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Fluidization bed reactor is an attractive method to synthesize and process quantities of functional nanoparticles, due to the large gas–solid contact area and its potential scalability. Nanoparticles fluidize not individually but as a form of porous agglomerates with a typical porosity above 90%. The porous structure has a significant effect on the hydrodynamic behavior of a single nanoparticle agglomerate, but its influence on the flow behavior of nanoparticle agglomerates in a fluidized bed is currently unclear. In the present study, a drag model was developed to consider the porous structure effects of nanoparticle agglomerates by incorporating porous-structure-based drag laws in the Eulerian–Eulerian two-fluid model. Numerical simulations were performed from particulate to bubbling fluidization state to evaluate the applicability of porous-structure-based drag laws. Results obtained for the minimum fluidization and bubbling velocities, bed expansion ratio, and agglomerate dispersion coefficient show that, compared with the drag law of solid sphere, the porous-structure-based drag laws, especially the drag law of fractal porous spheres, provide a closer fit to the experimental data. This indicates that the pore structures have a great impact on gas–solid flow behavior of nanoparticle agglomerates, and the porous-structure-based drag laws are more suitable for describing flows in nanoparticle agglomerate fluidized beds. [ABSTRACT FROM AUTHOR]

Welding stands as a critical focus for the intelligent and digital transformation of the machinery industry, with automated laser welding playing a pivotal role in the sector's technological advancement. The management of welding deformation in such operations is fundamental, relying on advanced analysis and prediction methods. The endeavor to accurately analyze welding deformation in practical applications is compounded by the interplay of numerous variables, a pronounced coupling effect among these factors, and a reliance on expert intuition. Thus, effective deformation control in automated laser welding operations necessitates the gathering of pre-test laser welding data to develop a predictive approach that accurately reflects real-world conditions and is characterized by improved reliability and stability. To address the technological evolution in automated laser welding, a predictive model based on neural network technology is proposed to map the intricate relationship between process variables and the resulting deformation. At the heart of this approach is the formulation of a predictive model utilizing a back-propagation neural network (BP), with an emphasis on four essential welding parameters: speed, peak power, duty cycle, and defocusing amount. The model's predictive accuracy is then honed through the application of the whale optimization algorithm (WOA) and the differential evolutionary (DE) algorithm. Finally, extensive testing in an automated laser welding experimental setup is conducted to validate the accuracy and reliability of the proposed prediction model. It is demonstrated through these experiments that the deformation prediction model, enhanced by the DEWOA-BP neural network, accurately forecasts the relationship between laser welding parameters and the induced deformation, maintaining a prediction error margin of ±0.1mm. The model is employed to fulfill the requirements for a pre-welding quality evaluation, thereby facilitating a more calculated and informed approach to welding operations. This method of intelligent prediction is not only crucial for the intelligent transformation of laser welding but also holds significant implications for traditional machining technologies such as milling, grinding, and spraying. It offers innovative ideas and methods that are pivotal for the industrial revolution and technological advancement of the traditional machining industry. [ABSTRACT FROM AUTHOR]

In the intelligent transformation of spraying operations, the investigation into the robotic spraying process holds significant importance. The spraying process, however, falls within the realm of experience-driven technology, characterized by high complexity, diverse parameters, and coupling effects. Moreover, the quality of manual spraying processes relies entirely on manual experience. Thus, the crux of the intelligent transformation of spraying robots lies in establishing a mapping model between the spraying process and the resultant spraying quality. To address the challenge of intelligently transforming empirical spraying processes and achieving the mapping from the spraying process to spraying quality, an algorithm employing an enhanced extreme learning machine-based neural network is proposed for predicting spraying process parameters with respect to the evaluation index of spraying quality. In this approach, an algorithmic model based on the Extreme Learning Machine (ELM) neural network is initially constructed utilizing five spraying process parameters: spraying speed, spraying height, spraying width pressure, atomization pressure, and oil spraying pressure. Two spraying quality evaluation indexes, namely average film thickness at the center point and surface roughness, are also incorporated. Subsequently, the prediction neural network is optimized using the K-means improved predator optimization algorithm (KHPO) to enhance the model's prediction accuracy. This optimization step aims to improve the efficiency of the model in predicting spraying quality based on the specified process parameters. Finally, data collection and model validation for the spraying quality prediction algorithm are conducted using a designed robotic automated waterborne paint spraying experimental system. The experimental results demonstrate a significant reduction in the prediction error of the KHPO-ELM neural network model for the average film thickness center point, showcasing a decrease of 61.95% in comparison to the traditional ELM neural network and 50.81% in comparison to the BP neural network. Likewise, the improved neural network model yields a 2.31% decrease in surface roughness prediction error compared to the traditional ELM neural network and a substantial 54.0% reduction compared to the BP neural network. Consequently, the KHPO-ELM neural network, incorporating the prediction algorithm, effectively facilitates the prediction of multi-spraying process parameters for the center point of average film thickness and surface roughness in automated robot spraying. Notably, the prediction algorithm exhibits a commendable level of accuracy in these predictions. [ABSTRACT FROM AUTHOR]

