1
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Iduoku K, Ngongang M, Kulathunga J, Daghighi A, Casanola-Martin G, Simsek S, Rasulev B. Phenolic Acid-β-Cyclodextrin Complexation Study to Mask Bitterness in Wheat Bran: A Machine Learning-Based QSAR Study. Foods 2024; 13:2147. [PMID: 38998653 PMCID: PMC11241027 DOI: 10.3390/foods13132147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Revised: 06/23/2024] [Accepted: 07/03/2024] [Indexed: 07/14/2024] Open
Abstract
The need to solvate and encapsulate hydro-sensitive molecules drives noticeable trends in the applications of cyclodextrins in the pharmaceutical industry, in foods, polymers, materials, and in agricultural science. Among them, β-cyclodextrin is one of the most used for the entrapment of phenolic acid compounds to mask the bitterness of wheat bran. In this regard, there is still a need for good data and especially for a robust predictive model that assesses the bitterness masking capabilities of β-cyclodextrin for various phenolic compounds. This study uses a dataset of 20 phenolic acids docked into the β-cyclodextrin cavity to generate three different binding constants. The data from the docking study were combined with topological, topographical, and quantum-chemical features from the ligands in a machine learning-based structure-activity relationship study. Three different models for each binding constant were computed using a combination of the genetic algorithm (GA) and multiple linear regression (MLR) approaches. The developed ML/QSAR models showed a very good performance, with high predictive ability and correlation coefficients of 0.969 and 0.984 for the training and test sets, respectively. The models revealed several factors responsible for binding with cyclodextrin, showing positive contributions toward the binding affinity values, including such features as the presence of six-membered rings in the molecule, branching, electronegativity values, and polar surface area.
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Affiliation(s)
- Kweeni Iduoku
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
- Biomedical Engineering Program, North Dakota State University, Fargo, ND 58102, USA
| | - Marvellous Ngongang
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | - Jayani Kulathunga
- Cereal Science Graduate Program, Department of Plant Sciences, North Dakota State University, Fargo, ND 58102, USA (S.S.)
- Department of Multidisciplinary Studies, Faculty of Urban and Aquatic Bioresources, University of Sri Jayewardenepura, Gangodawila, Nugegoda 10250, Sri Lanka
| | - Amirreza Daghighi
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
- Biomedical Engineering Program, North Dakota State University, Fargo, ND 58102, USA
| | - Gerardo Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | - Senay Simsek
- Cereal Science Graduate Program, Department of Plant Sciences, North Dakota State University, Fargo, ND 58102, USA (S.S.)
- Whistler Center for Carbohydrate Research, Department of Food Science, Purdue University, West Lafayette, IN 47907, USA
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
- Biomedical Engineering Program, North Dakota State University, Fargo, ND 58102, USA
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2
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Uddin MJ, Fan J. Interpretable Machine Learning Framework to Predict the Glass Transition Temperature of Polymers. Polymers (Basel) 2024; 16:1049. [PMID: 38674969 PMCID: PMC11054142 DOI: 10.3390/polym16081049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
The glass transition temperature of polymers is a key parameter in meeting the application requirements for energy absorption. Previous studies have provided some data from slow, expensive trial-and-error procedures. By recognizing these data, machine learning algorithms are able to extract valuable knowledge and disclose essential insights. In this study, a dataset of 7174 samples was utilized. The polymers were numerically represented using two methods: Morgan fingerprint and molecular descriptor. During preprocessing, the dataset was scaled using a standard scaler technique. We removed the features with small variance from the dataset and used the Pearson correlation technique to exclude the features that were highly connected. Then, the most significant features were selected using the recursive feature elimination method. Nine machine learning techniques were employed to predict the glass transition temperature and tune their hyperparameters. The models were compared using the performance metrics of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). We observed that the extra tree regressor provided the best results. Significant features were also identified using statistical machine learning methods. The SHAP method was also employed to demonstrate the influence of each feature on the model's output. This framework can be adaptable to other properties at a low computational expense.
