Melt-Processing of Electrodes for Lithium-Ion Batteries: A New Solvent-Free Approach
Bibliographic record
Abstract
Lithium-ion is currently the leading technology for electrochemical energy storage, especially in the transportation sector. The electrification of vehicles through the use of lithium-ion batteries (LiBs) is at the center of the world efforts to decrease atmospheric pollution by reducing CO 2 emission. Due to the high efficiency of electrical motors, a net reduction in greenhouse gases is achieved upon electrification of vehicles even in areas where electricity is generated from fossil fuels. Yet, the rapidly growing production of LiB brings new concerns on the environmental cost of the technology. The analysis of a complete their life cycle, from mining precursors to recycling batteries at the end of their life, reveals that several steps of the cycle can be improved to further reduce the environmental footprint of lithium-ion batteries. It is in this context that our group got interested in developing alternative approached to electrode fabrication, a key aspect of LiB manufacturing. Currently, most of the electrode fabrication processes involves the use of organic solvents, typically N-methyl-2-pyrrolidone (or NMP), which is toxic and costly. As such, a considerable effort is spent during electrode fabrication to recuperate NMP vapors and prevent exposure to the workers and environment. Avoiding the use of any solvents during electrode fabrication would not only minimize the environmental impact but would also result into lower energy consumption. This contribution presents a study of a new solvent-free melt process technique to LiB electrode fabrication through the use of elastomeric binders. The impact of formulation on active material dispersion, microscopic morphology, electronic percolation and porosity will be discussed. With such parameters optimized, the electrochemical response of composite electrodes based on LiFePO 4 and Li 4 Ti 5 O 12 active materials were characterized both individually and as full cells. The battery performance will be compared with PVDF-based electrodes made with a conventional approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.000 |
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 teacher head, 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".