Effects of lithium perchlorate on the electrochemical performance of PVDF-HFP/CA composite-based solid-state electrolytes
Bibliographic record
Abstract
Composite-based polymer electrolytes have attracted considerable attention due to their high ionic conductivity and excellent electrochemical performance in energy storage applications. This study explores the influence of Lithium Perchlorate (LiClO 4 ) loading on the electrochemical performance of composite-based solid-state electrolytes (SSEs) based on a composite of poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) and 20% cellulose acetate (CA), referred to as PVDF-HFP/20%CA. Utilizing a solution casting method, four SSE samples were fabricated: a control (0% LiClO 4 ) and three samples with varying LiClO 4 concentrations (5%, 10%, and 15%). Comprehensive characterizations, including scanning electron microscopy (SEM), Fourier-transform infrared (FTIR), physical characterization, and electrochemical impedance spectroscopy (EIS) were conducted to assess the impact of LiClO 4 on the composites. The Nyquist plot and DC ionic conductivity analysis further validate the superior performance of PH20CA-15Li (PVDF-HFP/20% cellulose acetate with 15% LiClO 4 ), with ionic conductivity increasing from 1.15 × 10 −6 S cm −1 for the control sample to 3.7 × 10 −6 S cm −1 . Loss Tangent and Cyclic Voltammetry analyses underscore the dynamic electrochemical behavior and stability of PH20CA-15Li, with the voltage window expanding from 0.996 V in the control sample to 1.398 V, highlighting its enhanced dielectric properties and energy storage capabilities. Electrolyte uptake and porosity analyses reveal a decrease in both parameters with increased LiClO 4 content, suggesting a trade-off between electrochemical performance and physical properties. SEM and FTIR analyses show structural and chemical changes, confirming the interaction between LiClO 4 and the composite matrix. The study concludes that 15% LiClO 4 loading optimally balances conductivity with physical properties, making it a promising candidate for advanced solid-state electrolyte applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".