Ultrathin metal films have a wide variety of applications, especially in microelectronics. A key method to deposit these films is plasma‐enhanced atomic layer deposition (PEALD), which is known for ...its ability to deposit thin films conformally and at relatively low temperatures. Building on the recent work, an improved recipe is reported on for the development of nickel PEALD technology, through which fully epitaxial nickel thin films are deposited. The effect of continuous heating on the phase structure and agglomeration in the metastable thin films is investigated in this paper. The variations of the phase structure are monitored via in situ synchrotron X‐ray diffraction, as well as optical roughness analysis. The temperature windows for phase transformation and particle formation are determined. It is noted that, after the hcp‐to‐fcc transformation and particle coalescence processes are complete, the particles reshape to acquire the thermodynamically stable shapes dictated by the Wulff theorem. Additionally, a crystallographic orientation relationship between the fcc particles and the sapphire substrate is observed, i.e., Ni (111)||Sapphire(002).
Epitaxial nickel thin films are prepared via atomic layer deposition. These films have a hexagonal crystal structure, as dictated by the sapphire substrate. The nickel hexagonal phase is metastable at lower temperatures. The effect of continuous heating of the films to various temperatures is investigated, and the nature of phenomena such as phase transformation and particle formation is discussed.
Creating computationally efficient models that link processing methods, material structures, and properties is essential for the development of new materials. Translating microstructural details to ...macro-level mechanical properties often proves to be an arduous challenge. This paper introduces a novel deep learning-based framework to predict 3D material stress fields, mechanical behavior, and progressive damage in ceramic materials informed by the microstructural features of the material. We construct a dataset of synthetic representative volume elements utilizing X-ray computed tomography scans and employ an automated finite element (FE) modeling approach to generate datasets of alumina ceramics with varying inclusion morphologies. The deep learning model, a U-Net based convolutional neural network (CNN), is trained to understand the structure-property linkages and mechanical responses directly from FE-generated data without transforming them into image format. The CNN's architecture is optimized for capturing both local and global contextual information from the microstructural data, enabling accurate prediction of stress fields and damage evolution. Inclusions within the material are shown to play a crucial role in the initiation and propagation of damage. The CNN model demonstrated robust performance in predicting the stress field, stress-strain curve, and progressive damage curve, with training and test data both showing high and consistent similarity between predictions and the ground truth. Overall, this research offers a generalized approach that can be adapted for different materials and structures toward creating efficient and accurate digital replicas for optimizing material performance in real-world applications.
The objective of this study was to examine, by gender, whether emotional intelligence (EI), peer social support, and/or family social support partially mediated the influence of verbal IQ on Grade 10 ...grade point average (GPA) for 192 students (96 male, 96 female). For male students, EI and peer social support predicted GPA and EI mediated the association between verbal IQ and GPA. For female students, EI, peer social support, and family support predicted GPA but did not mediate the association between verbal IQ and GPA. This study further examined whether subscales of EI (intrapersonal, interpersonal, adaptability, and stress management abilities), peer social support and family social support (emotional, socialising, practical, financial, and advice) added to the prediction of GPA after verbal IQ, gender, and socioeconomic status were controlled. Adaptability, stress management and practical family social support each added to the explanation of variability. None of the peer social support subscales predicted additional variance in GPA.
The Army Research Office funded an invitation-only workshop entitled “Identifying Mathematical Challenges Associated with Failure of Brittle Materials” at the Johns Hopkins University, Maryland on ...May 20–21, 2019. The workshop brought together mathematicians, statisticians, and mechanics of materials researchers with diverse academic and research backgrounds to discuss the state-of-the-art in brittle material failure prediction and to identify new directions for future research. Three specific goals of the workshop were: (1) to identify the state-of-the-art for modeling failure of brittle materials (e.g., ceramics, glasses); (2) to discuss the major mathematical and statistical challenges experienced by academics and scientists studying brittle failure; and (3) to propose novel and unexplored research collaborations between mechanics researchers and mathematicians to address the identified challenges. By virtue of the Army Research Office’s interests and the expertise of participants, these three goals were broadly pursued within the context of understanding and predicting dynamic behavior of brittle materials. This document provides a summary of workshop presentations, discussions, and recommendations for future work (and research funding) that emerged from the workshop. The recommendations for future work are organized into four major thrusts: (i) defining robust quantities of interest; (ii) understanding and modeling variability and stochasticity; (iii) model parameter importance and calibration; and (iv) transitioning from discrete to continuum behaviors. For each thrust, specific future work discussed in the workshop is described in this article.
