Controlled/living polymerization,owing to its excellent capability for regulating relative molecular mass and topological architecture,has become an important strategy for the synthesis of high-performance polymeric materials.However,such polymerization processes are typically characterized by multi-factor coupling,multiscale reaction dynamics,and highly nonlinear mappings between reaction conditions and polymer properties,which render conventional polymer design approaches reliant on empirical screening or mechanistic modeling limited by high computational cost,poor cross-system transferability,and restricted global optimization capability.In recent years,machine learning(ML)methods,leveraging multi-source data from experiments,literature reports,and computational simulations,have demonstrated strong capability in efficiently uncovering nonlinear relationships between reaction conditions,monomer/catalyst structures,and polymer properties.Even under limited data availability,ML enables rapid property prediction and inverse reaction design,providing a new paradigm for the rational design of controlled/living polymerization processes.This review summarizes recent advances in the application of ML to atom transfer radical polymerization,reversible addition-fragmentation chain transfer polymerization,ring-opening polymerization,and controlled/living polymerization,covering key aspects such as dataset construction,feature engineering,model training and optimization,as well as physical constraints and model interpretability.Furthermore,the integration of ML with high-throughput synthesis,online/in situ characterization techniques,and self-driving experimental platforms is discussed.Finally,in view of challenges including data scarcity,limited model interpretability,and insufficient automation,future perspectives are proposed,emphasizing the development of standardized polymerization databases,physically informed ML models,and self-driving experimental systems.
This paper proposes the Quaternion Hilbert Transform (QHT) and applies it to the encryption of color red, green and blue (RGB) images. Firstly, by decomposing an arbitrary quaternion signal into the linear sum of two complex signals and based on the Hilbert transform of complex signals, a Hilbert transform suitable for quaternion signals is constructed. Secondly, various commonly used properties satisfied by the Quaternion Hilbert Transform are derived. Finally, color RGB images are represented as quaternion signals, and a color RGB image encryption method based on the Quaternion Hilbert Transform is proposed. Experiments show that the proposed method has excellent visual effects and index performance, exhibits robustness against attacks such as noise and cropping, and has high sensitivity to keys, which verifies its effectiveness.
Electrocardiogram (ECG) anomaly detection plays a critical role in the early diagnosis and timely treatment of patients with heart disease. This is particularly important for medical devices such as implantable cardioverter defibrillators (ICD), where it is essential to have detection algorithms that are not only highly accurate but also consume minimal power. Traditional detection methods often face challenges in achieving optimal performance on embedded devices with limited resources, which necessitates the exploration of new and innovative solutions. In this paper, we present the design of an ECG anomaly detection device that leverages the capabilities of artificial intelligence algorithms. For this purpose, the STM32F303K8T6 development board has been selected as the platform for model design and deployment. Additionally, we have implemented an evaluation system that thoroughly tests and assesses the performance of the deployed algorithms. The study undertakes a comprehensive comparison between deep learning algorithms and traditional machine learning algorithms, culminating in the proposal of a novel classification algorithm that combines hybrid features. Experimental results highlight that this algorithm not only demonstrates superior memory efficiency and reduced latency on resource-constrained ICD devices but also exhibits higher precision and robust generalization capabilities. This research provides a novel and effective solution for ECG anomaly detection, offering significant implications for the design of medical devices and the optimization of detection algorithms.
