Sponge-Templated Macroporous Graphene Network for Piezoelectric ZnO Nanogenerator
Bibliographic record
Abstract
We report a simple approach to fabricate zinc oxide (ZnO) nanowire based electricity generators on three-dimensional (3D) graphene networks by utilizing a commercial polyurethane (PU) sponge as a structural template. Here, a 3D network of graphene oxide is deposited from solution on the template and then is chemically reduced. Following steps of ZnO nanowire growth, polydimethylsiloxane (PDMS) backfilling and electrode lamination completes the fabrication processes. When compared to conventional generators with 2D planar geometry, the sponge template provides a 3D structure that has a potential to increase power density per unit area. The modified one-pot ZnO synthesis method allows the whole process to be inexpensive and environmentally benign. The nanogenerator yields an open circuit voltage of ∼0.5 V and short circuit current density of ∼2 μA/cm(2), while the output was found to be consistent after ∼3000 cycles. Finite element analysis of stress distribution showed that external stress is concentrated to deform ZnO nanowires by orders of magnitude compared to surrounding PU and PDMS, in agreement with our experiment. It is shown that the backfilled PDMS plays a crucial role for the stress concentration, which leads to an efficient electricity generation.
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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".