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Affiliation(s)
| | - Jitang Fan
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
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3
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Zhuravskyi Y, Iduoku K, Erickson ME, Karuth A, Usmanov D, Casanola-Martin G, Sayfiyev MN, Ziyaev DA, Smanova Z, Mikolajczyk A, Rasulev B. Quantitative Structure-Permittivity Relationship Study of a Series of Polymers. ACS MATERIALS AU 2024; 4:195-203. [PMID: 38496050 PMCID: PMC10941280 DOI: 10.1021/acsmaterialsau.3c00079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/01/2023] [Accepted: 12/13/2023] [Indexed: 03/19/2024]
Abstract
Dielectric constant is an important property which is widely utilized in many scientific fields and characterizes the degree of polarization of substances under the external electric field. In this work, a structure-property relationship of the dielectric constants (ε) for a diverse set of polymers was investigated. A transparent mechanistic model was developed with the application of a machine learning approach that combines genetic algorithm and multiple linear regression analysis, to obtain a mechanistically explainable and transparent model. Based on the evaluation conducted using various validation criteria, four- and eight-variable models were proposed. The best model showed a high predictive performance for training and test sets, with R2 values of 0.905 and 0.812, respectively. Obtained statistical performance results and selected descriptors in the best models were analyzed and discussed. With the validation procedures applied, the models were proven to have a good predictive ability and robustness for further applications in polymer permittivity prediction.
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Affiliation(s)
- Yevhenii Zhuravskyi
- Department of Technology of Organic Products, Lviv Polytechnic National University, Lviv 79013, Ukraine
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Kweeni Iduoku
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Meade E Erickson
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Anas Karuth
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Durbek Usmanov
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Institute of the Chemistry of Plant Substances AS RUz, Tashkent 100170, Uzbekistan
| | - Gerardo Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Maqsud N Sayfiyev
- Department of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan
| | - Dilshod A Ziyaev
- Department of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan
| | - Zulayho Smanova
- Department of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan
| | - Alicja Mikolajczyk
- Laboratory of Environmental Chemometrics, Institute for Environmental and Human Health Protection, Faculty of Chemistry, University of Gdansk, Gdansk 80-308, Poland
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Department of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan
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4
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Erickson M, Casañola-Martin G, Han Y, Rasulev B, Kilin D. Relationships between the Photodegradation Reaction Rate and Structural Properties of Polymer Systems. J Phys Chem B 2024; 128:2190-2200. [PMID: 38386478 DOI: 10.1021/acs.jpcb.3c06854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
The development of reusable polymeric materials inspires an attempt to combine renewable biomass with upcycling to form a biorenewable closed system. It has been reported that 2,5-furandicarboxylic acid (FDCA) can be recovered for recycling when incorporated as monomers into photodegradable polymeric systems. Here, we develop a procedure to better understand the photodegradation reactions combining density functional theory (DFT) based time-dependent excited-state molecular dynamics (TDESMD) studies with machine learning-based quantitative structure-activity relationships (QSAR) methodology. This procedure allows for the unveiling of hidden structural features between active orbitals that affect the rate of photodegradation and is coined InfoTDESMD. Findings show that electrotopological features are influential factors affecting the rate of photodegradation in differing environments. Additionally, statistical validations and knowledge-based analysis of descriptors are conducted to further understand the structural features' influence on the rate of photodegradation of polymeric materials.
