Distribution Of Material Research Articles

Neural network-based reconstruction of steady-state temperature systems with unknown material composition

Effective thermal management is crucial for the performance and longevity of devices such as computer chips and batteries. A fundamental challenge in this field is accurately determining temperature distributions, which are often limited by incomplete observations. We introduce a neural-network-based approach to overcome these challenges. Specifically, a physics-informed fully convolutional auto-encoder coupled with a preceding spatial propagator network module is used to reconstruct complete temperature fields from partial observations, without requiring additional information about the spatial distribution of materials with varying thermal properties. This paper details the development and validation of our model, demonstrating its effectiveness in virtual sensing setups for both simulated and real-world thermal systems. The reconstruction capabilities for different sampling strategies are investigated. The most challenging of which is the “grid-edge” sampling where only discrete temperature sensors on the periphery of the systems are taken as input, amounting to a sampling ratio as low as 3.12%. The presented method is able to reconstruct the full temperature field with a relative average error of 1.1%. The NN-based approach outperforms the standard Kriging method in predicting the maximum temperature in a system, and proves more robust when the available measurements contain little information.

A novel method for shape pre-optimization of fir-tree couplings in blade-disk connections

This paper presents an innovative approach to the pre-optimization of the blade-disk fir-tree coupling, with the intent of advancing the design and analysis of such connections in turbomachinery. These couplings are subjected to extreme mechanical loads, primarily from centrifugal forces during turbine operation, compounded by aerodynamic and vibrational forces. These conditions pose substantial challenges to ensuring the structural integrity and long-term reliability of the connections. Failure of the blade-disk attachment can lead to catastrophic consequences, making the optimization of these joints crucial.The proposed pre-optimization method addresses this by optimizing the distribution of material between the blade-root, disk-post, and engaging teeth, ensuring uniform load distribution across all teeth. A novel one-dimensional radial spring model is introduced, which simplifies the representation of the joint. The method achieves accurate load-sharing without requiring knowledge of the spring stiffness values, instead relying on stiffness ratios, which in turn are approximated equal to ratios between the areas of throat cross-sections. This approach effectively balances tensile stresses, contact pressures, and, proportionally, local stress peaks, all while streamlining the design process.Validation against finite element models demonstrates the model’s accuracy across different joint configurations with varying numbers of teeth. This method serves as a foundation for more detailed analysis and future optimization efforts, including those targeting specific failure mechanisms such as fretting fatigue and peak stress mitigation. Overall, this work offers a practical and efficient approach to enhancing the performance and reliability of blade-disk connections in turbomachinery.

A simultaneous shape and topology optimization approach of shell structures based on isogeometric analysis and density distribution field

This paper presents a novel simultaneous shape and topology optimization approach of shell structures based on isogeometric analysis and density distribution field. In the optimization approach, Non-Uniform Rational B-Splines (NURBS) technology is utilized to describe the geometry and material distribution of the shell structures. The coordinates and densities of the NURBS control points are utilized as design variables to simultaneously optimize the shape and topology of shell structures. The proposed approach offers significant advantages, including ease of implementation, seamless integration with CAD models, high efficiency, and smooth, clear boundaries. Two representative examples are performed to demonstrate the effectiveness of the proposed approach. The optimized configurations are compared with other works and commercial software results.

Automatic Differentiation Accelerated Shape Optimization Approaches to Photonic Inverse Design in FDFD/FDTD

AbstractShape optimization approaches to inverse design offer low‐dimensional, physically‐guided parameterizations of structures by representing them as combinations of primitives. However, on fixed grids, computing the gradient of a user objective via the adjoint variables method requires a product of forward/adjoint field solutions and the Jacobian of the simulation material distribution with respect to the structural shape parameters. Shape parameters often perturb global parts of the simulation grid resulting in many non‐zero Jacobian entries. These are often computed by finite‐difference (FD) in practice, and hence can be non‐trivial. In this work, the gradient calculation is accelerated by invoking automatic differentiation (AD) in instantiations of structural material distributions, enabled by the development of extensible differentiable feature‐mappings from parameters to primitives and differentiable effective logic operations (denoted AutoDiffGeo or ADG). ADG can also be used to accelerate FD‐based shape optimization by efficient boundary selection. AD‐enhanced shape optimization is demonstrated using three integrated photonic examples: a blazed grating coupler, a waveguide transition taper, and a polarization‐splitting grating coupler. The accelerations of the gradient calculation by AD relative to FD with boundary selection exceed 10, resulting in total optimization wall time accelerations of – on the same hardware with no compromise to device figure‐of‐merit.

