Relevant bibliographies by topics / Solute clustering

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Solute clustering'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Solute clustering"

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Söhnel, O., and J. Garside. "Solute clustering and nucleation." Journal of Crystal Growth 89, no. 2-3 (June 1988): 202–8. http://dx.doi.org/10.1016/0022-0248(88)90403-4.

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Peng, Jian, Sumit Bahl, Amit Shyam, J. Allen Haynes, and Dongwon Shin. "Solute-vacancy clustering in aluminum." Acta Materialia 196 (September 2020): 747–58. http://dx.doi.org/10.1016/j.actamat.2020.06.062.

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Larson, M. A., and J. Garside. "Solute clustering in supersaturated solutions." Chemical Engineering Science 41, no. 5 (1986): 1285–89. http://dx.doi.org/10.1016/0009-2509(86)87101-9.

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Larson, M. A., and John Garside. "Solute clustering and interfacial tension." Journal of Crystal Growth 76, no. 1 (July 1986): 88–92. http://dx.doi.org/10.1016/0022-0248(86)90013-8.

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Liu, Zhixiao, Mingyang Ma, Wenfeng Liang, and Huiqiu Deng. "A Mechanistic Study of Clustering and Diffusion of Molybdenum and Rhenium Atoms in Liquid Sodium." Metals 11, no. 9 (September 9, 2021): 1430. http://dx.doi.org/10.3390/met11091430.

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Liquid Na is widely used as the heat transfer medium in high-temperature heat pipes based on Mo-Re alloys. In this study, ab initio molecular dynamics are employed in order to understand the interactions between the Na solvent and Mo or Re solute in the liquid phase. Both the temperature and concentration effects on the clustering and diffusion behaviors of solute atoms are investigated. It is found that Mo2 and Re2 dimers can be stabilized in liquid Na, and the higher temperature leads to a stronger binding force. Pure Re and Mo-Re mixed solutes can form tetramers at the highest concentration
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Fretwell, H. M., J. A. Duffy, M. A. Alam, and H. P. Leighly. "Solute Clustering in Al-Li Alloys." Materials Science Forum 175-178 (November 1994): 359–62. http://dx.doi.org/10.4028/www.scientific.net/msf.175-178.359.

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Niemeyer, Emily D., Richard A. Dunbar, and Frank V. Bright. "On the Local Environment Surrounding Pyrene in Near- and Supercritical Water." Applied Spectroscopy 51, no. 10 (October 1997): 1547–53. http://dx.doi.org/10.1366/0003702971939091.

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We use steady-state and time-resolved fluorescence spectroscopy to probe local solvent–solute interactions between pyrene (the solute) and supercritical water (SCW). Toward this end, we have developed a new fiber-optic-based titanium high-pressure optical cell which can withstand the temperatures and pressure needed to generate supercritical water. Static fluorescence measurements indicate that there is an increase in the local water density surrounding the pyrene molecules (clustering) up to five times the bulk fluid density. This extent of clustering is most prevalent at about one-half the c
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Fan, Zengwei, Jianan Zhu, Xintong Lian, Tengshi Liu, Dexiang Xu, Xicheng Wei, and Han Dong. "Microstructure, Inclusions, and Elemental Distribution of a Compacted Graphite Iron Alloyed by Ce and La Rare Earth (RE) Elements." Metals 12, no. 5 (April 30, 2022): 779. http://dx.doi.org/10.3390/met12050779.

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This work investigates the microstructure and inclusions of a compacted graphite iron (CGI) alloyed by Ce and La rare earth (RE) elements. In our study, alloying elemental distribution and solute segregation were characterized by methods of secondary ion mass spectrometry (SIMS) and a three-dimensional atom probe (3DAP) with high sensitivity and spatial resolution. RE sulfide, MgS, carbide, and composite inclusions formed during solidification and provided heterogeneous nucleation cores for the nucleation of the graphite. Significant solute clustering in the matrix, coupled with the segregatio
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Dupasquier, A., Rafael Ferragut, M. M. Iglesias, C. E. Macchi, Mario Massazza, P. Mengucci, G. Riontino, and Alberto Somoza. "Early Solute Clustering in an AlZnMg Alloy." Materials Science Forum 445-446 (January 2004): 16–20. http://dx.doi.org/10.4028/www.scientific.net/msf.445-446.16.

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Zhang, D., and R. C. Picu. "Solute clustering in Al–Mg binary alloys." Modelling and Simulation in Materials Science and Engineering 12, no. 1 (November 7, 2003): 121–32. http://dx.doi.org/10.1088/0965-0393/12/1/011.