Solution adsorption theory, as the core teaching content of surface chemistry, serves as a crucial link between fundamental chemistry and application fields such as industrial separation, environmental governance, and material preparation. The quality of its teaching is of vital importance to the cultivation of talents in chemistry, chemical engineering, environmental science, and other related fields. However, there are significant contradictions in current teaching and research. For instance, when traditional gas-solid adsorption theories (such as Langmuir's molecular layer adsorption theory and Brunauer-Emmett-Teller (BET) multi-molecule adsorption theory, etc.) are directly applied to solution systems, logical contradictions arise due to the neglect of the solvent effect, substitution effect and other special properties of solutions. Moreover, the theoretical derivation is complex and lacks coverage of practical systems such as non-uniform adsorbents and multi-component solutions, resulting in a disconnection between theory and practice. To fill the current gap in teaching resources, this paper takes statistical thermodynamics as the core theoretical basis and constructs a systematic and complete teaching system for solution adsorption theory. In teaching, it is essential to first clarify the three core assumptions. (1) The essence of solution adsorption is the monolayer adsorption of solutes at the interface, and the chemical potential of solutes is equal at equilibrium. (2) The intermolecular interaction energy is composed of van der Waals forces and hydrogen bonds, and the coordination number is directly proportional to the volume fraction of the components. (3) The surface of the non-uniform adsorbent is a collection of multiple types of active centers, and the total adsorption capacity is the linear superposition of the adsorption capacities of each center. Based on the uniform potential model (liquid phase) and the ideal potential well model (adsorption state), the derivation process of the monolayer adsorption equation for binary solutions is simplified, making the physical meaning of the equation clearer. At low concentrations, it conforms to Henry's law, and at high concentrations, it approaches the saturated adsorption capacity, which is in line with the essential characteristics of monolayer adsorption. On this basis, the teaching content of the theoretical boundary was further expanded. The adsorption equation of non-uniform adsorbents and the adsorption equation of multi-component solutions were established. To verify the validity of the theory, multi-dimensional experimental verification was carried out simultaneously. The adsorption experiments covered 7 types of adsorbates, including aniline, phenol and cyclohexanol, and 6 types of carbon material adsorbents, and combined a large number of chromatographic experiments, temperature effect studies and system verifications of adsorption of organic acids, alcohols and alkali metal ions. The results show that the theoretical calculated values are highly consistent with the experimental values, confirming that the solution adsorption is monolayer adsorption rather than the multi-molecular adsorption as traditionally understood. In addition, the universality of the theory in different adsorption systems was further verified through the adsorption of acetic acid by bone charcoal, the adsorption of alkali metal ions by silica gel, and the adsorption of n-butanol by blood charcoal, and the influence mechanisms of factors such as temperature, molecular structure, and the active center of the adsorbent on the adsorption behavior were clarified. The theoretical system of solution adsorption constructed in this paper not only provides a concise and systematic teaching tool for related majors in colleges and universities, but also helps students quickly master the core principles. At the same time, it provides scientific guidance for practical applications and demonstrates significant application value in fields such as liquid chromatography retention value prediction and adsorption separation process optimization.
Although air quality in Beijing has improved year by year,regional heavy air pollution episodes still occur in autumn and winter.Investigating the characteristics,influencing factors,and causes of such typical regional-scale heavy pollution events is of great significance for formulating differentiated control strategies.This study focuses on Shijingshan District and analyzes three heavy pollution episodes that occurred in 2024,comparing them with non-pollution periods.The Hybrid Single-Particle Lagrangian Integrated Trajectory(HYSPLIT)model was employed to calculate 72 h backward trajectories for source attribution,while real-time traffic flow monitoring was conducted on major local roads during the pollution episodes.In addition,meteorological factors such as wind speed and boundary layer height were analyzed to explore their correlations with pollutant concentrations.Results indicate that PM2.5 was the dominant pollutant,with the three heavy pollution episodes increasing the monthly mean concentration by 30.8%-44.1%.NO2 exhibited both pronounced evening peaks and stepwise declines,along with spatial variability.Regional transport pathways determined pollutant accumulation characteristics,with short-distance low-altitude transport contributing 77.0%.Wind speed and boundary layer height had significant dilution effects,showing stable negative correlations with NO2,whereas PM2.5 displayed stage-dependent relationships,reflecting uncertainties in the meteorological dilution effect.Although local vehicular emissions exerted some influence on air quality,they were not the dominant factor during heavy pollution episodes.Instead,regional pollutant accumulation,atmospheric transformation,and unfavorable dispersion conditions were identified as the main causes.
Reticular framework materials,distinguished by their precisely engineered architectures and highly tailorable functional environments,have emerged as a versatile platform for applications in energy storage,drug delivery,and environmental remediation.However,the complexity of their synthetic routes and the vast diversity of accessible topologies and compositions still pose substantial obstacles to rational design and large-scale production.In recent years,artificial intelligence(AI),in particular transformer-based models and large language models,has begun to transform reticular chemistry by enabling large-scale data mining,accurate prediction of materials properties,and algorithmic guidance for experimental design.This review summarizes the disruptive impact of AI on the discovery and synthesis of reticular framework materials,with a focus on its roles in structural design,performance prediction,and optimization of synthetic conditions.We further discuss the deep integration of AI with automated experimental platforms to build autonomous laboratories capable of generating experimental protocols,adaptively adjusting reaction parameters,and iteratively refining conditions based on real-time feedback.These developments not only accelerate the discovery cycle and improve experimental reproducibility,but also greatly expand the accessible design space of reticular frameworks.The close coupling between AI methodologies and laboratory automation is expected to steer the field toward a new research paradigm that is more intelligent,efficient,and predictive,and that enables genuinely innovation-driven discovery and synthesis.
