Flexible Piezoelectric Nanogenerator with a Ferroelectric Metal–Ligand Cage for Self-Powered Sensor Applications
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Ferroelectric materials have emerged as promising candidates for piezoelectric nanogenerators, attributed to their superior energy conversion efficiency derived from inherent polarization characteristics. Polar metal–ligand assemblies represent advantageous alternatives to conventional inorganic ceramics and organic polymers, offering tunable electronic properties, environmental benignity, and enhanced energy conversion capabilities. We demonstrate an octahedral [[Co 6 (H 2 O) 12 (TPTA) 8 ](NO 3 ) 12 ·50H 2 O] cage assembly exhibiting pronounced ferroelectric behavior, characterized by a P–E hysteresis loop with a remnant polarization of 6.84 μC cm –2 . The ferroelectric and piezoelectric properties of 1 were unambiguously confirmed through the visualization of electrical domains in single crystals and crystalline thin films via piezoresponse force microscopy (PFM). Single-point, bias-dependent PFM spectroscopy measurements revealed characteristic amplitude-butterfly and phase-hysteresis loops, substantiating the piezoelectric nature of the material. Piezoelectric energy harvesting investigations conducted on polydimethylsiloxane (PDMS) composite materials revealed a maximum peak output voltage of 12.20 V and a power density of 14.85 μW cm –2 for the optimized 20 wt % 1 -PDMS composite device. The practical utility was validated through the implementation of a smart pressure sensor, wherein a mat device, constructed from five parallel-connected independent devices, successfully functioned as a sensor capable of illuminating a commercial LED under gentle mechanical stimulation. These findings establish the potential of this cage system for integration into self-powered sensor technologies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".