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Effect of battery electrode manufacturing heterogeneity on transport properties

Ph.D. student Chance Norris published his first first-author paper in ACS Applied Materials & Interfaces! In this work, we study the heterogeneity of commercially manufactured graphite electrodes for lithium-ion batteries using x-ray computed tomography and computational simulation. This heterogeneity spans multiple length scales, from the particle shape/morphology to that spanning multiple images samples. We show that heterogeneity at all scales influences the eventual transport properties of the electrode. DOI: 10.1021/acsami.1c19694

Cryo SEM identifies relationship between pressure and short circuits

In a collaborative paper recently published in iScience, we use novel cryogenic electron microscopy to show the relationship between eternally applied pressure, lithium dendrite growth, and short circuits in lithium batteries. While pressure improves Li inventory retention at high currents, higher pressures promotes sh ort circuits. Thicker or multiple separators only masks this effect. Available open source at DOI: 10.1016/j.isci.2021.103394

Method of calculating diffusion coefficient from GITT published in ACS AEM

What is “the diffusion coefficient”? A harder question than we first thought. In a new paper in ACS Applied Energy Materials, we show a more objective and accurate way to calculate the diffusion coefficient from GITT data specific to its end use. We realized that the diffusion coefficient used in a non-ideal (non-Fickian) diffusion model is different than the standard Fickian one. The value you calculate depends on its end use. Also interesting was that the non-ideal diffusion model fit discharge data across discharge rates much better than the Fickian model, even when each used their best fit diffusion coefficients. DOI: 10.1021/acsaem.1c02218

A checklist for verifiable battery modeling research

Do you ever find battery modeling papers that don’t quite have all of the requisite modeling details for you to replicate or understand the results? In collaboration with 28 other researchers from around the globe through the BMWS community, we published an article in ACS Energy Letters proposing a checklist that would be used by all future battery modeling papers to make sure they provide the necessary information to interpret the work. DOI: 10.1021/acsenergylett.1c01710

Uncertainty in image-based simulation published in Nature Communications

Image segmentation is a necessary component of image-based simulations for digital twins. In our recent paper in Nature Communications we develop a method for automated simulations based on 3D images that incorporates the impact of image segmentation uncertainty on physics simulations. This work will prove useful for a variety of applications, including in energy storage, composite materials, and health care. M. Krygier et al. Quantifying the unknown impact of segmentation uncertainty on image-based simulations, Nature Communications 12 (2021) 5414 doi: 10.1038/s41467-021-25493-8.

Perspective on moving boundary conditions published in JES

In a recent perspective article in the Journal of the Electrochemical Society, Scott Roberts, in collaboration with the research group of Prof. Venkat Subramanian at the University of Texas, Austin, discussed how proper formulation of boundary conditions for moving boundary problems (such as growth of dendrites on lithium during cycling) is paramount to ensuring mass conservation and therefore proper interpretation of simulation results. DOI: 10.1149/1945-7111/ac2091

Jamming simulations published in Physical Review Research

Our latest article on simulations of the jamming of bidisperse frictional spheres was published in Physical Review Research. In this study, led by former post-doc Ishan Srivastava, we performed discrete element simulations on domains as large as 10 million particles to determine the maximum packing fractions for a wide selection of particle sizes. DOI: 10.1103/PhysRevResearch.3.L032042

Job Openings in Multi-Scale, Multi-Physics Modeling

We have multiple post-doctoral appointment openings in the broad area of multi-scale, multi-physics modeling. Areas of study include electrochemistry, batteries, composites, and manufacturing processes. Work will involve physics-informed machine learning, image-based simulation, and multi-physics modeling using high performance computing. Interested US citizen candidates can apply at the Sandia Careers page.