With the emergence of people's demand for high-quality life in the new era,various urban cultural practices are increasingly abundant.In recent years,the study of scene theory has received a large number of domestic and foreign scholars'attention.Through combing the literature related to scene theory at home and abroad,it is found that scene research presents diversified development in terms of research perspectives,scales and methods.The elements of the scene are summarized into space environment,amenities,people,values and so on.Then,scene consumption shows the characteristics of emotional resonance,identity and locality.Both domestic and foreign scene studies will adapt to the evaluation index system,and the impact of scene is mainly reflected in the promotion of urban development,residents'identity,tourism consumption and other aspects.Based on this,combined with the systematic sorting of the above scene research,"scene-amenities-people"interactive influence mechanism is formed.The research is proposed:strengthen the scene theory research from the perspective of consumers.Explore tourists'perception difference of premium products and reasons for purchase.Explore the influence of local context on the construction of scene authenticity.(4)Carry out research on the path of creating urban scenes shared by hosts and guests.
A systematic theoretical study was investigated on the electronic transport characteristics in group-u2163 graphene-like structures using the non-equilibrium Green's function method. This article mainly analyzes the mechanism of intrinsic effective spin orbit coupling(SOC)and Rashba SOC in small-sized systems, and their effects on transport pathways, conductivity, and band characteristics. The results show that under weak intrinsic SOC, electrons tend to be transported through tunneling channels between edge states. When the intrinsic coupling strength increases, the interaction between edge states diminishes, causing more electrons to propagate along the edges. After introducing Rashba SOC, spin-flipping can occur between different spin orientations, enhancing quantum tunneling, and weakening the localization of edge-state currents. When the Rashba SOC is weak, electrons with different spins in the valence band move toward higher- and lower-energy regions, leading to an increased overlap of bands and enhancing the conductance in the nanoribbon. Furthermore, when the Rashba SOC exceeds the intrinsic SOC, a tunable band gap emerges near the electron bands. The results provide theoretical insights into spin-orbit coupling effects that may facilitate the design of future spintronic devices.
A novel porous CuO/Cu2O composite hollow sphere assembled with one-dimensional (1D) nanostructures was successfully synthesized by a facile one-pot surfactant and template-free hydrothermal method. The electrochemical performance evidently demonstrated that the achieved porous CuO/Cu2O composite hollow sphere was a very effective structure for solving the large volume expansion problem, which was serious issue for metal oxide anode materials. Compared to the initial cycle, the porous CuO/Cu2O composite hollow spheres maintain a high reversible capacity of 680 mAh/g after 500 cycles at 100 mA/g without capacity fading. Meanwhile, the porous CuO/Cu2O composite hollow spheres exhibit a good rate capability. The reversible capacity and cycling life significantly superior to those of reported CuO and Cu2O nanostructures as well as CuO/Cu2O h composite nanostructures, and the capacity fade was significantly inhibited. The excellent electrochemical performance of the CuO/Cu2O h composite hollow spheres for anode materials in lithium-ion (Li+) batteries are attributed to the efficient diffusion of Li+ and electron facilitated by porous and 1D nanostructures, which promotes the diffusion of Li+ and electrons, effectively mitigates the volume changes caused by mesopores and internal hollow spaces, and the synergistic effect between CuO and Cu2O within the porous CuO/Cu2O hollo composite hollow spheres.
As a core technique in the field of analytical chemistry, chromatography has been widely applied in chemistry, biology, medicine, and numerous other disciplines. To address the fragmentation challenge in traditional chromatographic theory, this paper systematically presents two unified equations for chromatographic thermodynamics and chromatographic kinetics based on prior research. In 1990, based on the unified equation of chromatographic retention values using statistical thermodynamics and lattice models, we broke through the barriers of retention value formulas across multiple chromatographic modes, predicted the retention behavior of various types of chromatography (gas-solid chromatography, gas-liquid chromatography, liquid-solid chromatography, supercritical fluid chromatography, etc.) through molecular parameters, verified the high prediction accuracy through experiments, which provided theoretical support for the conversion of retention values for different modes. In 2020, we established a unified equation for the height of liquid chromatography trays, which extended the study of chromatographic kinetics from one-dimensional space to three-dimensional space, by introducing the heat conduction equation and integrating the contributions of radial diffusion and thermal effects to the tray height equation, we achieved a unified description of column efficiency rules for multiple chromatographic modes such as high-performance liquid chromatography (HPLC), ultra-high performance liquid chromatography (UPLC), capillary electrochromatography (CEC), and clarified the conditions for achieving high efficiency in high-speed chromatography. This study will integrate the dispersion theory of multiple chromatographic modes such as gas chromatography (GC), liquid chromatography (LC), and supercritical fluid chromatography (FSC) to reveal the common physical and chemical essence of different chromatographic techniques, achieving a theoretical leap from macroscopic description to microscopic mechanism. On the one hand, it provides quantitative theoretical support for optimizing chromatographic conditions, transferring methods, and developing new separation technologies, clarifying the conditions for achieving high-speed and efficient separation. On the other hand, simplifying the knowledge system of chromatographic analysis courses, reducing the burden of teaching and learning, providing scientific basis for teaching reform, and helping students establish a systematic chromatographic knowledge framework. The two unified equations respectively address the thermodynamic essence of "whether separation is feasible"and the kinetic key of "how to achieve efficient separation", realizing a leap forward in the systematization of chromatographic theories. This study clarifies the core values of the unified theories in academic integration, technological optimization, and teaching reform, and prospects their development directions in integration with artificial intelligence and the exploration of new separation technologies, thus offering an important theoretical reference for the advancement of chromatography as a discipline.