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Affiliation(s)
- Meade Erickson
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Gerardo Casañola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Yulun Han
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Dmitri Kilin
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58105, United States
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5
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Karuth A, Casanola-Martin GM, Lystrom L, Sun W, Kilin D, Kilina S, Rasulev B. Combined Machine Learning, Computational, and Experimental Analysis of the Iridium(III) Complexes with Red to Near-Infrared Emission. J Phys Chem Lett 2024; 15:471-480. [PMID: 38190332 DOI: 10.1021/acs.jpclett.3c02533] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Various coordination complexes have been the subject of experimental and theoretical studies in recent decades because of their fascinating photophysical properties. In this work, a combined experimental and computational approach was applied to investigate the optical properties of monocationic Ir(III) complexes. An interpretative machine learning-based quantitative structure-property relationship (ML/QSPR) model was successfully developed that could reliably predict the emission wavelength of the Ir(III) complexes and provide a foundation for the theoretical evaluation of the optical properties of Ir(III) complexes. A hypothesis was proposed to explain the differences in the emission wavelengths between structurally different individual Ir(III) complexes. The efficacy of the developed model was demonstrated by high R2 values of 0.84 and 0.87 for the training and test sets, respectively. It is worth noting that a relationship between the N-N distance in the diimine ligands of the Ir(III) complexes and emission wavelengths is detected. This effect is most probably associated with a degree of distortion in the octahedral geometry of the complexes, resulting in a perturbed ligand field. This combined experimental and computational approach shows great potential for the rational design of new Ir(III) complexes with the desired optical properties. Moreover, the developed methodology could be extended to other transition-metal complexes.
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Affiliation(s)
- Anas Karuth
- Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Gerardo M Casanola-Martin
- Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Levi Lystrom
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Wenfang Sun
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58108, United States
- Department of Chemistry and Biochemistry, The University of Alabama, Tuscaloosa, Alabama 35487, United States
| | - Dmitri Kilin
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Svetlana Kilina
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58108, United States
| | - Bakhtiyor Rasulev
- Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58108, United States
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6
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Yuan Q, Li Y, Wang S, He E, Yang B, Nie R. A Molecular Dynamics Simulation Study on Enhancement of Mechanical and Tribological Properties of Nitrile-Butadiene Rubber with Varied Contents of Acrylonitrile. Polymers (Basel) 2023; 15:3799. [PMID: 37765653 PMCID: PMC10535401 DOI: 10.3390/polym15183799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 09/08/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
The molecular models of nitrile-butadiene rubber (NBR) with varied contents of acrylonitrile (ACN) were developed and investigated to provide an understanding of the enhancement mechanisms of ACN. The investigation was conducted using molecular dynamics (MD) simulations to calculate and predict the mechanical and tribological properties of NBR through the constant strain method and the shearing model. The MD simulation results showed that the mechanical properties of NBR showed an increasing trend until the content of ACN reached 40%. The mechanism to enhance the strength of the rubber by ACN was investigated and analyzed by assessing the binding energy, radius of gyration, mean square displacement, and free volume. The abrasion rate (AR) of NBR was calculated using Fe-NBR-Fe models during the friction processes. The wear results of atomistic simulations indicated that the NBR with 40% ACN content had the best tribological properties due to the synergy among appropriate polarity, rigidity, and chain length of the NBR molecules. In addition, the random forest regression model of predicted AR, based on the dataset of feature parameters extracted by the MD models, was developed to obtain the variable importance for identifying the highly correlated parameters of AR. The torsion-bend-bend energy was obtained and used to successfully predict the AR trend on the new NBR models with other acrylonitrile contents.
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Affiliation(s)
- Quan Yuan
- School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China; (Q.Y.); (S.W.); (B.Y.)
| | - Yunlong Li
- School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China; (Q.Y.); (S.W.); (B.Y.)
| | - Shijie Wang
- School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China; (Q.Y.); (S.W.); (B.Y.)
| | - Enqiu He
- School of Chemical Equipment, Shenyang University of Technology, Liaoyang 111003, China
| | - Bin Yang
- School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China; (Q.Y.); (S.W.); (B.Y.)