Characteristics, drivers and ecological risk assessment of microplastics in the surface water of urban rivers in Guangdong-Hong Kong-Macao Greater Bay Area cities - A case study of Dongguan city

In the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), microplastic pollution in urban rivers is a prominent problem due to the developed economy and high industrial intensity. Using the Xiaohai River, Hanxi River and Dongguan Canal in Dongguan City, an important node city in the GBA, as an example, microplastic characteristics, drivers and ecological risks in the surface water of three rivers were investigated. Results showed that the average abundance of rivers in the wet period (1646.22 ± 154.73 items/m3) was 4.7 times higher than that in the dry period (351.09 ± 34.2 items/m3). Microplastics were mainly in the form of fragments and fibers, with a size range of 30–500 μm, and appeared transparent with white color. The microplastic polymer types PE, PP, PET and PA accounted for more than 70%. There are large differences in the characteristics of microplastic pollution during different hydrological periods. Redundancy analysis showed that the distribution of plastics, chemical materials, packaging and printing industries along the rivers dominated the differences in microplastic abundance. The electronic information industry contributed most to the composition of microplastic polymer types. The polymer hazard index, pollution load index, and potential ecological risk index for rivers indicate a medium-high risk classification or higher. Therefore, the industrial layout along the urban rivers should be rationalized, the disposal of microplastics in wastewater should be increased, and the use of green plastic products should be promoted. This study provides support for the management of microplastic pollution in urban surface water in the GBA.

Breaking the silence of female genital schistosomiasis in Ghana's health system: A case of health workers within the FAST project.

Female Genital Schistosomiasis (FGS) remains a critical and yet neglected topics in Neglected Tropical Diseases (NTDs), significantly affecting the health of women and girls worldwide. Health workers' knowledge of FGS is vital to the prevention and management of the disease. This study adopted an implementation research approach to identify and address the existing knowledge gap regarding FGS among healthcare workers in Ghana. This study was a 3-year (2020-2022) implementation research applying a pragmatic uncontrolled quasi-experimental study design. The study involved a baseline assessment, FGS training intervention for health workers and student nurses, distribution of FGS educational materials, and an endline assessment. A mixed-method approach was applied to data collection involving health workers from two schistosomiasis endemic districts and across the country. NVIVO 12 and STATA 14 were used for qualitative and quantitative data analysis, respectively. Prior to the intervention, the level of awareness about FGS among health workers was less than 8%, and most participants only understood FGS as merely urogenital schistosomiasis in females. In response to this gap, an FGS education intervention in the form of training of health workers, student nurses alongside the distribution of FGS educational materials were carried out. The intervention enhanced health workers' awareness of FGS to more than 61%, encompassing an enhanced understanding of the disease's signs and symptoms to more than 60%, as well as its management strategies. However, access to praziquantel, the primary treatment, remained a significant challenge. The FGS intervention effectively raised healthcare workers' awareness and knowledge. Expanding training and improving praziquantel access are essential for optimal FGS management. A multi-faceted approach involving individuals, communities, and the healthcare system is necessary for comprehensive FGS prevention and control.

