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arXiv 2024年

作者： Zhang, Hanlei Bai, Jincheng Chen, Xiabo Li, Can Zhong, Chuanjian Fang, Jiye Zhou, Guangwen Advanced Materials Characterization Laboratory Materials Research Center Missouri University of Science and Technology RollaMO65409 United States Materials Science and Engineering Program Department of Mechanical Engineering State University of New York BinghamtonNY13902 United States Department of Chemistry State University of New York BinghamtonNY13902 United States

Scanning transmission electron microscopy (STEM) is a powerful tool to reveal the morphologies and structures of materials, thereby attracting intensive interests from the scientific and industrial communities. The outstanding spatial (atomic level) and temporal (ms level) resolutions of the STEM techniques generate fruitful amounts of high-definition data, thereby enabling the high-volume and high-speed analysis of materials. On the other hand, processing of the big dataset generated by STEM is time-consuming and beyond the capability of human-based manual work, which urgently calls for computer-based automation. In this work, we present a deep-learning mask region-based neural network (Mask R-CNN) for the recognition of nanoparticles imaged by STEM, as well as generating the associated dimensional analysis. The Mask R-CNN model was tested on simulated STEM-HAADF results with different Gaussian noises, particle shapes and particle sizes, and the results indicated that Gaussian noise has determining influence on the accuracy of recognition. By applying Gaussian and Non-Local Means filters on the noise-containing STEM-HAADF results, the influences of noises are largely mitigated, and recognition accuracy is significantly improved. This filtering-recognition approach was further applied to experimental STEM-HAADF results, which yields satisfying accuracy compared with the traditional threshold methods. The deep-learning-based method developed in this work has great potentials in analysis of the complicated structures and large data generated by STEM-HAADF. © 2024, CC BY.

关键词： Gaussian noise (electronic)

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The Effect of air carbon arc gouging on the mechanical and electrochemical properties of AISI 316L submerged arc welds 11

The Effect of air carbon arc gouging on the mechanical and e...

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11th European Stainless Steel Conference science and Market, ESSC 2022 and 7th European Duplex Stainless Steel Conference and Exhibition, DUPLEX 2022

作者： Campesan, Gabriel Roberto de Bribean Guerra, Ana Paula Koga, Guilherme Yuuki Bolfarini, Claudemiro Federal University of São Carlos Department of Materials Science and Engineering Rod. Washington Luis São Carlos SP CEP 13565-905 Brazil Federal University of São Carlos Graduate Program in Materials Science and Engineering Rod. Washington Luis São Carlos SP CEP 13565-905 Brazil

ISBN: (纸本)9788898990269

In the maintenance and manufacturing practices of equipment in the petrochemical industry, the air carbon arc gouging-ACG is used in the preparation of carbon steel joints to be repaired or joined by welding. However, the Standards impose several restrictions to the use of this cost-effective process to stainless steels. The main concern is the pick-up of carbon and sensitization of the steels against corrosion. Therefore, this paper aimed to evaluate the effect of ACG of AISI 316L steel on its electrochemical and mechanical properties. The repaired/welded joints were studied in three different situations, namely: i) any type of cleaning, ii) brushing and iii) machining after gouging. The 20-mm-thick plate was submerged arc welded. Different regions on cross-section of welds were inspected to assess the degree of sensitization by double-loop electrochemical potentiokinetic reactivation (DL-EPR) method, and the risk of intergranular corrosion by oxalic acid etching tests. The electrochemical techniques and mechanical tests (only welded joints) such as tensile, hardness and impact measurements showed no impairing of such properties with the use of ACG, irrespective to the cleaning method used. © ESSC and DUPLEX 2022. All rights reserved.

关键词： Welds

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Study of Precipitates Formed During Solidification and Post-solidification Stage in Low-Sc Al-Mg-Sc Alloys

Study of Precipitates Formed During Solidification and Post-...

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Light Metals Symposium held at the TMS Annual Meeting and Exhibition, TMS 2025

作者： Nunes, Anderson Thadeu Afonso, Conrado Ramos Moreira Spinelli, José Eduardo Graduate Program in Materials Science and Engineering Federal University of São Carlos São CarlosSP 13565-905 Brazil Department of Materials Engineering Federal University of São Carlos UFSCar São CarlosSP 13565-905 Brazil

ISBN: (纸本)9783031806759

Low-Sc (3Mg2 eutectic was identified in the intercellular/interdendritic regions of the alloys. The β-Al3Mg2 phase was detected in the micrographs of the three Al-Mg-Sc alloys, with more detailed observation in the fracture region of the AlMg3Sc0.1 alloy sample. The Al3Sc phase was identified forming isolated primary clusters during solidification in the AlMg5Sc0.1 and AlMg10Sc0.1 alloys. Nanometric Al3Sc p s with irregular shapes and dimensions smaller than 10 nm were identified in the AlMg5Sc0.1 alloy solidified at 0.7 K/s. © The Minerals, Metals & materials Society 2025.

