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Abstract δ-MnO2 is an important component of environmental minerals and is among the strongest sorbents and oxidants. The crystalline morphology of δ-MnO2 is one of the key factors affecting its reactivity. In this work, δ-MnO2 was initially synthesized and placed in an acidic environment to react with Mn2+ and undergo a crystalline transformation. During the transformation of crystalline δ-MnO2, kinetic sampling was conducted, followed by analyses of the structures and morphologies of the samples. The results showed that at pH 2.5 and 4, δ-MnO2 nanoflakes spontaneously self-assembled into nanoribbons via edge-to-edge assembly in the initial stage. Subsequently, these nanoribbons attached to each other to form primary nanorods through a face-to-face assembly along the c-axis. These primary nanorods then assembled along the (001) planes and lateral surfaces, achieving further growth and thickening. Since a lower pH is more favorable for the formation of vacancies in δ-MnO2, δ-MnO2 can rapidly adsorb Mn2+ directly onto the vacancies to form tunnel walls. At the same time, the rapid formation of the tunnel walls leads to a quick establishment of hydrogen bonding between adjacent nanoribbons, enabling the assembly of these nanoribbons into primary nanorods. Therefore, in a solution with the same concentration of Mn2+, the structure transformation and morphology evolution of δ-MnO2 to α-MnO2 occur faster at pH 2.5 than at pH 4. These findings provide insights into the mechanism for crystal growth from layer-based to tunnel-based nanorods and methods for efficient and controlled syntheses of nanomaterials.

A novel coated urea (MVCU) was prepared, and its application effect was verified by field trials of oilseed rape in three main cultivation areas. Meanwhile, the nutrient release and coating layer changes of MVCU in static water at 25 °C and different soils were systematically evaluated. MVCU showed a long nutrient release time under static water (77 days) and soil incubation (140 days) conditions due to the slow degradation of the coating layer in MVCU, and its nitrogen release coincided well with oilseed rape nitrogen demand. The above results were further confirmed by FT-IR spectra and SEM analysis. Compared with conventional urea (U), the field trials of MVCU in the three main cultivation areas showed high nitrogen utilization efficiency and yield advantages in oilseed rape. The field trials results indicated that the MVCU significantly enhanced the aboveground dry matter (28.7%), the seed nitrogen concentration (9.5%) and aboveground nitrogen accumulation (42.5%) of oilseed rape at the mature stage as compared to that of the U. The oilseed rape yield enhanced by 932.8 kg/hm2, the average growth rate was 65.1%, and nitrogen utilization efficiency increased by 21.2%. In short, MVCU has the advantages of excellent slow-release performance and strong applicability, and its yield-increasing effect on oilseed rape could reach or even be better than that of traditional fertilization.

Magnetic seeded filtration (MSF) is a multidimensional solid–liquid separation process capable of fractionating a multimaterial suspension based on particle size and surface properties. It relies on the selective hetero-agglomeration between nonmagnetic target and magnetic seed particles followed by a magnetic separation. Experimental investigations of multimaterial suspensions are challenging and limited. Therefore, a Monte Carlo model for the simulation of hetero-agglomeration processes is developed, validated, and compared to a discrete population balance model. The numerical investigation of both charge-based and hydrophobicity-based separation in an 11-material system, using synthetic agglomeration kernels based on real-world observations, yields results consistent with prior experimental studies and expectations: Although a multidimensional separation is indeed possible, unwanted hetero-agglomeration between target particles results in a reduced selectivity. This effect is more pronounced when separation is based on a dissimilarity rather than a similarity in the separation criterion and emphasizes the advantages of hydrophobicity-based systems. For the first time, 2D grade efficiency functions T(φ,d) are presented for MSF. However, it is shown that these functions strongly depend on the initial state of the suspension, which casts doubt on their general definition for agglomeration-based processes and underlines the importance of a simulation tool like the developed MC model.

Crystallization is an important process that affects the properties of final products and is essential in nearly all chemical processing industries. In recent years, ultrasonic technology has received widespread attention due to its ability to enhance crystallization yield, improve crystal morphology and shape, and regulate the particle size and distribution of crystal products. It holds promising prospects for industrial crystallization. In this work, the ultrasonic cavitation effect and ultrasonic crystallization mechanism are described, and the influence of ultrasound on the crystallization effect of products is analysed and discussed. In addition, the application status of ultrasonic reactors and ultrasonic crystallization processes is introduced in detail, and the change trend from laboratory to industrialization is analyzed. Finally, the challenges and opportunities facing the industrialization of ultrasonic crystallization in future developments are discussed. The purpose of this work is to make the selective promotion or inhibition of ultrasound more helpful for industrial crystallization.

Abstract Currently three major problems seriously limit the practical application of cancer photodynamic therapy (PDT): (i) the hypoxic tumor microenvironment (TME); (ii) low generation efficiency of toxic reactive oxygen species (ROS) in aggregates and (iii) shallow tissue penetration depth of excitation light. Very limited approaches are available for addressing all the above three problems with a single design. Herein, a rational “three birds with one stone” molecular and nanoengineering strategy is demonstrated: a photodynamic nanoplatform U‐Ir@PAA‐ABS based on the covalent combination of lanthanide‐doped upconversion nanoparticles (UCNPs) and an AIE‐active dinuclear Ir(III) complex provides a low oxygen concentration‐dependent type‐I photochemical process upon 980 nm irradiation by Föster resonance energy transfer (FRET). U‐Ir@PAA‐ABS targets mitochondria and has excellent phototoxicity even in severe hypoxia environments upon 980 nm irradiation, inducing a dual‐mode cell death mechanism by apoptosis and ferroptosis. Taken together, the in vitro and in vivo results demonstrate a successful strategy for improving the efficacy of PDT against hypoxic tumors.

