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Effect of steel slag on shrinkage characteristics of calcium sulfoaluminate cement 2nd

Effect of steel slag on shrinkage characteristics of calcium...

2nd Conference on Research and Application of Advanced Cementitious Materials and Advanced Materials Forum, CRAACM and AMF 2020

作者： Huang, Haoran Liao, Yishun Qunaynah, Siraj Ai Jiang, Guoxi Guo, Dawei Yuan, Wenjie School of Urban Construction Wuhan University of Science and Technology Wuhan430065 China State Key Laboratory of Refractories and Metallurgy Wuhan University of Science and Technology Wuhan430081 China Department of Civil Construction and Environmental Engineering Iowa State University AmesIA50011 United States

ISBN: (纸本)9783035718119

The effects of steel slag with 0, 10%, 20 % and 40% content on the chemical shrinkage, autogenous shrinkage, internal relative humidity, and drying shrinkage of calcium sulfoaluminate cement paste were studied. The results show that the compressive strength of calcium sulfoaluminate cement paste at an early stage decreases gradually when the content of steel slag increases. When the steel slag content is 0 and 10%, the compressive strength of hardened cement pastes gradually decreases at 90 and 180 days, but the samples with steel slag content of 20% and 40% maintain the compressive strength growth within 180 d. With the extension of curing period, the gap of compressive strength is gradually narrowed. The autogenous shrinkage decreases with the increase of steel slag content and has a good linear relationship with the relative humidity inside the paste. The proportion of autogenous shrinkage to chemical shrinkage is deficient, and most chemical shrinkage occurs in the form of the pore volume. Although the trends of drying shrinkage and autogenous are consistent, the former is more severe than the latter. © 2021 Trans Tech Publications Ltd, Switzerland.

关键词： Compressive strength

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Interplay between magnetism and band topology in the kagome magnets RMn6Sn6

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Physical Review B 2023年第4期108卷 045132-045132页

作者： Y. Lee R. Skomski X. Wang P. P. Orth Y. Ren Byungkyun Kang A. K. Pathak A. Kutepov B. N. Harmon R. J. McQueeney I. I. Mazin Liqin Ke Ames Laboratory U.S. Department of Energy Ames Iowa 50011 USA Department of Physics and Astronomy Nebraska Center for Materials and Nanoscience University of Nebraska Lincoln Nebraska 68588 USA Sophyics Technology LLC McLean Virginia 22102 USA Department of Physics and Astronomy Iowa State University Ames Iowa 50011 USA Department of Materials Science and Engineering University of Washington Seattle Washington 98195 USA College of Arts and Sciences University of Delaware Newark Delaware 19716 USA Department of Physics SUNY Buffalo State Buffalo New York 14222 USA 5 Carriage Lane Roxbury Connecticut 06783 USA Department of Physics and Astronomy George Mason University Fairfax Virginia 22030 USA Quantum Science and Engineering Center George Mason University Fairfax Virginia 22030 USA

Kagome-lattice magnets RMn6Sn6 recently emerged as a new platform to exploit the interplay between magnetism and topological electronic states. Some of the most exciting features of this family are the dramatic dependence of the easy magnetization direction on the rare-earth specie, despite other magnetic and electronic properties being essentially unchanged, and the kagome geometry of the Mn planes that in principle can generate flat bands and Dirac points; gapping of the Dirac points by spin-orbit coupling has been suggested recently to be responsible for the observed anomalous Hall response in the member TbMn6Sn6. In this paper, we address both issues with ab initio calculations. We have discovered the significant role played by higher-order crystal-field parameters and rare-earth magnetic anisotropy constants in these systems. We demonstrate that the microscopic origin of rare-earth magnetic anisotropy can also be quantified and understood at various levels: ab initio, phenomenological, and analytical. In particular, using a simple and physically transparent analytical model based on perturbation theory, we are able to explain, with full quantitative agreement, the evolution of rare-earth magnetic anisotropy across the series. We analyze in detail the topological properties of Mn-dominated bands and demonstrate how they emerge from the multiorbital planar kagome model. We further show that, despite this fact, most of the topological features at the Brillouin zone corner K are strongly 3D and therefore cannot explain the observed quasi-2D anomalous Hall effect, while the most pronounced quasi-2D dispersion are too far removed from the Fermi level. By employing self-consistent calculations with ab initio many-body approaches, we demonstrate that the exchange-correlation effects beyond the density functional theory for itinerant Mn-d electrons do not significantly alter the obtained electronic and magnetic structure. Therefore, we conclude that, contrary to previou

