| Predicting glass transition of amorphous polymers by application of cheminformatics and molecular dynamics simulations A Karuth, A Alesadi, W Xia, B Rasulev Polymer 218, 123495, 2021 | 63 | 2021 |
| Experimental and simulation studies on nonwoven polypropylene–nitrile rubber blend: recycling of medical face masks to an engineering product G Varghese P. J, DA David, A Karuth, JF Manamkeri Jafferali, SB P. M, ... ACS omega 7 (6), 4791-4803, 2022 | 33 | 2022 |
| Reactive molecular dynamics study of hygrothermal degradation of crosslinked epoxy polymers A Karuth, A Alesadi, A Vashisth, W Xia, B Rasulev ACS Applied Polymer Materials 4 (6), 4411-4423, 2022 | 30 | 2022 |
| Energy dissipation characteristics of crosslinks in natural rubber: an assessment using low and high-frequency analyzer K Anas, S David, RR Babu, M Selvakumar, S Chattopadhyay Journal of Polymer Engineering 38 (8), 723-729, 2018 | 25 | 2018 |
| A Quantitative Structure-Permittivity Relationship Study of a Series of Polymers Y Zhuravskyi, K Iduoku, M Erickson, A Karuth, D Usmanov, ... ACS Materials Au, 2024 | 11 | 2024 |
| Combined machine learning, computational, and experimental analysis of the iridium (III) complexes with red to near-infrared emission A Karuth, GM Casanola-Martin, L Lystrom, W Sun, D Kilin, S Kilina, ... The Journal of Physical Chemistry Letters 15 (2), 471-480, 2024 | 10 | 2024 |
| Integrated machine learning, computational, and experimental investigation of compatibility in oil-modified silicone elastomer coatings A Karuth, S Szwiec, GM Casanola-Martin, A Khanam, M Safaripour, ... Progress in Organic Coatings 193, 108526, 2024 | 6 | 2024 |
| Machine learning analysis of a large set of homopolymers to predict glass transition temperatures GM Casanola-Martin, A Karuth, H Pham-The, H González-Díaz, ... Communications Chemistry 7 (1), 226, 2024 | 2 | 2024 |
| Phytochemical constituents isolated from Silene popovii Schischk UY Yusupova, KM Bobakulov, AR Khurramov, VN Syrov, FR Egamova, ... Medicinal Chemistry Research, 1-9, 2024 | | 2024 |
| Machine learning analysis of a large set of homopolymers to predict glass transition temperatures G Casañola, A Karuth, H González-Díaz, DC Webster, B Rasulev Springer Nature, 2024 | | 2024 |
| Integrated Machine Learning and Computational Framework for Predicting the Thermophysical and Functional Properties of Polymers A Karuth North Dakota State University, 2023 | | 2023 |
| Predicting the Glass Transition Temperature of Amorphous Polymers via Integration of Cheminformatics and Molecular Dynamics Simulations A Karuth, A Alesadi, W Xia, B Rasulev | | |
| Machine learning potential for predicting the properties and simulating the degradation behavior in polymeric materials A Karuth ACS spring 2022, 0 | | |
| Energy dissipation characteristics of elastomer under dynamic conditions-A comprehensive assessment using a conventional and a very high frequency dynamic mechanical analyzer K Anas, M Selvakumar, S David, RR Babu, S Chattopadhyay | | |
Bakhtiyor RasulevAssociate Professor, North Dakota State University, Fargo, USAยืนยันอีเมลแล้วที่ ndsu.edu
