Relevant bibliographies by topics / Solute clustering

Academic literature on the topic 'Solute clustering'

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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. However, for the pure Mo solute, Mo4 is not observed. The diffusivities of a single solute atom and clusters are calculated. It is found that the Mo species diffuse faster than the Re species, and the diffusivity decreases as the cluster size increases.
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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 critical density. Consistent with previous work on more mild supercritical fluids (e.g., CO2, CF3H, C2H6), the extent of this solute–fluid clustering decreases as the system temperature and pressure are increased. Time-resolved fluorescence measurements show that the excited-state decay kinetics are exponentially activated and not themselves affected by this solute–fluid clustering process.
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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 segregation of solute to grain boundaries, was observed. C, Mn, Cr, and V were soluted in cementite and promoted the precipitation of cementite, while Si was found to be soluted in ferrite. Cu is usually distributed uniformly in ferrite, but some Cu-rich atom clusters were observed to segregate towards the interface between the ferrite and cementite, stabilizing the pearlite. In addition, P, as a segregation element, was enriched along the boundaries continuously. The RE elements participated in the formation of inclusions, consuming harmful elements such as As and P, and also promoted the heterogeneous nucleation of the graphite and segregated, in the form of solute atoms, at its interfaces.
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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 donnent la chimie et les dimensions sub-nanométriques des amas. L'effet du Mg sur les cinétiques de vieillissement naturel est discuté dans le contexte de son interaction avec les lacunes disponibles pour la diffusion. De courts traitements isothermes à températures relativement basses sont utilisés pour dissoudre les amas présents après vieillissement naturel et obtenir une solution solide avec moins de lacunes qu’après mise en solution. Lorsque du Mg est présent dans le système Al-Cu-Li, le soluté libéré après dissolution se regroupe avec une cinétique comparable à celle obtenue immédiatement après la trempe du traitement de mise en solution. L'augmentation immédiate de la cinétique de mise en amas quand une concentration quelconque de Mg est présente dans les alliages Al-Cu- (Li, Mg) est révélée avec couple de diffusion
Decomposition 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
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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 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.
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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)
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 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.
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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 facilitator superfamily (MFS) type. These share evolutionary history with the remaining SLCs, but are orphans regarding expression, structure and/or function. They are not classified into any of the existing 52 SLC families. The overall aim in this thesis was to study the atypical SLCs with a focus on their phylogenetic clustering, evolutionary conservation, structure, protein expression in mouse brains and if and how their gene expressions were affected upon changed food intake. In Papers I-III, the focus was on specific proteins, MFSD5 and MFSD11 (Paper I), MFSD1 and MFSD3 (Paper II), and MFSD4A and MFSD9 (Paper III). They all shared neuronal expression, and their transcription levels were altered in several brain areas after subjecting mice to food deprivation or a high-fat diet. In Paper IV, the 29 atypical SLCs of MFS type were examined. They were divided into 15 families, based on phylogenetic analyses and sequence identities, to facilitate functional studies. Their sequence relationships with other SLCs were also established. Some of the proteins were found to be well conserved with orthologues down to nematodes and insects, whereas others emerged at first in vertebrates. The atypical SLCs of MFS type were predicted to have the common MFS structure, composed of 12 transmembrane segments. With single-cell RNA sequencing and in situ proximity ligation assay, co-expression of atypical SLCs was analysed to get a comprehensive understanding of how membrane-bound transporters interact.   In conclusion, the atypical SLCs of MFS type are suggested to be novel SLC transporters, involved in maintaining nutrient homeostasis through substrate transport.
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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.
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 ainsi que la modélisation par réseau Bayésien. Le premier algorithme sert au diagnostic ex-situ et est appliqué pour traiter les donnés issues des essais de polarisation. Il permet de déterminer les variables de réponse significatives qui indiquent l'état de santé du stack. L'indice Silhouette a été calculé comme mesure de qualité de classification afin de trouver le nombre optimal de classes dans la base de données.La détection de défaut en temps réel peut se réaliser par le deuxième algorithme. Puisque le stack est employé en tant que capteur, son état de santé doit être vérifié préalablement. La transformée des ondelettes a été utilisée pour décomposer les signaux de tension de la pile SOFC dans le but de chercher les variables caractéristiques permettant d'indiquer l'état desanté de la pile et également assez discriminatives pour différentier les conditions d'opération normales et anormales.Afin d'identifier le défaut du système lorsqu'une condition d'opération anormale s'est détectée, les paramètres opérationnelles réelles du stack doivent être estimés. Un réseau Bayésien a donc été développé pour accomplir ce travail.Enfin, tous les algorithmes ont été validés avec les bases de données expérimentales provenant de systèmes SOFC variés, afin de tester leur généricité.
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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
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.
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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 model is proposed to learn a graph based on the given data graph such that the new obtained optimal graph is more suitable for the clustering task. We also propose an efficient algorithm to solve the model. Extensive experimental results illustrate that the proposed model outperforms other state-of-the-art clustering algorithms.
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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 each task by introducing linear regression model into symmetric nonnegative matrix factorization; (2) cross-task relatedness learning: updating the parameter of the linear regression model in each task by learning the model parameter relatedness between the clusters in each pair of tasks. We present an effective alternating algorithm to solve the non-convex optimization problem. Experimental results show the superiority of the proposed method over traditional single-task clustering methods and existing multi-task clustering methods.
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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, we propose a simple but effective multi-view algorithm termed {Multi-view Clustering via Late Fusion Alignment Maximization (MVC-LFA)}. In specific, MVC-LFA proposes to maximally align the consensus partition with the weighted base partitions. Such a criterion is beneficial to significantly reduce the computational complexity and simplify the optimization procedure. Furthermore, we design a three-step iterative algorithm to solve the new resultant optimization problem with theoretically guaranteed convergence. Extensive experiments on five multi-view benchmark datasets demonstrate the effectiveness and efficiency of the proposed MVC-LFA.
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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 network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through a scalable algorithm that is able to efficiently solve for tens of millions of observations. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile dataset how TICC can be used to learn interpretable clusters in real-world scenarios.
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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. It consists of sampling, FRC and clustering. RCC randomly samples a small number of data points, quickly calculates the codes of all data points by using the non-linear function learned from FRC, and employs a large-scale spectral clustering method to cluster the codes. Besides, we provide a theorem guarantee that the non-linear function has a first-order approximation ability and a group effect. The theorem manifests that the codes are easily used to construct a dividable similarity graph. Compared with the state-of-the-art LSSC methods, our model achieves better clustering results in large-scale datasets.
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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-MR) and co-regularized multi-view spectral clustering (CRSC). After that, we design the corresponding algorithms with proved convergence to solve the resultant optimization problems. To the best of our knowledge, our framework fills the gap between kernel adaption and clustering procedure for the first time in the literature and is readily extendable. Extensive experimental research has been conducted on 7 MKC benchmarks. As is shown, our algorithms consistently and significantly improve the performance of the base MKC algorithms, indicating the effectiveness of the proposed framework. Meanwhile, our framework shows better performance than compared ones with imperfect kernels.
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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 different views, which implicitly makes it more comprehensive and well-adapted to subspace clustering. With the introduction of kernel dependence measure, the latent representation can flexibly encode complementary information from different views and explore nonlinear, high-order correlations among these views. We employ the Alternating Direction Minimization (ADM) method to solve our problem. Empirical studies on real-world datasets show that our method achieves superior clustering performance over other state-of-the-art methods.
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