ALD TiO2 coated silicon nanowires for lithium ion battery anodes with enhanced cycling stability and coulombic efficiency
Bibliographic record
Abstract
We demonstrate that silicon nanowire (SiNW) Li-ion battery anodes that are conformally coated with TiO2 using atomic layer deposition (ALD) show a remarkable performance improvement. The coulombic efficiency is increased to ∼99%, among the highest ever reported for SiNWs, as compared to 95% for the baseline uncoated samples. The capacity retention after 100 cycles for the nanocomposite is twice as high as that of the baseline at 0.1 C (60% vs. 30%), and more than three times higher at 5 C (34% vs. 10%). We also demonstrate that the microstructure of the coatings is critically important for achieving this effect. Titanium dioxide coatings with an as-deposited anatase structure are nowhere near as effective as amorphous ones, the latter proving much more resistant to delamination from the SiNW core. We use TEM to demonstrate that upon lithiation the amorphous coating develops a highly dispersed nanostructure comprised of crystalline LiTiO2 and a secondary amorphous phase. Electron energy loss spectroscopy (EELS) combined with bulk and surface analytical techniques are employed to highlight the passivating effect of TiO2, which results in significantly fewer cycling-induced electrolyte decomposition products as compared to the bare nanowires.
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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".