Atomic Layer Deposition ZnO-Enhanced Negative Electrode for Lithium-Ion Battery: Understanding of Conversion/Alloying Reaction via 7Li Solid State NMR Spectroscopy
Bibliographic record
Abstract
Understanding the mechanism for capacity delivery in conversion/alloying materials (CAM) electrodes, such as ZnO, in lithium-ion batteries (LIBs) requires careful investigation of the electrochemical reactions. Here, we used magic angle spinning (MAS at 60 kHz) 7Li nuclear magnetic resonance (NMR) as a sensitive analytical means to probe the reactions occurring between electrode materials and Li+ ions. The ZnO nanolayer generated on carbon substrate by atomic layer deposition (ALD) enhanced the cyclic capacity of half cell LIB up to 40%. 7Li NMR revealed LixZn alloy formation through an irreversible conversion reaction during discharge. MAS results revealed the dealloying of LixZn at the full charge step which left atomic zinc nanograins that do not undergo the re-oxidation of zinc atoms according to the cyclic voltammetry. An in situ formation of elemental zinc at the initial cycles facilitates uniform lithium deposition on subsequent cycles due to the reduced energy barrier for lithium nucleation on pure zinc as compared to ZnO. X-ray diffraction analysis indicated the crystalline formation of the LixZn alloy while scanning electron microscope showed the uniform morphology for the lithiated discharge products. Cyclic voltammetry and differential capacity functions initially predicted the conversion and alloying 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".