This study offers novel insights into the Mode I tensile response of an alumina ceramic through the use of computational modeling and the flattened Brazilian disk (FBD) experiments. A modified hybrid ...finite-discrete element method (HFDEM) is developed, integrating a coupled damage and friction cohesive model and a microscopic stochastic fracture model with a Weibull strength distribution by Monte Carlo simulation. The model is used to simulate direct tensile failure processes under quasi-static loading conditions, providing qualitative and quantitative predictions of direct tensile failure processes of an alumina ceramic. Concurrently, quasi-static flattened Brazilian disk tests (indirect tensile tests) are performed on a standard MTS machine coupled with a high-speed camera. The modified HFDEM model is also applied to reproduce the FBD experiments, and our simulated tensile strength is consistent with the experimental results. The results of the modified HFDEM model show three kinds of phenomena (i.e., “underestimation”, “reasonable estimation”, and “overestimation” of the indirect tensile strength) and four different types of associated fracture and fragment patterns of FBD testing. The integration of simulation and experimental results reveal relationships between fracture patterns, fragment geometry, tensile strength, and indirect tensile strength. The fracture and fragmentation patterns derived from our modified HFDEM model can be utilized to analyze the “tensile strength” measured in BD testing. Overall, this research offers important insights and direction for future Brazilian disk experiments and tensile strength assessments, enhancing our comprehension of ceramic failure mechanisms under tensile loadings.
•Hybrid finite-discrete element method is modified by incorporating friction and flaws.•Our model probes over, reasonable, and underestimation in tensile strength.•Simulations and tests reveal links between fracture patterns, fragment geometry and tensile strength.•Study offers insights for Brazilian disk tests and tensile strength measurements in brittle materials.
The relationship between emotional intelligence and academic achievement in high school was examined. Students (
N=667) attending a high school in Huntsville, Alabama completed the Emotional Quotient ...Inventory (EQ-i:YV). At the end of the academic year the EQ-i:YV data was matched with students’ academic records for the year. When EQ-i:YV variables were compared in groups who had achieved very different levels of academic success (highly successful students, moderately successful, and less successful based on grade-point-average for the year), academic success was strongly associated with several dimensions of emotional intelligence. Results are discussed in the context of the importance of emotional and social competency on academic achievement.
A numerical approach combining finite element modeling and machine learning is used to inform the material performance of an alumina ceramic tile undergoing high-velocity impact. In this study, the ...alumina ceramic tile is simulated by incorporating a user-defined Johnson–Holmquist–Beissel (JHB) material model within the framework of smoothed particle hydrodynamics (SPH) in LS-DYNA finite element software. The implementation of the JHB model is verified by comparing equivalent stress–pressure responses through a single element simulation test. After implementation, the computational framework is simulated across our chosen range of conditions by matching the results from both plate impact experiments and ballistic testing from the literature. The computational model is then used to generate training data sets for an artificial neural network (ANN) to predict the residual velocity and projectile erosion for an alumina ceramic tile undergoing high-velocity impact in the SPH framework. The ANN is then used to perform a sensitivity analysis involving exploring the effect of mechanical properties (e.g., strength and shear modulus) and impact simulation geometries (e.g., thickness of ceramic tile) on material performance (i.e., residual projectile velocity and erosion). Overall, this study shows the capability of the FEM-ANN approach in studying the high-velocity impact on ceramic tiles and is applicable to guide the structural-scale design of ceramic-based protection systems.
This paper explores microstructural and mechanical variability in porous ceramics, combining advanced X-ray computed tomography (XCT) and digital image correlation (DIC) techniques to characterize an ...alumina material. The results show low variability in microstructure, with median pore size values for this alumina ranging from 16.0 μm to 17.2 μm across ten samples. Spatial analysis showed internal pores are regularly distributed, and though spacing was found to be largely independent of pore size, the variability in spacing was shown to be greater for smaller pores. Mechanical results show a strain-rate dependence and greater scatter at quasi-static rates, with the coefficient of variation for compressive strength and failure strain decreasing from 10.28% and 10.23% at quasi-static to 5.20% and 4.17% at dynamic rates. In view of the consistency demonstrated in the microstructure, the difference in variability between the quasi-static and dynamic mechanical properties is attributed to variability in testing conditions (e.g., misalignment of platens) and the activation of a greater number of pores in dynamic compression. In summary, these results motivate the use of new spatial characterization parameters via XCT for links to manufacturing, the integration of realistic microstructures into computational models, and focus on the role of defect distributions in dynamic compressive failure events.
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•Pores in an aluminum oxide ceramic found to have tightly controlled sizes.•These pores are shaped as slightly prolate spheroids, with no preferred orientation.•Voronoi tessellation shows pores are regularly distributed in space, with greater spatial variability for smaller pores.•Greater mechanical variability observed in quasi-static compression than in dynamic compression.
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