Manganese oxide-based materials as electrochemical supercapacitor electrodes
Bibliographic record
Abstract
Electrochemical supercapacitors (ECs), characteristic of high power and reasonably high energy densities, have become a versatile solution to various emerging energy applications. This critical review describes some materials science aspects on manganese oxide-based materials for these applications, primarily including the strategic design and fabrication of these electrode materials. Nanostructurization, chemical modification and incorporation with high surface area, conductive nanoarchitectures are the three major strategies in the development of high-performance manganese oxide-based electrodes for EC applications. Numerous works reviewed herein have shown enhanced electrochemical performance in the manganese oxide-based electrode materials. However, many fundamental questions remain unanswered, particularly with respect to characterization and understanding of electron transfer and atomic transport of the electrochemical interface processes within the manganese oxide-based electrodes. In order to fully exploit the potential of manganese oxide-based electrode materials, an unambiguous appreciation of these basic questions and optimization of synthesis parameters and material properties are critical for the further development of EC devices (233 references).
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".