Breaking dielectric dilemma via polymer functionalized perovskite piezocomposite with large current density output
Bibliographic record
Abstract
Organometal halide perovskite (OHP) composites are flexible and easy to synthesize, making them ideal for ambient mechanical energy harvesting. Yet, the output current density from the piezoelectric nanogenerators (PENGs) remains orders of magnitude lower than their ceramic counterparts. In prior composites, high permittivity nanoparticles enhance the dielectric constant (ϵr) but reduce the dielectric strength (Eb). This guides our design: increase the dielectric constant by the high ϵr nanoparticle while enhancing the Eb by optimizing the perovskite structure. Therefore, we chemically functionalize the nanoparticles to suppress their electrically triggered ion migration for an improved piezoelectric response. The polystyrene functionalizes with FAPbBr2I enlarges the grains, homogenizes the halide ions, and maintains their structural integrity inside a polymer. Consequently, the PENG produces a current density of 2.6 µAcm−2N−1. The intercalated electrodes boost the current density to 25 µAcm−2N−1, an order of magnitude enhancement for OHP composites, and higher than ceramic composites. By functionalizing polystyrene with perovskite FAPbBr2I, both the dielectric constant and dielectric strength are controlled, leading to energy-dense composite films for piezoelectric nanogenerators with normalized current density of 25 µA.cm−2 N−1
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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) | 0.002 | 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 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".