Electrosynthesis of formate via CO2 reduction (CO2ER) is of great significance for completing carbon cycle, however, low CO2 utilization efficiency caused by slow reaction kinetics greatly hinder its large‐scale application. Herein, we reported a ~50 nm‐thick SnSe2 nanosheets with enriched Se vacancies, which exhibits an excellent catalytic activity for pH‐universal formate electrosynthesis. In alkaline, neutral, and acidic electrolytes, this catalyst reached up to the maximum formate Faradaic efficiencies of 94.1%, 81.7%, and 78.1%, respectively. Under corresponding pH conditions, the maximum formate partial current densities achieved 800, 568, and 495 mA cm−2, respectively. Kinetic experiments combined with in situ electrochemical spectroscopy unveiled that the abundant Se vacancies promoted the dissociation of water to produce more protons, thereby accelerating the formation rate of key *OOCH intermediate. Theoretical calculations validated that the formation of enriched Sn active sites lowered the energy barrier for the *OOCH formation, eventually facilitating the formate electrosynthesis process. [ABSTRACT FROM AUTHOR]

Calcium carbonate (CaCO3), the most abundant biogenic mineral on earth, plays a crucial role in various fields such as hydrosphere, biosphere, and climate regulation. Of the four polymorphs, calcite, aragonite, vaterite, and amorphous CaCO3, vaterite is the most enigmatic one due to an ongoing debate regarding its structure that has persisted for nearly a century. In this work, based on systematic transmission electron microscopy characterizations, crystallographic analysis and machine learning aided molecular dynamics simulations with ab initio accuracy, we reveal that vaterite can be regarded as a polytypic structure. The basic phase has a monoclinic lattice possessing pseudohexagonal symmetry. Direct imaging and atomic-scale simulations provide evidence that a single grain of vaterite can contain three orientation variants. Additionally, we find that vaterite undergoes a second-order phase transition with a critical point of ~190 K. These atomic scale insights provide a comprehensive understanding of the structure of vaterite and offer advanced perspectives on the biomineralization process of calcium carbonate. The structure of vaterite has been debated for a century. Combining systematic TEM characterisations, crystallographic analysis, and machine learning aided molecular dynamics simulations, the authors unlock the structural mystery in vaterite. [ABSTRACT FROM AUTHOR]

Thin films of Er2O3 films were grown by atomic layer deposition using the Er precursor tris(1-(dimethylamino)-3,3-dimethylbut-1-en-2-olate)erbium(III) (Er(L1)3), with water as the co-reactant. Saturative, self-limited growth was observed at a substrate temperature of 200 °C for pulse lengths of ≥4.0 s for Er(L1)3 and ≥0.2 s for water. An ALD window was observed from 175 to 225 °C with a growth rate of about 0.25 Å per cycle. Er2O3 films grown at 200 °C on Si(100) and SiO2 substrates with a thickness of 33 nm had root mean square surface roughnesses of 1.75 and 0.75 nm, respectively. Grazing incidence X-ray diffraction patterns showed that the films were composed of polycrystalline Er2O3 at all deposition temperatures on Si(100) and SiO2 substrates. X-ray photoelectron spectroscopy revealed stoichiometric Er2O3, with carbon and nitrogen levels below the detection limits after argon ion sputtering to remove surface impurities. Transmission electron microscopy studies of Er2O3 film growth in nanoscale trenches (aspect ratio = 10) demonstrated conformal coverage. [ABSTRACT FROM AUTHOR]

