Optimizing multilayer graphite-silicon anodes: A computational approach to enhancing lithium-Ion battery performance
Bibliographic record
Abstract
This study evaluated the performance of multilayer anodes for lithium-ion batteries, composed of an outer graphite layer in direct contact with the electrolyte and an inner graphite–silicon composite layer, using finite-element simulations and multivariate statistical analysis. Various silicon contents such as 10, 20 percent and 30 %, layer thickness configurations including 30–30 µm, 20–40 µm and 10–50 µm, and graphite particle sizes of 2.5, 5 and 7.5 µm were systematically examined while maintaining a total anode thickness of 60 µm. In addition, the cathode material NMC 622 and the electrolyte LiPF6 in 3:7 EC:EMC were specified in the simulated cell configuration. The methodology integrated COMSOL Multiphysics® simulations with a simulation design (DOE) constructed in JMP, enabling the identification of key response parameters such as capacity loss percentage, solid-electrolyte interphase (SEI) layer thickness, potential drop across the SEI and electrolyte consumption over 2000 simulated cycles. Simulation results indicated that a 30–30 µm configuration, employing 2.5 µm graphite particles and a silicon content in the range of 20–30 % within the composite layer, substantially reduces potential drop, electrolyte consumption and SEI growth compared to modeled single-layer 100 % graphite or homogeneous silicon–graphite anodes. These findings underscore the viability of dual-layer structures for leveraging silicon’s high theoretical capacity without compromising electrochemical stability, and they highlight the crucial role of simulation-driven optimization in predicting long-term performance in batteries with enhanced energy density and extended cycle life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".