NCL Publicationshttps://hdl.handle.net/20.500.12252/102024-03-28T14:37:59Z2024-03-28T14:37:59ZDoes Viscosity Drive the Dynamics in an Alcohol-Based Deep Eutectic Solvent?Chatterjee, SrijanDeshmukh, SamadhanBagchi, Sayanhttps://hdl.handle.net/20.500.12252/62562023-10-10T06:41:22Z2022-10-06T00:00:00ZDoes Viscosity Drive the Dynamics in an Alcohol-Based Deep Eutectic Solvent?
Chatterjee, Srijan; Deshmukh, Samadhan; Bagchi, Sayan
Deep eutectic solvents, consisting of heterogeneous nanodomains of hydrogen-bonded networks, have evolved into a range of viscous fluids that find applications in several fields. As viscosity is known to influence the dynamics of other neoteric solvents like ionic liquids, understanding the effect of viscosity on deep eutectic solvents is crucial to realize their full potential. Herein, we combine polarization-selective pump–probe spectroscopy, two-dimensional infrared spectroscopy, and molecular dynamics simulations to elucidate the impact of viscosity on the dynamics of an alcohol-based deep eutectic solvent, ethaline. We compare the solvent fluctuation and solute reorientation time scales in ethaline with those in ethylene glycol, an ethaline constituent. Interestingly, we find that the solute’s reorientation apparently scales the bulk viscosity of the solvent, but the same is not valid for the overall solvation dynamics. Using the variations in the estimated intercomponent hydrogen bond lifetimes, we show that a dissolved solute does not sense the bulk viscosity of the deep eutectic solvent; instead, it senses domain-specific local viscosity determined by the making and breaking of the hydrogen bond network. Our results indicate that understanding the domain-specific local environment experienced by the dissolved solute is of utmost importance in deep eutectic solvents.
2022-10-06T00:00:00ZOn–Off Infrared Absorption of the S═O Vibrational Probe of Dimethyl SulfoxideChakrabarty, SuranjanaDeshmukh, SamadhanBarman, AnjanBagchi, SayanGhosh, Anuphttps://hdl.handle.net/20.500.12252/62552023-10-10T06:32:49Z2022-06-08T00:00:00ZOn–Off Infrared Absorption of the S═O Vibrational Probe of Dimethyl Sulfoxide
Chakrabarty, Suranjana; Deshmukh, Samadhan; Barman, Anjan; Bagchi, Sayan; Ghosh, Anup
Dimethyl sulfoxide (DMSO), a polar solvent molecule, is used in a wide range of therapeutic and pharmacological applications. Different intermolecular interactions, such as dimerization and hydrogen bonding with water, are crucial to understanding the role of DMSO in applications. Herein, we study DMSO in various solvation environments to decipher the environment-dependent dimerization and hydrogen-bonding propensity. We use a combination of infrared spectroscopy, quantum mechanical calculations, and molecular dynamics simulations to reach our conclusions. Although DMSO can exist in a dynamic equilibrium between monomers and dimers, our results show that the relative intensity of the S═O stretch and the CH3 rocking modes is a spectroscopic indicator of the extent of DMSO dimerization in solution. The dimerization (self-association) is seen to be maximum in neat DMSO. When dissolved in different solvents, the dimerization propensity decreases with increasing solvent polarity. In the presence of a protic solvent, such as water, DMSO forms a hydrogen bond with the solvent molecules, thereby reducing the extent of dimerization. Further, we estimate the hydrogen-bond occupancy of DMSO. Our results show that DMSO predominantly exists as doubly hydrogen-bonded in water.
2022-06-08T00:00:00ZLigand Dynamics Time Scales Identify the Surface–Ligand Interactions in Thiocyanate-Capped Cadmium Sulfide NanocrystalsDeshmukh, SamadhanChatterjee, SrijanGhosh, DeborinBagchi, Sayanhttps://hdl.handle.net/20.500.12252/62542023-10-10T06:26:54Z2022-03-30T00:00:00ZLigand Dynamics Time Scales Identify the Surface–Ligand Interactions in Thiocyanate-Capped Cadmium Sulfide Nanocrystals
Deshmukh, Samadhan; Chatterjee, Srijan; Ghosh, Deborin; Bagchi, Sayan
The nanocrystal surface, which acts as an interface between the semiconductor lattice and the capping ligands, plays a significant role in the attractive photophysical properties of semiconductor nanocrystals for use in a wide range of applications. Replacing the long-chain organic ligands with short inorganic variants improves the conductivity and carrier mobility of nanocrystal-based devices. However, our current understanding of the interactions between the inorganic ligands and the nanocrystals is obscure due to the lack of experiments to directly probe the inorganic ligands. Herein, using two-dimensional infrared spectroscopy, we show that the variations in the inorganic ligand dynamics within the heterogeneous nanocrystal ensemble can identify the diversities in the inorganic ligand–nanocrystal interactions. The ligand dynamics time scale in SCN– capped CdS nanocrystals identifies three distinct ligand populations and provides molecular insight into the nanocrystal surface. Our results demonstrate that the SCN– ligands engage in a dynamic equilibrium and stabilize the nanocrystals by neutralizing the surface charges through both direct binding and electrostatic interaction.
2022-03-30T00:00:00ZPredicting the Redox Potentials of Phenazine Derivatives Using DFT-Assisted Machine LearningGhule, SiddharthDash, Soumya RanjanBagchi, SayanJoshi, KavitaVanka, Kumarhttps://hdl.handle.net/20.500.12252/62532023-10-10T06:24:01Z2022-03-29T00:00:00ZPredicting the Redox Potentials of Phenazine Derivatives Using DFT-Assisted Machine Learning
Ghule, Siddharth; Dash, Soumya Ranjan; Bagchi, Sayan; Joshi, Kavita; Vanka, Kumar
This study investigates four machine-learning (ML) models to predict the redox potentials of phenazine derivatives in dimethoxyethane using density functional theory (DFT). A small data set of 151 phenazine derivatives having only one type of functional group per molecule (20 unique groups) was used for the training. Prediction accuracy was improved by a combined strategy of feature selection and hyperparameter optimization, using the external validation set. Models were evaluated on the external test set containing new functional groups and diverse molecular structures. High prediction accuracies of R2 > 0.74 were obtained on the external test set. Despite being trained on the molecules with a single type of functional group, models were able to predict the redox potentials of derivatives containing multiple and different types of functional groups with good accuracies (R2 > 0.7). This type of performance for predicting redox potential from such a small and simple data set of phenazine derivatives has never been reported before. Redox flow batteries (RFBs) are emerging as promising candidates for energy storage systems. However, new green and efficient materials are required for their widespread usage. We believe that the hybrid DFT-ML approach demonstrated in this report would help in accelerating the virtual screening of phenazine derivatives, thus saving computational and experimental costs. Using this approach, we have identified promising phenazine derivatives for green energy storage systems such as RFBs.
2022-03-29T00:00:00Z