Large piezoelectric response in a Jahn-Teller distorted molecular metal halide
Bibliographic record
Abstract
Abstract Piezoelectric materials convert between mechanical and electrical energy and are a basis for self-powered electronics. Current piezoelectrics exhibit either large charge (d33) or voltage (g33) coefficients but not both simultaneously, and yet the maximum energy density for energy harvesting is determined by the transduction coefficient:d33*g33. In prior piezoelectrics, an increase in polarization usually accompanies a dramatic rise in the dielectric constant, resulting in trade off betweend33andg33. This recognition led us to a design concept: increase polarization through Jahn-Teller lattice distortion and reduce the dielectric constant using a highly confined 0D molecular architecture. With this in mind, we sought to insert a quasi-spherical cation into a Jahn-Teller distorted lattice, increasing the mechanical response for a large piezoelectric coefficient. We implemented this concept by developing EDABCO-CuCl4(EDABCO = N-ethyl-1,4-diazoniabicyclo[2.2.2]octonium), a molecular piezoelectric with ad33of 165 pm/V andg33of ~2110 × 10−3 V m N−1, one that achieved thusly a combined transduction coefficient of 348 × 10−12 m3 J−1. This enables piezoelectric energy harvesting in EDABCO-CuCl4@PVDF (polyvinylidene fluoride) composite film with a peak power density of 43 µW/cm2(at 50 kPa), the highest value reported for mechanical energy harvesters based on heavy-metal-free molecular piezoelectric.
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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 | 0.000 |
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".