Academic literature on the topic 'Solute clustering'
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Journal articles on the topic "Solute clustering"
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.
Full textPeng, 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.
Full textLarson, 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.
Full textLarson, 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.
Full textLiu, 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.
Full textFretwell, 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.
Full textNiemeyer, 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.
Full textFan, 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.
Full textDupasquier, 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.
Full textZhang, 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.
Full textDissertations / Theses on the topic "Solute clustering"
Ivanov, Rosen. "Solute clustering in multi-component aluminium alloys." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAI012/document.
Full textDecomposition of super saturated solid solutions of Al-Cu-(Li,Mg) alloys pose theoretical and experimental challenges. The chemical fluctuations - clusters - formed at room temperature are critically analysed using a combination of in-situ small angle scattering (SAS), differential scanning calorimetry (DSC), atom probe tomography (APT), and micro-hardness. A methodology for combined interpretation of SAS data from experiments using neutron and X-ray radiation is proposed and allows for comparison with standard analysis performed by APT. The results effectively capture the chemistry and sub-nanometer dimensions of clusters. The profound positive effect of Mg on clustering of Cu via excess vacancies available for diffusion is captured through the clustering kinetics over the course of natural ageing. Short isothermal treatments at relatively low temperatures are used to dissolve naturally aged clusters and obtain a solid solution with less expected vacancies. When Mg is present in the Al-Cu-Li system, released solute after dissolution exhibits clustering behaviour with kinetics comparable to those immediately after quench from solution treatment. The immediate increase of clustering kinetics when any concentration of Mg is present in Al-Cu-(Li,Mg) alloys is revealed through a composition graded sample
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.
Full textMarceau, 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.
Full textThe 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 designed for use with three-dimensional APT data. Prior to this detailed characterisation work, the actual thermal impact from both heat treatment and quenching of small, lab-scale specimens was determined through correlation of both experimental results and calculations that modelled the heat transfer conditions using the lumped capacitance method. Subsequently, the maximum diffusion distance by random walk of the solute atoms was calculated for these periods, bearing significance on the propensity for these atoms to have the ability to cluster together, rather than segregate to the dislocation loops in the microstructure, which have a relatively larger interspacing distance. Age-hardening curves for the Al-1.1Cu-xMg (x = 0, 0.2, 0.5, 0.75, 1.0, 1.7 at.%) alloys at 150ºC show that the rapid hardening phenomenon (RHP) exists for Mg compositions ≥ 0.5Mg. Given that zone-like precipitate structures were unable to be detected by TEM or APT during the early stages of ageing at 150ºC, and that statistically significant dispersions of clusters were found in the APT data after ageing for 60 s, the RHP is attributed to these clustering reactions. Identification of clusters in the APT data has been achieved using the core-linkage algorithm and they have been found to be quite small, containing only a few atoms up to a couple of tens of atoms. The RHP is governed by some critical number density of both Mg clusters and Cu-Mg co-clusters of a critical size, whereas Cu clusters do not contribute significantly to the hardening mechanism. Significance testing indicates that Mg clusters are more significant at smaller clusters sizes and Cu-Mg co-clusters more important at larger cluster sizes. Hardness results also confirm the existence of rapid early hardening during secondary ageing at 65ºC in Al-1.1Cu-1.7Mg. The mechanism of secondary rapid hardening involves a combination of both secondary clustering from solute (mainly Mg atoms) residual in solution, and pre-existing amorphous primary clusters that have slower growth kinetics at the lower secondary ageing temperature. The latter occurs mainly by vacancy-assisted diffusion of Mg atoms as evidenced by the gradual increase of the Mg:Cu ratio of co-clusters. From an alloy design point of view it is important to fully understand the solute distribution in the microstructure to be able to subsequently optimise the configuration for enhanced material properties. The change in dispersion of solute atoms during ageing was determined by combining calculations of % vacancy-solute associations with detailed measurements of the dislocation loops to estimate the solute distribution within the microstructure. The implication of the balance of solute atoms segregated to the loops compared with that in the matrix is then discussed in the context of hardnening mechanisms.
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.
Full textMarceau, 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.
Full textTitle 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.
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.
Full textLu, 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.
Full text長庚大學
化工與材料工程研究所
98
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 clusters reach the critical nucleus size. Meanwhile, solute clusters might dissociation in the aggregation process due to the thermodynamic instability before clusters reach the critical nucleus size. The aggregation and dissociation rate constants can be recovered by fitting the experimental induction period data with the proposed model. This model is successfully applied to determine the induction period of CaCO3 homogeneous nucleation by assuming that the mean cluster size reaches critical nucleus size at the induction period. In addition, a model is proposed to simulate the solute clustering process in the diffusion layer around a growing crystal in a stirred solution. The aggregation and dissociation rate constants recovered previously for homogeneous nucleation of CaCO3 are employed to determine the number-average degree of clustering and the cluster size distribution in the diffusion layer around a growing CaCO3 precipitate. The effects of supersaturation, diffusion layer thickness and temperature on the number-average degree of clustering and the size distribution of solute clusters in the diffusion layer are studied in detail. The obtained results of this part will elucidate the solute clustering process of solute molecules during nucleation and crystal growth in a stirred solution. Crystallization has very broad applications in the chemical industry. The required shape, size and size distribution of the product crystals depends on the type of crystallizer selected. In the second part, the operation and control for various crystallizers, including a batch cooling crystallizer, a continuous multi-stage MSMPR (mixed-suspension, mixed-product-removal) crystallizer with recycle flow, and a continuous fluidized bed crystallizer with liquor recycling, is investigated in detail. Depending on the type of crystallizer, various models are developed based on the population balance to examine the effects of the design and operating parameters on the resulting CSD (crystal size distribution) of the product crystals. As the nonhomogeneous mixing of crystals can significantly influence the overall performances of a stirred crystallizer, the nonhomogeneous suspension of crystals due to the particle gravity in a non-ideal stirred-crystallizer is also studied. The simulation results of this part can provide valuable information for design and operation of various types of industrial crystallizers.
Book chapters on the topic "Solute clustering"
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.
Full textJiang, 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.
Full textLee, 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.
Full textChen, 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.
Full textChen, 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.
Full textMarquis, 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.
Full textMarquis, 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.
Full textDukelsky, 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.
Full textSha, 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.
Full textHono, 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.
Full textConference papers on the topic "Solute clustering"
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.
Full textLiu, 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.
Full textZhang, 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.
Full textWang, 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.
Full textHallac, 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.
Full textLi, 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.
Full textWang, 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.
Full textOmar, 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.
Full text"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.
Full textLi, 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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