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Journal of magnesium and Alloys 2022年第11期10卷 3041-3053页

作者： Victoria m.miller Jian-Feng Nie tresa m.pollock Department of materials Science and Engineering University of FloridaGainesvilleFL 32611USA Department of materials Science and Engineering Monash UniversityVictoria 3800Australia materials Department University of California Santa BarbaraSanta BarbaraCA 93106-5050USA

Selection of appropriate thermomechanical processing parameters is crucial for control of the crystallographic texture and grain size distribution in wrought magnesium alloys, which in turn dictate their mechanical properties. In this work, the recrystallization behavior of binary mg-Zn alloys are examined after warm rolling as a function of alloy content and heat treatment temperature. The Avrami exponent increased with increasing heat treatment temperature up to the mg Zn solvus, while the strength of the recrystallization texture decreased.Neither the Avrami exponent nor the qualitative trends in texture evolution were sensitive to the Zn alloying content in the examined range.EBSD and transmission electron microscopy observations revealed that the changes in texture and recrystallization kinetics could be attributed to differences in the nucleation behavior of basal and off-basal orientations.

关键词： magnesium alloys Recrystallization Dislocation substructure Texture

Adaptable physics-based super-resolution for electron backscatter diffraction maps

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npj Computational materials 2022年第1期8卷 2444-2452页

作者： Devendra K.Jangid Neal R.Brodnik michael G.Goebel Amil Khan SaiSidharth majeti mcLean P.Echlin Samantha H.Daly tresa m.pollock B.S.manjunath Electrical and Computer Engineering University California Santa BarbaraSanta Barbara93106-5050CAUSA mechanical Engineering University California Santa BarbaraSanta Barbara93106-5050CAUSA Computer Science University California Santa BarbaraSanta Barbara93106-5050CAUSA materials Department University California Santa BarbaraSanta Barbara93106-5050CAUSA

In computer vision,single-image super-resolution(SISR)has been extensively explored using convolutional neural networks(CNNs)on optical images,but images outside this domain,such as those from scientific experiments,are not well investigated.Experimental data is often gathered using non-optical methods,which alters the metrics for image quality.One such example is electron backscatter diffraction(EBSD),a materials characterization technique that maps crystal arrangement in solid materials,which provides insight into processing,structure,and property relationships.We present a broadly adaptable approach for applying state-of-art SISR networks to generate super-resolved EBSD orientation maps.This approach includes quaternion-based orientation recognition,loss functions that consider rotational effects and crystallographic symmetry,and an inference pipeline to convert network output into established visualization formats for EBSD maps.The ability to generate physically accurate,high-resolution EBSD maps with super-resolution enables high-throughput characterization and broadens the capture capabilities for three-dimensional experimental EBSD datasets.

关键词： maps resolution networks

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先进高温合金单晶材料

先进高温合金单晶材料

Data-driven Bayesian model-based prediction of fatigue crack nucleation in Ni-based superalloys

2004年中国材料研讨会

作者：冯强 Laura J.Rowland tresa m.pollock University of michigan Department of Materials Science and EngineeringAnn ArborMI 48109-2136USA. University of michigan Department of Materials Science and EngineeringAnn ArborMI 48109-2136USA. University of michigan Department of Materials Science and EngineeringAnn ArborMI 48109-2136USA.

＜正＞在过去的35年里,由于定向凝固技术的发明和四代镍基高温合金单晶材料的研发,使飞机发动机叶片的金属使用温度提高了近150℃。与此同时,随着热障涂层和先进冷却技术的应用,飞机涡轮叶片的进口燃烧温度提高了275℃。第四代单晶材料... 详细信息

＜正＞在过去的35年里,由于定向凝固技术的发明和四代镍基高温合金单晶材料的研发,使飞机发动机叶片的金属使用温度提高了近150℃。与此同时,随着热障涂层和先进冷却技术的应用,飞机涡轮叶片的进口燃烧温度提高了275℃。第四代单晶材料通过新的钌（Ru）合金化改变合金元素在γ和γ’相的成分分配比,提高材料的相稳定性:并允许添加更多的高熔点合金元素,从而提高材料的高温强度。本文通过对近30种多组元含钌镍基高温实验合金的系统研究,准备从（1）材料制备,（2）凝固偏析,（3）

