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Record W1157829151 · doi:10.1021/acsami.5b05702

Sponge-Templated Macroporous Graphene Network for Piezoelectric ZnO Nanogenerator

2015· article· en· W1157829151 on OpenAlexafffund
Xinda Li, Yi Chen, Amit Kumar, Ahmed Mahmoud, John A. Nychka, Hyun‐Joong Chung

Bibliographic record

VenueACS Applied Materials & Interfaces · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of Alberta
KeywordsMaterials scienceNanogeneratorPiezoelectricityGrapheneNanotechnologySpongeComposite material

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.229
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations68
Published2015
Admission routes2
Has abstractyes

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