Two‐dimensional (2D) intrinsic vacancy semiconductors possess great application prospects in optoelectronics fields, originating from abundant intrinsic vacancy structures and exceptional physical properties. Understanding the structure‐activity relationship between vacancies and physical/photoelectric properties is significant for building advanced photoelectric devices. However, limited by the increasing instability of 2D structure induced by intrinsic vacancies, the precise synthesis of 2D intrinsic vacancy semiconductors faces great challenges. Here, high‐quality 2D intrinsic vacancy semiconductor α‐Ga2Se3 is synthesized via low‐pressure and space‐confined physical vapor deposition. The vacancy structures induce an intermediate energy level and a super‐bandgap photoluminescence (PL) peak, which are verified by temperature‐dependent PL spectra. Furthermore, the vacancy energy level favorably endows 2D α‐Ga2Se3‐based photodetector with a UV‐vis‐NIR super‐bandgap photoresponse, excellent ultraviolet detection ability (photoresponsivity (41.86 A W−1), epitaxial quantum efficiency (1.42 × 104%), and detectivity (6.69 × 1011 Jones) @365 nm), fast photoresponse (20 ms), and superior photocurrent‐light power‐fitting factors. In addition, 2D α‐Ga2Se3‐based phototransistor displays p‐type transport behavior with a responsivity of 105 A W−1@365 nm at a gate voltage of −40 V. This work suggests a bright future of 2D intrinsic vacancy semiconductors in tailoring physical properties and enabling sophisticated device functionality.

Fluorine-19 (19F) is a nucleus of great importance in the field of Nuclear Magnetic Resonance (NMR) spectroscopy due to its high receptivity and wide chemical shift dispersion. 19F NMR plays crucial roles in both organic synthesis and biomedicine. Herein, a machine learning-based comprehensive 19F NMR chemical shift prediction model was established based on the experimental 19F NMR dataset from the book by Dolbier and the open NMR database nmrshiftdb2. Fluorine radical SMILES (Fr-SMILES) that reflected the fluorine chemical equivalence, was designed as the representation of fluorine in the molecule. Model trained with the graph convolution network (GCN) algorithm gave a low mean absolute error (MAE) of 3.636 ppm on the testing set. This model exhibits broad applicability and can effectively predict 19F NMR shifts for a wide range of organic fluorine molecules. We believe that the current work will provide a powerful tool for not only predicting 19F NMR shifts but also aiding in the analysis and identification of these shifts in diverse organic fluorine compounds. An online prediction platform was constructed based on the current model, which can be found at https://fluobase.cstspace.cn/fnmr.

Ultrasound enhanced evaporating crystallization has been proposed to solve the problems of low crystallization yield and uneven particle size in the evaporating crystallization process of ammonium sulfate solution at atmospheric pressure. The effects of key operating parameters, including the ultrasound power, stirring speed, pH value, and ultrasound time, on the yield of ammonium sulfate product and the duration of solid–liquid transformation time are studied. The results show that the ultrasound crystallization can increase the ammonium sulfate yield by 52.9 %, reduce the solid–liquid transformation time of ammonium sulfate by 10 %, and obtain ammonium sulfate products with higher crystallinity and more uniform particle size. Ultrasound promotes the crystallization of ammonium sulfate by enhancing the transfer of heat in the solution and reducing the supersolubility of the ammonium sulfate solution from 937.5 g/L to 833.33 g/L. This study provides experimental justification for the use of ultrasound in atmospheric evaporative crystallization.

With the development of IoT technology, central cloud servers and edge-computing servers together form a cloud–edge communication network to meet the increasing demand for computing tasks. The data transmitted in this network is of high value, so the ability to quickly and accurately predict the traffic load of each link becomes critical to ensuring the security and stable operation of the network. In order to effectively counter the potential threat of flood attacks on network stability, we combine the Bi-directional Gated Recurrent Unit (BiGRU) model with the Dung Beetle Optimizer (DBO) algorithm to design a DBO-BiGRU short-term traffic load prediction model. Experimental validation on a public dataset shows that the proposed model has better prediction accuracy and fit than the mainstream models of RNN, LSTM, and TCN.

The purpose of this paper is to analyze the profitability of wind energy and demand response (DR) resources participating in the energy and frequency regulation markets. Since wind power producers (WPPs) must reduce their output to provide up-regulation and DR aggregators (DRAs) have to purchase additional power to facilitate down-regulation, this may result in revenue loss. If WPPs coordinate with DRAs, these two costs could be reduced. Thus, it would be profitable for WPPs and DRAs to form a coalition to participate in the regulation market. To better utilize the frequency response characteristics of wind and DR resources, this paper proposes a cooperation scheme to optimize the bidding strategy of the coalition. Furthermore, cooperative game theory methods, including Nucleolus- and Shapley-value-based models, are employed to fairly allocate additional benefits among WPPs and DRAs. The uncertainties associated with wind power and the behavior of DR customers are modeled through stochastic programming. In the optimization process, the decision-maker’s attitude toward risks is considered using conditional value at risk (CVaR). Case studies demonstrate that the proposed bidding strategy can improve the performance of the coalition and lead to higher benefits for both WPPs and DRAs. Specifically, the expected revenue of the coordinated strategies increased by 12.1% compared to that of uncoordinated strategies.

