Bibliographic record
Abstract
This chapter explores the innovative field of K-ion hybrid capacitors, with a particular focus on their design and functionality through the lens of nanotechnology. In the Introduction, we provide an overview of K-ion capacitors, positioning them as a promising alternative to other energy-storage technologies. We then focus on the working principles of K-ion hybrid capacitors, elucidating the electrochemical mechanisms involved and explaining the dynamics of K-ions during charge and discharge cycles, as well as their interaction with the electrodes. The chapter also discusses innovations aimed at enhancing anode performance through the use of nanomaterials such as carbon nanotubes, graphene, and metal oxides. The unique properties of these materials, including high surface area and electrical conductivity, are examined in relation to their ability to improve K-ion intercalation and storage capacities. The cathode section focuses on the design and optimization of cathode materials and addresses the challenges of improving energy density and cycle life in K-ion systems. Finally, we present a forward-looking perspective on K-ion hybrid capacitors, discussing recent advancements, potential breakthroughs, and the future role these capacitors may play in the evolving landscape of energy-storage technologies.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".