关键词： Electronic structure Magnetic anisotropy Magnetic coupling Magnetic interactions Magnetism Spin-orbit coupling Kagome lattice Rare-earth magnetic materials Topological materials Crystal-field theory DFT+U Density functional theory

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Mapping effective connectivity by virtually perturbing a surrogate brain

arXiv

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arXiv 2022年

作者： Luo, Zixiang Peng, Kaining Liang, Zhichao Cai, Shengyuan Xu, Chenyu Li, Dan Hu, Yu Zhou, Changsong Liu, Quanying Department of Biomedical Engineering Southern University of Science and Technology Shenzhen518055 China Department of Electrical and Computer Engineering Iowa State University AmesIA50011 United States Department of Mathematics Division of Life Science The Hong Kong University of Science and Technology Hong Kong Department of Physics Centre for Nonlinear Studies Hong Kong Baptist University Hong Kong

Effective connectivity (EC), indicative of the causal interactions between brain regions, is fundamental to understanding information processing in the brain. Traditional approaches, which infer EC from neural responses to stimulations, are not suited for mapping whole-brain EC in humans due to being invasive and having limited spatial coverage of stimulations. To address this gap, we present Neural Perturbational Inference (NPI), a data-driven framework designed to map EC across the entire brain. NPI employs an artificial neural network trained to learn large-scale neural dynamics as a computational surrogate of the brain. NPI maps EC by perturbing each region of the surrogate brain and observing the resulting responses in all other regions. NPI captures the directionality, strength, and excitatory/inhibitory properties of brain-wide EC. Our validation of NPI, using models having ground-truth EC, shows its superiority over Granger causality and dynamic causal modeling. Applying NPI to resting-state fMRI data from diverse datasets reveals consistent and structurally supported EC. Further validation using a cortico-cortical evoked potentials dataset reveals a significant correlation between NPI-inferred EC and real stimulation propagation pathways. By transitioning from correlational to causal understandings of brain functionality, NPI marks a stride in decoding the brain’s functional architecture and facilitating both neuroscience research and clinical applications. © 2022, CC BY-NC-SA.

关键词： Brain mapping

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Elevated Temperature Deposition of Metal (Nb, Ti) – Max (Ti2alc) Phase Nanolaminates: Synthesis, Microstructure and Micro-Mechanical Characterization

SSRN

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SSRN 2024年

作者： Supakul, Skye Jain, Manish Yaddanapudi, Krishna Gruber, Jacob El-Atwani, Osman Tucker, Garritt J. Pathak, Siddhartha Materials Science and Engineering Department Iowa State University IA50011 United States Laboratory for Mechanics of Materials and Nanostructures Department of Materials Science and Engineering University of California DavisCA95616 United States Materials Science Program Colorado School of Mines GoldenCO80401 United States Materials Science and Technology Division Department of Physics Baylor University WacoTX76706 United States

We utilize elevated temperature physical vapor deposition techniques to design metal/MAX multilayered nanocomposite thin films with alternating nanoscale metallic (Nb, Ti) and MAX phase (Ti2AlC) layer thicknesses. These metal/MAX nanolaminate architectures attempt to exploit a unique hierarchical topology – as interfaces between the layers are expected to be in direct competition with the internal interfaces within the MAX layers, to drive their tunable macroscopic mechanical behavior. Our first deposition attempt with the Nb/Ti2AlC metal/MAX system showed highly diffused layer interfaces with distinct Ti rich and Nb-Al rich layers, with the presence of MAX phase alongside TiC and other Ti-Al and Nb-Al intermetallic phases. The Nb/Ti2AlC system possessed a layered architecture, though the MAX phases were not found to be continuously present in each alternating layer. The second Ti/Ti2AlC system showed a non-lamellar nanocomposite microstructure and the formation of mixed Tin+1AlCn phases (a mix of n = 1, 2), and no indication of layering. Diffusion occurring between the metal/MAX layers in both cases, likely due to the elevated temperatures during the deposition process, is speculated as the likely cause of these resultant microstructures. The mechanical properties of both systems were evaluated using micromechanical (nanoindentation and micro-pillar compression) techniques, and were found to be within the bounds predicted by composite theory for the Nb system (yield and instability strengths of 4.88±0.1 GPa and 5.57±0.03 GPa ) and just short of expectations for the Ti system (yield and instability strength of 5.61±0.28 GPa and 6.21±0.25 GPa). This work provides important insights for future depositions, such as the need to effectively block/mitigate interlayer diffusion by either lowering the deposition temperature or adding a third layer that can act as a diffusion barrier. © 2024, The Authors. All rights reserved.

