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The basics of atomistic simulation methods, density functional theory and molecular dynamics, are first presented in Chapter 2. Then we demonstrate how to calculate some basic materials properties (including lattice parameter, thermodynamic properties, elastic properties, and defect properties) through first-principles (FP) methods. Because of the remarkable accuracy in predicting such physical and chemical properties of materials, FP is widely used in computational materials science. Finally, we take the design of Mg–Li alloys for ultralightweight application as an example to show the important role of atomistic simulation methods in material design.
The advance of human civilization with materials development from the Stone Age to the Information Age is the starting point of Chapter 1, highlighting significant roles of computational design of materials. Important terms (model, simulation, database, and materials design) used in computational materials science are defined. The past and present development of computational design of materials is then introduced. A few milestones for alloy design, such as the Hume–Rothery rule, the Phase Computation (PHACOMP) method, and the calculation of phase diagrams (CALPHAD) approach, are highlighted. The past two-decade focus on three aspects in computational design of materials (multiscale/multilevel modeling methodologies, simulation software, and scientific database) in the core of the Materials Genome Initiative is emphasized. A general framework of materials design is demonstrated with two flowcharts: through-process simulation of Al alloys during heat treatment, and the three stages for the development of engineering materials. The two-part structure of the book – fundamentals and case studies – is explained.
Chapter 13 starts with brief summary of Chapters 1–12. Subsequently, to show that the strategy described in this book is valid for design of other materials, computational designs for other four materials (Mo2BC thin film, Cu3Sn interconnect material, slag/metal/gas LD-converter steel process, and slag recycling) were highlighted. In view of the need for establishing more quantitative relationships among four cornerstones (composition/processing-structure–properties–performance) in materials science and engineering as well as advancing product design methods, several future orientations and challenges for computational design of engineering materials are suggested. These are (1) advancement of models and approaches for more quantitative simulation in materials design, such as interfacial thermodynamics, thermodynamics under external fields, and a more quantitative phase-field model; (2) the need for scientific databases and materials informatics; (3) enhanced simulation software packages; and (4) concurrent design of materials and products (CDMP). Finally, the correlations among ICME, MGI, and CDMP are discussed.
In Chapter 4, firstly a few basic terms (object and configuration, stress, strain, and constitutive relation between stress tensor and strain tensor), three coordinate systems (shape coordinate, lattice coordinate, and laboratory coordinate), deformation gradient as well as fundamental equations in continuum mechanics are briefly recalled for the sake of understanding fundamental equations of the crystal plasticity finite element method (CPFEM). A few advantages of CPFEM (including its abilities to analyze multiparticle problems and solve crystal mechanics problems with complex boundary conditions) are highlighted. Then, representative mechanical constitutive laws of crystal plasticity including dislocation-based constitutive models and constitutive models for displacive transformation are briefly described, followed by a short introduction to the finite element method (FEM), several FEM software packages (including Adina, ABAQUS, Deform, and ANSYS) and a procedure for CPFEM simulation. Finally, a case study of plastic deformation-induced surface roughening in Al polycrystals is demonstrated to show important features of crystal plasticity finite element method in materials design.
Chapter 7 briefly introduces steels, including classification, production processes, microstructure, and properties as well as computational tools for design of steels. Two case studies for S53 and AISI H13 steels are demonstrated. For S53 steel, high strength and good corrosion resistance are needed. For that purpose, plots of thermodynamic driving forces for precipitates were established, guaranteeing the accurate precipitation of M2C strengthener in steels. In addition, a martensite model is developed, designing maximal strengthening effect and appropriate martensite start temperature to maintain an alloy with lath martensite as the matrix. The corrosion resistance was designed by analyzing thermodynamic effects to maximize Cr partitioning in spinel oxide and enhance the grain boundary cohesion. In the case of AISI H13 steel, precipitations of carbides were simulated. Then simulated microstructure was coupled with structure–property models to predict the stress–strain curve and creep properties. Subsequently, those simulated properties were coupled with FEM to predict the relaxation of internal stresses and deformation behavior at the macroscopic scale during tempering of AISI H13
Test anxiety refers to maladaptive cognitive and physiological reactions that interfere with optimal performance. Self-regulatory models suggest test anxiety occurs when there is a perceived discrepancy between current functioning and mental representations of desired academic goals. Interestingly, prior investigations have demonstrated those with greater interhemispheric communication are better able to detect discrepancies between current functioning and preexisting mental representations. Thus, the current study was designed to investigate the relationship between test anxiety and handedness—a commonly used proxy variable for interhemispheric communication. Undergraduate and graduate students (N = 277, 85.20% female, 68.19% Caucasian, $ \overline{\chi} $age = 29.88) (SD = 9.53) completed the FRIEDBEN Test Anxiety Scale and Edinburgh Handedness Inventory – Short Form. A series of Mann–Whitney U tests were used to test for differences in the cognitive, physiological, and social components of test anxiety between mixed- and consistent-handers. The results indicated that mixed-handers had significantly higher levels of cognitive test anxiety than consistent-handers. We believe this information has important implications for our understanding of the role of discrepancy detection and interhemispheric communication in eliciting and maintaining test-anxious responses.
