<sup>7</sup>Li and <sup>29</sup>Si NMR Enabled by High-Density Cellulose-Based Electrodes in the Lithiation Process in Silicon and Silicon Monoxide Anodes
Bibliographic record
Abstract
To meet the energy density requirement for our next-generation electric vehicle applications, new electrode materials with higher capacity are required. Silicon monoxide (a-SiO) is one of the most promising anode materials because it can provide 1500 mAh/g specific capacity compared to 372 mAh/g for graphite and nevertheless overcome some of the inherent structural disadvantages of its parent material, silicon (Si) itself. The present work discusses the electrochemical reaction mechanisms of lithium insertion into a-SiO using multinuclear solid-state NMR (nuclear magnetic resonance). An in situ 7 Li NMR study on both Si and a-SiO using a jelly-roll-type battery design shows the intrinsic difference between the lithiation of those two materials. In addition, 29 Si MAS (magic-angle spinning) NMR data obtained at 20 T provide sufficient sensitivity to acquire these spectra on electrode active materials, in spite of the low natural abundance of 29 Si. Additionally, the electrochemical method developed here using porous cellulosic substrates provides a means to substantially enhance the amount of active material available for the NMR study of the cycled anode materials as a function of charge state. We demonstrate that this unorthodox cell design achieves reasonable capacity retention for the a-SiO anodes, and we suggest that this approach could be applied to a wide range of electrode materials.
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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".