Beyond Thin Films: Clarifying the Impact of <i>c</i> -Li <sub>15</sub> Si <sub>4</sub> Formation in Thin Film, Nanoparticle, and Porous Si Electrodes
Bibliographic record
Abstract
The formation of the c -Li 15 Si 4 phase has well-established detrimental effects on the capacity retention of thin film silicon electrodes. However, the role of this crystalline phase with respect to the loss of capacity is somewhat ambiguous in nanoscale morphologies. In this work, three silicon-based morphologies are examined, including planar films, porous planar films, and silicon nanoparticle composite powder electrodes. The cycling conditions are used as the lever to induce, or not induce, the formation of c -Li 15 Si 4 through application of constant-current (CC) or constant-current constant-voltage (CCCV) steps. In this manner, the role of this phase on capacity retention and Coulombic efficiency can be determined with few other convoluting factors such as alteration of the composition or morphology of the silicon electrodes themselves. The results here confirm that the c -Li 15 Si 4 phase increases the rate of capacity decay in planar films but has no major effect on capacity retention in half-cells based on porous silicon films or silicon nanoparticle composite powder electrodes, although this conclusion is nuanced. Besides using a constant-voltage step, formation of the c -Li 15 Si 4 phase is influenced by the dimensions of the Si material and the lithiation cutoff voltage. Porous Si films, which, in this work, comprise primary Si particle sizes that are smaller than those in the preformed Si nanoparticle slurries, do not undergo the formation of c -Li 15 Si 4 at 50 mV, whereas Si nanoparticle slurries are accompanied by the formation of c -Li 15 Si 4 up to 80 mV. The solid-electrolyte interphase (SEI) formed from reaction of the c -Li 15 Si 4 with the carbonate-based electrolyte causes polarization in both nanoparticle and porous film silicon electrodes and lowers the average Coulombic efficiency. A comparison of the cumulative irreversibilities due to SEI formation between different lithiation cutoff voltages in silicon nanoparticle slurry electrodes confirmed the connection between higher SEI buildup and formation of the c -Li 15 Si 4 phase. This work indicates that concerns about the c -Li 15 Si 4 phase in silicon nanoparticles and porous silicon electrodes should mainly focus on the stability of the SEI and a reduction of irreversible electrolyte reactions.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".