Electrochemical Changes in Lithium-Battery Electrodes Studied Using <sup>7</sup>Li NMR and Enhanced <sup>13</sup>C NMR of Graphene and Graphitic Carbons
Bibliographic record
Abstract
An anode composed of tin-core, graphitic-carbon-shell nanoparticles distributed on graphene nanosheets, Sn@C-GNs, is studied during the lithiation process. 7 Li NMR provides an accurate measure of the stepwise reduction of metallic Sn to lithium–tin alloys and reduction of the graphitic carbon. The metallic nanoparticle cores are observed to form ordered, crystalline phases at each step of the lithiation process. The 7 Li 2D experiments presented provide insight into the proximity of the various phases, reflecting the mechanism of the electrochemical reaction. In particular, a sequential model of nanoparticle lithiation, rather than a simultaneous process, is suggested. Movement of lithium ions between two elements of the nanostructured Sn@C-GNs material, the metallic core and carbon shell, is also observed. Conventional 13 C solid-state NMR, SSNMR, experiments on <5 mg of active material from electrochemical cells were found to be impossible, but signal enhancements (up to 18-fold) via the use of extended echo trains in conjunction with magic-angle spinning enabled NMR characterization of the carbon. We demonstrate that the 13 C data is extremely sensitive to the added electron density when the graphitic carbon is reduced. We also investigate ex situ carbon electrodes from cycled Li–O 2 cells, where we find no evidence of charge sharing between the electrochemically active species and the graphitic carbon in the 13 C NMR spectroscopy.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".