Applications of Laser Material Processing for Solid-State Lithium Batteries
Bibliographic record
Abstract
Laser material processing is emerging as a critical manufacturing technology in the advancement of solid-state lithium batteries (SSLBs), offering numerous advantages in precision, efficiency, and versatility. This mini-review explores the applications and benefits of laser material-processing techniques, such as laser sintering, laser cutting, laser surface cleaning, laser ablation for nanoparticle generation, and pulsed laser deposition, in the fabrication and performance enhancement of SSLBs’ materials and components. It will demonstrate that laser material processing can enhance material properties such as density and surface morphology, improve ionic conductivity and reduce interfacial resistance. Laser material-processing techniques are adaptable to a variety of materials, including polymers, metal oxides, metal sulfides, and metals, making them suitable for processing various SSLB components like electrolytes, electrodes, and current collectors. In addition, the use of laser material-processing technologies reduces manufacturing costs by minimizing material waste and streamlining production processes. Looking forward, integrating laser material processing with other advanced manufacturing technologies, such as roll-to-roll (R2R) manufacturing, for SSLBs holds promise for further scalability and efficiency. It is expected that laser material processing will be positioned to significantly contribute to the development of safer, more efficient, and cost-effective SSLBs, supporting their broader adoption across industries and paving the way for future innovations in energy storage technology.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".