Nucleation Under Shear Flow

We present a general formalism for calculating the nucleation rates of simply sheared systems [5-6]. We have derived an extension to the conventional Classical Nucleation Theory, explicitly embodying the shear rate. The framework can be used for moderate supercooling, at which temperatures brute-force methods are practically infeasible. We show how the theory can be used to identify shear regimes of ice nucleation behavior for the mW water model, unifying disparate trends reported in the literature [7]. [1] Tschauner, O et al., Science, 359, 1136-1139 (2018) [2] Goswami, A., Singh, J. K., Phys. Chem. Chem. Phys., (2020) [3] Goswami, A., Singh, J. K., J. Phys. Chem. C., in press (2020) [4] Goswami A, Singh JK, J. Chem. Phys. 154, 154502 (2021) [5] Goswami A and Singh JK, Phys. Phys. Chem. Phys. 23, 150402 (2021) [6] Goswami, A, Dalal IS, Singh JK, J. Chem. Phys, 153, 09502 (2021) [7] Goswami, A, Dalal IS, Singh JK, Phys. Rev. Letts, 126, 195702 (2021

Fabrication of Light Weight Farms Yoke With The Use of Composite Material

Natural fibers are subdivided based on their origins, coming from plants, animals or minerals. All plant fibers are composed of cellulose while animal fibers consist of proteins (hair, silk, and wool). Plant fibers include bask (or stem or soft sclerenchyma) fibers, leaf or hard fibers, seed, fruit, wood, cereal straw, and other grass fibers. Over the last few years, a number of researchers have been involved in investigating the exploitation of natural fibers as load bearing constituents in composite materials. The use of such materials in composites has increased due to their relative cheapness, ability to recycle and for the fact that they can compete well in terms of strength per weight of material. Natural fibers can be considered as naturally occurring composites consisting mainly of cellulose fibrils embedded in lignin matrix. The cellulose fibrils are aligned along the length of the fiber, which render maximum tensile and flexural strengths, in addition to providing rigidity. The reinforcing efficiency of natural fiber is related to the nature of cellulose and its crystallinity. The existing traditional yoke is made up of the wood of trees like Tun and Haldu which are not easily available in different parts of India. The life of a yoke is about 3 to 5 years, due to this 628 tons of wood is required at every 3-5 years in India that would create a burden to the presently available forests. To overcome these problems, there is a need to develop yoke from waste bio-materials. Keeping this in view, a yoke from composite material was designed and developed and relative comparison between traditional wooden and developed yoke has been done

Characterization of mono- and divacancy in fcc and hcp hard-sphere crystals

We determine and compare the thermodynamic properties of mono- and divacancies in the face-centered-cubic and hexagonal-close-packed hard-sphere crystals via a modified grand canonical ensemble. Widom-type particle insertion was employed to estimate the free energy of formation of mono- and divacancies, and the results are supported by an alternative approach, which quantifies the entropy gain of the neighbor particles. In hcp crystal, we found a strong anisotropy in the orientational distribution of vacancies and observe an eightfold increase in the number of divacancies in the hexagonal plane compared to the one in the out of plane at highest density of interest. This phenomenon is induced by the different arrangement and behavior of the shared nearest neighbor particles, which are located at the same distance from each vacant site in divacancy. The effect of divacancies on the free energy is to reduce that of the hcp crystal relative to the fcc by around 7?? 10-6 kB T at melting.open8

Surface tension and vapor-liquid phase coexistence of confined square-well fluid

Phase equilibria of a square-well fluid in planar slit pores with varying slit width are investigated by applying the grand-canonical transition-matrix Monte Carlo (GC-TMMC) with the histogram-reweighting method. The wall-fluid interaction strength was varied from repulsive to attractive such that it is greater than the fluid-fluid interaction strength. The nature of the phase coexistence envelope is in agreement with that given in literature. The surface tension of the vapor-liquid interface is calculated via molecular dynamics simulations. GC-TMMC with finite size scaling is also used to calculate the surface tension. The results from molecular dynamics and GC-TMMC methods are in very good mutual agreement. The vapor-liquid surface tension, under confinement, was found to be lower than the bulk surface tension. However, with the increase of the slit width the surface tension increases. For the case of a square-well fluid in an attractive planar slit pore, the vapor-liquid surface tension exhibits a maximum with respect to wall-fluid interaction energy. We also report estimates of critical properties of confined fluids via the rectilinear diameter approach.open312