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Dissertations / Theses on the topic "Solute clustering"

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Ivanov, Rosen. "Solute clustering in multi-component aluminium alloys." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAI012/document.

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La décomposition de solutions solides sursaturées d'alliages multiconstitués Al-Cu- (Li, Mg) pose des défis théoriques et expérimentaux. La formation de fluctuations chimiques à température ambiante est analysée de façon critique en utilisant une combinaison de diffusion centrale (SAS), de calorimétrie différentielle à balayage (DSC), de sonde atomique tomographique (APT) et de micro-dureté. Une méthodologie pour l'interprétation combinée de données SAS d'expériences utilisant des neutrons et des rayons X est proposée et permet une comparaison avec les données de sonde atomique. Les résultats
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Marceau, Ross Kevin William. "Design in Light Alloys by Understanding the Solute Clustering Processes During the Early Stages of Age Hardening in Al-Cu-Mg Alloys." Thesis, The University of Sydney, 2008. http://hdl.handle.net/2123/4008.

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The evolution of atomistic-level nanostructure during the early stages of both standard, high-temperature T6 heat treatment, and low-temperature secondary ageing after interruption of the former (T6I4), has been investigated in rapid hardening Al-Cu-Mg alloys using a variety of microscopy and microanalytical techniques, including transmission electron microscopy (TEM), positron annihilation spectroscopy (PAS) and atom probe tomography (APT). In order to carry out this objective, quantitative data-analysis methods were developed with respect to new cluster-finding algorithms, specifically desig
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Marceau, Ross Kevin William. "Design in Light Alloys by Understanding the Solute Clustering Processes During the Early Stages of Age Hardening in Al-Cu-Mg Alloys." University of Sydney, 2008. http://hdl.handle.net/2123/4008.

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Doctor of Philosophy (PhD)<br>The evolution of atomistic-level nanostructure during the early stages of both standard, high-temperature T6 heat treatment, and low-temperature secondary ageing after interruption of the former (T6I4), has been investigated in rapid hardening Al-Cu-Mg alloys using a variety of microscopy and microanalytical techniques, including transmission electron microscopy (TEM), positron annihilation spectroscopy (PAS) and atom probe tomography (APT). In order to carry out this objective, quantitative data-analysis methods were developed with respect to new cluster-finding
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Perland, Emelie. "Atypical Solute Carriers : Identification, evolutionary conservation, structure and histology of novel membrane-bound transporters." Doctoral thesis, Uppsala universitet, Institutionen för neurovetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-324206.

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Solute carriers (SLCs) constitute the largest family of membrane-bound transporter proteins in humans, and they convey transport of nutrients, ions, drugs and waste over cellular membranes via facilitative diffusion, co-transport or exchange. Several SLCs are associated with diseases and their location in membranes and specific substrate transport makes them excellent as drug targets. However, as 30 % of the 430 identified SLCs are still orphans, there are yet numerous opportunities to explain diseases and discover potential drug targets. Among the novel proteins are 29 atypical SLCs of major
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Marceau, Ross K. W. "Design in light alloys by understanding solute clustering processes during the early stages of age hardening in Al-Cu-Mg alloys." Connect to full text, 2008. http://ses.library.usyd.edu.au/handle/2123/4008.

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Thesis (Ph. D.)--University of Sydney, 2008.<br>Title from title screen (viewed Jan 07, 2009). Includes two published articles co-authored with others. Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the Australina Key Centre for Microscopy and Microanalysis, Electron Microscope Unit, Faculty of Science. Includes bibliographical references. Also available in print form.
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Wang, Kun. "Algorithmes et méthodes pour le diagnostic ex-situ et in-situ de systèmes piles à combustible haute température de type oxyde solide." Phd thesis, Université de Franche-Comté, 2012. http://tel.archives-ouvertes.fr/tel-01017170.

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Le projet Européen " GENIUS " ambitionne de développer les méthodologies génériques pour le diagnostic de systèmes piles à combustible à haute température de type oxyde solide (SOFC). Le travail de cette thèse s'intègre dans ce projet ; il a pour objectif la mise en oeuvre d'un outil de diagnostic en utilisant le stack comme capteur spécial pour détecter et identifierles défaillances dans les sous-systèmes du stack SOFC.Trois algorithmes de diagnostic ont été développés, se basant respectivement sur la méthode de classification k-means, la technique de décomposition du signal en ondelettes ain
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Lu, Yun Fang, and 呂永方. "The solute clustering during nucleation and crystal growth in a stirred solution and the operation and control for various crystallizers." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/44254645202940724468.