| | - Rui Nie
- Ningbo Institute of Technology, Beihang University, Ningbo 315800, China;
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7
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Wu FY, Yin J, Chen SC, Gao XQ, Zhou L, Lu Y, Lei J, Zhong GJ, Li ZM. Application of machine learning to reveal relationship between processing-structure-property for polypropylene injection molding. POLYMER 2023. [DOI: 10.1016/j.polymer.2023.125736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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8
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Li D, Dong C, Chen Z, Dong Y, Liu J. A combinatorial machine-learning-driven approach for predicting glass transition temperature based on numerous molecular descriptors. MOLECULAR SIMULATION 2023. [DOI: 10.1080/08927022.2023.2181019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Affiliation(s)
- Dazi Li
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, People’s Republic of China
| | - Caibo Dong
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, People’s Republic of China
| | - Zhudan Chen
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, People’s Republic of China
| | - Yining Dong
- School of Data Science and Hong Kong Institute for Data Science, Centre for Systems Informatics Engineering, City University of Hong Kong, Kowloon, Hong Kong
| | - Jun Liu
- Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials, Beijing University of Chemical Technology, Beijing, People’s Republic of China
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9
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Yu M, Shi Y, Jia Q, Wang Q, Luo ZH, Yan F, Zhou YN. Ring Repeating Unit: An Upgraded Structure Representation of Linear Condensation Polymers for Property Prediction. J Chem Inf Model 2023; 63:1177-1187. [PMID: 36651860 DOI: 10.1021/acs.jcim.2c01389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Unique structure representation of polymers plays a crucial role in developing models for polymer property prediction and polymer design by data-centric approaches. Currently, monomer and repeating unit (RU) approximations are widely used to represent polymer structures for generating feature descriptors in the modeling of quantitative structure-property relationships (QSPR). However, such conventional structure representations may not uniquely approximate heterochain polymers due to the diversity of monomer combinations and the potential multi-RUs. In this study, the so-called ring repeating unit (RRU) method that can uniquely represent polymers with a broad range of structure diversity is proposed for the first time. As a proof of concept, an RRU-based QSPR model was developed to predict the associated glass transition temperature (Tg) of polyimides (PIs) with deterministic values. Comprehensive model validations including external, internal, and Y-random validations were performed. Also, an RU-based QSPR model developed based on the same large database of 1321 PIs provides nonunique prediction results, which further prove the necessity of RRU-based structure representation. Promising results obtained by the application of the RRU-based model confirm that the as-developed RRU method provides an effective representation that accurately captures the sequence of repeat units and thus realizes reliable polymer property prediction by data-driven approaches.
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Affiliation(s)
- Mengxian Yu
- School of Chemical Engineering and Materials Science, Tianjin University of Science and Technology, Tianjin300457, P. R. China
| | - Yajuan Shi
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai200240, P. R. China
| | - Qingzhu Jia
- School of Marine and Environmental Science, Tianjin University of Science and Technology, Tianjin300457, P. R. China
| | - Qiang Wang
- School of Chemical Engineering and Materials Science, Tianjin University of Science and Technology, Tianjin300457, P. R. China
| | - Zheng-Hong Luo
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai200240, P. R. China
| | - Fangyou Yan
- School of Chemical Engineering and Materials Science, Tianjin University of Science and Technology, Tianjin300457, P. R. China
| | - Yin-Ning Zhou
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai200240, P. R. China
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10
