Quantitative <i>Operando</i> <sup>7</sup>Li NMR Investigations of Silicon Anode Evolution during Fast Charging and Extended Cycling
Bibliographic record
Abstract
The development and optimization of fast battery charging protocols require detailed information regarding lithium speciation inside a battery. Nuclear magnetic resonance (NMR) spectroscopy has the unique capability of identifying the Li phases formed in an anode during Li-ion cell operation and quantifying their relative amounts. In addition, both Li metal films and dendrites are readily detected and quantified. Here, our recently reported parallel-plate resonator radio frequency (RF) probe and the cartridge-type single-layer full cell were used to track the behavior of Si electrodes during cycling and during fast charging. The Li x Si compounds formed during electrochemical cycling exhibit an unexpected intrinsic nonequilibrium behavior at both slow and fast rates, evolving toward increasingly disordered local environments. The evolution with time of lithiated phases is nonlinear during both charging and discharging at constant current, unlike the case for pure graphite, and asymmetric between charge and discharge. During charging at rates of 1C, 2C, and 3C, metallic Li in both films and (to a lesser extent) dendritic forms are deposited on the Si anode. Part of the Li metal film formation is reversible, but a fraction remains on the electrode surface as dead Li, while all of the dendritic Li, even though formed in a considerably smaller amount, is entirely irreversible. Such performance-governing properties are critical to the development of fast-charging protocols for lithium-ion batteries (LIBs) and are exceptionally well evaluated and quantified by 7 Li magnetic resonance strategies such as those presented here.
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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) | 0.002 | 0.001 |
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".