Optimization Of Material Properties Research Articles

Surface evolution of thermoelectric material KCu4Se3 explored by scanning tunneling microscopy

Abstract Novel two-dimensional thermoelectric materials have attracted significant attention in the field of thermoelectric due to their low lattice thermal conductivity. A comprehensive understanding of their microscopic structures is crucial for driving further the optimization of materials properties and developing novel functional materials. Here, by using in situ scanning tunneling microscopy, we report the atomic layer evolution and surface reconstruction on the cleaved thermoelectric material KCu4Se3 for the first time. We clearly revealed each atomic layer, including the naturally cleaved K atomic layer, the intermediate Se2- atomic layer, and the Se- atomic layer that emerges in the thermodynamic-stable state. Departing from the majority of studies that predominantly concentrate on macroscopic measurements of the charge transport, our results reveal the coexistence of potassium disorder and complex reconstructed patterns of selenium, which potentially influences charge carrier and lattice dynamics. These results provide the direct insight into the surface microstructures and evolution of KCu4Se3, and sheds useful lights on designing functional materials with superior performance.

Two-photon absorption in quantum dots with Hellmann potential

Abstract We study the nonlinear optical absorption properties of a spherical quantum dot (SQD) with Hellmann potential, focusing on the two-photon absorption (TPA) process, using GaAs/AlGaAs material as an example. The radial Schrödinger equation is solved using the Nikiforov-Uvarov method, while the two-photon absorption coefficient is determined through second-order perturbation theory concerning the electron-photon interaction. Our study shows that the intraband transition has a smaller energy transition than the interband transition, leading to TPA spectra for the intraband transition that is restricted within a smaller energy range and exhibits a higher peak value than those for the interband transition. The peak corresponding to the orbital quantum number of electrons in SQD ℓ = 2 consistently appears to the left of the peak corresponding to ℓ = 1 in both intraband and interband transition cases. Additionally, the dependence of the absorption peak position on the order of transition, n, differs between intra- and inter-band transitions. We also observe blue shift behavior in the TPA spectra as all three parameters, r 0, V 0e , and η, increase. Our investigation has the potential to enable the design of novel photonic devices, ultra-fast optical switches, and highly efficient solar cells through the optimization of quantum dot material properties.

Step-edge-guided nucleation and growth mode transition of α-Ga2O3 heteroepitaxy on vicinal sapphire

Abstract Controlling the epitaxial growth mode of semiconductor layers is crucial for optimizing material properties and device performance. In this work, the growth mode of α-Ga2O3 heteroepitaxial layers was modulated by tuning miscut angles (θ) from 0° to 7° off the (10-10) direction of sapphire (0002) substrate. On flat sapphire surfaces, the growth undergoes a typical three-dimensional (3D) growth mode due to the random nucleation on wide substrate terraces, as evidenced by the hillock morphology and high dislocation densities. As the miscut angle increases to θ=5°, the terrace width of sapphire substrate is comparable to the distance between neighboring nuclei, and consequently, the nucleation is guided by terrace edges, which energetically facilitates the growth mode transition into the desirable two-dimensional coherent growth. Consequently, the mean surface roughness decreases to only 0.62 nm, accompanied by a significant reduction in screw and edge dislocations to 0.16×107 cm-2 and 3.58×109 cm-2, respectively. However, the further increment of miscut angles to θ=7° shrink the terrace width less than nucleation distance, and the step-bunching growth mode is dominant. In this circumstance, the misfit strain is released in the initial growth stage, resulting in surface morphology degradation and increased dislocation densities.

Tackling Data Scarcity Challenge through Active Learning in Materials Processing with Electrospray

Machine learning (ML) has been harnessed as a promising modelling tool for materials research. However, small data, or data scarcity, is a bottleneck when incorporating ML in studies involving experimentation. Current experiment planning methods show several disadvantages: one‐factor‐at‐a‐time (OFAT) experimentation became impractical due to limited laboratory resources; conventional design of experiments (DoE) failed to incorporate high‐dimensional features in ML; Surrogate‐based or Bayesian optimization (BO) shifted the goal to optimize material properties rather than guiding training data accumulation. The present research proposes leveraging active learning (AL) to strategically select critical data for experimentation. Two AL strategies, query‐by‐Committee (QBC) algorithm and Greedy method, are benchmarked against random query baseline on various materials datasets. AL is shown to efficiently reduce model prediction errors with minimal additional experiment data. Investigation of hyperparameters revealed benefits of applying AL at an early stage of experimental dataset construction. Moreover, AL is implemented and validated for an in‐house materials development task ‐ electrospray modelling. AL exploration as a paradigm is highlighted to guide experiment design for efficient data accumulation purposes, and its potential for further ML modelling. In doing so, the power of ML is expected to be fully unleashed to experimental researchers.