Submerged plants and related disturbances can affect both the phosphorus (P) release and the microbial communities in sediments. In this study, a sediment resuspension system was constructed, and P variability characteristics influenced by Vallisneria natans (V. natans) and the response mechanism of the microbial community were studied. The results indicated that the total phosphorus (TP) content increased from 678.875 to 1019.133 mg/kg and from 1126.017 to 1280.679 mg/kg in sediments and suspended solids (SSs) during the sediment resuspension process, respectively. Organic P (OP) increased by 127.344 mg/kg and 302.448 mg/kg in sediments and SSs after the disturbance, respectively. The microbial communities in the sediments and the leaves of V. natans had higher Chao values after the disturbance, while Shannon values decreased after the disturbance compared to the control in SSs. Proteobacteria had the highest abundance with the value of 51.1% after the disturbance in the sediments and SSs, and the abundance values of Proteobacteria in rhizomes and leaves of V. natans could reach 73.2% on average. Chloroflexi, Acidobacteria, and Firmicutes were also the main phyla in the sediment resuspension system. Sodium hydroxide extractable P (NaOH-P) in sediments could reduce the bioavailability of this P fraction under disturbance conditions. The decrease in the abundance of Bacteroidetes and Nitrospirae indicated that they were more sensitive to the disturbance, and the rotational speed changed the survival conditions for the Bacteroidetes and Nitrospirae. The response mechanism of microbial community during the sediment resuspension process could reflect the influence of the microbial community on the changing characteristics of P and could provide a theoretical foundation for P control at the micro level.

Pre-trained language models perform well in various natural language processing tasks. However, their large number of parameters poses significant challenges for edge devices with limited resources, greatly limiting their application in practical deployment. This paper introduces a simple and efficient method called Autocorrelation Matrix Knowledge Distillation (AMKD), aimed at improving the performance of smaller BERT models for specific tasks and making them more applicable in practical deployment scenarios. The AMKD method effectively captures the relationships between features using the autocorrelation matrix, enabling the student model to learn not only the performance of individual features from the teacher model but also the correlations among these features. Additionally, it addresses the issue of dimensional mismatch between the hidden states of the student and teacher models. Even in cases where the dimensions are smaller, AMKD retains the essential features from the teacher model, thereby minimizing information loss. Experimental results demonstrate that BERTTINY-AMKD outperforms traditional distillation methods and baseline models, achieving an average score of 83.6% on GLUE tasks. This represents a 4.1% improvement over BERTTINY-KD and exceeds the performance of BERT4-PKD and DistilBERT4 by 2.6% and 3.9%, respectively. Moreover, despite having only 13.3% of the parameters of BERTBASE, the BERTTINY-AMKD model retains over 96.3% of the performance of the teacher model, BERTBASE.

With the increasing development of productivity, new materials that allow for the efficient use of energy are slowly becoming a sought-after goal, as well as a challenge that is currently being faced. For this reason, we have made aerogels as the target of our research and prepared different series (CLPI (1–5)) of cross-linked polyimide aerogels by mixing and cross-linking the heat-insulating cross-linking agent 1,3,5-tris(4-aminobenzylamino)benzene (TAB) with polyamic acid solution. We created a three-dimensional spatial organization by using vacuum freeze-drying and programmed high-temperature drying, then controlled the concentration of the polyamidate solution to investigate the concentration and TAB’s influence on aerogel-related properties. Among them, the shrinkage is reduced from 40% in CLPI-1 to 28% in CLPI-5, and it also shows excellent mechanical characteristics, the highest compression strength (CLPI-5) reaches 0.81 MPa and specific modulus reaches 41.95 KN m/Kg. In addition, adding TAB improves the aerogel thermal resistance, T5 in N2 from PI-2 519 °C to CLPI-2 556 °C. The three-dimensional network-type structure of the aerogel shows an excellent thermal insulation effect, where the thermal conductivity can be as low as 24.4 mWm−1 K−1. Compared with some protective materials, cross-linked polyimide aerogel presents better flame-retardant properties, greatly improving the scope of its application in the industrial protection.

The advancements in the field of micro-robots for drug delivery systems have garnered considerable attention. In contrast to traditional drug delivery systems, which are dependent on blood circulation to reach their target, these engineered micro/nano robots possess the unique ability to navigate autonomously, thereby enabling the delivery of drugs to otherwise inaccessible regions. Precise drug delivery systems can improve the effectiveness and safety of synthetic lethality strategies, which are used for targeted therapy of solid tumors. MYC-overexpressing tumors show sensitivity to CDK1 inhibition. This study delves into the potential of Ro-3306 loaded magnetic-driven hydrogel micro-robots in the treatment of MYC-dependent osteosarcoma. Ro-3306, a specific inhibitor of CDK1, has been demonstrated to suppress tumor growth across various types of cancer. We have designed and fabricated this micro-robot, capable of delivering Ro-3306 precisely to tumor cells under the influence of a magnetic field, and evaluated its chemosensitizing effects, thereby augmenting the therapeutic efficacy and introducing a novel possibility for osteosarcoma treatment. The clinical translation of this method necessitates further investigation and validation. In summary, the Ro-3306-loaded magnetic-driven hydrogel micro-robots present a novel strategy for enhancing the chemosensitivity of MYC-dependent osteosarcoma, paving the way for new possibilities in future clinical applications.

The CuMgAl-x catalysts derived from hydrotalcite precursors with different Mg/Al molar ratios were synthesized and applied to CO2 hydrogenation to methanol reaction. In this study, the effects of Mg/Al molar ratio on the structure and surface properties of CuMgAl-x catalysts were investigated by XRD, N2 adsorption-desorption, SEM, TEM, H2-TPR, CO2-TPD, XPS, and in situ DRIFTS characterization methods. The results showed that an appropriate Mg/Al molar ratio can enhance the Cu-MgO interaction, increasing the basic sites and obtaining suitable acid sites. The dispersion of active Cu on the CuMgAl-x catalysts can be improved by strong Cu-MgO interaction, which enhances the adsorption capacity of CO2 and makes H2 activation easier, accelerates the conversion of intermediate species CO3* and HCO3*to HCOO*, and facilitates further conversion to CH3O* and CH3OH. The strong interaction between Cu and MgO was conducive to the formation of Cu+, which can inhibit the desorption of CO in the reverse water gas shift reaction. The CuMgAl-3 catalyst showed the highest CO2 Conversion rate (14.3%), methanol selectivity (94.5%), and STY of methanol (419.3 g⋅kgcat.−1⋅h−1) at 240°C and 2.5 MPa. The results obtained in this paper can provide a new idea for the design of high-performance catalysts for CO2 hydrogenation to methanol.