In order to understand the characteristics of heavy metal content in the surface sediments of the coastal waters of Weihai, this study determined the contents of Cr, Cu, Zn, As, Cd, Hg and Pb in the surface sediments of 15 stations in the coastal waters of Weihai by using inductively coupled plasma mass spectrometry (ICP-MS) and catalytic hot injection cold atomic absorption spectrometry. The environmental quality was evaluated by using the geoaccumulation index method, potential ecological risk method and toxicity effect prediction method. The average mass fraction of Cr, Cu, Zn, As, Pb in the surface sediments of the coastal waters of Weihai were 64.1, 26.4, 81.2, 8.1 and 26.2 mg/kg, Cd and Hg were 124.0 and 20.7 u03BCg/kg, respectively. The average pollution degree of heavy metals from high to low was Zn, Cu, Pb, Cr, Hg, As, Cd, but the overall pollution level was slight and the ecological environment was good. The order of ecological risk of single heavy metals was Hg, Cd, As, Cu, Pb, Cr, Zn. Except for station 15 which was of medium ecological risk, the other stations were of low ecological risk. The probability of heavy metal biological toxicity hazard in the coastal sediments of Weihai was low. Apart from natural sources, the Cu, Zn, Cd, and Pb present in the surface sediments of the coastal waters may derived from direct discharge of sewage, ship emissions and offshore aquaculture activities, while As mainly comes from nearby industrial production activities, and some of it also comes from the discharge of treated sewage.
This paper proposes the trinion Hartley Transform (THT) and presents its commonly used properties, such as linearity, cyclic shift property, convolution theorem, and so on. In addition, the relationship between THT and Fourier Transform is derived, and a fast computation for THT based on FFT is proposed. Finally, based on the proposed THT and its fast computation, encryption and decryption methods suitable for color images are proposed, and the effectiveness of the proposed methods are verified by simulation results.
Cu2O microspheres were prepared by a room-temperature liquid-phase method using glucose as the reducing agent and ethylenediaminetetraacetic acid as the morphology control agent. After that, Au nanoparticles were grown on the surface of Cu2O microspheres by room temperature liquid-phase method with sodium citrate as the reducing agent, and Cu2O/Au micro-nanostructures were successfully fabricated. The phase composition of the samples was characterized by X-ray powder diffraction (XRD), and the morphology and size of the micro-nanostructures were analyzed by scanning electron microscopy (SEM). Photocatalytic performance showed that the degradation rate of methyl orange (MO) by Cu2O/Au micro-nanostructures reached as high as 87% under visible light irradiation for 90 min. Based on the heterojunction structure between Cu2O and Au interface, the enhancement mechanism of the photocatalytic performance of the micro-nanostructures was analyzed and discussed.
Negative visual attention bias, a core pathological feature of depression, is crucial for its diagnosis and treatment. Related research has become a cutting-edge focus in the cognitive neuroscience of depression. This article focuses on the diagnostic and therapeutic value of negative visual attention bias as a key pathological target in depression, systematically reviewing the paradigm shifts, key advances, and current challenges in this research field driven by artificial intelligence (AI) over the past three years. First, we outline the basic concepts of negative visual attention bias and its central role in the pathogenesis of depression. Next, we focus on three breakthrough directions driven by AI: dynamic modeling of neural mechanisms, multimodal AI assessment techniques, and innovative AI-assisted precision intervention strategies. We also provide an in-depth analysis of the technical limitations and standardization challenges currently facing AI-driven research. Finally, we explore future developments, such as the development of closed-loop precision diagnosis and treatment systems integrating multidimensional data from dynamic brain networks, multi-omics, and digital phenotyping. This review of existing research aims to provide both theoretically insightful and technologically cutting-edge insights for deepening our understanding of the neural mechanisms of negative visual attention bias and promoting the clinical translation of precision diagnosis and treatment for depression.