Liquid crystal elastomers (LCEs) are programmable materials par excellence. I review the history and state of the art of LCE materials and processing development from the perspective of the important remaining step of moving out of the academic research lab and applying LCEs as soft actuators or strain sensors. After a brief introduction for the non-expert of what LCEs are and which their main advantages and limitations are, I discuss the key breakthroughs that LCE research has undergone over its 50-year history. Building on this and drawing from fresh results from on-going research, I consider possible future development trajectories that would help address the outstanding key obstacles to reach mass production at competitive cost. I end with discussing a selected set of application scenarios with good opportunities for LCEs to perform functions that no other material could deliver. Specifically, I focus on responsive buildings incorporating LCE actuator fibres and sheets/ribbons, structural health monitoring with LCE strain sensors monitoring crack growth and propagation or alerting residents of buildings exposed to dangerous levels of deformation, and kinetic and responsive garments incorporating LCE fibre actuators and/or strain sensors.
Biodesign is a recent discipline broad in scale and scope, reaching towards solving complex ecological issues such as climate breakdown, pollution, biodiversity loss and social justice. Designers manipulate living matter and translate scientific discoveries and methods into real-world applications. Although some specialization and knowledge have emerged from working with specific organisms like algae, mycelium, bacteria, for example, the variety and vastness of the natural sciences require a greater understanding of the intricacies of the collaborative nature and interrelationships between lab protocols, scientific tools and methods and the tools and techniques for design production. As a result, the ingenuity of biodesigners to adapt, transform and invent new interdisciplinary methods is an emerging space. We are looking at presenting and discussing the invention of new technical skills, toolkits and machines that allow for the calibration and manipulation of living systems for the the advancement of the discipline of Biodesign.
Yield decline has been the hallmark of Ethiopian sugarcane plantations. However, the extent and causes of the decline have not yet been empirically studied, making it difficult to manage the problem. This study aimed at analyzing the long-term yield data (1954–2022) with respect to variety and soil type. Thus, 8,923 records of yield data were summarized and sorted into decades, varieties, and soil types and then analyzed by applying Mann-Kendall and Tukey’s tests. The fields were classified and mapped using ArcGIS 10.3. The results revealed that 69% of the plantation fields were classified as “yield declining,” and the overall rate of decline has been 8.4 quintals ha−1 year−1 (R2 = 0.76). The rate of decline was higher for older than newer varieties and for vertisols than cambiols. Therefore, the older varieties should be micropropagated or replaced with improved ones, and the vertisols should be amended through practices such as green manuring, improved fallows, etc.
The crystal structure of 5-(3-methoxyphenyl)indoline-2,3-dione (C15H11NO3) was solved and refined using laboratory powder diffraction data and optimized using density functional techniques. The title compound crystallizes in space group Pbca with a = 11.1772(3) Å, b = 7.92536(13) Å, c = 27.0121(7) Å, and V = 2392.82(10) Å3. The asymmetric unit contains one molecule. Isatin molecules are joined into almost flat chains along the a direction by N–H⋯O bonds. The chains are linked into layers by π-stacking interactions. Finally, the third dimension of the crystal is formed by weaker C–H⋯π and C–H⋯O contacts.
The crystal structure of meglumine diatrizoate has been solved and refined using synchrotron X-ray powder diffraction data and optimized using density functional theory techniques. Meglumine diatrizoate crystallizes in space group P21 (#4) with a = 10.74697(4), b = 6.49364(2), c = 18.52774(7) Å, β = 90.2263(3), V = 1292.985(5) Å3, and Z = 2. Two different crystal structures, which yielded essentially identical refinement residuals and positions of the non-H atoms, were obtained. The differences were in the H atom positions and the hydrogen bonding. One structure was 123.0 kJ/mol/cell lower in energy than the other and was adopted for the final description. The crystal structure consists of alternating double layers of cations and anions along the c-axis. The hydrogen bonds link the cations and anions into a three-dimensional framework. Each of the hydrogen atoms on the ammonium nitrogen of the cation acts as a donor in a strong N–H⋯O hydrogen bond. One of these is to a hydroxyl group of another cation, and the other is to the carboxylate group of the anion. Each of the amide nitrogen atoms of the anion forms a strong N–H⋯O intermolecular hydrogen bond, one to a carbonyl and the other to a carboxylate group. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).
