2D Material Doping in Ion Conducting Membranes Used for Energy Storage Applications
Bibliographic record
Abstract
The electrolyte component of a battery plays a crucial part in its power density and ion balancing as it acts as an ion-carrier between electrodes. However, the use of liquid electrolytes in batteries can cause issues with thermal runaway, leakage, flammability and ultimately battery failure if cell damage occurs. In this thesis, a Gel Polymer Electrolyte (GPE) is reinforced with exfoliated 2D Boron Nitride (BN) nanosheets — a unique 2D nanomaterial that possesses electronically insulating properties while having high specific surface area and thermal conductivity to reinforce the electrolyte component used in batteries. The properties of the GPE are analyzed by looking at the mechanism of ion conduction within polymer chains and how BN can be used as nanofillers to enhance electrochemical performances. The optimal doping of BN to a porous GPE can then lead to improved thermal stability, ionic conductivity and electrochemical stability for safer operation conditions 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".