关键词： Nanocomposites

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Porous FeAl alloys via powder sintering:Phase transformation,microstructure and aqueous corrosion behavior

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Journal of Materials science & technology 2021年第27期86卷 64-69页

作者： Gang Chen Klaus-Dieter Liss Chao Chen Yuehui He Xuanhui Qu Peng Cao Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing100083PR China Materials and Engineering Science Program Guangdong Technion-Israel Institute of TechnologyShantouGuangdong515063PR China Technion-Israel Institute of Technology Haifa32000Israel School of Mechanical MaterialsMechatronic and Biomedical EngineeringFaculty of Engineering&Information SciencesUniversity of WollongongWollongongNSW2522Australia State Key Laboratory for Powder Metallurgy Central South UniversityChangsha410083PR China Department of Chemical and Materials Engineering The University of AucklandPrivate Bag 92019Auckland1142New Zealand MacDiarmid Institute of Advanced Materials and Nanotechnology Victoria University of WellingtonPO Box 600Wellington6140New Zealand

This study investigates the phase transformation and microstructure of porous Fe Al parts sintered from elemental powder mixtures using in-situ neutron diffraction and in-situ thermal dilatometry.A single B2 structured Fe Al phase was determined in the sintered Fe Al *** combined effects of the Kirkendall porosity,transient liquid phase,and phase transformations associated with powder sintering all contribute to the swelling phenomenon of the final sintered *** aqueous corrosion test indicates that the corrosion products include iron oxides in the porous Fe Al *** accumulation of corrosion products blocks the pore channel and decreases pore size and permeability over the soaking time.

关键词： Powder sintering Intermetallic Porous Corrosion Neutron diffraction

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Enhancing Screen Time Identification in Children with a Multi-View Vision Language Model and Screen Time Tracker

arXiv

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arXiv 2024年

作者： Hou, Xinlong Shen, Sen Li, Xueshen Gao, Xinran Huang, Ziyi Holiday, Steven J. Cribbet, Matthew R. White, Susan W. Sazonov, Edward Gan, Yu Department of Biomedical Engineering Stevens Institute of Technology United States Department of Computer Science Iowa State University United States Department of Electrical Engineering Columbia University The City of New York United States Nokia Bell Labs United States Department of Communication & Information Science University of Alabama United States Department of Psychology University of Alabama United States Department of Electrical and Computer Engineering University of Alabama United States

Being able to accurately monitor the screen exposure of young children is important for research on phenomena linked to screen use such as childhood obesity, physical activity, and social interaction. Most existing studies rely upon self-report or manual measures from bulky wearable sensors, thus lacking efficiency and accuracy in capturing quantitative screen exposure data. In this work, we developed a novel sensor informatics framework that utilizes egocentric images from a wearable sensor, termed the screen time tracker (STT), and a vision language model (VLM). In particular, we devised a multi-view VLM that takes multiple views from egocentric image sequences and interprets screen exposure dynamically. We validated our approach by using a dataset of children’s free-living activities, demonstrating significant improvement over existing methods in plain vision language models and object detection models. Results supported the promise of this monitoring approach, which could optimize behavioral research on screen exposure in children’s naturalistic settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Ultrathin-shell epitaxial Ag@Au core-shell nanowires for high-performance and chemically-stable electronic, optical, and mechanical devices

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Nano Research 2021年第11期14卷 4294-4303页