Characterization of fluid-solid phase transition of hard-sphere fluids in cylindrical pore via molecular dynamics simulation

Equation of state and structure of hard-sphere fluids confined in a cylindrical hard pore were investigated at the vicinity of fluid-solid transition via molecular dynamics simulation. By constructing artificial closed-packed structures in a cylindrical pore, we explicitly capture the fluid-solid phase transition and coexistence for the pore diameters from 2.17?? to 15??. There exist some midpore sizes, where the phase coexistence might not exist or not clearly be observable. We found that the axial pressure including coexistence follows oscillatory behavior in different pore sizes; while the pressure tends to decrease toward the bulk value with increasing pore size, the dependence of the varying pressure on the pore size is nonmonotonic due to the substantial change of the alignment of the molecules. The freezing and melting densities corresponding to various pore sizes, which are always found to be lower than those of the bulk system, were accurately obtained with respect to the axial pressure.open9

Fusing machine learning strategy with density functional theory to hasten the discovery of MXenes for hydrogen generation

The complexity of the topological and combinatorial configuration space of MXenes can give rise to gigantic design challenges that cannot be addressed through traditional experimental or routine theoretical approaches. To this end, we establish a robust and more broadly applicable multistep workflow from the toolbox of supervised machine learning (ML) algorithms for predicting the hydrogen evolution reaction (HER) activity over 4,500 MM$^{\prime}$XT$_2$-type MXenes, where 25\% of the material space (1125 systems) is randomly selected to evaluate the HER performance using density functional theory (DFT) calculations. As the most desirable ML model, the random forest regression method with recursive feature elimination and hyperparameter optimization accurately and rapidly predicts the Gibbs free energy of hydrogen adsorption ($\Delta$G$_{H}$) with a low predictive mean absolute error of 0.374 eV. Based on these observations, the H-atom adsorbed directly on top of the outermost metal atomic layer of the MM$^{\prime}$XT$_2$-type MXenes (site-2) with Nb, V, Mo, Cr and Ti metals composed of carbon based O-functionalization are discovered to be highly stable and active catalysts, surpassing that of commercially available platinum based counterparts. Overall, the physically meaningful predictions and insights of the developed ML/DFT-based multistep workflow will open new avenues for accelerated screening, rational design and discovery of potential HER catalysts

Accelerating the Discovery of g-C3_3N4_4-Supported Single Atom Catalysts for Hydrogen Evolution Reaction: A Combined DFT and Machine Learning Strategy

Two-dimensional materials supported by single atom catalysis (SACs) are foreseen to replace platinum for large-scale industrial scalability of sustainable hydrogen generation. Here, a series of metal (Al, Sc, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn) and non-metal (B, C, N, O, F, Si, P, S, Cl) single atoms embedded on various active sites of g-C$_3$N$_4$ are screened by DFT calculations and six machine learning (ML) algorithms (support vector regression, gradient boosting regression, random forest regression, AdaBoost regression, multilayer perceptron regression, ridge regression). Our results based on formation energy, Gibbs free energy and bandgap analysis demonstrate that the single atoms of B, Mn and Co anchored on g-C$_3$N$_4$ can serve as highly efficient active sites for hydrogen production. The ML model based on support vector regression (SVR) exhibits the best performance to accurately and rapidly predict the Gibbs free energy of hydrogen adsorption (${\Delta}$GH ) for the test set with a lower mean absolute error (MAE) and a high coefficient of determination (R$^2$) of 0.45 and 0.81, respectively. Feature selection based on the SVR model highlights the top five primary features: formation energy, bond length, boiling point, melting point, and valance electron as key descriptors. Overall, the multistep work-flow employed through DFT calculations combined with ML models for efficient screening of potential hydrogen evolution reaction (HER) from g-C$_3$N$_4$-based single atom catalysis can significantly contribute to the catalyst design and fabrication.Comment: 10 pages, 4 figure