Deep learning with plasma plume image sequences for anomaly detection and prediction of growth kinetics during pulsed laser deposition

Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the need arises to develop machine learning (ML) techniques that are capable of extracting information from in situ diagnostics. Here, we demonstrate that intensified-CCD image sequences of the plasma plume generated during PLD can be used for anomaly detection and the prediction of thin film growth kinetics. We develop multi-output (2 + 1)D convolutional neural network regression models that extract deep features from plume dynamics that not only correlate with the measured chamber pressure and incident laser energy, but more importantly, predict parameters of an auto-catalytic film growth model derived from in situ laser reflectivity experiments. Our results demonstrate how ML with in situ plume diagnostics data in PLD can be utilized to maintain deposition conditions in an optimal regime. Further, the predictive capabilities of plume dynamics on the kinetics of film growth or other film properties prior to deposition provides a means for rapid pre-screening of growth conditions for the non-expert, which promises to accelerate materials optimization with PLD.

Mechanistic study on the role of 3D-Printed biomimetic coral bone scaffolds in bone defect repair

With the continuous innovation and development of 3D printing technology, 3D-printed biomimetic coral bone scaffolds have demonstrated significant potential in the field of bone defect repair. This paper aims to explore in-depth the mechanistic study of 3D-printed biomimetic coral bone scaffolds in bone defect repair, by systematically reviewing relevant literature and analyzing their potential mechanisms in promoting bone growth and improving the success rate of bone defect repair. Firstly, this paper introduces the fabrication process and material characteristics of 3D-printed biomimetic coral bone scaffolds. Secondly, the paper discusses the mechanisms of 3D-printed biomimetic coral bone scaffolds in terms of biocompatibility, biomechanical performance, as well as their roles in vascularization and bone formation. Finally, the paper outlines future research directions for 3D-printed biomimetic coral bone scaffolds, including further optimization of material properties, improvement of printing precision, and expansion of clinical applications.

Reduced model and nonlinear analysis of localized instabilities of residually stressed cylinders under axial stretch

In this paper, we present a dimensional reduction to obtain a one-dimensional model to analyze localized necking or bulging in a residually stressed circular cylindrical solid. The nonlinear theory of elasticity is first specialized to obtain the equations governing the homogeneous deformation. Then, to analyze the nonhomogeneous part, we include higher-order correction terms of the axisymmetric displacement components leading to a three-dimensional form of the total potential energy functional. Details of the reduction to the one-dimensional form are given. We focus on a residually stressed Gent material and use numerical methods to solve the governing equations. Two loading conditions are considered. First, the residual stress is maintained constant, while the axial stretch is used as the loading parameter. Second, we keep the pre-stretch constant and monotonically increase the residual stress until bifurcation occurs. We specify initial conditions, find the critical values for localized bifurcation, and compute the change in radius during localized necking or bulging growth. Finally, we optimize material properties and use the one-dimensional model to simulate necking or bulging until the Maxwell values of stretch are reached.

Three-dimensional simulation of heat and moisture transfer in woven fabric structures

Fabric structure parameters have a significant impact on the comfort of heat and moisture transfer in garments. Previous numerical simulations required extensive mathematical calculations and mostly investigated one- or two-dimensional models of fiber assembly without considering the weave structure, which is a key parameter, that significantly influences the porosity of the woven structure and consequently its heat and moisture management. While the finite element method supports the simulation of the optimal shape and material properties with better visibility, previous finite element models focused on heat transfer and neglected water vapor transfer in fabrics. In this article, the finite element simulation of heat and moisture transfer in woven fabrics is established based on the testing principle of thermal resistance and moisture resistance tester using COMSOL Multiphysics software. In this simulation, three-dimensional parametric geometrical models of the fabric are created using curve interpolation methods by acquiring the control point coordinates of different weaves (plain, 2/2 balanced twill, and 4/1 unbalanced twill weaves). Heat and moisture transfer properties of fabric models in the horizontal and vertical directions were analyzed, including the heat flux, moisture resistance, water vapor permeability, and water vapor concentration. The article also deals with the effects of weave structure and fabric cover in a range of 72.1–85.1% on the fabric heat flux and water vapor concentration. Comparison between model and experimental results revealed that the three-dimensional simulation can accurately predict the impact of weave pattern and fabric cover on the fabric heat and moisture transfer performance. In addition, this model can be utilized to study the distribution of heat and water vapor within fabrics, providing a theoretical foundation for optimizing heat and moisture comfort in woven fabrics.