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博士<br>長庚大學<br>化工與材料工程研究所<br>98<br>This thesis is divided into two parts. In the first part, the solute clustering process of solute molecules during nucleation and crystal growth is studied in a stirred solution. In the second part, the operation and control for various crystallizers is investigated in detail. In the first part, a kinetic model is developed to simulate the solute clustering due to the aggregation and dissociation of solute molecules during nucleation in a stirred solution. Solute clusters are formed through a series of particle collisions and become stable nuclei when cluster
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Book chapters on the topic "Solute clustering"

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Combes, J. R., K. P. Johnston, K. E. O'Shea, and M. A. Fox. "Influence of Solvent—Solute and Solute—Solute Clustering on Chemical Reactions in Supercritical Fluids." In ACS Symposium Series, 31–47. Washington, DC: American Chemical Society, 1992. http://dx.doi.org/10.1021/bk-1992-0488.ch003.

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Jiang, Lu, Kathleen Wood, Anna Sokolova, Robert Knott, Timothy Langan, and Thomas Dorin. "Solute Clustering During Natural Ageing in Al-Cu-(Sc)-(Zr) Alloys." In The Minerals, Metals & Materials Series, 1247–51. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-22532-1_167.

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Lee, Byeong-Joo, Hyo-Sun Jang, Jong-Kwan Lee, Antonio João Seco Ferreira Tapia, and Nack Joon Kim. "Solute-Dislocation Binding and Solute Clustering as a Mechanism for Room Temperature Ductility and Formability of Mg Alloys." In The Minerals, Metals & Materials Series, 93–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92533-8_16.

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Chen, Yimeng, Yan Dong, Emmanuelle Marquis, Zhijie Jiao, Justin Hesterberg, Gary Was, and Peter Chou. "Solute Clustering in As-irradiated and Post-irradiation-Annealed 304 Stainless Steel." In The Minerals, Metals & Materials Series, 2189–207. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-030-04639-2_147.

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Chen, Yimeng, Yan Dong, Emmanuelle Marquis, Zhijie Jiao, Justin Hesterberg, Gary Was, and Peter Chou. "Solute Clustering in As-irradiated and Post-irradiation-Annealed 304 Stainless Steel." In The Minerals, Metals & Materials Series, 973–91. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68454-3_71.

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Marquis, Emmanuelle A., Vicente Araullo-Peters, Yan Dong, Auriane Etienne, Svetlana Fedotova, Katsuhiko Fujii, Koji Fukuya, et al. "On the Use of Density-Based Algorithms for the Analysis of Solute Clustering in Atom Probe Tomography Data." In The Minerals, Metals & Materials Series, 2097–113. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-030-04639-2_141.

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Marquis, Emmanuelle A., Vicente Araullo-Peters, Yan Dong, Auriane Etienne, Svetlana Fedotova, Katsuhiko Fujii, Koji Fukuya, et al. "On the Use of Density-Based Algorithms for the Analysis of Solute Clustering in Atom Probe Tomography Data." In The Minerals, Metals & Materials Series, 881–97. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68454-3_65.

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Dukelsky, J., C. Esebbag, and M. de Llano. "Fermion Clustering in an Exactly-Soluble N-Fermion Model for Hadronic, Nuclear and Superconductivity Physics." In Symmetries in Physics, 35–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77284-9_3.

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Sha, G., R. K. W. Marceau, and S. P. Ringer. "Precipitation and solute clustering in aluminium: advanced characterisation techniques." In Fundamentals of Aluminium Metallurgy, 345–66. Elsevier, 2011. http://dx.doi.org/10.1533/9780857090256.2.345.

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Hono, K. "Atom Probe Characterization of Nanoscale Precipitates in Aluminum Alloys." In Encyclopedia of Aluminum and Its Alloys. Boca Raton: CRC Press, 2019. http://dx.doi.org/10.1201/9781351045636-140000394.

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Atomic Probe Field Ion Microscopy (APFIM) is used to solve many critical problems related to microstructures of metallic materials such as nanostructures that are composed of nanoscale precipitates dispersed in a matrix phase. The atom probe technique provides unique information on metallic nanostructures not attainable with other analytical microscopy techniques such as Transmission Electron Microscopy (TEM). In this article the an overview of the contribution of the atom probe technique to enhance the current understanding of solute clustering and characterization of fine precipitates of aluminum alloys.
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Conference papers on the topic "Solute clustering"

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Thronsen, Elisabeth. "Studying solute clustering in Al alloys by scanning nanobeam electron diffraction." In European Microscopy Congress 2020. Royal Microscopical Society, 2021. http://dx.doi.org/10.22443/rms.emc2020.1030.