Occupational exposure to herbicides is an important factor for the development of Parkinson's disease (PD). Atrazine (ATR) and simazine (SIM) are dopamine neurotoxins that can cause PD-like symptoms, but the mechanism is unknown. We analyzed the differentially expressed genes (DEGs) in the substantia nigra of PD patients and controls by using the PD dataset GSE20333 from the Gene Expression Omnibus (GEO) database. The potential intersectional targets of ATR and SIM combined exposure induced PD were screened by network pharmacology. The intersection targets were used for enrichment analysis and to construct protein–protein interaction networks. The binding potentials between hub targets and triazine herbicides were validated using molecular docking, and the cellular exposure models were also constructed to validate the screened results. We identified 934 genes that showed differential expression between individuals with PD and healthy controls. Using network pharmacology, we further refined our search to 151 targets of herbicides known to contribute to PD, and identified eight potential core targets: CDK2, CYCS, FOS, LCK, NTRK2, PDGFRB, PGR, and VEGFA. Finally, molecular docking and molecular biological detection results showed that ATR and SIM each exhibit strong binding affinity to CDK2, CYCS and PGR, both ATR and SIM exposure alone or in combination can affect the expression of core targets. Our study indicates that combined exposure to ATR and SIM may play a critical role in the development of PD and offers a new perspective on occupational prevention and treatment strategies.

Both phloretin and phlorizin (phloretin 2′-O-glucoside) extracted from the peel of apples are attributed to particular flavonoid dihydrochalcones with multiple pharmacological activities. However, metabolite structural characterization of these two components, which may accumulate to exert their pharmacological effects, remains insufficient. The present study aimed to comparatively clarify the metabolic pathways of phloretin and phlorizin after oral administration individually to Sprague-Dawley (SD) rats. Therefore, a rapid, integrate and systematic analytical strategy based on characteristic fragment ion fishing was proposed for the screening and identification of metabolites coming from phloretin and phlorizin using UHPLC-Q-Exactive Orbitrap mass spectrometry in parallel reaction monitoring mode. As a result, a total of 50 phloretin metabolites and 52 phlorizin metabolites were individually identified from different biological samples including rat plasma, urine, and faeces. Moreover, glucuronidation, sulfation, carbonylation and hydrolyzation were revealed to be the main metabolic pathways of phlorizin, while decarbonylation, glucuronidation and sulfation were regarded as the predominant biotransformation pathways as for phloretin. This is the first systematic study on comparison of metabolic profiles of phlorizin and phloretin in disease states, which was helpful to declare the complicated structure activity relationships between phlorizin and phloretin and shed light to their action mechanism.

Abstract Selenocompounds protect against damage to healthy cells and induce the death of tumor cells by apoptosis; for this reason, they are attractive compounds for cancer research. In the present study, two series of novel phenoxy-((phenylethynyl) selanyl) propan-2-ol derivatives were synthesized, and their anti-proliferation activities were evaluated. Of the 23 compounds synthesized, most showed potent anti-proliferative activity against human cancer cell lines. Specifically, compounds 3h, 3g, and 3h-2, which had a 2- or 4-position halogen substituent on 1-((phenylethynyl)selanyl)-3-phenoxypropan-2-ol, exhibited the best anti-proliferative activity against tumor cells. Flow cytometry demonstrated that 3h, 3g, and 3h-2 induced G2/M phase arrest and apoptosis in A549 cells. Cellular studies demonstrated that the induction of apoptosis by 3h correlated with changes in the expression of cell cycle-related proteins and apoptosis-related proteins. Xenograft tumor experiments in nude mice revealed that compound 3h has antitumor effects in vivo and no evident toxic effects in nude mice. In addition, compound 3h alleviated cisplatin-induced liver and kidney damage. These findings uncover the applicability of compound 3h as a novel lead compound for cancer treatment.

The multi-microwave sources reactor can significantly reduce energy consumption and processing time with broad application prospects in industrial processing. In order to optimize the temperature field of materials, this paper proposes the heating strategy of multi-microwave sources based on collaborative switching, particularly in distributed combined heat source networks with poor communication conditions. Firstly, simplifying system control variables to enhance the design of the microwave intelligent agent system, and optimizing the time-frequency characteristics of combined power output from multi-microwave sources to emphasize the process of energy partition. Meanwhile, an event-triggered strategy reduces communication frequency and energy consumption between agents. Secondly, a fixed positive lower limit τmin is used in event-triggered to avoid Zeno behavior caused by DoS attacks. Finally, The finite element method was used with the time domain for thermal analysis. The simulation results of SiC show that the energy utilization efficiency of microwave heating equipment is increased by 4.3∼10.7%, temperature uniformity is improved by 25.6∼43.6%, and the results of the potato experiment simulation showed that the multi-microwave source collaborative switching heating strategy can effectively optimize the temperature field distribution of the material.