Emission Impacts of Direct Reduced Iron‐Ore Processing Systems: A Systems Thinking Approach in Case Studies of Canada and Peru

This study presents a novel methodology for quantifying CO2 emissions in direct reduced iron (DRI)‐grade iron‐ore processing plants using system dynamics modeling (SDM) with an emphasis on associated uncertainties. Two plants in Canada and Peru are analyzed, where similar ore grades (34.89% and 39.10%) but different ore types (hematite and magnetite) lead to distinct mineral processing systems. By incorporating the breakdown of power grid generation sources, the annual CO2 emissions are quantified, finding significantly higher emissions in Peru (22,241.63 tCO2e ton−1) compared to Canada (2,252.85 tCO2e ton−1). These results highlight the critical impact of local energy grids on emissions, underscoring the need to consider both ore characteristics and regional energy profiles in developing decarbonization strategies. This study offers a structured approach to assessing the CO2 impact of raw materials in low‐carbon steel production. It emphasizes the effect of the spatial distribution of raw materials in the steel‐making process. The analysis reveals that emissions from the plant in Peru are nearly 10 times higher than those in Canada, highlighting the significant influence of the local energy grid. These results underscore the influence of ore characteristics and regional energy profiles in developing effective decarbonization strategies.

Application of Deep Learning to Optimize Gradient Porosity Profile for Improved Energy Density of Lithium-Ion Batteries

Lithium-ion batteries with high active material loading can yield a high energy density at low C-rates. However, the sluggish ion transport caused by longer and more tortuous pathways hinders high energy delivery when extracting high power. This study presents the implementation of neural networks to optimize the gradient active material distribution profile throughout the thickness of electrodes to enhance energy density. The profiles were randomly generated, while maintaining a constant average active material in each electrode. An electrochemical–thermal model was used to investigate the impact of different profiles. A neural network model was then developed to establish the connection between the profiles and the resulting energy density for various electrode thicknesses and C-rates, utilizing a limited amount of simulation data. The neural network model could replicate the performance of the electrochemical–thermal model, but with significantly reduced computational time. This enabled the possibility of efficiently exploring a vast number of candidate profiles to identify the most optimal one for each of the positive and negative electrodes. The results showed that the gradient profiles were mostly influenced by the average active material, rather than the thickness of the electrode. Finally, at high currents, the optimal gradient profiles increased the energy density by over four times compared to uniform electrodes.

Optimisation of microstructures from filament extrusion additive manufacturing based on numerical simulation with VOLCO-X

The mechanical properties of parts built with material extrusion additive manufacturing are highly dependent on the material distribution within parts’ microstructure. This varies with the choice of process parameters. Therefore, when designing a functional printed part, one must tailor the printing parameters in order to obtain the desired properties, such as minimal voids. The present work proposes an optimisation method that designs printing parameters to minimise manufacturing time while keeping the void volume fraction at very low values (hence improving mechanical properties), keeping dimensions within tight tolerances and guaranteeing structural integrity. The new optimisation method utilises the authors’ previously developed software VOLCO-X, which is capable of efficiently predicting material distribution from filament extrusion within printed parts, including print track dimensions and microstructure geometry, without the need for any experimental calibration. In order to validate the proposed optimisation scheme, optimised printed parts using the scheme and parts using printing parameters determined by a commercial slicing software were manufactured and compared for different printing speeds and deposition strategies. At printing speed of 16 mm/s, it was possible to decrease the manufacturing time by more than 20% and structural mass by more than 5% in comparison to the commercial slicer printed part, whilst maintaining similar mechanical properties. At printing speed of 96 mm/s, due to the high printing speed, the commercial printed part presented gap faults between deposited strands, while the optimised part had structural integrity. At this printing speed, the optimised printed part presented significant improvements in terms of mechanical properties. The proposed optimisation methodology, in conjunction with VOLCO-X, is a powerful tool that can be used to improve manufacturing by filament extrusion. This innovative tool allows the identification of printing parameters without experiments and trial-and-error approaches, thus saving time and expense.

