Multimodal Characterization of Nucleation and Progression of Interfacial Degradation in All Solid-State Batteries
Bibliographic record
Abstract
We face an immediate need for more energy-dense batteries that are stable over long-term cycling, to address the increased electrification of the transportation sector. All-solid state batteries (ASSBs) that combine a solid-state electrolyte with a Li metal anode offer the potential to achieve this objective by replacing intercalation anodes such as graphite with Li metal. However, interfacial degradation at the Li | solid electrolyte interface currently compromises the safety and cycling stability of ASSBs [1]. This interfacial degradation is usually a combination of instabilities of mechanical, chemical or electrochemical origin. This in turn compromises the structural and morphological stability of ASSBs over cycling, causing them to fail in fewer cycles than required for implementation as next-generation batteries in automobiles [2]. Thus, in order to improve their cycling stability, the understanding of the origin and nature of these interfacial instabilities needs to be multimodal, to understand the interplay between the different degradation mechanisms. Argyrodite (Li6PS5Cl) is a particularly attractive solid-state electrolyte (SSE) due to a high ionic conductivity (comparable to liquid electrolytes), as well as potential for batch processing [3]. However, its brittle nature and chemical composition makes it susceptible to cracking and deleterious side reactions, which hamper its stability. This work will focus on elucidating the effect of (1) current density and (2) processing conditions on the cycling stability of Li | LPSCl | Li ASSBs. We use 4-D XRD-CT (X-ray diffraction computed tomography) combined with phase contrast micro-computed tomography (μCT) to conduct the multimodal investigation of interfacial degradation, under pseudo operando conditions. The methodology is to obtain a 3-D XRDCT scan around the interface in a particular cycled condition, followed by a higher-resolution 3D μCT scan on the same region. This sequence is repeated after every stripping/plating cycle, starting from the pristine cell up to cell failure. These experiments are conducted at a synchrotron source, to enable the acquisition at high spatial resolution (~5 μm) over large volumes (~mm3), in a reasonable amount of time. A standard swagelok-style cell is used for repeatability of results and optimal geometry for conducting tomography. XRD-CT can furnish quantitative phase maps of all phases (argyrodite and reaction by-products), as well as the elastic strain in the sample. The μCT on the other hand gives information on the evolution of morphology around the interface with cycling (cracks/voids). Thus, by correlating the two datasets together over cycling, we are able to connect mechanical instabilities to the chemical/electrochemical instabilities. Here, we focus on varying two specific processing parameters for argyrodite: the sintering temperature and pressure, and studying their effect on nucleation and propagation of interfacial instabilities over repeated cycling. Through cycling, we track the evolution of cracks/voids, chemical by-products such as LiCl, Li3P and Li2S using XRD-CT and μCT. Finally, we track the filling of metallic Li into certain cracks in dendritic form, which leads to shorting and failure of the cell. We find that both of these parameters heavily influence the behavior of the interface, with cells failing between 4-20 cycles, and a marked difference in how the degradation initiates and propagates. Finally, for the processing condition that shows the best cycling behavior, we do a C-rate study, by increasing the current density for plating/stripping with each cycle up to failure. This discusses the utility of a parameter such as critical current density for a cell, where the local current densities are very heterogeneous and different from the global current reading while cycling. We envisage these results being used as inputs into modeling studies to optimize strategies for interfacial stabilization in ASSBs going forward. [1] Paul, P. P. et al. Interfaces in all solid state Li-metal batteries: a review on instabilities, stabilization strategies, and scalability. Energy Storage Materials 2022, 45, 969-1001. [2] Hao, S. et al. Tracking lithium penetration in solid electrolytes in 3D by in-situ synchrotron X-ray computed tomography. Nano Energy 2021, 82, 105744. [3] Chen, Y-T. et al., Investigating dry room compatibility of sulfide solid-state electrolytes for scalable manufacturing, Journal of Materials Chemistry A 2022, 10, 7155-7164.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".