(Invited) Green Materials for Sustainable Supercapacitors
Bibliographic record
Abstract
The exponential increase of energy storage systems like batteries and supercapacitors is paralleled by the growing need of minimizing the environmental and economic impact of disassembly and recycling of waste devices. Today, electrode and separator processing greatly contribute to device cost and environmental footprint. The use of water-processable, biodegradable electrodes and separators is a viable approach to develop green and easy-to-dispose devices. Natural polymers processed by aqueous solutions are a very promising alternative to fluorinated polymers like polyvinylidene difluoride that also requires the use of the toxic N-Methyl-2-pyrrolidone solvent. Electrospinning is an emerging technology for the preparation of free-standing fiber mats to be used as electrode materials and separators in supercapacitors. Here, the strategies to develop green supercapacitors making use of natural binders and separators and bio-derived electrodes are presented. In particular, the performance of supercapacitors making use of cellulose and pullulan membranes prepared by electrospinning are reported and compared to those of devices featuring cellulose separators produced by bacteria. Acknowledgments The research has been carried out under the Italy-South Africa joint Research Programme 2018-2020 and the Executive Bilateral Program Italy-Quebec 2017-2019, Italian Ministers of Foreign Affairs and of the Environment. References [1] M. Yassine, D. Fabris, Energies, 10 (2017) 1340 [2] B. Dyatkin, V. Presser, M. Heon, M. R. Lukatskaya, M. Beidaghi, Y. Gogotsi, ChemSusChem, 6 (2013) 2269 -2280. [3] D. Bresser, D. Buchholz, A. Moretti, A. Varzi, S. Passerini, Energy & Environmental Science, 11 (2018) 3096-3127 [4] P. Kumar, E. Di Mauro, S. Zhang, A. Pezzella, F. Soavi, C. Santato, F. Cicoira, J. Mater. Chem. C, 4 (2016) 9516. [5] S. Chen, S. He, H. Hou, Current Organic Chemistry, 17 (2013) 1402-1410 [6] F. Poli, D. Momodu, A. Terella, M. L. Focarete, N. Manyala, F. Soavi, Energy Storage Materials, submitted.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.115 | 0.067 |
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".