Recent Progress in Biomass‐Derived Electrode Materials for High Volumetric Performance Supercapacitors
Bibliographic record
Abstract
Abstract Tremendous efforts have been spent on the development of electrical energy storage (EES) systems with high volumetric performance in the past few years due to the evergrowing demand of miniaturized, portable electronic devices, and electric vehicles. Among all the EES devices, supercapacitors with electrode materials derived from biosources have attracted special attention due to their eco‐friendliness, natural abundance, their intrinsic porous structures as well as their renewable and sustainable features. However, the relatively low packing densities make their specific volumetric capacitance intrinsically low, which has largely limited their further application in the supercapacitors. To address these issues, various promising approaches ranging from structural manufacture to compositional design are applied and significant breakthroughs are witnessed in recent years. In this progress report, key factors influencing the volumetric performance of biomass‐derived electrode materials are systematically discussed with a particular focus spanning from fundamental to operational aspects. This work provides insights into the development of high‐volumetric‐performance biomass‐derived supercapacitors by comprehensively summarizing recent advances in the rational structural design and fabrication. Perspectives regarding the future challenges and promising research directions on the design of next‐generation EES devices are also provided.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".