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Liu, Yang, Quanxue Gao, Zhaohua Yang, and Shujian Wang. "Learning with Adaptive Neighbors for Image Clustering." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/344.

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Due to the importance and efficiency of learning complex structures hidden in data, graph-based methods have been widely studied and get successful in unsupervised learning. Generally, most existing graph-based clustering methods require post-processing on the original data graph to extract the clustering indicators. However, there are two drawbacks with these methods: (1) the cluster structures are not explicit in the clustering results; (2) the final clustering performance is sensitive to the construction of the original data graph. To solve these problems, in this paper, a novel learning mo
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Zhang, Xiaotong, Xianchao Zhang, Han Liu, and Jiebo Luo. "Multi-Task Clustering with Model Relation Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/435.

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Multi-task clustering improves the clustering performance of each task by transferring knowledge among the related tasks. An important aspect of multi-task clustering is to assess the task relatedness. However, to our knowledge, only two previous works have assessed the task relatedness, but they both have limitations. In this paper, we propose a multi-task clustering with model relation learning (MTCMRL) method, which automatically learns the model parameter relatedness between each pair of tasks. The objective function of MTCMRL consists of two parts: (1) within-task clustering: clustering e
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Wang, Siwei, Xinwang Liu, En Zhu, Chang Tang, Jiyuan Liu, Jingtao Hu, Jingyuan Xia, and Jianping Yin. "Multi-view Clustering via Late Fusion Alignment Maximization." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/524.

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Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Although demonstrating promising performance in many applications, we observe that most of existing methods directly combine multiple views to learn an optimal similarity for clustering. These methods would cause intensive computational complexity and over-complicated optimization. In this paper, we theoretically uncover the connection between existing k-means clustering and the alignment between base partitions and consensus partition. Based on this observation, w
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Hallac, David, Sagar Vare, Stephen Boyd, and Jure Leskovec. "Toeplitz Inverse Covariance-based Clustering of Multivariate Time Series Data." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/732.

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Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation net
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Li, Jun, Handong Zhao, Zhiqiang Tao, and Yun Fu. "Large-scale Subspace Clustering by Fast Regression Coding." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/297.

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Large-Scale Subspace Clustering (LSSC) is an interesting and important problem in big data era. However, most existing methods (i.e., sparse or low-rank subspace clustering) cannot be directly used for solving LSSC because they suffer from the high time complexity-quadratic or cubic in n (the number of data points). To overcome this limitation, we propose a Fast Regression Coding (FRC) to optimize regression codes, and simultaneously train a non-linear function to approximate the codes. By using FRC, we develop an efficient Regression Coding Clustering (RCC) framework to solve the LSSC problem
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Wang, Yueqing, Xinwang Liu, Yong Dou, and Rongchun Li. "Multiple Kernel Clustering Framework with Improved Kernels." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/418.

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Multiple kernel clustering (MKC) algorithms have been successfully applied into various applications. However, these successes are largely dependent on the quality of pre-defined base kernels, which cannot be guaranteed in practical applications. This may adversely affect the clustering performance. To address this issue, we propose a simple while effective framework to adaptively improve the quality of these base kernels. Under our framework, we instantiate three MKC algorithms based on the widely used multiple kernel $k$-means clustering (MKKM), MKKM with matrix-induced regularization (MKKM-
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Omar, Larbi, and Bassou Abdesselam. "Applying Clustering Algorithms to Solve E-learning Problems." In ICCES '17: International Conference of Computing for Engineering and Sciences. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3129186.3129195.

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"Weighted Agglomerative Clustering to Solve Normalized Cuts Problems." In 8th International Workshop on Pattern Recognition in Information Systems. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001740100670076.

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Li, Ruihuang, Changqing Zhang, Qinghua Hu, Pengfei Zhu, and Zheng Wang. "Flexible Multi-View Representation Learning for Subspace Clustering." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/404.

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In recent years, numerous multi-view subspace clustering methods have been proposed to exploit the complementary information from multiple views. Most of them perform data reconstruction within each single view, which makes the subspace representation unpromising and thus can not well identify the underlying relationships among data. In this paper, we propose to conduct subspace clustering based on Flexible Multi-view Representation (FMR) learning, which avoids using partial information for data reconstruction. The latent representation is flexibly constructed by enforcing it to be close to di
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