Klasterisasi Penggunaan Ban dengan Cost Per Kilometer Terendah pada PT. PL menggunakan Metode K-Means

Established in 1969, PT. Puninar has grown to become one of Indonesia's leading logistics companies. The company provides a broad range of logistics solutions through its subsidiaries, encompassing material management, manufacturing, warehousing, distribution, customs services, freight forwarding, and post-delivery operations. After fuel, tires represent the second-largest expenditure for the company. Proper tire management can reduce maintenance costs, making it a key cost-reduction strategy in the logistics sector. This study utilizes data mining techniques, specifically the K-means method, to analyze and classify tires based on travel distance and the lowest cost per kilometer, enabling monthly cost monitoring. PT. Puninar allocates approximately two billion rupiah per month for tire inventory due to its fleet of over seven hundred trucks. The company currently employs various tire brands, including Bridgestone, Doublecoin, Effiplus, Chengsang, and others, with varying levels of durability.

“Poor and Dirty”: Unintended consequences in a hygiene material Distribution in Burkina Faso

The practical interrelationships between the African environment and hygiene practices during a pandemic period is a topic of significant importance. Specifically, this research explores the intersection of cultural dynamics and hygiene practices in Burkina Faso, focusing on a recent example of distribution of hygiene materials. It highlights the negative reactions to these foreign interventions, perceived as patronizing and stigmatizing, which underscores the complexities of implementing health initiatives in diverse socio-cultural landscapes. These findings stress the importance of culturally sensitive approaches and greater engagement in public health initiatives like the latrine usage with specific designs for communities.

Framework for electrochemical-mechanical behavior of all-solid-state batteries: From the reconstruction method to multi-physics and multi-scale modeling

All-solid-state batteries (ASSBs) are high-energy, high-power batteries. To enhance the understanding of the electrochemical-mechanical behavior in ASSBs across different scales, we developed a multi-physics and multi-scale modeling framework. This framework incorporates elastoplastic finite deformation and electrode microstructures of ASSBs, and the role of gradient plasticity in the governing equation for multiple physical fields was discussed. Utilizing X-ray computed tomography, we reconstructed the microstructure through a machine learning (ML)-informed image segmentation process. Our study clarifies the impact of electrode microstructures on concentration, stress, voltage, delamination and buckling from AM to electrode scale. Comparative analysis of the Feret diameter distribution of active materials (AMs) shows that ML-informed image segmentation outperforms two traditional segmentation methods. We observed that the asynchronous diffusion saturation of AMs, varying in shape and size, significantly influences the electrochemical-mechanical behavior of ASSBs, resulting in complicated debonding indices and J-integral distribution at the interface. The proposed upscaling homogenization procedure is demonstrated to be efficient for buckling analysis, with the shape mode closely matching existing experimental observations. These results shed light on the critical multi-physics and multi-scale coupling mechanisms in ASSBs.

“One stone, two birds”: Salt template enabling porosity engineering and single metal atom coordinating toward high-performance zinc-ion capacitors

Zinc-ion hybrid capacitors (ZIHCs) have received increasing attention as energy storage devices owing to their low cost, high safety, and environmental friendliness. However, their progress has been hampered by low energy and power density, as well as unsatisfactory long-cycle stability, mainly due to the lack of suitable electrode materials. In this context, we have developed manganese single atoms implanted in nitrogen-doped porous carbon nanosheets (MnSAs/NCNs) using a metal salt template method as cathodes for ZIHCs. The metal salt serves a dual purpose in the synthesis process: It facilitates the uniform dispersion of Mn atoms within the carbon matrix and acts as an activating agent to create the porous structure. When applied in ZIHCs, the MnSAs/NCNs electrode demonstrates exceptional performance, including a high capacity of 203 mAh g−1, an energy density of 138 Wh kg−1 at 68 W kg−1, and excellent cycle stability with 91% retention over 10,000 cycles. Theoretical calculations indicate that the introduced Mn atoms modulate the local charge distribution of carbon materials, thereby improving the electrochemical property. This work demonstrates the significant potential of carbon materials with metal atoms in zinc-ion hybrid capacitors, not only in enhancing electrochemical performance but also in providing new insights and methods for developing high-performance energy storage devices.

