Greener Rechargeable Lithium-Ion Batteries Using Plasma Processes at Atmospheric Pressure
Bibliographic record
Abstract
Numerous efforts have been made to develop new thin films by using atmospheric pressure plasma process. Complex materials such as nanocomposite thin films [1.2], biodegradable coatings [3] and advanced chemical treatments on complex surfaces [4] have for example been studied. Recently, we reported the use of an atmospheric pressure dielectric barrier discharge (DBD) to strongly modify the interfacial properties in lithium-ion batteries [5]. This work focuses on the chemical functionalization of composite electrodes used for aqueous electrolyte rechargeable batteries (ARLB). In this presentation, the physicochemical properties of the coating and electrochemical performance of such electrodes are investigated. Plasma-deposited coatings were then characterized by SEM-EDX, FTIR and XPS. The electrical analysis indicates that the discharge remains stable during the process. This suggests that the substrate slightly affect the physical regime of the gaseous discharge. The SEM analysis of the plasma-treated electrode suggests a homogeneous surface treatment over a wide area. From the FTIR analysis, different chemical groups were highlighted. Finally, the electrochemical performance of such electrodes was also investigated in a battery operation. Preliminary explanation of the fundamental mechanisms allowing the operation of the battery and ions diffusion through the plasma layer is attempted. [1] J. Profili et al., Plasma Processes and Polymers, volume13, issue10, October 2016, 981-989 [2] P. Brunet et al., Plasma Processes and Polymers, volume 14, issue12, December 2017, 1700049 [3] M. Laurent et al., Plasma Processes and Polymers, volume 13, issue7, July 2016, 711-721 [4] S. Asadollahi et al., Materials 2019, 12(2), 219 [5] J. Profili et al., ACS Sustainable Chemistry & Engineering 8 (12), 4728-4733
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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 | 0.001 |
| 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".