(Invited) Composite Electrodes – the Challenge of Characterizing Ionic Transport on the Liquid Side of the Solid-Liquid Interface
Bibliographic record
Abstract
Composite electrodes are at the hart of modern batteries, since they overcome critical limitations of the solid active materials including volume changes as well as poor electronic and ionic conduction. Due to the very nature of the composite electrodes the solid-liquid interface of electron transfer reaction become buried within the bulk. Moreover, the bulk of the electrode has a complex internal structure, which is exceeding difficult to characterize using even advanced electron and X-ray techniques. This is in part due the nanometric size of the carbon black used to improve electron transport. In this talk, we will present some of our latest development based on scanning electrochemical microscopy and synchrotron radiation to characterize the electrolyte side of the composite electrode. Specifically, we will present data to suggest that the canonical electrolyte transport can be determined in a single sided measurement, greatly simplifying analysis in quality control and providing experimental data for this critical parameter in battery modeling. Moreover, we will present operando measurements of the electrolyte concentration gradient within the composite electrode. This matrix is particularly changeling due to its paramagnetic, conducting and opaque nature. The talk will conclude with an outlook on opportunities and challenges related to these techniques.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.015 |
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".