{"title":"A Comprehensive Solute-Medium Interaction Model for Anisotropic NMR Data Prediction","authors":"Yizhou Liu","doi":"10.1039/d4cp04136d","DOIUrl":null,"url":null,"abstract":"Prediction of order parameters and hence anisotropic NMR data of a molecule dissolved in a dilute alignment medium can provide a unique means of solving certain challenging structural elucidation problems. Here, a general prediction model based on solute-medium interactions is developed featuring the use of medium-specific parameters as only fitting variables to depict intermolecular interactions in an alignment system, including repulsive, dispersive, and electrostatic interactions between the solute and the medium polymer, as well as their interactions with implicit solvent. This model is implemented on the framework of a surface decomposition method previously designed to formalize medium-induced solute alignment without a priori knowledge of medium structure. Having descriptors for all interactions, the new model performs significantly better than the original hard-body model when the medium and solute exhibit strong electrostatic and dispersion interactions. This model offers a general method to extract physical properties of a medium polymer by combining the experimental NMR data of different model compounds and then use these properties to predict the NMR data of other molecules of interest.","PeriodicalId":99,"journal":{"name":"Physical Chemistry Chemical Physics","volume":"22 1","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical Chemistry Chemical Physics","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1039/d4cp04136d","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
A Comprehensive Solute-Medium Interaction Model for Anisotropic NMR Data Prediction
Prediction of order parameters and hence anisotropic NMR data of a molecule dissolved in a dilute alignment medium can provide a unique means of solving certain challenging structural elucidation problems. Here, a general prediction model based on solute-medium interactions is developed featuring the use of medium-specific parameters as only fitting variables to depict intermolecular interactions in an alignment system, including repulsive, dispersive, and electrostatic interactions between the solute and the medium polymer, as well as their interactions with implicit solvent. This model is implemented on the framework of a surface decomposition method previously designed to formalize medium-induced solute alignment without a priori knowledge of medium structure. Having descriptors for all interactions, the new model performs significantly better than the original hard-body model when the medium and solute exhibit strong electrostatic and dispersion interactions. This model offers a general method to extract physical properties of a medium polymer by combining the experimental NMR data of different model compounds and then use these properties to predict the NMR data of other molecules of interest.
期刊介绍:
Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions.
The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.