Xerogel-like Materials from Sustainable Sources: Properties and Electrochemical Performances
Bibliographic record
Abstract
Energy storage is currently one of the most significant technological challenges globally, and supercapacitor is a prominent candidate over batteries due to its ability for fast charging and long lifetime. Supercapacitors typically use porous carbon as electrodes, because of both the high conductivity and surface area of the material. However, the state-of-the-art porous carbon described in the literature uses toxic chemicals and complex procedures that enhance costs and pollute the environment. Thus, a more sustainable procedure to produce porous carbon is highly desirable. In this context, xerogel-like carbons were prepared by a new, cheap, simple route to polymerization reactions of tannin-formaldehyde in a bio-oil by-product. Using bio-oil in its natural pH allowed a cost reduction and avoided using new reactants to change the reactional medium. Textural properties and electrochemical performances were improved by fast activating the material per 20 min. The non-activated carbon xerogel presented a capacitance of 92 F/g, while the activated one had 132 F/g, given that 77% of the components used are eco-friendly. These results demonstrate that renewable materials may find applications as carbon electrodes for supercapacitors. Overhauling the synthesis route with a different pH or replacing formaldehyde may enhance performance or provide a 100% sustainable carbon electrode.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".