Bio-Based PLA Membranes for Ion Transport and Ion Filtration
Bibliographic record
Abstract
Lithium-ion batteries require battery separators for both safety and electrochemical performance. Due to that, they have received a lot of attention. In order to prevent any electronic current from moving within the negative and positive electrodes and allow ions to flow through while avoidance of electric contact between them, a porous membrane used as a separator is positioned between the electrodes with opposing polarities. Accordingly, the objective of the present work is to build a biodegradable PLA based battery separator, which has exceptional thermal capabilities and can endure temperatures of up to 300°C. They also seem to serve as the least degree of barrier for the flow of an ionic current. In this study bio-polymer battery separator membranes were developed using PLA as matrix material and fillers such as Copper slag (CS) and Cardanol resin (CNSL). CS and CNSL were preferred for the reason to realize the concept of a wealth reclaimed from wastes that act as toughening and pore forming agent for PLA matrix. It is found that at PLA-CS film has more brittleness when compared to neat PLA and PLA-CNSL resin. On the other hand, PLA-CNSL films are the toughest ones. Overall, it has been demonstrated that obtaining more sustainable and high-performance is possible by the usage of such sustainable materials for futuristic developments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 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".