Holistic comparison of environmental barrier coating material candidates through design of a figure of merit

AbstractThe first figure of merit for environmental barrier coating (EBC) materials was designed through a ranking system for material properties pertaining to established EBC failure modes in service. Seven past, present, and novel EBC candidate materials were used in the figure of merit design: SiO2, Ba0.75Sr0.25Al2Si2O8 (BSAS), HfSiO4, Yb2Si2O7, Yb2SiO5, Yb2O3, and YbPO4. Utilizing compiled data from the literature, the presented figure of merit verified Yb2Si2O7 as the state‐of‐the‐art candidate with optimal EBC properties and inferred that YbPO4 should be considered as a potentially viable EBC material candidate. The figure of merit allows for a holistic comparison of EBC candidates and informs experimental and computational search efforts for next‐generation complex EBCs. Clear knowledge gaps found through this work include CaO–MgO–Al2O3–SiO2 (CMAS)‐resistant coatings, EBC lifetimes before delamination, and oxidant diffusion rates in relevant EBC microstructures. It was shown that while some materials show promise for solving a single key failure mode for EBCs (i.e., CMAS reactivity), a community‐wide goal should be placed on materials development to achieve acceptable resistance against all major failure modes, which are interconnected. Novel compositionally complex EBC materials, in addition to layered EBC architectures, show promise for the optimization of material properties for long‐lifetime EBCs in combustion environments.

Prediction and optimization of civil engineering material properties based on artificial intelligence

In the modern research field of predicting and optimizing the performance of civil engineering materials, the incorporation of artificial intelligence (AI) has provided a new perspective. The research is based on a thorough examination of the central role of AI technology and the application of different mathematical models in the task of material performance prediction and optimization, summarizing the current state of AI technology application in the field of civil engineering material performance prediction. Against this backdrop, the research further proposes a brand-new AI-based civil engineering material performance prediction method. This study, aiming to realize the optimization of material performance prediction tasks, utilized cutting-edge machine learning techniques and mathematical models, proposing this new AI prediction framework. Detailed discussions on design, implementation, and evaluation were conducted in the forecast framework, which includes a large number of tables, mathematical functions, and references. Through rigorous study and implementation, the prediction framework exhibited admirable performance, demonstrating extensive applicability for civil engineering material performance prediction and optimization. In conclusion, this new AI-based civil engineering material performance prediction method has demonstrated encouraging results. This study provides a new horizon, illustrating the tremendous potential and value of AI for predicting the performance of civil engineering materials, thereby improving efficiency and effectiveness in the civil engineering field. Overall, this new AI prediction method provides a fresh pathway for future research, making an essential contribution to anticipating and optimizing in civil engineering field.

Strategies for the fast optimization of the glass transition temperature of sustainable epoxy resin systems via machine learning

AbstractAligned with the prevailing sustainability paradigm, the imperative adoption of bio‐based substitutes for constituents within petroleum‐derived epoxy resin becomes evident. Blending bio‐based and petroleum‐based epoxy resins and curing agents, establishes a synergistic compromise addressing both sustainability imperatives and the mechanical efficacy of thermosets. The conventional approach to discovering optimal compositions for multi‐component mixtures under specific boundary conditions includes empirical trial and error and is seen as a protracted and inefficient endeavor. Conversely, leveraging machine learning might afford a streamlined and confident resolution to this challenge. This investigation elucidates the requisite strategies for maximizing the efficiency of material property optimization through the application of Bayesian optimization and active learning. Illustratively, the study demonstrates the proficient optimization of the glass transition temperature within a four‐component epoxy resin system. This optimization is conducted across varying ranges of bio‐content and cost considerations. The study underscores the utility of machine learning in achieving this task with notable efficiency. The efficacy of least squares, kernel ridge regression, Gaussian process regression, and artificial neural networks, is meticulously evaluated through comprehensive seven‐fold cross‐validation and validated against experimental data.

