(Invited) HQ Characterisations Techniques for Li-Ion and Solid State Batteries
Bibliographic record
Abstract
In these presentation, we will present the data and movies of several an operando techniques to study lithium ion and solid state batteries such in situ SEM, In situ TEM, in situ Raman spectroscopy , in situ X-ray diffraction and in situ UV visible. These studies help to understand several mechanism such volume expansion of anodes such lithium metal (20 %), Graphite (10 %), LTO (0 %) and another example the measurements of the thickness of anode, cathode and electrolyte during charge discharge. The mechanism of lithium dendrite was also study and details will be shown during this presentation. Bleand Lithium/solid polymer electrolyte (SPE)/sulfur cells were studied in operando by two techniques: Scanning Electron Microscope (SEM) and ultraviolet-visible absorption spectroscopy (UV-vis). During the operation of the cell, extensive polysulfide dissolution in the solid polymer electrolyte (cross-linked polyethylene oxide) leads to the formation of a catholyte. A clear micrograph of the thick passivation layer on the sulfur-rich anode and the decreased SPE thickness by cycling confirmed the failure mechanism; the capacity decays by reducing the amount of active material, and by contributing to a charge inhibiting mechanism called polysulfide shuttle. The formation of elemental sulfur is clearly visible in real time during the charge process beyond 2.3 V. The non-destructive in operando UV-vis study also shows the presence of characteristic absorption peaks evolving with cycling, demonstrating the accumulation of various polysulfide species, and the predominant formation of S4 2- and of S6 2- during discharge and charge, respectively. This finding implies that the charge and discharge reactions are not completely reversible and proceed along different pathways.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.024 |
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".