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Schmid F. Understanding and Modeling Polymers: The Challenge of Multiple Scales. ACS POLYMERS AU 2022. [DOI: 10.1021/acspolymersau.2c00049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Friederike Schmid
- Institut für Physik, Johannes Gutenberg-Universität Mainz, Staudingerweg 9, 55128Mainz, Germany
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11
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Zheng X, Guo Y, Douglas JF, Xia W. Understanding the role of cross-link density in the segmental dynamics and elastic properties of cross-linked thermosets. J Chem Phys 2022; 157:064901. [PMID: 35963735 DOI: 10.1063/5.0099322] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Cross-linking is known to play a pivotal role in the relaxation dynamics and mechanical properties of thermoset polymers, which are commonly used in structural applications because of their light weight and inherently strong nature. Here, we employ a coarse-grained (CG) polymer model to systematically explore the effect of cross-link density on basic thermodynamic properties as well as corresponding changes in the segmental dynamics and elastic properties of these network materials upon approaching their glass transition temperatures (Tg). Increasing the cross-link density unsurprisingly leads to a significant slowing down of the segmental dynamics, and the fragility K of glass formation shifts in lockstep with Tg, as often found in linear polymer melts when the polymer mass is varied. As a consequence, the segmental relaxation time τα becomes almost a universal function of reduced temperature, (T - Tg)/Tg, a phenomenon that underlies the applicability of the "universal" Williams-Landel-Ferry (WLF) relation to many polymer materials. We also test a mathematical model of the temperature dependence of the linear elastic moduli based on a simple rigidity percolation theory and quantify the fluctuations in the local stiffness of the network material. The moduli and distribution of the local stiffness likewise exhibit a universal scaling behavior for materials having different cross-link densities but fixed (T - Tg)/Tg. Evidently, Tg dominates both τα and the mechanical properties of our model cross-linked polymer materials. Our work provides physical insights into how the cross-link density affects glass formation, aiding in the design of cross-linked thermosets and other structurally complex glass-forming materials.
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Affiliation(s)
- Xiangrui Zheng
- Department of Mechanics, School of Physical Science and Engineering, Beijing Jiaotong University, Beijing 100044, China
| | - Yafang Guo
- Department of Mechanics, School of Physical Science and Engineering, Beijing Jiaotong University, Beijing 100044, China
| | - Jack F Douglas
- Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
| | - Wenjie Xia
- Department of Civil, Construction and Environmental Engineering, North Dakota State University, Fargo, North Dakota 58108, USA
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12
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Cravero F, Díaz MF, Ponzoni I. Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break. J Chem Phys 2022; 156:204903. [DOI: 10.1063/5.0087392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
The artificial intelligence-based prediction of the mechanical properties derived from the tensile test, plays a key role in assessing the application profile of new polymeric materials, specifically in the design stage, prior to synthesis. This strategy saves time and resources when creating new polymers with improved properties that are increasingly demanded by the market. A quantitative structure-property relationship (QSPR) model for tensile strength at break is presented in this work. The QSPR methodology applied here is based on machine learning tools, visual analytics methods, and expert-in-the-loop strategies. From the whole study, a QSPR model composed of five molecular descriptors that achieved a correlation coefficient of 0.9226 is proposed. We applied visual analytics tools at two levels of analysis: a more general one in which models are discarded for redundant information metrics and a deeper one in which a chemistry expert can make decisions on the composition of the model in terms of subsets of molecular descriptors, from a physical-chemical point of view. In this way, with the present work, we close a contribution cycle to polymer informatics, providing QSPR models oriented to the prediction of mechanical properties related to the tensile test.
