Adhesion and Surface Layers on Silicon Anodes Suppress Formation of <i>c</i> -Li <sub>3.75</sub> Si and Solid-Electrolyte Interphase
Bibliographic record
Abstract
The formation of c -Li 3.75 Si is known to be detrimental to silicon anodes in lithium-ion batteries. To suppress the formation of this crystalline phase and improve the electrochemical performance of Si-based anodes, three approaches were amalgamated: addition of a nickel adhesion sublayer, alloying of the silicon with titanium, and addition of either carbon or TiO 2 as a capping layer. The silicon-based films were analyzed by a suite of methods, including scanning electron microscopy (SEM) and a variety of electrochemical techniques, as well as X-ray photoelectron spectroscopy (XPS) to provide insights into the composition of the resulting solid-electrolyte interphase (SEI). A nickel adhesion layer decreased the extent of delamination of the silicon from the underlying copper substrate, compared to Si deposited directly on Cu, which resulted in less capacity loss. Alloying of silicon with titanium (85% silicon, 15% titanium) further increased the stability. Finally, capping these multilayer electrodes with either a thin 10 nm layer of carbon or TiO 2 resulted in the best electrode behavior and lowest cumulative relative irreversible capacity. TiO 2 is slightly more effective in enhancing the capacity retention, most likely due to differences in the resulting solid-electrolyte interphase (SEI). The combination of an adhesion layer, alloying, and surface coatings shows a cumulative suppression of the formation of c -Li 3.75 Si and SEI, resulting in the greatest improvement of capacity retention when all three are incorporated together. However, these strategies appear to only delay the onset of the c -Li 3.75 Si phase; eventually, the c -Li 3.75 Si phase will form, and at that point, the capacity degradation rate of all the electrodes becomes similar.
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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".