Indigo As Single Additive in Liquid Electrolyte for Lithium Metal Batteries
Bibliographic record
Abstract
Rechargeable lithium ion batteries (LIBs) have been successfully developed and widely used to power today’s portable electronic devices. The long term success in electric vehicles and energy storage system relies on rising the energy density, low temperature efficiency, safety and increased cycle life. In parallel, lithium metal batteries (LMBs), described as a system with Li0 as anode and metal oxide as cathode (NMC, LFP, LMO) are arising as aim of research for a plethora of groups. Lithium metal anode is considered as ideal anode due to high theoretical capacity (3860 mAhg-1), lower negative electrochemical potential and lower density (0.534 g.cm-3).1 Researchers used complex liquid electrolyte (ionic liquid, etc.) systems in order to avoid lithium dendrite and degradation, conventional liquid electrolyte (LiPF6, carbonate solvants) degraded faster than expected, and consequently, generated cells fading. Therefore conventional liquid electrolyte in such cell was not used. Use organic additive in electrolyte and/or electrodes is considered as one of the most economical and effective’s approaches for solved many problems as cited previously. The additive can interact with electrolyte or anode to prevent degradation or enhanced cell performances. This presentation will outline commercial organic compounds: Indigo and his derivatives use as single additive in NMC/Li batteries with conventional liquid electrolyte, which considerably increased the cycle life of Li metal batteries. Dyes such Indigoïd contain electron density donor (-NH-) and acceptor (-C=O) groups linked by conjugated bonds, which participate to their versatile electrochemical properties. Used as additive in cathode materials or electrolyte, indigo modified the electrode structure and changed the composition of the Li-ions battery electrolyte and solid electrolyte interface (SEI) on lithium metal. 1.Zhang, H., Eshetu, G.G.,Judez, X., Li, C., Armand, M., Angew. Chem. Int. Ed. 2018, 57, 15002-15027 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 | 0.000 | 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".