Study of Transport Phenomena in Next Generation Lithium Batteries, Assisted by 3D Printing
Bibliographic record
Abstract
Lithium-ion batteries (LIB) have a major role in the global energy future as they currently offer the best alternative for energy storage. The increase in energy demand is driving the development and optimization of high-performance batteries. With the specific objective of improving energy densities, studying the limiting phenomena such as the transport of species through the batteries’ components is essential. In this regard, using a new emerging technology such as 3D printing to process some of the batteries’ components could answer some issues. Thanks to its design flexibility compared to conventional manufacturing methods, this technique offers the possibility of creating customizable and complex architectures that could help unravel limiting phenomenon. More specifically, 3D printing is an interesting way to design solid electrolytes. This study aims at showing how the structure, the composition and the configuration of the solid electrolyte will affect the systems properties and thus its electrochemical performances. A first part of the study consists in evaluating the feasibility of 3D printing a solid composite electrolyte with a complex architecture. Thus, 3D printing techniques will be discussed as well as the formulation of a ceramic ink. Other aspects will be addressed such as the ink printability studies via rheology, the processing of different structures via printing and the print fidelity. Using different compositions and architectures, different structural characterizations and conductivity tests are performed to evaluate the impact of the processing on the system properties. The optimization of this proof of concept involving various polymer/ceramic ratios, geometrical architectures and processing studies is an interdisciplinary project that can lead to significant advances in the field of batteries.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".