关键词：镍基合金单晶钌

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npj Computational materials 2022年第1期8卷 354-368页

作者： maxwell Pinz George Weber Jean Charles Stinville tresa pollock Somnath Ghosh Civil&Systems Engineering Johns Hopkins UniversityBaltimoreMD21218USA materials Department University of CaliforniaSanta BarbaraCA93106USA Departments of Civil&Systems Engineering Mechanical Engineeringand Materials Science&EngineeringJohns Hopkins UniversityBaltimoreMD21218USA

This paper develops a Bayesian inference-based probabilistic crack nucleation model for the Ni-based superalloy René88DT under fatigue loading.A data-driven,machine learning approach is developed,identifying underlying mechanisms driving crack nucleation.An experimental set of fatigue-loaded microstructures is characterized near crack nucleation sites using scanning electron microscopy and electron backscatter diffraction images for correlating the grain morphology and crystallography to the location of crack nucleation sites.A concurrent multiscale model,embedding experimental polycrystalline microstructural representative volume elements(RVEs)in a homogenized material,is developed for fatigue simulations.The RVE domain is modeled by a crystal plasticity finite element model.An anisotropic continuum plasticity model,obtained by homogenization of the crystal plasticity model,is used for the exterior domain.A Bayesian classification method is introduced to optimally select informative state variable predictors of crack nucleation.From this principal set of state variables,a simple scalar crack nucleation indicator is formulated.

关键词： crack fatigue superalloy

New frontiers for the materials genome initiative

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npj Computational materials 2019年第1期4卷 776-798页

作者： Juan J.de Pablo Nicholas E.Jackson michael A.Webb Long-Qing Chen Joel E.moore Dane morgan Ryan Jacobs tresa pollock Darrell G.Schlom Eric S.Toberer James Analytis Ismaila Dabo Dean m.DeLongchamp Gregory A.Fiete Gregory m.Grason Geoffroy Hautier Yifei mo Krishna Rajan Evan J.Reed Efrain Rodriguez Vladan Stevanovic Jin Suntivich Katsuyo Thornton Ji-Cheng Zhao Institute for molecular Engineering University of ChicagoChicagoIL 60615USA Department of materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Physics University of CaliforniaBerkeleyBerkeleyCA 94720USA Department of materials Science and Engineering University of Wisconsin-MadisonMadisonWI 53706USA materials Department and materials Research Laboratory University of CaliforniaSanta BarbaraCA 93106USA Department of materials Science and Engineering Cornell UniversityIthacaNY 14853USA Department of Physics Colorado School of MinesGoldenCO 80401USA materials measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD 20899USA Department of Physics University of Texas at AustinAustinTX 78712USA Department of Polymer Science and Engineering University of MassachusettsAmherstMA 01003USA Institute of Condensed matter and Nanosciences UniversitéCatholique de Louvain1348 Louvain-la-NeuveBelgium Department of materials Science and Engineering University of MarylandCollege ParkMD 20742USA Department of materials Design and Innovation University of Buffalo–The State University of New YorkBuffaloNY 14260-5030USA Department of materials Science and Engineering Stanford UniversityStanfordCA 94305USA Department of Chemistry and Biochemistry University of MarylandCollege ParkMD 20742USA Department of metallurgical and materials Engineering Colorado School of MinesGoldenCO 80401USA Department of materials Science and Engineering University of MichiganAnn ArborMI 48109USA Department of materials Science and Engineering The Ohio State UniversityColumbusOH 43210USA

The materials Genome Initiative(mGI)advanced a new paradigm for materials discovery and design,namely that the pace of new materials deployment could be accelerated through complementary efforts in theory,computation,and experiment.Along with numerous successes,new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the mGI.In may 2017,the National Science Foundation sponsored the workshop“Advancing and Accelerating materials Innovation Through the Synergistic Interaction among Computation,Experiment,and Theory:Opening New Frontiers”to review accomplishments that emerged from investments in science and infrastructure under the mGI,identify scientific opportunities in this new environment,examine how to effectively utilize new materials innovation infrastructure,and discuss challenges in achieving accelerated materials research through the seamless integration of experiment,computation,and theory.This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts.