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Affiliation(s)
- Fiorella Cravero
- Instituto de Ciencias e Ingeniería de la Computación (CONICET-UNS) . Argentina, Argentina
| | | | - Ignacio Ponzoni
- Instituto de Ciencias e Ingeniería de la Computación (CONICET-UNS) . Argentina, Argentina
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13
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Xu X, Xu WS. Melt Properties and String Model Description of Glass Formation in Graft Polymers of Different Side-Chain Lengths. Macromolecules 2022. [DOI: 10.1021/acs.macromol.2c00327] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Xiaolei Xu
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, P. R. China
| | - Wen-Sheng Xu
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, P. R. China
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14
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Patnode K, Rasulev B, Voronov A. Synergistic Behavior of Plant Proteins and Biobased Latexes in Bioplastic Food Packaging Materials: Experimental and Machine Learning Study. ACS APPLIED MATERIALS & INTERFACES 2022; 14:8384-8393. [PMID: 35119263 DOI: 10.1021/acsami.1c21650] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Plant-based proteins are attractive components which may serve as sustainable alternatives to current petrochemical products. Both soy protein and major corn protein, zein, are of interest in food packaging applications due to their sustainability, biodegradation properties, and inherent physicochemical properties. This study discusses the development of bioplastic materials, where it explores the effects of combining zein, soy protein, and plasticizing latexes derived from plant oil-based monomers (POBMs) on properties of resulting bioplastic films. By looking for synergistic effects of soy protein's inherent film formation ability and zein's higher strength, we prepare strong yet flexible soy-zein films as materials, called proteoposites. Incorporation of natural additive POBM-latexes helps to plasticize and hydrophobize the bioplastic films and thus to improve mechanical and barrier properties. Variation of the POBM-latexes' particle size further aims to enhance the performance of resulting bioplastic films. As a result, modified soy-zein proteoposite films with improved moisture resistance, enhanced mechanical behavior, and greater barrier properties were developed. Machine learning-based computational models were utilized in order to find main structural factors affecting the bioplastic's properties and develop a quantitative structure-property relationship model between the physicochemical properties of the film components and the resulted bioplastics' properties and performance. The developed model effectively predicts experimental outcomes with >85% (R2: 0.85) accuracy. The newly synthesized proteoposites confirmed the machine learning model predictions. As a result, proteoposite films made of two plant proteins and modified with POBM-latexes can be considered as an attractive and viable replacement for petrochemical food packaging products.
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Affiliation(s)
- Kristen Patnode
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102-6050, United States
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102-6050, United States
| | - Andriy Voronov
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102-6050, United States
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15
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Varghese P. J G, David DA, Karuth A, Manamkeri Jafferali JF, P. M SB, George JJ, Rasulev B, Raghavan P. Experimental and Simulation Studies on Nonwoven Polypropylene-Nitrile Rubber Blend: Recycling of Medical Face Masks to an Engineering Product. ACS OMEGA 2022; 7:4791-4803. [PMID: 35187299 PMCID: PMC8851451 DOI: 10.1021/acsomega.1c04913] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 12/08/2021] [Indexed: 05/05/2023]
Abstract
The battle against the COVID-19 pandemic counters the waste management system, as billions of single-use face masks are used per day all over the world. Proper disposal of used face masks without jeopardizing the health and the environment is a challenge. Herein, a novel method for recycling of medical face masks has been studied. This method incorporates the nonwoven polypropylene (PP) fiber, which is taken off from the mask after disinfecting it, with acrylonitrile butadiene rubber (NBR) using maleic anhydride as the compatibilizer, which results in a PP-NBR blend with a high percentage economy. The PP-NBR blends show enhanced thermomechanical properties among which, 70 wt % PP content shows superior properties compared to other composites with 40, 50, and 60 wt % of PP. The fully Atomistic simulation of PP-NBR blend with compatibilizer shows an improved tensile and barrier properties, which is in good agreement with the experimental studies. The molecular dynamics simulation confirms that the compatibility between non-polar PP and polar NBR phases are vitally important for increasing the interfacial adhesion and impeding the phase separation.