作者： Yangzhi Zhu Sanggon Kim Xuezhi Ma Peter Byrley Ning Yu Qiushi Liu Xiaoming Sun Da Xu Sangshan Peng Martin C.Hartel Shiming Zhang Vadim Jucaud Mehmet R.Dokmeci Ali Khademhosseini Ruoxue Yan Department of Chemical and Environmental Engineering Bourns College of EngineeringUniversity of California-RiversideRiversideCA92521USA Terasaki Institute for Biomedical Innovation Los AngelesCA90064USA Department of Electrical and Computer Engineering University of California-RiversideRiversideCA92521USA State Key Laboratory of Chemical Resource Engineering Beijing University of Chemical TechnologyBeijing100029China State Key Laboratory of Chemo/Biosensing and Chemometrics College of Chemistry and Chemical EngineeringHunan UniversityChangsha410082China Department of Bioengineering University of California-Los AngelesLos AngelesCA90095USA Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina Material Science and Engineering Program University of California-RiversideRiversideCA92521USA

Silver nanowires (AgNWs) hold great promise for applications in wearable electronics, flexible solar cells, chemical and biological sensors, photonic/plasmonic circuits, and scanning probe microscopy (SPM) due to their unique plasmonic, mechanical, and electronic properties. However, the lifetime, reliability, and operating conditions of AgNW-based devices are significantly restricted by their poor chemical stability, limiting their commercial potentials. Therefore, it is crucial to create a reliable oxidation barrier on AgNWs that provides long-term chemical stability to various optical, electrical, and mechanical devices while maintaining their high performance. Here we report a room-temperature solution-phase approach to grow an ultra-thin, epitaxial gold coating on AgNWs to effectively shield the Ag surface from environmental oxidation. The Ag@Au core-shell nanowires (Ag@Au NWs) remain stable in air for over six months, under elevated temperature and humidity (80 °C and 100% humidity) for twelve weeks, in physiological buffer solutions for three weeks, and can survive overnight treatment of an oxidative solution (2% H2O2). The Ag@Au core-shell NWs demonstrated comparable performance as pristine AgNWs in various electronic, optical, and mechanical devices, such as transparent mesh electrodes, surface-enhanced Raman spectroscopy (SERS) substrates, plasmonic waveguides, plasmonic nanofocusing probes, and high-aspect-ratio, high-resolution atomic force microscopy (AFM) probes. These Au@Ag core-shell NWs offer a universal solution towards chemically-stable AgNW-based devices without compromising material property or device performance.

关键词： epitaxial growth core-shell nanowire plasmonic waveguides atomic force microscopy(AFM)probe transparent electrode wearable electronics

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Effects of graphene layer and gold nanoparticles on sensitivity of humidity sensors

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Journal of Micromanufacturing 2020年第1期3卷 20 - 27页

作者： Jiawei Bao Niloofar Hashemi Jingshuai Guo Nicole N. Hashemi Department of Mechanical Engineering Iowa State University Ames USA University of Applied Science and Technology Tehran Iran Department of Mechanical Engineering Iowa State University Ames USADepartment of Biomedical Sciences Iowa State University Ames USA

Humidity sensors can be used to monitor body sweat. Here, we studied a humidity sensor that comprised of a graphene layer between two electrodes. The operating principle is that the humidity sensor will respond when vapor reaches the graphene layer from the top. Based on the humidity diffusion, the sensor measures the relative humidity (RH) with different response times. Graphene is a material with high diffusivity and small thickness that can increase the sensitivity of a sensor. Based on the micro electro mechanical systems (MEMS) method, we modeled the humidity sensor using COMSOL Multiphysics® transport of diluted species software. Additionally, we used the concentration values from the simulations to determine the relationship between capacitance and relative humidity. The sensitivity was found to be 3.379 × 10−11 pF/%RH for the 4-layer graphene, 1.210 × 10−14 pF/%RH for the 8-layer graphene, and 3.597 × 10−11 pF/%RH for the 16-layer graphene sensor. The sensitivity of 4-layer graphene with gold sensor is 3.872 × 10−13 pF/%RH which is smaller than 4-layer graphene sensor, and graphene with gold nanoparticles shows better response time than 4-layer graphene sensor.

关键词： Graphene humidity sensor COMSOL gold nanoparticles sensitivity

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