Revealing the Impact of Hierarchical Pore Organization in Supercapacitor Electrodes by Coupling Ionic Dynamics at Micro‐ and Macroscales
Bibliographic record
Abstract
Abstract The rate of charging of supercapacitors depends on how quickly ions can reach and accommodate the surface of electrodes. Diffusivity, a parameter reflecting the speed of ions’ migration, is believed to be crucial in designing supercapacitor electrodes. Herein, this belief is questioned, shedding light on a puzzling and potentially critical feature of ionic dynamics denoted as confinement‐induced ion–solvent separation. This effect can lead to a strong slowdown of the ion mobility inside hierarchical pore networks. Explanations for when such an effect occurs and how it can be circumvented are provided. Furthermore, this microscopic picture of diffusion seen by NMR is bridged with the macroscopic charging behavior of supercapacitors investigated by impedance spectroscopy. Quantifying the average residence time of ions within carbon particles shows that the nanopore environment may not be the rate‐limiting factor for the overall ion mobility and thus performance of a cell—as commonly expected. Combining direct diffusion studies performed with neat and solvated ionic liquids and those on organic electrolytes, the so far lacking criteria for the rational selection of electrolyte–carbon systems is developed and recommendations for the preparation of transport‐optimized materials for supercapacitors to minimize ionic diffusion limitations are given.
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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".