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Affiliation(s)
- George Varghese P. J
- Department
of Metallurgical and Materials Engineering, Indian Institute of Technology Patna (IIT P), Patna 801106, Bihar, India
- Materials
Science and NanoEngineering Lab, Department of Polymer Science and
Rubber Technology, Cochin University of
Science and Technology (CUSAT), Kochi 682022, Kerala, India
| | - Deepthi Anna David
- Materials
Science and NanoEngineering Lab, Department of Polymer Science and
Rubber Technology, Cochin University of
Science and Technology (CUSAT), Kochi 682022, Kerala, India
- Department
of Applied Chemistry, Cochin University
of Science and Technology (CUSAT), Kochi 682022, Kerala, India
| | - Anas Karuth
- Department
of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Jabeen Fatima Manamkeri Jafferali
- Materials
Science and NanoEngineering Lab, Department of Polymer Science and
Rubber Technology, Cochin University of
Science and Technology (CUSAT), Kochi 682022, Kerala, India
| | - Sabura Begum P. M
- Department
of Applied Chemistry, Cochin University
of Science and Technology (CUSAT), Kochi 682022, Kerala, India
| | - Jinu Jacob George
- Materials
Science and NanoEngineering Lab, Department of Polymer Science and
Rubber Technology, Cochin University of
Science and Technology (CUSAT), Kochi 682022, Kerala, India
| | - Bakhtiyor Rasulev
- Department
of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Prasanth Raghavan
- Materials
Science and NanoEngineering Lab, Department of Polymer Science and
Rubber Technology, Cochin University of
Science and Technology (CUSAT), Kochi 682022, Kerala, India
- Department
of Materials Engineering and Convergence Technology, Gyeongsang National University, 501 Jinju-daero, Jinju 52828, Republic of Korea
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16
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Wu C, Li K, Ning X, Zhang L. An Enhanced Scheme for Multiscale Modeling of Thermomechanical Properties of Polymer Bulks. J Phys Chem B 2021; 125:8612-8626. [PMID: 34291641 DOI: 10.1021/acs.jpcb.1c02663] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
While multiscale modeling significantly enhances the capability of molecular simulations of polymer systems, it is well realized that the systematically derived coarse-grained (CG) models generally underestimate the thermomechanical properties. In this work, a charge-based mapping scheme has been adopted to include explicit electrostatic interactions and benchmarked against two typical polymers, atactic poly(methyl methacrylate) (PMMA) and polystyrene (PS). The CG potentials are parameterized against the oligomer bulks of nine monomers per chain to match the essential structural features and the two basic pressure-volume-temperature (PVT) properties, which are obtained from the all-atomistic (AA) molecular dynamics (MD) simulations at a single elevated temperature. The so-parameterized CG potentials are extended with the MD method to simulate the two polymer bulks of one hundred monomers per chain over a wide temperature range. Without any scaling, all the simulated results, including mass densities and bulk moduli at room temperature, thermal expansion coefficients at rubbery and glassy states, and glass transition temperatures (Tg), compare well with the corresponding experimental data. The proposed scheme not only contributes to realistically simulating various thermomechanical properties of both apolar and polar polymers but also allows for directly simulating their electrical properties.
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Affiliation(s)
- Chaofu Wu
- Hunan Provincial Key Laboratory of Fine Ceramics and Powder Materials, School of Materials and Environmental Engineering, Hunan University of Humanities, Science and Technology, Loudi, Hunan 417000, P. R. China
| | - Kewen Li
- Hunan Provincial Key Laboratory of Fine Ceramics and Powder Materials, School of Materials and Environmental Engineering, Hunan University of Humanities, Science and Technology, Loudi, Hunan 417000, P. R. China
| | - Xutao Ning
- Hunan Provincial Key Laboratory of Fine Ceramics and Powder Materials, School of Materials and Environmental Engineering, Hunan University of Humanities, Science and Technology, Loudi, Hunan 417000, P. R. China
| | - Lei Zhang
- Hunan Provincial Key Laboratory of Fine Ceramics and Powder Materials, School of Materials and Environmental Engineering, Hunan University of Humanities, Science and Technology, Loudi, Hunan 417000, P. R. China
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17
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Xu X, Douglas JF, Xu WS. Influence of Side-Chain Length and Relative Rigidities of Backbone and Side Chains on Glass Formation of Branched Polymers. Macromolecules 2021. [DOI: 10.1021/acs.macromol.1c00834] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Xiaolei Xu
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, P. R. China
| | - Jack F. Douglas
- Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Wen-Sheng Xu
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, P. R. China
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