The Development of Pseudocapacitor Electrodes and Devices with High Active Mass Loading
Bibliographic record
Abstract
Abstract Pseudocapacitive materials are used for supercapacitor applications due to their exceptionally high capacitance and low cost. Good capacitive performance of the pseudocapacitive materials at high active mass loadings is vital for the development of the next generation of supercapacitor devices. This review describes recent advances in materials and nanotechnologies, which allows the development of advanced pseudocapacitive devices with high active mass. An important breakthrough is the discovery of novel dispersing and capping agents for the colloidal processing of nanoparticles. Particularly important are novel co‐dispersants that exhibit enhanced adsorption on materials of different types, such as inorganic nanoparticles, carbon nanotubes, and graphene. Conceptually new strategies are designed to fabricate coated particles. Recent innovations pave the way for the development of multifunctional redox‐active dopants‐dispersants and dopants‐oxidants to manufacture conductive polymer composites. Among the most important advances in nanotechnology is the development of template methods and heterocoagulation techniques for composite manufacturing. The progress in the design of novel surface modification techniques and materials, discovery of advanced anchoring groups, and development of liquid–liquid extraction allows agglomerate‐free processing of nanomaterials and composites. This review describes fundamental aspects of novel technologies and their applications in the manufacturing of pseudocapacitive devices for energy storage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".