(Invited) In Operando and in Situ techniques for Intercalation Compounds in Li-Ion and All-Solid-State Batteries
Bibliographic record
Abstract
We will present the results and supporting video files of several in operando techniques used to study lithium-ion and solid-state batteries, such as in situ scanning electron microscopy (SEM), in situ transmission electron microscopy (TEM), in situ Raman spectroscopy, in situ X-ray diffraction and in situ UV visible. These studies help understand several mechanisms, including volume expansion of anodes such as Si (300%), lithium metal (20%), graphite (10%) and LTO (0%). They are also used to measure the thickness of the anode, cathode and electrolyte through the charge/discharge cycle. The mechanism of lithium dendrite was also studied and its details will be presented. Blend-based solid polymer electrolyte (SPE) lithium-sulfur cells were studied in operando using two techniques: SEM and ultraviolet-visible absorption spectroscopy (UV-vis). During the operation of these cells, 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 due to cycling confirmed the failure mechanism: 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 showed the presence of characteristic absorption peaks evolving with cycling, demonstrating the accumulation of various polysulfide species, and the predominant formation of S 4 2- and of S 6 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".