Operando monitoring of dendrite formation in lithium metal batteries via ultrasensitive tilted fiber Bragg grating sensors
Bibliographic record
Abstract
Lithium (Li) dendrite growth significantly deteriorates the performance and shortens the operation life of lithium metal batteries. Capturing the intricate dynamics of surface localized and rapid mass transport at the electrolyte-electrode interface of lithium metal is essential for the understanding of the dendrite growth process, and the evaluation of the solutions mitigating the dendrite growth issue. Here we demonstrate an approach based on an ultrasensitive tilted fiber Bragg grating (TFBG) sensor which is inserted close to the electrode surface in a working lithium metal battery, without disturbing its operation. Thanks to the superfine optical resonances of the TFBG, in situ and rapid monitoring of mass transport kinetics and lithium dendrite growth at the nanoscale interface of lithium anodes have been achieved. Reliable correlations between the performance of different natural/artificial solid electrolyte interphases (SEIs) and the time-resolved optical responses have been observed and quantified, enabling us to link the nanoscale ion and SEI behavior with the macroscopic battery performance. This new operando tool will provide additional capabilities for parametrization of the batteries' electrochemistry and help identify the optimal interphases of lithium metal batteries to enhance battery performance and its safety.
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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".