High-performance concrete (HPC) is commonly used in the main structures of bridges. HPC is widely applied in the main structures of bridges, yet some skepticism remains with integrating fly ash and mineral powder as admixtures into prestressed HPC bridges. To address this, this study conducted scaled-model experiments to analyze the flexural fatigue behavior of prestressed HPC bridges with double-mineral fine powder admixtures (PB-DA). This study derives the similarity criteria for a simply supported beam bridge under a concentrated load based on similarity theory. Subsequently, in following these criteria, a 30 m long actual bridge is scaled down to a 6 m PB-DA at a 1:5 scale. For this scaled PB-DA, the concentrated load is reduced to 1/25 of the actual bridge, while the strain remains the same as in the actual bridge. The double-mineral fine powder admixture (D-A) was produced and used to fabricate PB-DA by mixing fly ash and mineral powder. Five PB-DAs were constructed, with C50 and C80 concrete strength grades, and admixture ranges from 10% to 32%. Sinusoidal half-wave constant stress amplitude loading at 5 Hz frequency was applied, with 2 million fatigue loading cycles. After fatigue loading, a continuously increasing static load was applied until the PB-DA failed. The experimental results show that the upper part of the PB-DA is compressed, and the lower part is in tension. The PB-DA strain distribution from top to bottom generally conforms to the plane section assumption. During 2 million fatigue loading cycles, 200,000 cycles mark the beam strain and stiffness evolution boundary. Below 200,000 cycles, the PB-DA strain rapidly increases, and flexural stiffness quickly decreases. Beyond 200,000 cycles, the rate of increase in strain and the rate of decrease in flexural stiffness significantly slow down. After fatigue loading, the PB-DA displacement increases exponentially under a continuously increasing static load. The crack distribution is uniform across all PB-DA, with the cracks being sparsest at a 30% admixture. A comprehensive analysis shows that all PB-DAs demonstrate good flexural fatigue behavior. Notably, when D-A content reaches 30%, strain increases, but reductions in flexural stiffness and damage in PB-DA significantly decrease. This paper’s conclusions provide a reference for applying D-A at PB-DA.

In the context of crowd innovation and the generative design driven by big language models, the exploration of personalized requirements has become a key in significantly improving product innovation, concept feasibility, and design interaction efficiency. To mine a large number of vague and unexpressed implicit requirements of personalized products, a domain knowledge graph-based method is proposed in this research. First, based on the classical theory of design science, the characteristics and categories of personalized implicit requirements are analyzed, and the theoretical basis of implicit requirement mining is formed. Next, in order to improve the practicability and construction efficiency of the domain knowledge graph, a more informative ontology is constructed, and better-performing natural language processing (NLP) models are proposed. Then, a multi-category personalized implicit requirement mining method based on a knowledge graph is proposed. Finally, a platform was developed based on the technical solution proposed in this study, and an example verification was conducted in the field of electromechanical engineering. The efficiency improvement of the training model proposed in the research was analyzed, and the practicality of implicit requirement mining methods are discussed.

In this paper, a novel intelligent controller for the trajectory tracking control of a nonholonomic mobile robot with time-varying parameter uncertainty and external disturbances in the case of tire hysteresis loss is proposed. Based on tire dynamics principles, a dynamic and kinematic model of a nonholonomic mobile robot is established, and the neural network approximation model of the system’s nonlinear term caused by many coupling factors when the robot enters a roll is given. Then, in order to adaptively estimate the unknown upper bounds on the uncertainties and perturbations for each subsystem in real time, a novel adaptive law employed online as a gain parameter is designed to solve the problem of inter-system coupling and reduce the transient response time of the system with lower uncertainties. Additionally, based on improved gray wolf optimizer and fuzzy system techniques, an adaptive algorithm using the gray wolf optimizer study space as the output variable of the fuzzy system to expand the search area of the gray wolves is developed to optimize the controller parameters online. Finally, the efficacy of the proposed intelligent control scheme and the feasibility of the proposed algorithm are verified by the 2023a version of MATLAB/Simulink platform.

Nitrobenzene liquid-phase catalytic hydrogenation is commonly regarded as one of the most effective technologies for aniline production. The traditional granular catalysts have the disadvantages that the reactor bed pressure drop is large and the mass transfer efficiency between gas and liquid phases is low. In this study, a novel structured mesh-type Pd/γ-Al2O3/Al catalyst was prepared by anodic oxidation and pore structures of γ-Al2O3/Al supports were constructed by acid pore-widening treatments. The results showed that acid pore-widening treatments can improve the pore size of γ-Al2O3/Al supports; the support with HNO3 pore-widening treatment exhibited the largest pore size, being enlarged from 3.7 nm to 4.6 nm. The Pd/γ-Al2O3/Al catalysts prepared with different acid pore-widening treatment supports contribute to the increased active metal Pd loading, more Pd0 content, and better dispersion of the Pd particles. The catalyst prepared with HNO3 pore-widening treatment support exhibited the largest active metal Pd loading, enlarging from 1.82% to 1.95%, the largest Pd0 content being enlarged from 52.1% to 58.5% and the smallest Pd particle size being reduced from 103 nm to 41 nm, resulting in the highest nitrobenzene conversion, increasing from 67.2% to 74.3%. Eventually, we calculated that the pressure drop of structured catalysts was 1/72 of that of granular catalysts, resulting in a better diffusion of the H2 through nitrobenzene solution to active sites on the catalyst surface and a significant increase in the catalytic activity.

The remarkable optoelectronic properties of organometal halide perovskite solar cells have captivated significant attention in the energy sector. Nevertheless, the instability of 3D perovskites, despite their extensive study and attainment of high-power conversion efficiency, remains a substantial obstacle in advancing PSCs for practical applications and eventual commercialization. To tackle this issue, researchers have devised mixed-dimensional perovskite structures combining 1D and 3D components. This innovative approach entails incorporating stable 1D perovskites into 3D perovskite matrices, yielding a significant improvement in long-term stability against various challenges, including moisture, continuous illumination, and thermal stress. Notably, the incorporation of 1D perovskite yields a multitude of advantages. Firstly, it efficiently passivates defects, thereby improving the overall device quality. Secondly, it retards ion migration, a pivotal factor in degradation, thus further bolstering stability. Lastly, the inclusion of 1D perovskite facilitates charge transport, ultimately resulting in an elevated device efficiency. In this succinct review, we thoroughly encapsulate the recent progress in PSCs utilizing 1D/3D mixed-dimensional architectures. These advancements encompass both stacked bilayer configurations of 1D/3D structures and mixed monolayer structures of 1D/3D. Additionally, we tackle critical challenges that must be surmounted and offer insights into the prospects for further advancements in this domain.

