Élaboration d'une nouvelle architecture pour anodes en silicium poreux dans les batteries lithium-ion, intégrant une approche économique et écologique via l'utilisation de silicium grade solaire
Bibliographic record
Abstract
Silicon is one of the most promising anode materials for Li-ion batteries, especially to meet the growing demand for energy storage in the form of microbatteries for mobile and autonomous devices. However, the development of such batteries is hindered by mechanical and electrochemical failures resulting from massive Si volume expansion and continuous growth of the solid electrolyte interphase. This thesis is dedicated to advancing lithium-ion batteries by focusing on silicon anodes. The primary objective is the development of a novel anode architecture, termed "panel sandwich like," aiming to mitigate silicon's volumetric expansion and the growth of the solid electrolyte interphase (SEI). Applied on a chip, this structure exhibits outstanding electrochemical performance, with high areal capacities up to 10 mAh/cm² and an extended cycle life of up to 1000 cycles. Concurrently, the thesis explores the potential of solar-grade silicon as an economical alternative. These findings open innovative perspectives for energy storage, supporting the global transition to renewable energies. The significant contribution of this research lies in proposing an economical and technological solution to overcome challenges associated with silicon as an anode material. The "panel sandwich like" structure simplifies battery manufacturing while delivering exceptional electrochemical performance. Additionally, the exploration of solar-grade silicon represents an innovative breakthrough, paving the way for more affordable anodes. In conclusion, this thesis lays the groundwork for future research, including the potential application of the "panel sandwich like" structure to different silicon types and ongoing optimization of anode formulations. These developments offer opportunities for innovation and continuous growth in the field of energy storage, supporting the global transition to renewable energies.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".