Quantitative Investigation of Lithium Metal Plating via Operando 7Li NMR Spectroscopy of a Unique Three Electrode Lithium-Ion Battery
Bibliographic record
Abstract
This work represents the first investigation that simultaneously uses both 7Li NMR chemical analysis and three-electrode (3E) electrochemical analysis in real time to observe SiO-graphite anodes experiencing lithium plating during fast charge. NMR has the unparalleled ability to distinguish lithium in various forms, such as plated metal, alloyed lithium-silicon, or intercalated lithium-graphite compounds. This permits a detailed accounting of the whereabouts of lithium in an anode undergoing high-rate charge. This highly specialized technique comes at a high cost, however, in terms of time, equip-ment, and expertise. Establishing correlation between NMR and the more common and readily implemented 3E tech-nique for detecting the onset of plating allows the latter to be applied with confidence to the type of extensive testing required to build fast charge tables. NMR provides additional insight into the disposition of lithium after plating has occurred, allowing a clear analysis of the fraction of lithium metal that is spontaneously re-dissolved and taken up by the anode (reversible lithium plating).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".