator with extensive applicability in coal combustion flue gas but also to take a step toward the rational design of selenide-based sorbents for diverse environmental remediation by the facile surface functionalization of coordinatively adjustable ligands.

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In this paper a novel path planning algorithm based on improved ant colony optimization is proposed for dual-robot cooperative welding. We seek to provide a low-cost, fast and effective solution for such problems. The optimization model of dual-robot collaborative welding is established based on actual welding process constraints and shortest welding path target. Then, a dynamic transfer strategy and information update strategy are proposed to improve the ant colony algorithm (ACO+) based on this model. Finally, the proposed ACO+ algorithm is implemented to solve the dual-robot path planning problem in the welding task of a large complex component. The simulation demonstrates that the proposed method has faster convergence rate and shorter welding path compared with the unmodified ant colony algorithm. Particularly, this algorithm is suitable for path planning of dual-robot cooperative welding for symmetrical complex component.

We develop a fractional-degree expectation dependence which is the generalization of the first-degree and second-degree expectation dependence. The motivation for introducing such a dependence notion is to conform with the preferences of decision makers who are mostly risk averse but would be risk seeking at some wealth levels. We investigate some tractable equivalent properties for this new dependence notion, and explore its properties, including the invariance under increasing and concave transformations, and the invariance under convolution. We also extend our results to a combined fractional-degree expectation dependence notion including ε-almost first-degree expectation dependence. Two applications on portfolio diversification problem and optimal investment in the presence of a background risk illustrate the usefulness of the approaches proposed in the present paper.