Toward Low-Cost All-Organic and Biodegradable Li-Ion Batteries
Bibliographic record
Abstract
Abstract This work presents an alternative method for fabricating Li-ion electrodes in which the use of aluminum/copper current collectors and expensive binders is avoided. Low-cost natural cellulose fibers with a 2-mm length are employed as binder and support for the electrode. The objective of this method is to eliminate the use of heavy and inactive current collector foils as substrates and to replace conventional costly binders with cellulose fibers. Moreover, no harmful solvents, such as N-methylpyrrolidone, are employed for film fabrication. Water-soluble carbons are also utilized to reduce the preparation time and to achieve a better repartition of carbon in the electrode, thus improving the electrochemical performance. Flexible and resistant LiFePO4(LFP), Li4Ti5O12(LTO), organic 3,4,9,10-perylenetetracarboxylic dianhydride (PTCDA), and graphite electrodes are obtained with active mass loadings similar to those obtained by the current casting method. The initial discharge capacity of approximately 130 mAh·g−1at 2 C is obtained for an LFP/LTO paper battery with an approximately 91.6% capacity retention after 1000 cycles. An all-organic prelithiated PTCDA/graphite cell without a transition metal is prepared and electrochemically tested. It is one of the first self-standing batteries that is composed of organic redox active molecules and biodegradable components reported in literature.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".