Stanford Energy Student Lectures - Magnesium Crystal Defects and Battery State Estimation

Join us as grad student/postdoc speakers from various technical areas deliver short and accessible presentations about their innovative clean energy research. Learn more about cutting-edge science and the most recent breakthroughs in areas such as renewables, energy conversion materials and devices, catalysis, and decarbonization from the researchers themselves!

Refreshments will be provided starting at 2:45pm. Share your feedback on the speakers for a chance to win a Coupa gift card!

Speaker Topics:

Edem Honu - High-temperature defect and microstructural evolution in single-crystal Mg

Abstract:

Weight reduction in structural applications is among the most direct routes to reducing fuel consumption and CO₂ emissions in transport. Magnesium (Mg), the lightest available structural metal (ρ =1.74 g/cm³), is 30% less than aluminium alloys with a high specific strength and is therefore a critical candidate of next-generation lightweight structures in automotive and aerospace applications. Realizing this potential requires well-controlled thermal processing cycles to restore workability and tailor microstructure; yet the intrinsic, thermally-driven dislocation behaviour of Mg, by grain-boundary effects or applied stress, is poorly understood.

Here, we present the first in-situ three-dimensional dark-field X-ray microscopy (DFXM) study of defect evolution in a single-crystal Mg during a high-temperature annealing cycle under no applied stress, resolving a bulk volume of 255 × 92 × 40 µm³ at sub-micrometre resolution. A pre-existing {11-22} compression twin dissolves by ~202°C, triggering thermally activated dislocation climb across multiple prismatic and pyramidal slip variants. Continued heating drives near-complete static recovery by 318°C, after which the crystal stabilizes as a hierarchical sub-grain boundary network.

Statistical analysis of centre-of-mass rocking-curve maps confirms a significant reduction in lattice orientation spread, confirming the progressive release of stored elastic energy. These critical temperature windows inform optimized annealing regimes directly applicable to the industrial processing of Mg components, with implications for reducing fuel consumption through wider deployment of lightweight Mg structures. The results simultaneously provide the experimental benchmark required to validate emerging thermal field dislocation mechanics (T-FDM) and phase-field dislocation dynamics (PFDD) models in low-symmetry crystal structures.

Speaker bio:

My background is in aerospace engineering, but currently, I am a structural and materials enthusiast with strong research interests in metal powder-based AM technology, hydrogen embrittlement, and machine learning. I work in the Dresselhaus-Marais group at the Geballe Laboratory for Advanced Materials at Stanford University and SLAC National Accelerator Laboratory, where I study hydrogen-induced mesoscale defects using advanced characterization techniques and imaging.

 

Joseph Lucero - Seeing Inside Batteries from the Outside: State Estimation for Reliable Energy Storage

Abstract:

Lithium-ion batteries are central to electrified transportation and grid energy storage, yet many internal states needed for effective operation cannot be measured directly. Battery management systems must therefore infer quantities such as state of charge from external measurements, typically current and voltage. This talk focuses on how physics-based models can support more trustworthy state estimation from these limited signals. In particular, I will discuss why high-resolution battery models that accurately predict voltage are not necessarily the best models for estimating internal states, and how observability-aware modeling can help identify model structures better suited for inference. Improved state estimation can clarify how battery systems interpret operating conditions, quantify uncertainty, and support more informed control decisions. Ultimately, current and voltage measurements contain valuable information about battery behavior, but extracting that information requires models designed not only to simulate battery dynamics, but also to infer the hidden states that inform battery operation.

Speaker bio:

Joseph N. E. Lucero is a final-year Ph.D. candidate in Chemistry at Stanford University, where he develops electrochemical battery models and estimation algorithms for next-generation battery management systems. His research spans lithium-ion battery modeling across multiple scales, from electrochemical model development and state estimation to vehicle energy analysis and grid-scale energy optimization. He has held research internships at Google and Oak Ridge National Laboratory, and his publications span battery systems, stochastic thermodynamics, biophysics, and medical physics. Joseph earned his M.Sc. in Physics and B.Sc. with Distinction in Biological Physics from Simon Fraser University.