Cage‐like metal–organic frameworks (MOFs) are promising for hexane isomer separation, but their aperture sizes are difficult to control precisely. Herein, a molecular valve strategy is proposed to fine‐tune the aperture of cage‐like MOFs, thereby enhancing their separation performance for hexane isomers. Three novel isostructural cage‐like MOFs, namely Cu‐MO4‐TPA, are synthesized using different oxometallate anions (MO42−, M = Cr, Mo, and W) and the ligand tri(pyridin‐4‐yl)amine (TPA). The different oxometallate anions induce varying degrees of twisting in the TPA ligand, leading to sub‐angstrom tuning in the aperture size of the MOFs. The materials can separate hexane isomers based on their degree of branching, with the smallest aperture material (Cu‐MoO4‐TPA) showing molecular sieving of di‐branched isomers. Adsorption isotherms, kinetic, and breakthrough measurements, Monte Carlo, and density functional theory calculations confirm the aperture size differences result in superior separation of hexane isomers by molecular sieving on Cu‐MoO4‐TPA. The results demonstrate the effectiveness of molecular valve strategy for fine‐tuning MOF aperture size for selective separation applications.

Silicon-based sulfonic solid acids have the advantages of high catalytic activity and selectivity, easy separation from products, low equipment corrosion, and environmental protection, and sulfuric acid loading is the key to their preparation. To overcome the shortcomings of low acid loading and uneven distribution in the existing preparation methods of micron-sized silicon-based sulfonic solid acids, a method was proposed to prepare micron-sized silicon-based sulfonic solid acids using ultrasonic enhanced technology. The effect of different reaction parameters, such as time, power, and temperature of ultrasonication, sulfonation temperature and time, and sulfuric acid concentration, on acid loading in solid acid was investigated in this work. The results showed that a micron-sized mesoporous silica-based solid acid was successfully synthesized with a high acid content of 0.8633 mmol/g, uniform acid distribution, high specific surface area of 269.332 m2/g, and large average particle size of 172.142 μm in this work. The introduction of ultrasound was found to expand the carrier's pore volume and increase the carrier's specific surface area and the number of hydroxyl groups, thereby increasing the acid loading capacity and the specific surface area of the solid acid sample by 66.6 % and 10.97 % respectively, compared with the case without ultrasound.

Endangered species detection plays an important role in biodiversity conservation and is significant in maintaining ecological balance. Existing deep learning-based object detection methods are overly dependent on a large number of supervised samples, and building such endangered species datasets is usually costly. Aiming at the problems faced by endangered species detection, such as low accuracy and easy loss of location information, an efficient endangered species detection method with fewer samples is proposed to extend the few-shot object detection technique to the field of endangered species detection, which requires only a small number of training samples to obtain excellent detection results. First, SE-Res2Net is proposed to optimize the feature extraction capability. Secondly, an RPN network with multiple attention mechanism is proposed. Finally, for the classification confusion problem, a weighted prototype-based comparison branch is introduced to construct weighted category prototype vectors, which effectively improves the performance of the original classifier. Under the setting of 30 samples in the endangered species dataset, the average detection accuracy value of the method, mAP50, reaches 76.54%, which is 7.98% higher than that of the pre-improved FSCE method. This paper also compares the algorithm on the PASCOL VOC dataset, which is optimal and has good generalization ability compared to the other five algorithms.

Dwarf apple is one of the most important forms of garden economy, which has become a new engine for rural revitalization. The effective detection of coloring and ripening apples in complex environments is important for the sustainable development of smart agricultural operations. Addressing the issues of low detection efficiency in the greenhouse and the challenges associated with deploying complex target detection algorithms on low-cost equipment, we propose an enhanced lightweight model rooted in YOLOv7. Firstly, we enhance the model training performance by incorporating the Squeeze-and-Excite attention mechanism, which can enhance feature extraction capability. Then, an SCYLLA-IoU (SIoU) loss function is introduced to improve the ability of extracting occluded objects in complex environments. Finally, the model was simplified by introducing depthwise separable convolution and adding a ghost module after up-sampling layers. The improved YOLOv7 model has the highest AP value, which is 10.00%, 5.61%, and 6.00% higher compared to YOLOv5, YOLOv7, and YOLOX, respectively. The improved YOLOv7 model has an MAP value of 95.65%, which provides higher apple detection accuracy compared to other detection models and is suitable for potted dwarf anvil apple identification and detection.

The discovery of antibiotics and pesticides has greatly contributed to the social and economic development of human society but, due to the long-term irrational application, it has led to drug-resistant microorganisms, environmental damage, and other hazards, so the selection of alternative natural, safe, and non-hazardous bioactive substances is an effective solution for this problem. D-limonene is a bioactive compound widely present in various plant essential oils, exhibiting excellent broad-spectrum bioactivity and promising prospects for development and clinical application. This review provides a detailed overview of the biological activities of D-limonene, emphasizing its antimicrobial, anthelmintic, insecticidal, and medicinal potential. While nanoencapsulation technology shows promise in improving the physicochemical properties of D-limonene and enhancing its practical applications, it is also crucial to comprehensively evaluate the potential side effects of D-limonene before use.

As urban underground spaces continue to develop, new engineering projects are increasingly closer to existing operating tunnels. This study focuses on the impact of excavation work, such as a new tunnel or deep excavation, on an existing shield tunnel. However, few studies have examined the simultaneous effects of excavation and over-crossing tunneling on operational tunnels. This paper presents a case study of Section 2 of Hangzhou Metro Line 7, employing MIDAS GTS to simulate the settlement of the existing tunnel and analyze the data collected through automatic measurements. Both methods demonstrated that excavating a foundation pit near the existing tunnel results in the tunnel’s settlement while over-crossing tunneling of the new tunnels leads to the uplift of the existing tunnel. The excavation prompted the existing tunnel to move horizontally toward the foundation pit, whereas over-crossing tunneling pushed it horizontally away. The numerical simulation revealed minor vertical displacement but significant horizontal displacement, aligning closely with the measured data. The impacts of foundation pit excavation and over-crossing tunneling on the vertical and horizontal displacement of existing tunnels are contrary, suggesting that the two construction stages will offset each other.

