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Shagurin A, Miannay FA, Kiselev MG, Jedlovszky P, Affouard F, Idrissi A. Widom Line in Supercritical Water in Terms of Changes in Local Structure: Theoretical Perspective. J Phys Chem Lett 2024; 15:5831-5837. [PMID: 38787641 DOI: 10.1021/acs.jpclett.4c01142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Performing molecular dynamics simulations with the TIP4P/2005 water model along 9 isobars (from 175 to 375 bar) in the temperature range between 300 and 1100 K, we have found that the loci of the extrema in the rate of change of specific structural properties can be used to define purely structure-based Widom lines. We have examined several parameters that describe the local structure of water, such as the tetrahedral arrangement, nearest neighbor distance, local density around the molecules, and the size of the largest dense domain. The last two parameters were determined using the Voronoi polyhedral and density-based spatial clustering of applications with noise methods, respectively. By analyzing the moments of the associated distributions, we show that along a given isobar, the temperature at which we observe a maximum in the fluctuation, the rate of change of the average values, or in the skewness values unambiguously determines the Widom line that is in agreement with the experimentally detected, thermodynamic response function-based ones.
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Affiliation(s)
- Artem Shagurin
- University of Lille, CNRS UMR 8516 -LASIRe - Laboratoire Avancé de Spectroscopie pour les Interactions la Réactivité et l'environnement, 59000 Lille, France
- Univ. Lille, CNRS, INRAE, Centrale Lille, UMR 8207 - UMET - Unité Matériaux et Transformations, F-59000 Lille, France
- Krestov Institute of Solution Chemistry, Russian Academy of Sciences, Ivanovo, 153045 Russia
| | - Francois A Miannay
- University of Lille, CNRS UMR 8516 -LASIRe - Laboratoire Avancé de Spectroscopie pour les Interactions la Réactivité et l'environnement, 59000 Lille, France
| | - Michael G Kiselev
- Krestov Institute of Solution Chemistry, Russian Academy of Sciences, Ivanovo, 153045 Russia
| | - Pal Jedlovszky
- Department of Chemistry, Eszterházy Károly Catholic University, Leányka u. 6, 3300 Eger, Hungary
| | - Frederic Affouard
- Univ. Lille, CNRS, INRAE, Centrale Lille, UMR 8207 - UMET - Unité Matériaux et Transformations, F-59000 Lille, France
| | - Abdenacer Idrissi
- University of Lille, CNRS UMR 8516 -LASIRe - Laboratoire Avancé de Spectroscopie pour les Interactions la Réactivité et l'environnement, 59000 Lille, France
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Gomes Souza F, Bhansali S, Pal K, Silveira Maranhão FD, Santos Oliveira M, Valladão VS, Brandão E Silva DS, Silva GB. A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence. MATERIALS (BASEL, SWITZERLAND) 2024; 17:1088. [PMID: 38473560 DOI: 10.3390/ma17051088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 02/18/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024]
Abstract
From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment analysis of nanocomposite literature, distinguishing itself from existing reviews through its unique computational methodology. Developed by our research group, this novel approach systematically investigates the evolution of nanocomposites, focusing on microstructural characterization, electrical properties, and mechanical behaviors. By deploying advanced Boolean search strategies within the Scopus database, we achieve a meticulous extraction and in-depth exploration of thematic content, a methodological advancement in the field. Our analysis uniquely identifies critical trends and insights concerning nanocomposite microstructure, electrical attributes, and mechanical performance. The paper goes beyond traditional textual analytics and bibliometric evaluation, offering new interpretations of data and highlighting significant collaborative efforts and influential studies within the nanocomposite domain. Our findings uncover the evolution of research language, thematic shifts, and global contributions, providing a distinct and comprehensive view of the dynamic evolution of nanocomposite research. A critical component of this study is the "State-of-the-Art and Gaps Extracted from Results and Discussions" section, which delves into the latest advancements in nanocomposite research. This section details various nanocomposite types and their properties and introduces novel interpretations of their applications, especially in nanocomposite films. By tracing historical progress and identifying emerging trends, this analysis emphasizes the significance of collaboration and influential studies in molding the field. Moreover, the "Literature Review Guided by Artificial Intelligence" section showcases an innovative AI-guided approach to nanocomposite research, a first in this domain. Focusing on articles from 2023, selected based on citation frequency, this method offers a new perspective on the interplay between nanocomposites and their electrical properties. It highlights the composition, structure, and functionality of various systems, integrating recent findings for a comprehensive overview of current knowledge. The sentiment analysis, with an average score of 0.638771, reflects a positive trend in academic discourse and an increasing recognition of the potential of nanocomposites. Our bibliometric analysis, another methodological novelty, maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While acknowledging its limitations, this study exemplifies the indispensable role of our innovative computational tools in synthesizing and understanding the extensive body of nanocomposite literature. This work not only elucidates prevailing trends but also contributes a unique perspective and novel insights, enhancing our understanding of the nanocomposite research field.
