Computational optimization of multilayer anode architectures for high–energy lithium–ion batteries
Bibliographic record
Abstract
This thesis addresses the computational optimization of multilayer anode architectures aimed at enhancing the performance of high-energy lithium-ion batteries.Given the critical need for improved energy density, cycle life, and sustainability, this research evaluates multilayer anode designs that strategically combine graphite with silicon and other alternative materials (hard carbon, lithium titanate oxide (LTO), and lithium metal).It validates a simulation-driven workflow for designing multilayer lithiumion-battery anodes that raise specific energy without sacrificing long-term stability.Finite-element electrochemical modelling in COMSOL Multiphysics®, coupled with factorial and multivariate statistics in JMP®, is used to isolate the geometric and compositional variables that govern capacity fade, solid-electrolyte-interphase (SEI) growth, overpotential and electrolyte loss.In Phase I a 3³ full factorial varies silicon content (10-30 wt %), graphite particle size (2.5-7.5 µm) and layer-thickness ratio (10-50, 20-40, 30-30 µm) across twentyseven graphite / graphite-silicon bilayers cycled 2 000 times at 1 C.The optimal configuration consists of a 30 µm graphite buffer layer over a 30 µm composite containing approximately 20 wt% silicon and 2.5 µm graphite particles.This setup limits capacity fade to around 20%, caps SEI thickness at 0.36 µm, maintains the SEI overpotential at 42 mV, and consumes only 44% of the initial electrolyte.It outperforms both a 100% graphite electrode and a homogeneous 90% graphite / 10% silicon blend by reducing direct electrolyte contact with silicon and promoting a more uniform current distribution.Phase II takes that graphite buffer and particle size while replacing the inner composite with hard carbon, Li₄Ti₅O₁₂ (LTO) or metallic lithium, each tested pure and as 10/20/30 wt % graphite mixtures.Among sixteen new cells, one stands out because it has a second layer with 100% hard-carbon core that retains ≈94 % of its initial capacity with negligible impedance rise.Another with a second layer of 10 wt % LTO generates the VI thinnest SEI (0.17 µm) and the lowest electrolyte loss (~19 %) while sacrificing only 11.5 % capacity, and finally, a configuration with a second layer with 30 wt % Limetal delivers the largest gravimetric capacity gain but stabilizes at ~83 % retention because of dead-lithium formation and higher mid-SOC polarization.Electrochemical impedance spectroscopy confirms that the graphite buffer effectively decouples SEI resistance from charge-transfer resistance, while the inner layer material dictates diffusional impedance and kinetic durability.This study provides a practical design framework: employ balanced layer thicknesses to facilitate diffusion processes and avoid bottlenecks.A graphite front layer with fine particles is recommended to control SEI chemistry, paired with a second high-capacity layer tailored to specific applications silicon-rich or lithium metal for high energy density, hard carbon for rapid charge resilience, or LTO for safety-critical, long-life battery packs thereby offering a scalable route to pouch cells suitable for electric vehicles and grid storage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".