A dichotomous angle refinement discrete material optimization method for fiber composite materials

The discrete material optimization method optimizes the distribution of materials by setting candidate materials using a multi-phase material penalty model. Addressing the issue of reduced efficiency in fiber orientation optimization due to a large number of candidate materials, this paper proposes a dichotomous angle refinement discrete material optimization method to enhance solution speed. This method leverages the efficiency of the bisection approach to rapidly refine the angle in discrete optimization results, thereby narrowing the range of candidate fiber orientations. Furthermore, by introducing fiber orientations perpendicular to the optimized orientation, it mitigates potential local optimization issues during the optimization process, thereby enhancing the method’s optimization capability. Compared to traditional discrete material optimization methods, this method employs a special dichotomy method to determine the candidate materials for each element, thereby decreasing the number of design variables. This method demonstrates higher computational efficiency when solving fiber orientation optimization problems with a large number of candidate materials. Several numerical examples provided in the paper validate the efficiency and stability of the proposed method in addressing optimization problems.

Unraveling the drivers of inequality in primary health-care resource distribution: Evidence from Guangzhou, China

BackgroundPrimary health-care (PHC) resource plays a critical role in addressing challenges related to healthcare accessibility and costs, as well as implementing a graded diagnosis and treatment system. This study aims to analyze the extent and components of inequality in PHC resource allocation in Guangzhou. MethodsBy utilizing data from the Annual Report on Healthcare Institutions for community and township health centers in Guangzhou from 2012 to 2020, this paper analyses the distribution of human, material and financial resources. It examines inequities in health resource allocation using the Gini coefficient. Additionally, it investigates the internal structure of overall inequality through the two-stage nested Theil decomposition method and explores the influencing mechanisms of inequality using the concentration index decomposition method. ResultsThe findings indicate that between 2012 and 2020, except for beds in 2018 and 2019, the Gini coefficients for resource allocation relative to population size remained below 0.3. Moreover, the Gini coefficient for geographical area ranged from 0.1228 to 0.3481. The two-stage nested Theil decomposition results reveal that within-district disparity contributes the most to the overall inequality, exceeding 46 %. The Concentration indexes show negative values, and the decomposition analysis highlights the significant contribution of the number of individuals served (over 72 %) to the inequality in health resource allocation. ConclusionAt the administrative district level, the allocation of PHC resources in Guangzhou demonstrates overall equity, with within-district inequality identified as the primary contributor to the overall inequality. The distribution of PHC resources in Guangzhou follows a pro-poor pattern. The key factors influencing equity enhancement in PHC resource allocation in Guangzhou include the number of individuals served, the presence of township health centers among institution types, the number of hospital admissions per capita, and the proportion of children aged 0–3.

D-BJH: The Intrinsic Model for Characterizing the Pore Size Distribution of Porous Materials.

A novel approach, the differential Barrett-Joyner-Halenda model (D-BJH), is proposed to address the limitations of the traditional BJH model in determining the pore size distribution (PSD). This method integrates multilayer adsorption and capillary condensation processes using advanced numerical techniques, notably, a Galerkin-based framework for solving the differential BJH equations. The D-BJH model offers a precise analytical framework, establishing itself as a benchmark in adsorption theory. It enables accurate PSD calculations and provides robust theoretical support for interpreting adsorption isotherms. Additionally, a simplified D-BJH method allows for direct point-by-point PSD calculation, reducing computational complexity and eliminating cumulative errors inherent in traditional methods like BJH, DH, CI, and VBS. The D-BJH model demonstrates that PSD is directly related to the differential function of the isotherm and pore size concerning relative pressure. Comparative analyses with BJH, DH, and NLDFT models show that D-BJH effectively explains fundamental adsorption mechanisms, such as the "spurious peak" observed in desorption branches. This work expands the applicability of the BJH model, proving that both the D-BJH model and its simplified method are accurate and comprehensive for characterizing the pore structures of microporous and mesoporous materials.

