Elucidating the Importance of Pore Structure in Determining the Double-Layer Capacitance of Nanoporous Carbon Materials
Bibliographic record
Abstract
Porous carbon is a common electrode material used in electrochemical double-layer capacitors, in which energy is stored by physical adsorption of electrolyte ions on the carbon’s surface, forming an electrical double layer (EDL). However, due to the complex nanoporous network of carbon materials, it is difficult to characterize the EDL structure. This work demonstrates that the understanding of the EDL structure in nanoporous carbon materials can be improved by defining the pore shapes using ultrahigh resolution scanning electron microscopy (SEM). The SEM images reveal a continuous network of curved pores. This characterization, along with the experimentally determined surface areas and pore sizes, enabled the investigation of the applicability of various models describing the EDL configuration. This study found that, by using the microscopic information to characterize the 3-D nanostructure and select the appropriate models for the pore shape, it is possible to predict a porous carbon material’s experimental capacitance within ±8%. This updated approach may be used to identify ideal pore structures and top-performing carbon materials. It is clear that using ultrahigh resolution SEM images to understand the relationship between pore shape, EDL structure, and capacitance provides valuable insight into the complexity of energy storage in nanoporous carbon materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".