Biomass Activated Carbon for Solid Supercapacitors
Bibliographic record
Abstract
Biomass activated carbon (AC), produced from the pyrolysis of biomass waste feedstock (wood, agricultural residues, etc.) has been studied as low cost Electrochemical Double Layer Capacitor (EDLC) electrodes. Their parent biomass materials also lead biomass ACs with different natural porous templates which can be further tuned or modified via chemical activation techniques. We have investigated several waste biomass ACs ranging from corn cobs, pine cones, to chitosan and found that, while all inherited porous structures from nature, they displayed quite different surface chemical compositions and pore structures. For example, the surfaces area of these biomass AC were in around 570 m2/g, 2400 m2/g and 3300 m2/g, respectively. These differences, especially in surface area, morphology and pore size distribution, have direct impact on the electrochemical performance of these biomass ACs in both liquid and solid electrolytes. Among the three biomass materials, chitosan-based carbon has demonstrated a high level of hierarchical meso-pore structure and the highest specific energy density. Solid-state supercapacitors, enabled by polymer electrolytes, are ideal solutions for future energy storage applications that require high performance, safe operation, light-weight, thin and flexible form factors. We have developed a series of aqueous-based polymer electrolytes that are proton-conducting, hydroxide ion-conducting or neutral salt ion-conducting to match various cell chemistries. Many electrolytes exhibited ionic conductivities >10-2mS/cm and maintained stable performance under ambient conditions (i.e. room temperature and 45% relative humidity). In this talk, we will present the development of solid EDLC devices leveraging biomass ACs and aqueous based polymer electrolytes. The performance of solid EDLC devices using chitosan AC carbon and polymer electrolyte containing Li2SO4 will be compared with their liquid counterpart (Fig. 1) as well as with the commercial baselines. Figure 1
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".