Experimental study on flexural behavior of steel-laminated concrete (NC and UHPC) composite beams with corrugated steel webs

The challenges of the substantial self-weight in prefabricated bridge deck slabs, the presence of numerous scattered steel beam components, and the low durability of bridge deck slabs have long been difficult issues to address in the context of traditional prefabricated steel-normal concrete (NC) composite girder bridges with flat steel webs. This paper proposes a solution through the optimization of structural forms and material properties by introducing a novel structural configuration as prefabricated steel-laminated concrete (LC) composite girder bridges with corrugated steel webs. The flat steel webs were substituted with corrugated steel webs, and the LC bridge deck slab was composed of a NC layer and an ultra-high performance concrete (UHPC) layer. This paper conducted three groups of experimental tests to comparatively analyze the effects of web forms and bridge deck materials on the flexural performance of composite beams. The results indicate that, under the conditions of consistent beam height and steel usage, composite beams with corrugated steel webs have fewer steel components, better stability, and smaller difference in bearing capacity compared to composite beams with flat steel webs. Compared with NC bridge deck slab, laminated bridge deck slab can increase the stiffness and bearing capacity of composite beam by about 13%, and the thickness of the bridge deck slab can be reduced by 20%. Furthermore, this paper presents a shear connection degree calculation method suitable for composite beams with corrugated steel webs, which effectively reduces the number of connectors.

Materials genome engineering accelerates the research and development of organic and perovskite photovoltaics

AbstractThe emerging photovoltaic (PV) technologies, such as organic and perovskite PVs, have the characteristics of complex compositions and processing, resulting in a large multidimensional parameter space for the development and optimization of the technologies. Traditional manual methods are time‐consuming and labor‐intensive in screening and optimizing material properties. Materials genome engineering (MGE) advances an innovative approach that combines efficient experimentation, big database and artificial intelligence (AI) algorithms to accelerate materials research and development. High‐throughput (HT) research platforms perform multidimensional experimental tasks rapidly, providing a large amount of reliable and consistent data for the creation of materials databases. Therefore, the development of novel experimental methods combining HT and AI can accelerate materials design and application, which is beneficial for establishing material‐processing‐property relationships and overcoming bottlenecks in the development of emerging PV technologies. This review introduces the key technologies involved in MGE and overviews the accelerating role of MGE in the field of organic and perovskite PVs.

Numerical Study of Crack Prediction and Growth in Automotive Wheel Rims.

Finite element analysis has become an essential tool for simulating and understanding crack growth. This technique holds significant importance in the field of mechanical engineering, where it finds wide application in the design and optimization of structural components and material properties. This work began with the identification of critical zones and estimated the number of load life repeats through fatigue analysis, specifically applied to automotive rims utilizing innovative finite element methods. To investigate crack behavior, we are used the Extended Finite Element Method (XFEM) with the volumetric approach to compute the Stress Intensity Factor (SIF). The results obtained by our study align closely with experimental tests in terms of detecting the critical zone where a crack can appear. Our findings contribute to the understanding of fatigue behavior in automotive rims, offering new insights into their structural integrity and performance under various load conditions.

Investigation of Evolutionary Computation Techniques for Enhancing Solar Photovoltaic Cell Performance

The pursuit of optimized solar photovoltaic (PV) cell parameters is critical for advancing renewable energy technologies amidst global energy security and climate change challenges. This research investigates the efficacy of particle swarm optimization (PSO) and gray wolf optimization (GWO) in fine-tuning PV cell behavior parameters. Leveraging evolutionary computation, the study aims to maximize energy output, minimize costs, and enhance system reliability by optimizing material properties, structural configurations, and operating conditions. Through iterative optimization, PSO and GWO navigate the parameter space with precision, yielding solutions that maximize energy yield and system efficiency.

Chemical Species Formed on FeB-Fe2B Layers during Wet Sliding Wear Test

In the present work, X-ray photoelectron spectroscopy (XPS) survey spectra of borided AISI 316L for two different times (1 and 6 h) of exposure to simulated body fluid (SBF) were obtained after wet sliding wear. A borided layer of ~39 microns was obtained on the surface of the AISI 316L stainless steel using the thermochemical treatment of boriding. As part of the mechanical and chemical characterization of sliding wear, Berkovich nanoindentation and X-ray spectroscopy tests were used to determine the main properties of the borided layer. The results of the specific wear rate values were higher at 5 mm/s sliding speed than those recorded at 30 mm/s due to the influence of the exposure time of the sample and the complex combinations of chemical reactions with boron (e.g., B2S3, Cr2O3, and Fe2O3) on the surface during the sliding during 6 h of exposure in Hank’s solution due to the formation of the passive film. The knowledge of chemical species formed during wet sliding wear tests on borided AISI 316L is essential for understanding wear mechanisms and materials’ performance and optimizing material properties and materials’ and components’ reliability in the biomedical industry for screws and fastening plates.