关键词： Aluminum alloys

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Manufacturing of PEEK-PDMS Composite materials for Intervertebral Disc Implants

Manufacturing of PEEK-PDMS Composite Materials for Intervert...

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International Conference on Electronics Packaging (ICEP)

作者： Heng-ching Mie Yu-chen Liu Jun Mizuno Department of Materials Science and Engineering National Cheng Kung University Program on Smart and Sustainable Manufacturing Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University Department of Mechanical Engineering National Cheng Kung University

ISBN: (数字)9784991191176

ISBN: (纸本)9798350385908

The current treatment approach for lower back pain caused by intervertebral disc (IVD) degeneration involves the use of PEEK (polyether ether ketone) polymer or titanium alloy based spinal cages to replace the degenerated IVD. However, this method not only restricts the natural mobility of the vertebrae but also fails to mimic the structure and function of the human IVD with a single material. Additionally, the use of high-hardness materials may lead to vertebral settling, further contributing to lower back pain. Therefore, this study is dedicated to utilizing high-biocompatibility polymer (PEEK and Polydimethylsiloxane (PDMS)) composites to create an artificial IVD closely mimics the real intervertebral disc. Through adjusting the manufacture parameters, the proportion of PEEK and PDMS, and the porosity we were able to achieve the elastic modulus that is close to those of humans' IVD. Furthermore, the dual-layer structure can better replicate the functions and structure of human intervertebral disc then spinal cage.

关键词： Titanium alloys Pain Spine Implants Back Mechanical factors Manufacturing

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Intercalated Graphene and Colloidal Quantum Dots as a novel Architecture for UV/ Vis/NIR Multispectral Photodetection

Intercalated Graphene and Colloidal Quantum Dots as a novel ...

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IEEE Research and Applications of Photonics In Defense Conference (RAPID)

作者： J.Y. Shang S. Ahn O. Vazquez Mena Program in Materials Science and Engineering University of California San Diego La Jolla USA Department of Nanoengineering University of California San Diego/ La Jolla USA

We present a compact hybrid architecture of intercalated graphene and PbS quantum dots with independent electrical contacts for each graphene layer that allowing for detection of different spectral bands for each grap... 详细信息

ISBN: (数字)9798350373097

ISBN: (纸本)9798350373103

关键词： Electrodes Optical interferometry Graphene Quantum dots Lead Photodetectors Photoconductivity

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Bone quality in zebrafish vertebrae improves after alendronate administration in a glucocorticoid-induced osteoporosis model

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Journal of the Mechanical Behavior of Biomedical materials 2024年 154卷 106521页

作者： Bohns, Fabio Rocha Akhtar, Riaz Chuang, Yung-Jen Chen, Po-Yu Department of Materials Science and Engineering National Tsing Hua University Hsinchu Taiwan Department of Mechanical Materials and Aerospace Engineering University of Liverpool Liverpool United Kingdom International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu Taiwan School of Medicine National Tsing Hua University Hsinchu Taiwan

Glucocorticoid-induced osteoporosis (GIOP) changes the microarchitecture of bones and often leads to the reduction of bone-mineral density (BMD) and increased fracture rates. Zebrafish has been used as an alternative model for GIOP, however, the interaction of GIOP, and its treatment, with zebrafish bone morphometrics and mechanical properties, remains a challenge. Thus, this study aimed to evaluate the effects of prednisolone and alendronate on the properties of zebrafish vertebrae. Adult 7-month-old zebrafish were distributed into four groups: control (CTRL), prednisolone-only (PN), alendronate-only (ALN), and the sequential use of both medicines (PN + ALN). Fish skeletons were scanned via micro-tomography (n = 3) to obtain vertebra morphometrics (e.g., BMD). Bone morphology was assessed using scanning electron microscopy (n = 4) and the biomechanical behaviour with nanoindentation technique (n = 3). The BMD decreased in PN (426.08 ± 18.58 mg/cm3) and ALN (398.23 ± 10.20 mg/cm3) groups compared to the CTRL (490.43 ± 41.96 mg/cm3) (p 3) (p > 0.05). The bone layered structures remain preserved in all groups. The vertebrae of the groups that received ALN and PN + ALN, displayed higher modulus of elasticity (27.27 ± 1.59 GPa and 25.68 ± 2.07 GPa, respectively) than the CTRL (22.74 ± 1.60 GP) (p © 2024 Elsevier Ltd

