Si and Si@C Nanoparticles for Lithium-Ion Batteries Anodes: Electrode/Electrolyte Interface Evolution
Bibliographic record
Abstract
Due to increasing demand in energy storage, much attention has been paid to Si as an anode material in Li-Ion batteries because of its theoretical capacity (3579 mAh/g in the Li15Si4 alloy vs 372 mAh/g for graphitic carbon). However, silicon suffers from several drawbacks, including rapid pulverization and SEI ripening, limiting its use. Nanostructuration and protection of silicon with a carbon coating are proven methods to improve the behavior of silicon-based anodes [1]. Using the laser pyrolysis method, the synthesis of silicon-carbon core-shell nanoparticles was achieved in a continuous way, without intermediate manipulations between the synthesis of the core from silane precursor and the shell from ethylene [2]. The influence of the carbon coating on electrochemical performances was studied in coin cells in operando conditions by using electrochemical impedance spectroscopy (EIS) as well as post mortem analysis of the anode by using (X-Ray Photon electron Spectroscopy (XPS), Scanning Electron Microscopy (SEM) and High Resolution Transmission Electron Microscopy (HRTEM)). Special attention was paid to the first cycle because of its major importance in the formation and growth of the SEI and the long term behavior of the battery. By comparing measurements on Si and Si@C materials, EIS clearly demonstrates the beneficial effect of the carbon shell in the SEI stabilization. The stability of the the SEI resistance shows the protective effect of the the carbon shell while the SEI resistance is strongly modified and increases during lithiation. Such behavior can be related to the evolution of the chemical composition determined by XPS at different potentials during lithiation and delithiation. Figure 1
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".