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Double transition metal (DTM) nitride MXenes offer enhanced electrical conductivity and tunable catalytic properties compared to conventional carbide-based MXenes. In this study, we employed first-principles density functional theory (DFT) calculations to discover and computationally validate a novel DTM nitride MXene, Nb2TiN2, derived from its MAX phase precursor and investigated its potential as an anchoring material (AM) for Li–Se batteries. This newly proposed MXene expands the compositional landscape of DTM nitrides and opens new avenues for functional material design. We performed a comprehensive analysis of the thermodynamic and electronic properties of Nb2TiAlN2, and the MAX phase precursor to Nb2TiN2 to assess its structural stability and exfoliation potential. Exfoliation energy calculations confirmed the feasibility of synthesizing Nb2TiN2 from Nb2TiAlN2. We then explored the functionalized form, Nb2TiN2S2, evaluating its capability to serve as an effective anchoring material (AM) in Li–Se batteries by analyzing the reaction mechanisms and kinetics of the selenium reduction reaction (SeRR). Our results indicate that Nb2TiN2S2 exhibits a strong binding affinity for lithium polyselenides (Li2Sen), effectively suppressing the shuttle effect. Gibbs free energy calculations for the rate-limiting step of the SeRR reveal favorable kinetics and reduced reaction barriers. Overall, this study provides a detailed evaluation of the structural and electronic properties of a newly proposed DTM nitride MXene and its S-functionalized derivative and the catalyzing effect of Nb2TiN2S2 in accelerating the reaction kinetics in Li–Se batteries. These findings underscore the potential importance of the further exploration of MXenes to address current challenges in high-performance Li–Se batteries. 

Abstract Challenge 3 of the 2022 NIST additive manufacturing benchmark (AM Bench) experiments asked modelers to submit predictions for solid cooling rate, liquid cooling rate, time above melt, and melt pool geometry for single and multiple track laser powder bed fusion process using moving lasers. An in-house developedAdditiveManufacturingComputationalFluidDynamics code (AM-CFD) combined with a cylindrical heat source is implemented to accurately predict these experiments. Heuristic heat source calibration is proposed relating volumetric energy density (ψ) based on experiments available in the literature. The parameters of the heat source of the computational model are initially calibrated based on a Higher Order Proper Generalized Decomposition- (HOPGD) based surrogate model. The prediction using the calibrated heat source agrees quantitatively with NIST measurements for different process conditions (laser spot diameter, laser power, and scan speed). A scaling law based on keyhole formation is also utilized in calibrating the parameters of the cylindrical heat source and predicting the challenge experiments. In addition, an improvement on the heat source model is proposed to relate the Volumetric Energy Density (VED_σ) to the melt pool aspect ratio. The model shows further improvement in the prediction of the experimental measurements for the melt pool, including cases at higher VED_σ. Overall, it is concluded that the appropriate selection of laser heat source parameterization scheme along with the heat source model is crucial in the accurate prediction of melt pool geometry and thermal measurements while bypassing the expensive computational simulations that consider increased physics equations.