The positioning accuracy of an industrial robot has a significant impact on its application in precision manufacturing, and it is necessary to calibrate robot kinematic parameters. Previous studies have established numerous nonlinear equations to solve the kinematic parameters, which are complicated and time consuming. A standard particle swarm optimization (PSO) algorithm is limited by long running time and low solution efficiency. Therefore, in this study, a dynamic particle swarm optimization algorithm based on roulette wheel selection (RWS-PSO) is proposed to realize the kinematic parameters calibration. First, a kinematics model is constructed using the standard Denavit-Hartenberg (D-H) method, and the theoretical and actual values of the spatial position of the robot endpoint are obtained via forward kinematics and a Laser Tracker, respectively. Next, the kinematic parameters calibration problem is transformed into a solution of a high-dimensional nonlinear equation using the proposed RWS-PSO algorithm. In the proposed RWS-PSO algorithm, the inertia factor is considered as linearly decreasing and the number of particles is selected by the roulette wheel selection (RWS) to improve its computational efficiency. The proposed RWS-PSO and standard PSO algorithms are compared based on various indices by simulation. The results reveal that the time cost of the RWS-PSO algorithm is much lower than that of the standard PSO algorithm on the basis of high precision and a reduced running time of approximately 53%. Finally, the kinematic parameter errors obtained by the two algorithms are compensated. According to the experimental results, the positioning accuracy of the robot in three (x -, y -, and z -) directions is improved by 78%, 46%, and 67%, respectively, compared to that of before compensation, which proves that the RWS-PSO algorithm is effective and practical for kinematic parameters calibration. [ABSTRACT FROM AUTHOR]

The application of polymer materials in sculpture art creation is extensive and plays a significant role in the development of sculpture art. This article aims to systematically explore the application of polymer materials in contemporary sculpture art creation. The research comprehensively applies various techniques such as literature research, data comparison, and case analysis to explore in detail the ways, methods, and paths of polymer materials employed in the shaping, decoration, and protection of sculptural artworks. First, the article analyzes three methods of shaping sculpture artworks with polymer materials (casting, printing, and constructing). Secondly, it explores two techniques of using polymer materials to adorn sculpture artworks (coloring and imitating texture); then it discusses the significant approach of using polymer materials to protect sculptural artworks (protective spray film). Finally, the research identifies the merits and demerits of using polymer materials in contemporary sculpture art creation. The findings of this study are expected to enrich the effective application of polymer materials in contemporary sculpture art creation and offer novel techniques and ideas for contemporary sculpture art creators. [ABSTRACT FROM AUTHOR]

This paper explores how to improve the sustainability of smart cities from a design perspective, by combining concepts of sustainable design with the Internet of Things and ecosystems. The paper uses a combination of the literature, cases, and model building to develop the research. First, the article analyzes six elements (human, environment, society, economy, and culture) and four dimensions (space, resources, management, and platform) that constitute a sustainable design system for smart cities and constructs a system model based on this analysis; then, the strategy for sustainable design in smart cities is discussed from the perspectives of management and spatial planning by combining CIM and specific cases. The study highlights the importance of prioritizing people and balancing the "people-environment-society-economy-culture" system using technology across the dimensions of management, space, resources, and platform. Moreover, based on the CIM platform, the sustainable design of smart cities can be considered in terms of urban management and spatial planning, enabling people, environment, society, economy, culture, space, resources, and platform to form a smart ecological system and enhance the sustainability of smart cities. [ABSTRACT FROM AUTHOR]

Magnetic van der Waals (vdW) materials offer an opportunity to design heterostructures that will lead to exotic functionalities that arise from interfacial interaction. In addition to coupling to different vdW materials, the naturally oxidized surface layer of a vdW material also forms a heterostructure with its bulk film, giving rise to intriguing phenomena. Here, we directly observe the impact of oxidation on the magnetic domains, namely, magnetic stripe domain and skyrmions, in a nanoscale Fe3GeTe2 flake using cryo Lorentz transmission electron microscopy. After the Fe3GeTe2 is exposed to ambient conditions, partial oxidation leads to an increase in the density of skyrmions even under zero magnetic field. Complete oxidation leads to a loss of the magnetic domain structure. We observe a gradual change in Fe3GeTe2 from single crystal to amorphous as the oxidation increases. The oxidized Fe3GeTe2 primarily consists of iron oxide, which could be antiferromagnetic in nature. We hypothesize that the interfacial interaction between these surface antiferromagnetic oxides and the bulk ferromagnetic Fe3GeTe2, as well as the effect of interfacial roughness, leads to the increase in Néel skyrmion creation. This work opens a path to harness controlled oxidation as a build block to create dense skyrmion lattices without the need for an external magnetic field, leading to potential future applications in spintronic devices. [ABSTRACT FROM AUTHOR]