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Affiliation(s)
- Fernando Gomes Souza
- Biopolymers & Sensors Lab., Instituto de Macromoléculas Professora Eloisa Mano, Universidade Federal do Rio de Janeiro, Centro de Tecnologia-Cidade Universitária, Rio de Janeiro 21941-853, Brazil
- Programa de Engenharia da Nanotecnologia, Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia (COPPE), Universidade Federal do Rio de Janeiro, Centro de Tecnologia-Cidade Universitária, Rio de Janeiro 21941-914, Brazil
| | - Shekhar Bhansali
- Biomolecular Sciences Institute, College of Engineering & Computing, Center for Aquatic Chemistry and Environment, Florida International University, 10555 West Flagler St EC3900, Miami, FL 33174, USA
| | - Kaushik Pal
- Department of Physics, University Center for Research and Development (UCRD), Chandigarh University, Mohali 140413, Punjab, India
| | - Fabíola da Silveira Maranhão
- Biopolymers & Sensors Lab., Instituto de Macromoléculas Professora Eloisa Mano, Universidade Federal do Rio de Janeiro, Centro de Tecnologia-Cidade Universitária, Rio de Janeiro 21941-853, Brazil
| | - Marcella Santos Oliveira
- Biopolymers & Sensors Lab., Instituto de Macromoléculas Professora Eloisa Mano, Universidade Federal do Rio de Janeiro, Centro de Tecnologia-Cidade Universitária, Rio de Janeiro 21941-853, Brazil
| | - Viviane Silva Valladão
- Biopolymers & Sensors Lab., Instituto de Macromoléculas Professora Eloisa Mano, Universidade Federal do Rio de Janeiro, Centro de Tecnologia-Cidade Universitária, Rio de Janeiro 21941-853, Brazil
| | - Daniele Silvéria Brandão E Silva
- Programa de Engenharia da Nanotecnologia, Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia (COPPE), Universidade Federal do Rio de Janeiro, Centro de Tecnologia-Cidade Universitária, Rio de Janeiro 21941-914, Brazil
| | - Gabriel Bezerra Silva
- Biopolymers & Sensors Lab., Instituto de Macromoléculas Professora Eloisa Mano, Universidade Federal do Rio de Janeiro, Centro de Tecnologia-Cidade Universitária, Rio de Janeiro 21941-853, Brazil
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Cencer MM, Moore JS, Assary RS. Machine learning for polymeric materials: an introduction. POLYM INT 2021. [DOI: 10.1002/pi.6345] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Morgan M Cencer
- Department of Chemistry University of Illinois at Urbana‐Champaign Urbana IL USA
- Materials Science Division Argonne National Laboratory Lemont IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana‐Champaign Urbana IL USA
| | - Jeffrey S Moore
- Department of Chemistry University of Illinois at Urbana‐Champaign Urbana IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana‐Champaign Urbana IL USA
| | - Rajeev S Assary
- Materials Science Division Argonne National Laboratory Lemont IL USA
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Li D, Liu J, Liu J. NNI‐SMOTE‐XGBoost: A Novel Small Sample Analysis Method for Properties Prediction of Polymer Materials. MACROMOL THEOR SIMUL 2021. [DOI: 10.1002/mats.202100010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Dazi Li
- College of Information Science and Technology Beijing University of Chemical Technology Beijing 100029 China
| | - Jianxun Liu
- College of Information Science and Technology Beijing University of Chemical Technology Beijing 100029 China
| | - Jun Liu
- Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials Beijing University of Chemical Technology Beijing 100029 China
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