Three-Dimensional Modeling of All-Solid-State Lithium-Ion Batteries Using Synchrotron Transmission X-ray Microscopy Tomography
Bibliographic record
Abstract
In this study, a synchrotron transmission X-ray microscopy tomography system has been utilized to reconstruct the three-dimensional (3D) morphology of all-solid-state lithium-ion battery (ASSB) electrodes. The electrode was fabricated with a mixture of Li(Ni 1/3 Mn 1/3 Co 1/3 )O 2 , Li 1.3 Ti 1.7 Al 0.3 (PO 4 ) 3 , and super-P. For the first time, a 3D numerical multi-physics model was developed to simulate the galvanostatic discharge performance of an ASSB, elucidating the spatial distribution of physical and electrochemical properties inside the electrode microstructure. The 3D model shows a wide range of electrochemical properties distribution in the solid electrolyte (SE) and the active material (AM) which might have a negative effect on ASSB performance. The results show that at high current rates, the void space hinders the ions’ movement and causes local inhomogeneity in the lithium-ion distribution. The simulation results for electrodes fabricated under two pressing pressures reveal that higher pressure decreases the void spaces, leading to a more uniform distribution of lithium ions in the SE due to more facile lithium ion transport. The approach in this study is a key step moving forward in the design of 3D ASSBs and sheds light on the physical and electrochemical property distribution in the SE, active material, and their interface.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".