A Study on Processing Parameters Affecting Solid Polymer Electrolytes Performances
Bibliographic record
Abstract
In the modern world, the rapid advancement of new technologies is accompanied by an increasing energetic demand. Following today`s trend, energy consumption will undoubtedly increase in the following years. Over the last 50 years, lithium-metal batteries have been considered as potential candidates for long-term high-performance electrochemical storage. (1) Although their operation is effective, they still present limitations mainly due to lithium dendrite growth associated with electrodeposited Li+ from the electrolyte on the anode material. This phenomenon causes internal short-circuits resulting in premature battery failure. (2) Various solid-state battery systems are being developed in hope to resolve the said issue. Amongst others, solid polymer electrolytes (SPE) have been investigated since Armand`s work in the 80`. (3) Even though these systems have non-flammable properties and successfully suppress dendrite growth, most SPEs do not reach ionic conductivities values higher than 10-3 S/cm at room temperature and offer lower energy densities and cycle number compared to the standard liquid electrolyte counterpart. (4) As a result of that, an increasing number of published literature shows off new engaging SPE systems. However, oftentimes, the presented performances are hardly reproducible due to the lack of precise and detailed experimental conditions. It is believed that some overlooked factors during processing may affect the aforementioned performances. (5-7) Certain parameters that affect ionic conductivities of SPEs have been investigated by techniques including but not limited to electrochemical impedance spectroscopy (EIS) and solid-state 7Li-NMR. It will be demonstrated that these parameters must be precisely controlled to ensure the reproducibility and the validity of measurements. Finally, it will be shown that this study can be applied to several types of polymers. 1: Hall P. J., Bain E. J., Energy Policy, 36, 4352 (2008). 2: Lisbona D., Snee T., Process. Saf. Environ., 89, 434 (2011). 3: Armand M. B., Ann. Rev. Mater. Sci. 16, 245 (1986). 4: Penghui Y., Haobin Y., Zhiyu D., Yanchen L., Juan L., Marino L., Junwei W., Xingjun L., Front. Chem., 7, 522 (2019). 5: Fullerton-Shirey S. K., Maranas J.K, Macromolecules, 42, 2142 (2009). 6: Devaux D., Bouchet R., Glé D., Denoyel R., Solid State Ion., 227, 119 (2012). 7: Wang X., Zhang L., Li G., Zhang G., Shao Z.G., Yi B., Electrochim. Acta, 158, 253 (2015)
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".