Multiphysics Optimization of Graphite-Buffered Bilayer Anodes with Diverse Inner Materials for High-Energy Lithium-Ion Batteries
Bibliographic record
Abstract
This study presents a multiphysics simulation approach to optimize graphite-buffered bilayer anodes for enhanced energy density in lithium-ion batteries, assessing the electrochemical impact of diverse inner-layer materials, including silicon, hard carbon, lithium titanate (LTO), and metallic lithium, in pure and graphite-composite forms. A coupled finite-element model implemented in COMSOL Multiphysics 6.2 was used to integrate spherical lithium diffusion, charge conservation, and the solid electrolyte interphase (SEI) formation kinetics. The evaluated anode structure consisted of a 60 µm-thick bilayer: a 30 µm graphite surface layer coupled with a 30 µm inner layer of alternative active materials. Simulations were performed using an NMC622 cathode, LiPF6 in EC:EMC electrolyte, at room temperature, under a charge rate of 1 C, considering realistic particle sizes (graphite: 2.5 µm; Si: 0.1 µm; hard carbon: 2.5 µm; LTO: 0.2 µm; Li metal: 0.5 µm), and evaluated over 2000 cycles. The hard carbon/graphite configuration exhibited a capacity fade of 5.8% compared with 7.1% in pure graphite. Additionally, the SEI thickness decreased to 0.20 µm (from 0.25 µm), the overpotential dropped to −17 mV (from −59 mV), and the electrolyte consumption was reduced to 20.8% (from 42.9%). The analysis highlights hard carbon and LTO inner layers as optimal trade-offs between capacity and cycle stability, whereas silicon and lithium metal significantly increased the initial capacity but accelerated SEI formation and impedance growth. These findings demonstrate the graphite-buffered bilayer’s potential to decouple interfacial degradation from high-capacity materials, providing valuable guidelines for the design of advanced lithium-ion battery anodes.
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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".