Hafnia-Based Ferroelectric Memory: Device Physics Strongly Correlated with Materials Chemistry.

Hafnia-based ferroelectrics and their semiconductor applications are reviewed, focusing on next-generation dynamic random-access-memory (DRAM) and Flash. The challenges of achieving high endurance and high write/read speed and the optimal material properties to achieve them are discussed. In DRAM applications, the trade-off between remanent polarization (Pr), endurance, and operation speed is highlighted, focusing on reducing the critical material property Ec (coercive field). Novel phase formation and interfacial redox chemistry are reviewed as potential game-changers for ferroelectric memories. Regarding Flash operation, the need for an ideal Pr and Ec ratio is emphasized, as excessive Pr can lead to charge trapping, resulting in fatigue and pass disturbance in the NAND array. Achieving the right balance of Pr and Ec for ferroelectric NAND with hafnia-based ferroelectrics remains challenging. This Perspective also recognizes technical advancements in FeFET technology, offering potential solutions for improved performance and casting a positive outlook on the future of ferroelectric memory technology.

A Review of Numerical Simulation and Modeling in High Strain Rate Deformation Processes

Numerical simulation and modeling play a crucial role in understanding and predicting the behavior of materials subjected to high strain rate deformation processes. These processes involve rapid deformation and loading rates, typically encountered in scenarios such as impact events, explosive detonations, metal forming, and crash simulations. By employing advanced computational techniques, researchers and engineers can gain insights into complex material behavior under extreme loading conditions. This paper provides an overview of numerical simulation and modeling approaches used in studying high-strain rate deformation processes. It discusses the challenges associated with capturing dynamic material response, the development of constitutive models, and the use of finite element analysis and computational fluid dynamics. The paper also highlights the importance of material characterization, model validation, and sensitivity analysis for accurate and reliable simulations. Additionally, it explores the application of numerical simulations in optimizing material properties, designing protective structures, and improving the performance of impact-resistant materials. Overall, this review paper emphasizes the significance of numerical simulation and modeling as powerful tools for advancing the understanding and design of high-strain rate deformation processes.

Enhancing Aluminum-Based Composite Manufacturing: Leveraging ZrB2 Reinforcement via Friction Stir Process

This study investigates the enhancement of aluminum-based composite manufacturing through the utilization of ZrB2 reinforcement via Friction Stir Process (FSP). Achieving a uniform distribution of ZrB2 particles is crucial for optimizing material properties. The addition of ZrB2 nanoparticles using FSP has led to notable improvements in various mechanical properties of aluminum. Tensile strength increased by 20.25%, hardness by 35.67%, fatigue strength by 23.67%, and wear resistance by 29.45%. These enhancements underscore the effectiveness of nanoparticle reinforcement in fortifying aluminum matrices against mechanical stresses and wear mechanisms. The results demonstrate the potential of FSP-based techniques in tailoring the mechanical properties of aluminum-based composites for diverse applications. This research contributes valuable insights into advanced manufacturing methods for developing high-performance materials with enhanced mechanical characteristics, facilitating the advancement of aluminum composite technologies for industries requiring superior strength, durability, and wear resistance.

Optimization of structural and electrical properties of graphene-based TiO2 thin film device using Bayesian machine-learning approach

The optimization of laser-induced graphene (LIG) patterning is crucial for achieving desirable electronic properties in flexible electronic devices. In this study, we applied state-of-the-art automated parameter optimization techniques, specifically Bayesian optimization, to enhance the electrical resistance of LIG patterns. By iteratively optimizing the laser power, irradiation time, pressure, and gas type, we achieved minimum LIG resistance within eight batch configurations suggested by the Bayesian optimization (BO) approach. Our method eliminates the reliance on skilled operators, as the initial surrogate models were trained with random parameter evaluations. Notably, our system enables the optimization of material properties even when characterizations are only available outside the experiment loop. Furthermore, the surrogate model provided insights into the underlying mechanisms of LIG growth on quartz substrates. Through partial dependence analysis, we identified relevant physical domains for further investigation, leading to the discovery of negative capacitance and a correlation between structural and electrical properties in LIG. These findings were supported by XPS, Raman, and optical characterizations. Our approach streamlines the experimental design, reducing time and cost while accelerating materials research, and offers human-interpretable conclusions for a deeper understanding of LIG patterning processes.