Three-dimensional magnetization reconstruction from electron optical phase images with physical constraints

The ability to characterize three-dimensional (3D) magnetization distributions in nanoscale magnetic materials and devices is essential to fully understand their static and dynamic magnetic properties. Phase contrast techniques in the transmission electron microscope (TEM), such as electron holography and electron ptychography, can be used to record two-dimensional (2D) projections of the in-plane magnetic induction of 3D nanoscale objects. Although the 3D magnetic induction can in principle be reconstructed from one or more tilt series of such 2D projections, conventional tomographic reconstruction algorithms do not recover the 3D magnetization within a sample directly. Here, we use simulations to describe the basis of an improved model-based algorithm for the tomographic reconstruction of a 3D magnetization distribution from one or more tilt series of electron optical phase images recorded in the TEM. The algorithm allows a wide range of physical constraints, including a priori information about the sample geometry and magnetic parameters, to be specified. It also makes use of minimization of the micromagnetic energy in the loss function. We demonstrate the reconstruction of the 3D magnetization of a localized magnetic soliton — a hopfion ring — and discuss the influence of noise, choice of magnetic constants, maximum tilt angle and number of tilt axes on the result. The algorithm can in principle be adapted for other magnetic contrast imaging techniques in the TEM, as well as for other magnetic characterization techniques, such as those based on X-rays or neutrons.

Isogeometric topology optimization for maximizing band gap of two-dimensional phononic crystal structures

A smooth and efficient isogeometric topology optimization (ITO) method for maximizing band gaps in two-dimensional phononic crystals is developed in the paper. The band gaps of phononic crystals are computed by the NURBS-based isogeometric analysis, and the material distributions of two-dimensional phononic crystals are represented by the smooth, high-order continuity of the NURBS surface. The densities defined at each control point of the NURBS surface are employed as optimized design variables. The isogeometric analysis optimization using the same spline technique for both geometry and numerical analysis can benefit the optimization procedure and obtain smooth optimized structures, which can be easily used in 3D printing. The maximizing band gaps of phononic crystals for both out-of-plane and in-plane wave modes are obtained here using the present ITO approach. Numerical examples validated the effectiveness and reliability of the ITO method in broadening the band gaps and finding the optimal phononic crystals. And the numerical results show that the smooth optimized structures are obtained with fewer iterations.

Constructing stress-release layer on Si nanoparticles for high-performance lithium storage

Tailing structures of silicon anode to alleviate the mechanical stress has an important significance in the development of lithium-ion batteries (LIBs). Herein, the stress-released layer of N-doped cubic carbon on the surface of Si nanoparticles (Si@NCC) is explored to stabilize the electrode. Endowed with the unique superiority, the Si@NCC electrode delivers an impressive lithium storage performance with high specific capacity of 630 mAh/g at 0.1 A/g, excellent cycling stability (72.0 % capacity retention after 100 cycles at 0.1 A/g) and good rate capability (375 mAh/g at 2 A/g). It has been demonstrated that the N-doped cubic carbon shell with high conductivity can effectively limit the expansion and agglomeration of Si nanoparticles. Besides, the COMSOL simulations of the stress distributions in materials confirm that the N-doped cubic carbon layer could remarkably decrease the stress originated from the volume expansion of Si nanoparticles during the lithiation. Therefore, this work provides ideas for the mechanical effect of Si-based anodes for lithium storage and sheds lights on the designing advanced materials toward high-performance energy storage devices.

Optimising outbound scheduling in shipyard steel stockyard

ABSTRACT In shipyard operations, steel stockyards manage the intake, storage and distribution of raw materials. However, the inherent customisation of steel plates presents challenges such as inefficient outbound scheduling and elevated energy consumption. To address these issues, this study introduces a novel outbound scheduling model that comprehensively considers manufacturing orders and the current warehouse status during the outbound process. To solve this model, an improved ant colony optimisation algorithm, based on an energy consumption selection strategy (ECS-IACO), is proposed. The algorithm adopts a dynamic pheromone matrix and updates rules to address the complexity of high-dimensional state spaces. The energy consumption selection strategy is integrated into initialisation and state transitions, guiding and decisions throughout. Extensive numerical tests show that the ECS-IACO is robust and highly efficient in solving problems. In real-world scenarios tests, the ECS-IACO reduced energy use by up to 22% more than other algorithms, supporting sustainability efforts in business operations.