Engineering topics
Publications and source records attributed to Krishnamurthy, Dilip.
The present disclosure is generally related to separators for use in lithium metal batteries, and associated systems and products. Certain embodiments are related to separators that form or are repaired when an electrode is held at a voltage. In some embodiments, an electrochemical cell may comprise an electrolyte that comprises a precursor for the separator.
Carbon-supported Ni–Mo composites catalyze alkaline hydrogen evolution and oxidation with negligible onset overpotential. Electrolyzers using Ni–Mo cathodes perform comparably to Pt–Ru cathodes, but oxidative instability limits fuel cell operation.
We report, in fluid physics, data-driven models to enhance or accelerate time to solution are becoming increasingly popular for many application domains, such as alternatives to turbulence closures, system surrogates, or for new physics discovery. In the context of reduced order models of high-dimensional time-dependent fluid systems, machine learning methods grant the benefit of automated learning from data, but the burden of a model lies on its reduced-order representation of both the fluid state and physical dynamics. In this work, we build a physics-constrained, data-driven reduced order model for Navier–Stokes equations to approximate spatiotemporal fluid dynamics in the canonical case of isotropic turbulence in a triply periodic box. The model design choices mimic numerical and physical constraints by, for example, implicitly enforcing the incompressibility constraint and utilizing continuous neural ordinary differential equations for tracking the evolution of the governing differential equation. We demonstrate this technique on a three-dimensional, moderate Reynolds number turbulent fluid flow. In assessing the statistical quality and characteristics of the machine-learned model through rigorous diagnostic tests, we find that our model is capable of reconstructing the dynamics of the flow over large integral timescales, favoring accuracy at the larger length scales. More significantly, comprehensive diagnostics suggest that physically interpretable model parameters, corresponding to the representations of the fluid state and dynamics, have attributable and quantifiable impact on the quality of the model predictions and computational complexity.
The present disclosure is generally related to separators for use in lithium metal batteries, and associated systems and products. Certain embodiments are related to separators that form or are repaired when an electrode is held at a voltage. In some embodiments, an electrochemical cell may comprise an electrolyte that comprises a precursor for the separator.
Batteries are an enabling technology for addressing sustainability through the electrification of various forms of transportation (1) and grid storage. (2) Batteries are truly multi-scale, multi-physics devices, and accordingly various theoretical descriptions exist to understand their behavior (3-5) ranging from atomistic details to techno-economic trends. As we explore advanced battery chemistries (6,7) or previously inaccessible aspects of existing ones, (8-10) new theories are required to drive decisions. (11-13) The decisions are influenced by the limitations of the underlying theory. Advanced theories used to understand battery phenomena are complicated and require substantial effort to reproduce. However, such constraints should not limit the insights from these theories. We can strive to make the theoretical research verifiable such that any battery stakeholder can assess the veracity of new theories, sophisticated simulations or elaborate analyses. We distinguish verifiability, which amounts to “Can I trust the results, conclusions and insights and identify the context where they are relevant?”, from reproducibility, which ensures “Would I get the same results if I followed the same steps?” With this motivation, we propose a checklist to guide future reports of theoretical battery research in Table 1. We hereafter discuss our thoughts leading to this and how it helps to consistently document necessary details while allowing complete freedom for creativity of individual researchers. Given the differences between experimental and theoretical studies, the proposed checklist differs from its experimental counterparts. (14,15) This checklist covers all flavors of theoretical battery research, ranging from atomic/molecular calculations (16-19) to mesoscale (20,21) and continuum-scale interactions, (9,22) and techno-economic analysis. (23,24) Finally, as more and more experimental studies analyze raw data, (25) we feel this checklist would be broadly relevant.
Improved activity and stability Pt-based catalysts for the oxygen reduction reaction (ORR) are needed to perpetuate the deployment of polymer electrolyte fuel cells (PEFCs) in the transportation sector. In this study, we use atomic layer deposition of TiO 2 and Pt coupled with thermal reductive annealing to prepare Pt 3 Ti electrocatalysts. The atomic level synthetic control resulted in Pt 3 Ti nanoparticles with high ORR performance, including a mass activity of 1.84 A/mg Pt and excellent electrochemical stability. The Pt 3 Ti nanoparticles show excellent specific activity — 5.3-fold higher than commercial Pt/C and 3-fold higher than polycrystalline Pt, exceeding the performance of any PtTi catalysts reported to date. Combined experimental and computational efforts indicate that Pt enrichment on the Pt 3 Ti enhances the activity, and the intrinsic stability of the Pt 3 Ti phase provides durability. Subsequently, this knowledge, along with the facile fabrication of alloys by atomic layer deposition, can be leveraged to designed improved performance catalysts.