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Subhasis Sarkar

Publications and source records attributed to Subhasis Sarkar.

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First-Principles Investigation of 2D Copper Boride as a High-Performance Anode for Lithium-Ion Batteries

In this study, we investigate the two-dimensional copper boride, Cu$_8$B$_{14}$, as a possible anode material for lithium-ion batteries using first-principles calculations. We found that the structural integrity of the monolayer was preserved even at elevated temperatures, while electronic calculations confirm the metallic character of the pristine and Li-loaded systems. On systematic lithiation on Cu$_8$B$_{14}$ a specific capacity of 430mAhg$^{-1}$ was obtained. A Li diffusion barrier of 0.32eV for the most favourable path, along with a diffusivity of approximately $2.26 \times 10^{-5}$cm$^2$s$^{-1}$ was obtained. The open-circuit voltage of 0.53 V falls within the optimal anode range of 0.1--1.0 V. These combined characteristics point to Cu$_8$B$_{14}$ as a compelling candidate for advanced battery anodes. Furthermore, to understand the defect and its effect on different parameters, we investigated an experimentally identified line-defect configuration of copper boride. The line defect monolayer retains a theoretical capacity of about 385mAhg$^{-1}$, while the introduced line defect further reduces the Li migration barrier to 0.21eV, yielding an enhanced macroscopic diffusivity of $\sim$5.6$\times$10$^{-4}$cm$^{2}$s$^{-1}$ and confirming that structural defects accelerate Li-ion transport kinetics in this material.

cond-mat.mtrl-sci

A Probabilistic Design Method for Fatigue Life of Metallic Component

In the present study, a general probabilistic design framework is developed for cyclic fatigue life prediction of metallic hardware using methods that address uncertainty in experimental data and computational model. The methodology involves (i) fatigue test data conducted on coupons of Ti6Al4V material (ii) continuum damage mechanics based material constitutive models to simulate cyclic fatigue behavior of material (iii) variance-based global sensitivity analysis (iv) Bayesian framework for model calibration and uncertainty quantification and (v) computational life prediction and probabilistic design decision making under uncertainty. The outcomes of computational analyses using the experimental data prove the feasibility of the probabilistic design methods for model calibration in presence of incomplete and noisy data. Moreover, using probabilistic design methods result in assessment of reliability of fatigue life predicted by computational models.

cs.CE