Herein, the starch nanocrystal/tannic acid (ST) complex particles, which were prepared based on the hydrogen bond between starch nanocrystal (SNC) and tannic acid (TA), were successfully used to stabilize the HIPPE gels. The optimal TA concentration of the ST complex particles resulted in better water dispersibility, surface wettability, and interfacial activity as compared to SNC. The hydrogen bond responsible for the formation of ST complex particles and subsequent stable emulsions was demonstrated by varying the pH and ionic strength of the aqueous phase. Notably, the HIPPE gels stabilized via the ST complex particles can maintain long-term stability for up to three months. The HIPPEs stabilized via the ST complex particles all displayed gel-like features and had smaller droplets and denser droplet networks than the SNC-stabilized HIPPEs. The rheological behavior of HIPPE gels stabilized via the ST complex particles can be readily changed by tuning the mass ratio of SNC and TA as well as pH. Finally, the prepared HIPPE gels used to effectively protect encapsulated β-carotene against high temperatures and ultraviolet radiation and its controllable release at room temperature were demonstrated. It is anticipated that the aforementioned findings will provide new perspectives on the preparation of Pickering emulsion for delivery systems.

As a strong interference source for the all-time optical imaging surveillance of maritime targets, sea surface glare is difficult to mitigate accurately because of its time-varying characteristics due to lighting conditions and seawater fluctuations. In this paper, we propose an adaptive suppression approach to sea surface glare, which establishes a geographic polarization suppression model based on real-time information regarding geographic positioning and the orientation information of the floating platform, and also combines dynamic polarization control and pixel normalization to achieve adaptive suppression of sea surface glare. Experimental results show that this approach can mitigate the influence of rapidly changing glare effectively, and the SSIM indexes between the images without glare and those with glare suppression of the same scenes exceed 0.8, which is suitable for all-time glare suppression on the sea surface under natural lighting conditions.

With the large-scale promotion and application of CO2 flooding, more and more engineering problems have emerged. Due to the high CO2 mole fraction, the associated gas of CO2 flooding very easily forms solid hydrates, compared to conventional natural gas. This has resulted in production decline or shutdown. Understanding the phase equilibrium conditions for hydrate formation in production fluids is crucial for hydrate prevention and control. In this study, accurate predictions of CO2-CH4 mixed gas hydrate formation conditions were performed using theoretical models. The temperature and pressure ranges for hydrate formation were calculated for different CO2 mole fraction, ranging from −11.5 °C to 20.85 °C and from 0.81 MPa to −28.1 MPa, respectively. Based on the calculated phase equilibrium data, a multi-parameter empirical model was developed using polynomial fitting. The calculation errors for the multi-parameter empirical model were 3.09%. The multi-parameter empirical model established in this study can avoid complex thermodynamic equilibrium calculations and has the advantages of simplicity, high accuracy, and wide coverage of downhole conditions. Based on the calculated phase equilibrium data, the dissociation enthalpy of CO2-CH4 hydrate below and above the freezing point of water was calculated. The results showed that an increase in CO2 mole fraction led to an increase in hydrate dissociation enthalpy and enhanced thermodynamic stability, making hydrate prevention more challenging. Our work can contribute to the optimization of CO2 production fluid treatment processes and the development of hydrate prevention and control technologies.

In situ combustion (ISC), an efficient and economical method for enhancing heavy oil recovery in high-pressure, high-viscosity, and thermally challenged reservoirs, relies on the kinetics of crude oil oxidation. Despite an increased focus on kinetic models, there is a gap in understanding how oxidation kinetic parameters impact ISC effectiveness in heavy oil reservoirs. This study addresses this by selecting heavy oil samples from the G Block in the Liaohe oilfield and the M Block in the Huabei oilfield and conducting ramped temperature oxidation (RTO), pressure differential scanning calorimetry (PDSC), and thermogravimetric analysis (TGA) experiments. RTO detailed the thermal conversion process, categorizing oxidation into low-temperature oxidation (LTO), fuel deposition (FD), and high-temperature oxidation (HTO) stages. PDSC and TGA provided thermal characteristics and kinetic parameters. The feasibility of fire flooding was evaluated. Using CMG-STARS, an ISC model was established to analyze the impact of kinetic parameter changes. Activation energy significantly affected coke combustion, while the pre-exponential factor had a notable impact on cracking reactions. The recommended values for activation energy and the pre-exponential factor are provided. This study not only guides fire flooding experiments but also supports field engineering practices, particularly for in situ combustion in heavy oil reservoirs.

Variational modal decomposition (VMD) is frequently employed for both signal decomposition and extracting features; however, the decomposition outcome is influenced by the quantity of intrinsic modal functions (IMFs) and the specific parameter values of penalty factors. To tackle this issue, we propose an algorithm based on the Halton sequence and the Laplace crossover operator for the sparrow search algorithm–VMD (HLSSA-VMD) to fine-tune the parameters of VMD. First, the population initialization by the Halton sequence yields higher-quality initial solutions, which effectively addresses the issue of the algorithm’s sluggish convergence due to overlapping and the lack of diversity of the initial solutions. Second, the introduction of the Laplace crossover operator (LX) to perturb the position of the best individual in each iteration helps to prevent the algorithm from becoming ensnared in a local optimum and improves the convergence speed of the algorithm. Finally, from the simulation of 17 benchmark test functions, we found that the HLSSA exhibited superior convergence accuracy and accelerated convergence pace, as well as better robustness than the particle swarm optimization (PSO) algorithm, the whale optimization algorithm (WOA), the multiverse optimization (MVO) algorithm, and the traditional sparrow search algorithm (SSA). In addition, we verified the effectiveness of the HLSSA-VMD algorithm on two simulated signals and compared it with PSO-VMD, WOA-VMD, MVO-VMD, and SSA-VMD. The experimental findings indicate that the HLSSA-VMD obtains better parameters, confirming the superiority of the algorithm.