Quantifying the microstructure of materials is of significance in material development, especially for building the relationship between structure and property. To establish a remarkable structure‐property (SP) linkage, a novel concept referred to as irregular‐representative volume element (IRVE) based on panoramic image stitching technology (PIST) is proposed and a data‐driven scheme integrating irregular domain‐oriented two‐point statistics, principal component analysis (PCA), and symbolic regression based on genetic programming (GPSR) is constructed. Combining with advanced image processing and genetic programming technologies, this scheme improves the microstructure quantization framework. This scheme can not only be applied in different complex conditions for extracting the information of a material microstructure, but can also to embody details of microstructure from the perspective of large scale. IRVE is demonstrated to have both strong statistical representativeness and sufficient physical interpretation, which makes the scheme robust and reliable. Performing the scheme on an example of ferrite heat‐resistant steels, it shows a powerful ability in building an equational SP linkage with high precision (R = 0.91, RMSE = 13.17), the generalization ability of the linkage is also validated by an unseen steel (relative percentage error is 2.66%). The scheme has bright application prospects in predicting mechanical property and accelerating alloy design. [ABSTRACT FROM AUTHOR]

Understanding the key factor driving the efficiency and stability of semiconductor devices is vital. To date, the key factor influencing the long‐term stability of perovskite solar cells (PSCs) remains unknown because of the many influencing factors. In this work, through machine learning, the influences of five factors, including grain size, defect density, bandgap, fluorescence lifetime, and surface roughness, on the efficiency and stability of PSCs have been revealed. It is found that the bandgap has the greatest influence on the efficiency, and the surface roughness and grain size are most influential to the long‐term stability. A mathematical model is given to predict efficiency based on fluorescence lifetime and bandgap. Guided by the model, four groups of experiments are conducted to confirm the machine‐learning predictions and a PSC with 23.4% efficiency and excellent long‐term stability is obtained, as manifested by retention of 97.6% of the initial efficiency after 3288 h aging in the ambient environment, the best stability under these conditions. This work shows that machine learning is an effective means to enrich semiconductor physical models. [ABSTRACT FROM AUTHOR]

Chinese traditional ceramic culture and art are an important foundation for the progress of civilization today, and these cultural foundations are closely related to the daily lives of modern people. Traditional ceramics, as a valuable cultural heritage leftover from ancient times, are inseparable from the various daily lives of modern people and have always played an important leading role in modern people's daily life. Ceramic painting is also an important embodiment form of traditional culture, and the integration of the two is very conducive to enriching people's spiritual world. This paper proposes a VRML-oriented method suitable for establishing a virtual ceramic product space model, that is, a method of building a VRML model with 3DS MAX and other three-dimensional modeling tools combined with a VRML visual editor. According to this method, based on the LOD node, the method is given the optimization and integration method of the virtual ceramic display space scene and completed the modeling of the outdoor background and building required by the virtual ceramic product display space scene. Subsequently, the interactivity of the virtual ceramic display space in the web application is explained, the realization method of the color switching and placement of the virtual ceramic product based on the built-in sensor is proposed, and the interaction based on the external data source of the web page is also given. Finally, in this article, the response time of the page can be basically controlled below 4 s/time, and after a long-term stress test, the system can still maintain the stability of operation, and the probability of serious errors is less than 0.512%. [ABSTRACT FROM AUTHOR]

Stainless steels are widely used in hospitals and public transportation vehicles as one of the most common touch surfaces. Retrofitting stainless steel surfaces with an antimicrobial layer can bring potential public health benefits by reducing the ability of inanimate objects, or fomites, to transmit infections. Here, a facile surface conversion reaction between stainless steel and a solution of KMnO4 and CuSO4 is reported, which leads to a conformal and robust oxyhydroxide layer. Microscopy observations show that the layer is amorphous, continuous, and pinhole‐free with a thickness of only 10–15 nm. The coating adheres strongly to stainless steel and can resist rubbing in simulated friction tests, which is attributed to its intermixing with the substrate without forming a sharp interface. Cu ions incorporated into the surface layer can be released into water droplets deposited on the surface and induce antimicrobial activities against bacteria (Pseudomonas aeruginosa PA14) after 30 min of contact. [ABSTRACT FROM AUTHOR]