This article considers a class of periodic network models with n-order rectangular structures, which contain any n unit cells. The equivalent resistance analytical formula of this network model is investigated using the recursion-transform method based on voltage parameters (RT-V). Two new analytical expressions for equivalent resistance are obtained by establishing the principal difference equations and the boundary condition difference equations, followed by theoretical calculations and derivations. At the end of the article, the correctness of the theoretical formulas is verified through simple circuits, and the conclusions under special cases and infinite networks are discussed. This research result can provide a new theoretical basis for relevant interdisciplinary research and simulation studies.
Optimization of photoelectrochemical (PEC) performance for electrode is very important to efficiently utilize solar energy in the form of chemical energy. The aim of this study is to optimize the PEC performance of TiO2 nanowire array electrodes via the optimization of Na2SO3 and Na2SO4 electrolytes. The electrode shows significant improvement for the PEC performance in Na2SO3 electrolyte with a photocurrent density of 0.8 mA/cm2, which is 5 times higher than that in Na2SO4 electrolyte. The incident photon-to-current conversion efficiency obtained in Na2SO3 electrolyte is 14.7% at u03BB = 390 nm, which is 2.6 times higher than that in Na2SO4 electrolyte. The Mott-Schottky and electrochemical impedance spectroscopy measurements experimentally show that the improved PEC performance is attributed to the efficient separation of photo-generated electrons and holes induced by the consumption of holes by S O 3 2 u2212 . The results of this study provide a promising strategy to improve the PEC performance of electrode by choosing the optimal electrolyte.
As a new technology platform developed in recent years, functional electrode interface has obvious advantages in the regulation of cell adhesion and migration behavior. The accurate regulation of the electrical, chemical and mechanical properties of the electrode interface can realize the real-time monitoring and dynamic intervention of cell behavior, which can better explain the law of cell microenvironment interaction. This paper summarizes the selection of electrode materials, morphology construction, chemical modification and preparation technology related to the functional electrode interface. The mechanism of the functional electrode interface regulating cell adhesion and migration was discussed. The physical and chemical characteristics of the functional electrode interface, the mechanism of electric field stimulation and biomimetic microenvironment regulating cell behavior were elaborated. Functional electrode interface brings new opportunities for research and application fields such as tissue regeneration, disease treatment and drug screening. However, there are some problems at this stage, such as lack of signal stability and biological safety. With the development of intelligent materials and the application of artificial intelligence, it is expected to improve the existing shortcomings, so that the application of functional electrode interface can be developed and improved. At the same time, this process is also conducive to the elucidation of the regulation of cell behavior and the discovery of new treatment methods for diseases.
Due to their excellent topological properties, cobweb models are widely used in fields such as material design and simulation calculations. This study focuses on the electrical characteristics of a 2u00D76u00D7n cascaded cobweb resistor network constructed with cobweb models as basic units. The complex structure of such networks makes it difficult to solve using traditional methods. To address this issue, an optimized recurrence-transformation method is proposed in this paper. First, the cascaded network is equivalently transformed into a single-stage network. Second, a nonlinear recurrence equation for the single-stage network is established. Third, the nonlinear recurrence equation is converted into a linear recurrence equation. Finally, the equivalent resistance of the target resistor network is solved with the help of the linear recurrence equation. This method avoids complex matrix operations involved in traditional methods and features simplicity and convenience. Additionally, it can be extended to other two-terminal cascaded networks.
To address the performance degradation of deep neural networks caused by noisy labels, we propose hard sample adaptive labeling with optimal reweighting for noisy labels, a novel hard sample adaptive weighting method. By reweighting and retraining the model multiple times in each epoch, learning from different subsets of hard samples, and iteratively predicting pseudo-labels, HEALON improves the accuracy of noisy label correction. Experiments demonstrate that our method outperforms existing approaches on noisy label learning tasks, showing significant performance gains and better generalization. This research provides a new perspective for tackling the label noise problem in real-world scenarios.
This article selects the hyperbolic sine function as the trial wave function for the two-component Bose-Einstein condensate soliton solution with Lee-Huang-Yang(LHY)corrections.The Euler-Lagrange equation is derived by variational method,and the static soliton analytical solution is obtained by solving the time-independent Euler-Lagrange equation.The static soliton analytical solution is compared with the static soliton numerical solution.The results show that in the absence of the harmonic potential,the trial wave function provides a good approximation for the static soliton solution,in the presence of the harmonic potential,when 0