Heavy metal contamination in soil, which is harmful to both ecosystem and mankind, has attracted worldwide attention from the academic and industrial communities. However, the most-widely used remediation technologies such as electrochemistry, elution, and phytoremediation. suffer from either secondary pollution, long cycle time or high cost. In contrast, in situ mineralization technology shows great potential due to its universality, durability and economical efficiency. As such, the development of mineralizers with both high efficiency and low-cost is the core of in situmineralization. In 2021, the concept of ‘Super-Stable Mineralization’ was proposed for the first time by Kong et al.[1] The layered double hydroxides (denoted as LDHs), with the unique host–guest intercalated structure and multiple interactions between the host laminate and the guest anions, are considered as an ideal class of materials for super-stable mineralization. In this review, we systematically summarize the application of LDHs in the treatment of heavy metal contaminated soil from the view of: 1) the structure–activity relationship of LDHs in in situ mineralization, 2) the advantages of LDHs in mineralizing heavy metals, 3) the scale-up preparation of LDHs-based mineralizers and 4) the practical application of LDHs in treating contaminated soil. At last, we highlight the challenges and opportunities for the rational design of LDH-based mineralizer in the future.

Isoquinoline alkaloids are a kind of small molecular compounds with isoquinoline structure which widely exist in natural plants. A large number of studies have confirmed that these substances have a variety of biological activities, such as antibacterial, anti-tumor, anti-hyperglycemia, anti-virus, anti-inflammation, regulation of metabolism and so on. However, the low solubility and over-mild action of these substances limit their application. Based on the pharmacodynamic characteristics, application prospect and clinical demand of isoquinoline alkaloids, we aim to review the antibacterial activity of isoquinoline alkaloids and find new modification sites and groups of isoquinoline alkaloids. to provide reference for the structural design of new drug molecules and fully explore its potential clinical application value.

Transition metal phosphates, as the most promising catalyst for oxygen evolution reaction (OER), have attracted much attention in recent years. In this work, CoP nanosheets are electrodeposited on NiMoO4 nanorods to form a self-supported core-shell structure electrode with CoP-NiMoO4 heterojunctions. Density functional theory (DFT) calculations demonstrate CoP-12@NiMoO4/NF electrode can reduce reaction energy barrier of rate-determining step (RDS) from 8.743 eV to 2.519 eV. In 1 M KOH, the electrode shows a low overpotential of 165 mV (10 mA cm−2), a low Tafel slope of 45 mV dec−1 and the significant stability. Compared with other non-noble metal OER electrocatalysts, the prepared CoP-12@NiMoO4/NF electrode exhibits better electrocatalytic activity, which can provide a new choice of electrocatalysts for future practical applications.

Abstract Deep learning methods, such as reaction prediction and retrosynthesis analysis, have demonstrated their significance in the chemical field. However, the de novo generation of novel reactions using artificial intelligence technology requires further exploration. Inspired by molecular generation, we proposed a novel task of reaction generation. Herein, Heck reactions were applied to train the transformer model, a state-of-art natural language process model, to generate 4717 reactions after sampling and processing. Then, 2253 novel Heck reactions were confirmed by organizing chemists to judge the generated reactions. More importantly, further organic synthesis experiments were performed to verify the accuracy and feasibility of representative reactions. The total process, from Heck reaction generation to experimental verification, required only 15 days, demonstrating that our model has well-learned reaction rules in-depth and can contribute to novel reaction discovery and chemical space exploration.

In rural locations, the application of clean heating technologies requires an appropriate design heating load. However, the variation characteristics of indoor temperatures in rural residential buildings are rarely taken into consideration by traditional techniques of calculating the design heating load, which may result in over- or under-design. As a result, a new method that took the uncertainty of the indoor temperature into account was presented to calculate the design heating load for rural residential buildings. First, for the “part-time, part-space” heating mode in rural residential buildings, an indoor temperature stochastic model was established to generate multiple indoor temperature scenarios; on the basis of this, heating loads under these scenarios were simulated and their probability distributions were counted; lastly, the design heating load was selected from the load probability distribution based on a predetermined confidence level. When the new method and the traditional method were compared, it was discovered that the new method can offer a more thorough guide to the determination of the design load value for the design of heating systems in rural residential buildings, while the traditional method’s result might not satisfy the reliability requirements.

The accurate prediction of small-scale three-dimensional wind fields is of great practical significance for aviation safety, wind power generation, and related fields. This study proposes a novel method for predicting small-scale three-dimensional wind fields by combining the mesoscale Weather Research and Forecasting (WRF) model with Computational Fluid Dynamics (CFD). The method consists of three components: the WRF module, the hybrid neural network prediction module, and the CFD module. First, mesoscale meteorological fields are simulated using the WRF module to establish a historical inflow boundary dataset for the CFD domain. Next, deep separable convolutions are incorporated, and convolutional long short-term memory (ConvLSTM) is combined with a deep separable convolution-gated recurrent unit (DSConvGRU) to construct a hybrid neural network prediction module named ConvLSTM-DSConvGRU. This module is employed for predicting inflow boundary data. Finally, the predicted inflow boundary conditions drive the CFD module to predict small-scale three-dimensional wind fields. The effectiveness of the WRF and CFD downscaling coupling method was validated using observed data from meteorological stations within the simulated domain, along with statistical indicators of errors. Additionally, a comparative evaluation was conducted between the proposed hybrid network model and the four commonly used spatiotemporal prediction models to assess its prediction performance. The results demonstrate that our proposed wind field prediction method achieves accurate simulation and short-term prediction of small-scale three-dimensional wind fields, and the hybrid network model exhibits comprehensive advantages in terms of model complexity and prediction accuracy.