关键词： Bone

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Computational Reverse engineering Analysis of Scattering Experiments Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D)

arXiv

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arXiv 2024年

作者： Akepati, Sri Vishnuvardhan Reddy Gupta, Nitant Jayaraman, Arthi Data Science Program University of Delaware NewarkDE19716 United States Department of Chemical and Biomolecular Engineering University of Delaware NewarkDE19716 United States Department of Materials Science and Engineering University of Delaware NewarkDE19716 United States

Characterization of structural diversity within soft materials is key for engineering new materials for various applications. Small-angle scattering (SAS) is a widely used characterization technique that provides structural information in soft materials at varying length scales (nm to microns). The typical output of a SAS measurement is the scattered intensity I(q) as a function of q, the scattered wavevector with respect to the incident wave. The scattered wavevector q is represented by its magnitude |q| ≡ q (in inverse distance units) and azimuthal angle θ. While structures with isotropic spatial arrangement can be interpreted by analysis of azimuthally averaged one-dimensional (1D) scattering profile, to understand anisotropic spatial arrangements, one has to interpret the two-dimensional (2D) scattering profile, I(q, θ). Manual interpretation of such 2D profiles is complicated, and usually involves fitting of approximate analytical models to azimuthally averaged sections of the 2D profile. In this paper, we present a new method called CREASE-2D that interprets, without any azimuthal averaging, the entire 2D scattering profile, I(q, θ), and outputs the relevant structural features. CREASE-2D is an extension of the ‘computational reverse engineering analysis for scatting experiments’ (CREASE) method that has been used successfully to analyze 1D SAS profiles for a variety of soft materials. CREASE uses a genetic algorithm for optimization and an artificial neural network (ANN) as the surrogate machine learning (ML) model for fast calculation of 1D ‘computed’ scattering profiles that are then compared to the experimental 1D scattering profiles in the optimization. CREASE-2D goes beyond CREASE by enabling analysis of 2D scattering profiles, which is far more challenging to interpret than the azimuthally averaged 1D profiles. Further, we use XGBoost as the surrogate ML model in CREASE-2D, in place of ANNs, to relate structural features to the I(q, θ) profile. The CREA

关键词： Inverse problems

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A preliminary data fusion study to assess the feasibility of Foundation Process-Property Models in Laser Powder Bed Fusion

arXiv

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arXiv 2025年

作者： Vendrell-Gallart, Oriol Negarandeh, Nima Foumani, Zahra Zanjani Amiri, Mahsa Valdevit, Lorenzo Bostanabad, Ramin Department of Mechanical and Aerospace Engineering University of California IrvineCA92697 United States Materials and Manufacturing Technology Program University of California IrvineCA92697 United States Department of Materials Science and Engineering University of California IrvineCA92697 United States Department of Civil and Environmental Engineering University of California IrvineCA92697 United States

Foundation models are at the forefront of an increasing number of critical applications. In regards to technologies such as additive manufacturing (AM), these models have the potential to dramatically accelerate process optimization and, in turn, design of next generation materials. A major challenge that impedes the construction of foundation process-property models is data scarcity. To understand the impact of this challenge, and since foundation models rely on data fusion, in this work we conduct controlled experiments where we focus on the transferability of information across different material systems and properties. More specifically, we generate experimental datasets from 17-4 PH and 316L stainless steels (SSs) in Laser Powder Bed Fusion (LPBF) where we measure the effect of five process parameters on porosity and hardness. We then leverage Gaussian processes (GPs) for process-property modeling in various configurations to test if knowledge about one material system or property can be leveraged to build more accurate machine learning models for other material systems or properties. Through extensive cross-validation studies and probing the GPs’ interpretable hyperparameters, we study the intricate relation among data size and dimensionality, complexity of the process-property relations, noise, and characteristics of machine learning models. Our findings highlight the need for structured learning approaches that incorporate domain knowledge in building foundation process-property models rather than relying on uninformed data fusion in data-limited applications. © 2025, CC BY.

关键词： Data fusion

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Optimizing cultivation strategies and scaling up for fucoxanthin production using Pavlova sp.