关键词： utilize frontier accomplishment

Damage mechanism identification in composites via machine learning and acoustic emission

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npj Computational materials 2021年第1期7卷 852-866页

作者： C.muir B.Swaminathan A.S.Almansour K.Sevener C.Smith m.Presby J.D.Kiser T.m.pollock S.Daly materials Department University of California-Santa BarbaraSanta BarbaraCAUSA NASA Glenn Research Center ClevelandOHUSA materials Scienceand Engineering Department University of Michigan-Ann ArborAnn ArborMIUSA mechanical Engineering Department University of California-Santa BarbaraSanta BarbaraCAUSA

Damage mechanism identification has scientific and practical ramifications for the structural health monitoring,design,and application of composite systems.Recent advances in machine learning uncover pathways to identify the waveform-damage mechanism relationship in higher-dimensional spaces for a comprehensive understanding of damage evolution.This review evaluates the state of the field,beginning with a physics-based understanding of acoustic emission waveform feature extraction,followed by a detailed overview of waveform clustering,labeling,and error analysis strategies.Fundamental requirements for damage mechanism identification in any machine learning framework,including those currently in use,under development,and yet to be explored,are discussed.

关键词： composites composite mechanism

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老年鼠海马自由基与学习记忆关系的研究

中华医学杂志 2003年第9期83卷 796-798页

作者：贾健民贾健平贾丽申玉贾琳 m.pollock 河北省邯郸市第一医院神经内科 056002 首都医科大学北京宣武医院神经内科 056002 新西兰Otago大学医学院

目的　探讨老年鼠海马自由基与学习记忆的关系。方法　测试 116只雄性Sprague Dawley大鼠在水迷宫中的学习记忆成绩 ,其中老年鼠 4 6只、青年鼠 36只和成年鼠 34只 ,大鼠海马透析测定各组海马中的羟自由基和超氧化物歧化酶 (SOD)的含量 ... 详细信息

目的　探讨老年鼠海马自由基与学习记忆的关系。方法　测试 116只雄性Sprague Dawley大鼠在水迷宫中的学习记忆成绩 ,其中老年鼠 4 6只、青年鼠 36只和成年鼠 34只 ,大鼠海马透析测定各组海马中的羟自由基和超氧化物歧化酶 (SOD)的含量 ,进行海马CA1和CA4段形态学检查 ,探讨自由基对海马细胞和学习记忆的影响。结果　老年鼠海马SOD活性 (U·g-1·min-1)为 4 10± 5 0 ,青年鼠为 880± 6 2 ,成年鼠为 86 0± 6 0。老年鼠比青年鼠和成年鼠海马自由基显著增加 (P <0 0 0 1) ,老年鼠海马细胞数目比对照组显著减少 (P <0 0 0 1) ,导致学习记忆成绩下降 (P <0 0 1)。结论老年鼠学习记忆能力的下降与自由基显著增加和海马神经元数量下降成正相关。

关键词：海马自由基学习记忆超氧化物歧化酶

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脑缺血海马透析自由基活性研究

中风与神经疾病杂志 1999年第1期16卷 12-14页

作者：贾健民贾健平申玉贾琳 m.pollock Otago大学医学院 056002 邯郸市第一医院 056002

研究脑反复缺血再灌流羟自由基活性动态变化，探讨脑缺血损伤的病理生理机制。方法采用Ｐｕｌｓｉｎｅｌｌｉ和Ｂｒｉｅｆｌｅｙ４血管关闭方法，５ｍｉｎ×３次缺血，用微透析针植人右侧海马ＣＡ１区，用水杨酸盐捕获生成稳定的加合物... 详细信息

