Bibliographic record
Abstract
Porous activated carbon is an important electrode material for devices such as supercapacitors, batteries and fuel cells. Despite the research and commercialization work that has been carried out, there is still a need to understand some of the fundamental characteristics of the role of activated carbon and the electrolyte in charge storage and ion migration within these structures. There has been two general types of electrolytes used; aqueous electrolytes with high concentrations of conductive salts and organic electrolytes modified to be conductive or directly conductive as in the form of ionic liquids. The former have high ionic conductance, the latter have a larger voltage range for stability. In this work we introduce an alternative to theses electrolytes, a low temperature molten inorganic salt. Potential advantages include high conductivity and a large potential operating range. The work demonstrates that inorganic salts can be used as electrolytes in supercapacitors at temperatures as low as 120oC . The charge discharge characteristics were similar to aqueous electrolyte performance and higher charging voltages than the aqueous limits are possible. The work also showed that the activated carbon pore sizes that can be used for charge discharge are similar for the molten salt ions and the aqueous ions.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".