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Bioresource Technology 2024年 399卷 130609页

作者： Chen, Chun-Yen Liu, Ping-Yung Chang, Yu-Han Nagarajan, Dillirani Latagan, Mary Joy D. de Luna, Mark Daniel G. Chen, Jih-Heng Chang, Jo-Shu University Center for Bioscience and Biotechnology National Cheng Kung University Tainan Taiwan Department of Biotechnology and Bioindustry Sciences National Cheng Kung University Tainan701 Taiwan Department of Chemical Engineering National Cheng Kung University Tainan Taiwan Institute of Aquatic Science and Technology College of Hydrosphere Science National Kaohsiung University of Science and Technology Nanzih Campus Kaohsiung City Taiwan Energy Engineering Program National Graduate School of Engineering University of the Philippines Diliman Quezon City1101 Philippines Environmental Engineering Program National Graduate School of Engineering University of the Philippines Quezon City Diliman1101 Philippines Department of Chemical Engineering University of the Philippines Diliman Quezon City1101 Philippines Department of Chemical and Materials Engineering National Kaohsiung University of Science and Technology Kaohsiung807 Taiwan Department of Chemical and Materials Engineering Tunghai University Taichung407 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung407 Taiwan Department of Chemical Engineering and Materials Science Yuan Ze University Chung-Li Taiwan

The microalgal-based production of fucoxanthin has emerged as an imperative research endeavor due to its antioxidant, and anticancer properties. In this study, three brown marine microalgae, namely Skeletonema costatum, Chaetoceros gracilis, and Pavlova sp., were screened for fucoxanthin production. All strains displayed promising results, with Pavlova sp. exhibiting the highest fucoxanthin content (27.91 mg/g) and productivity (1.16 mg/L·day). Moreover, the influence of various cultivation parameters, such as culture media, salinity, sodium nitrate concentration, inoculum size, light intensity, and iron concentration, were investigated and optimized, resulting in a maximum fucoxanthin productivity of 7.89 mg/L·day. The investigation was further expanded to large-scale outdoor cultivation using 50 L tubular photobioreactors, illustrating the potential of Pavlova sp. and the cultivation process for future commercialization. The biomass and fucoxanthin productivity for the large-scale cultivation were 70.7 mg/L·day and 4.78 mg/L·day, respectively. Overall, the findings demonstrated considerable opportunities for fucoxanthin synthesis via microalgae cultivation and processing. © 2024 Elsevier Ltd

关键词： Microalgae

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A Data-driven Framework to Accelerate the Discovery of Hybrid Cathode materials for Metal-based Batteries

arXiv

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arXiv 2024年

作者： Biby, Ahmed H. Rich, Benjamin S. Musgrave, Charles B. Materials Science and Engineering Program University of Colorado Boulder BoulderCO80309 United States Department of Chemistry University of Colorado Boulder BoulderCO80309 United States Department of Chemical Engineering University of Utah Salt Lake CityUT84112 United States Department of Materials Science and Engineering University of Utah Salt Lake CityUT84112 United States

Selecting materials for hybrid cathodes for batteries, which involve a combination of intercalation and conversion materials, has gained interest due to their combined synergistic and compromised properties that are not attainable by their homogeneous counterparts. Herein, we present a data-driven, chemistry-agnostic, and inverse material design framework for discovering hybrid cathode materials (HCMs) for metal-based batteries. This framework systematically explores the potential materials space for any given working ion, evaluates the candidate’s stability, and identifies the growth modes/adsorption of the components for a stable hybrid cathode. To demonstrate the application of the framework and its various possible outcomes, we performed a case study, for which the main design objective was to discover HCMs with an average gravimetric energy density surpassing that of the widely used high energy density NMC333 cathode material. The framework identified LiCr4GaS8-Li2S as a promising HCM that achieves an average energy density of 1,424 Wh/kg (on a lithiated cathode basis) that exceeds NMC333’s maximum theoretical energy density of 1,028 Wh/kg. The identified material has several additional desirable features: 1) possession of thermodynamically stable lithiated and delithiated intercalation and conversion phases;2) minimal volume change, upon (de)lithiation, that mitigates the high-volume change of the conversion material;3) high energy density that ameliorates the low energy density of the intercalation material;4) ability of the intercalation component to act as both a conductive additive and immobilizer of S, while simultaneously contributing to the total cathode energy density;5) the intercalation material serves as an ideal support for the soft sulfur species and finally, 6) we anticipate that the life span, self-discharge, mechanical integrity, and capacity fading are better than those of conventional Li-S batteries. The developed framework was instrumental f

关键词： Lithium compounds

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