研究脑反复缺血再灌流羟自由基活性动态变化，探讨脑缺血损伤的病理生理机制。方法采用Ｐｕｌｓｉｎｅｌｌｉ和Ｂｒｉｅｆｌｅｙ４血管关闭方法，５ｍｉｎ×３次缺血，用微透析针植人右侧海马ＣＡ１区，用水杨酸盐捕获生成稳定的加合物２，３和２，５二氧苯甲酸（２，３和２，５ＤＨＢＡ）。用高压液相（ＨＰＬＣ）电化学检测。结果腹腔内注射水杨酸盐和微管透析针内注射水杨酸盐，２，３和２，５ＤＨＢＡ出现相似变化。缺血期２，３和２，５ＤＨＢＡ稍降低，灌流２０ｍｉｎ显著增高，灌流１ｈ升高至顶峰，持续３ｈ。证明脑反复缺血后再灌流羟自由基显著增高，并且不是一个暂时的现象。结论自由基在脑缺血损害中起重要作用。

关键词：脑缺血自由基活性透析海马再灌注损伤

Type XIX collagen: a promising biomarker from the basement membranes

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Neural Regeneration Research 2020年第6期15卷 988-995页

作者： Ana C.Calvo Laura moreno Leticia moreno Janne m.Toivonen Raquel manzano Nora molina miriam de la Torre tresa López Francisco J.miana-mena maría J.munoz Pilar Zaragoza Pilar Larrodé Alberto García-Redondo Rosario Osta Laboratory of Genetics and Biochemistry(LAGENBIO) University of ZaragozaFaculty of Veterinary SciencesInstituto de Investigación Sanitaria de Aragón(IIS)IA2CIBERNEDZaragozaSpain Hospital Clínico Universitario Lozano Blesa ZaragozaSpain Neurology Department ALS UnitCIBERER U-723Health Research InstituteMadridSpain

Among collagen members in the collagen superfamily,type XIX collagen has raised increasing interest in relation to its structural and biological roles.Type XIX collagen is a Fibril-Associated Collagen with Interrupted Triple helices member,one main subclass of collagens in this superfamily.This collagen contains a triple helix composed of three polypeptide segments aligned in parallel and it is associated with the basement membrane zone in different tissues.The molecular structure of type XIX collagen consists of five collagenous domains,COL1 to COL5,interrupted by six non-collagenous domains,NCI to NC6.The most relevant domain by which this collagen exerts its biological roles is NCI domain that can be cleavage enzymatically to release matricryptins,exerting anti-tumor and anti-angiogenic effect in murine and human models of cancer.Under physiological conditions,type XIX collagen expression decreases after birth in different tissues although it is necessary to keep its basal levels,mainly in skeletal muscle and hippocampal and telencephalic interneurons in brain.Notwithstanding,in amyotrophic lateral sclerosis,altered transcript expression levels show a novel biological effect of this collagen beyond its structural role in basement membranes and its anti-tumor and anti-angiogenic properties.Type XIX collagen can exert a compensatory effect to ameliorate the disease progression under neurodegenerative conditions specific to amyotrophic lateral sclerosis in transgenic SOD1 G93 A mice and amyotrophic lateral sclerosis patients.This novel biological role highlights its nature as prognostic biomarker of disease progression in and as promising therapeutic target,paving the way to a more precise prognosis of amyotrophic lateral sclerosis.

关键词： anti-tumor and anti-angiogenic properties C1 domain COL19A1 gene and protein levels compensatory effect FACIT collagens hippocampal interneurons matricryptins multiplexins NC1 domain regenerative response skeletal muscle type XIX collagen

Performance and calibration of quark/gluon-jet taggers using 140 fb^(-1) of pp collisions at √s=13 TeV with the ATLAS detector

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Chinese Physics C 2024年第2期48卷 1-25页