Least-squares analysis on the diffraction intensity values certified for NIST SRM676a and SRM1976c α-Al2O3 (corundum) have shown that the intensities of SRM1976c can be simulated by the March-Dollase preferred orientation model along the (001)-direction. Diffraction intensities of randomly oriented corundum crystallites have been calculated from electron density data obtained by conventional and density functional theory (DFT) calculations, on the assumption of independent and similar atomic displacements for Al and O atoms. The results of DFT calculations support that the strongest peak of randomly oriented α-Al2O3 crystalline powder should be 113-reflection, though the intensities simulated by DFT calculations are not closer to NIST SRM676a intensities than those expected for a fully ionized model ${\rm Al}_2^{3 + } {\rm O}_3^{2-}$. Diffraction data of two types of relatively fine (nominally 2–3 μm and ca 0.3 μm) α-Al2O3 powder have been collected and processed by a deconvolutional treatment (DCT). Integrated peak intensities extracted from the DCT data by an individual peak profile fitting method also support that the 113-reflection is the strongest reflection of randomly oriented α-Al2O3 crystalline powder.
Quantitative phase analysis (QPA) of slags is complex due to the natural richness of phases and variability in sample composition. The number of phases frequently exceeds 10, with certain slag types (EAF, BOF, blends, stainless) having extreme peak overlap, making identification difficult. Another convolution arises from the variable crystallite sizes of phases found in slag, as well as the mixture of crystalline and amorphous components specific to each slag type. Additionally, polymorphs are common because of the complexity of the steelmaking and slag cooling processes, such as the cation-doped calcium aluminum silicate (Ca3Al2O6, C3A, Z = 24) supercell in LMF slag. References for these doped variants may not exist or in many cases are not known in advance, therefore it is incumbent on the analyzer to be aware of such discrepancies and choose the best available reference. All issues can compound to form a highly intricate QPA and have prevented previous methods of QPA from accurately measuring phase components in slag. QPA was performed via the internal standard method using 8 wt% ZnO as the internal standard and JADE Pro's Whole Pattern Fitting analysis. For each phase, five variables (lattice parameters, preferred orientation, scale factor, temperature factor, and crystallite size) must be accounted for during quantitation, with a specific emphasis on not refining crystallite sizes for iron oxides and trace phases as they are inclined to over-broaden and interact with the background to improve the goodness of fit (R/E value). Preliminary investigations show somewhat reliable results with the use of custom file sets created within PDF-4+ specifically targeted toward slag minerals to further regulate and normalize the analysis process. The objective of this research is to provide a standard protocol for collecting data, as well as to update methodologies and databases for QPA, to the slag community for implementation in a conventional laboratory setting. Currently, Whole Pattern Fitting “Modified” Rietveld block refinement coupled with the addition of a ZnO internal standard gives the most accurate QPA results, though further research is needed to improve upon the complex issues found in this study of the QPA of slags.
Neuromorphic computing is an approach to computing that is inspired by the structure and function of the brain. In terms of basic research, it aims at improving our understanding of biological intelligence by replicating aspects of its physical substrate – spiking neurons, synaptic plasticity etc. – and harness them towards also replicating its function. With respect to technological advances, it aims to inherit the brain’s combination of computational prowess and extreme energy efficiency; this is thought to foster a plethora of applications, from large-scale neuromorphic systems for machine learning to small-scale edge devices for signal processing and control, for example in the form of wearables for healthcare or adaptive sensors/processors for autonomous agents. The demand and usefulness of neuromorphic computing in bioelectronics is likely to increase in the future as researchers continue to explore its capabilities and develop new applications.
This article analyzes raw driving data of passenger cars in the city of Semnan in Iran, with the objective of understanding the impact of traffic conditions at different times of day (morning, noon, evening, and night). For this study, two cars, the Toyota Prius and the Peugeot Pars (or the IKCO Persia), were used, and the data of speed, longitude, latitude, and altitude of the vehicles were acquired. This data was collected over a week (July 21–28, 2022) for a distance of 670 km (13 hr), with the help of the Global Positioning System application, and were presented for both cars. In addition to this, the data on fuel consumption and average speed, based on the Electronic Control Unit in the Prius, was also collected. Finally, a sensitivity analysis was done on the features of the raw data, based on the Principal Component Analysis method.