作者： G. Aad B. Abbott K. Abeling N.J. Abicht S.H. Abidi A. Aboulhorma H. Abramowicz H. Abreu Y. Abulaiti B.S. Acharya C. Adam Bourdarios L. Adamczyk S.V. Addepalli m.J. Addison J. Adelman A. Adiguzel T. Adye A.A. Affolder Y. Afik m.N. Agaras J. Agarwala A. Aggarwal C. Agheorghiesei A. Ahmad F. Ahmadov W.S. Ahmed S. Ahuja X. Ai G. Aielli A. Aikot m. Ait Tamlihat B. Aitbenchikh I. Aizenberg m. Akbiyik T.P.A. Åkesson A.V. Akimov D. Akiyama N.N. Akolkar K. Al Khoury G.L. Alberghi J. Albert P. Albicocco G.L. Albouy S. Alderweireldt m. Aleksa I.N. Aleksandrov C. Alexa T. Alexopoulos F. Alfonsi m. Algren m. Alhroob B. Ali H.m.J. Ali S. Ali S.W. Alibocus m. Aliev G. Alimonti W. Alkakhi C. Allaire B.m.m. Allbrooke J.F. Allen C.A. Allendes Flores P.P. Allport A. Aloisio F. Alonso C. Alpigiani m. Alvarez Estevez A. Alvarez Fernandez m. Alves Cardoso m.G. Alviggi m. Aly Y. Amaral Coutinho A. Ambler C. Amelung m. Amerl C.G. Ames D. Amidei S.P. Amor Dos Santos K.R. Amos V. Ananiev C. Anastopoulos T. Andeen J.K. Anders S.Y. Andrean A. Andreazza S. Angelidakis A. Angerami A.V. Anisenkov A. Annovi C. Antel m.T. Anthony E. Antipov m. Antonelli F. Anulli m. Aoki T. Aoki J.A. Aparisi Pozo m.A. Aparo L. Aperio Bella C. Appelt A. Apyan N. Aranzabal S.J. Arbiol Val C. Arcangeletti A.T.H. Arce E. Arena J-F. Arguin S. Argyropoulos J.-H. Arling O. Arnaez H. Arnold G. Artoni H. Asada K. Asai S. Asai N.A. Asbah J. Assahsah K. Assamagan R. Astalos S. Atashi R.J. Atkin m. Atkinson H. Atmani P.A. Atmasiddha K. Augsten S. Auricchio A.D. Auriol V.A. Austrup G. Avolio K. Axiotis G. Azuelos D. Babal H. Bachacou K. Bachas A. Bachiu F. Backman A. Badea P. Bagnaia m. Bahmani A.J. Bailey V.R. Bailey J.T. Baines L. Baines O.K. Baker E. Bakos D. Bakshi Gupta V. Balakrishnan R. Balasubramanian E.m. Baldin P. Balek E. Ballabene F. Balli L.m. Baltes W.K. Balunas J. Balz E. Banas m. Bandieramonte A. Bandyopadhyay S. Bansal L. Barak m. Barakat E.L. Barberio D. Barberis m. Barbero m.Z. Barel K.N. Barends T. Barillari m-S. Barisits T. Barklow P. Baron D.A. Baron mo The ATLAS Collaboration

The identification of jets originating from quarks and gluons,often referred to as quark/gluon tagging,plays an important role in various analyses performed at the Large Hadron Collider,as Standard model measurements and searches for new particles decaying to quarks often rely on suppressing a large gluon-induced background.This paper describes the measurement of the efficiencies of quark/gluon taggers developed within the ATLAS Collaboration,using √s=13 TeV proton–proton collision data with an integrated luminosity of 140 fb^(-1) collected by the ATLAS experiment.Two taggers with high performances in rejecting jets from gluon over jets from quarks are studied:one tagger is based on requirements on the number of inner-detector tracks associated with the jet,and the other combines several jet substructure observables using a boosted decision tree.A method is established to determine the quark/gluon fraction in data,by using quark/gluon-enriched subsamples defined by the jet pseudorapidity.Differences in tagging efficiency between data and simulation are provided for jets with transverse momentum between 500 GeV and 2 TeV and for multiple tagger working points.

关键词： ATLAS JET QUARK GLUON TAGGING