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Record W2327141801 · doi:10.1021/am5008968

Piezoelectric Paper Fabricated via Nanostructured Barium Titanate Functionalization of Wood Cellulose Fibers

2014· article· en· W2327141801 on OpenAlexaff
Suresha K. Mahadeva, Konrad Walus, Boris Stoeber

Bibliographic record

VenueACS Applied Materials & Interfaces · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceSurface modificationBarium titanateComposite materialPiezoelectricityCelluloseNanoparticleZeta potentialFiberChemical engineeringCellulose fiberNanofiberNanotechnologyCeramic

Abstract

fetched live from OpenAlex

We have successfully developed hybrid piezoelectric paper through fiber functionalization that involves anchoring nanostructured BaTiO3 into a stable matrix with wood cellulose fibers prior to the process of making paper sheets. This is realized by alternating immersion of wood fibers in a solution of poly(diallyldimethylammonium chloride) PDDA (+), followed by poly(sodium 4-styrenesulfonate) PSS (-), and once again in PDDA (+), resulting in the creation of a positively charged surface on the wood fibers. The treated wood fibers are then immersed in a BaTiO3 suspension, resulting in the attachment of BaTiO3 nanoparticles to the wood fibers due to a strong electrostatic interaction. Zeta potential measurements, X-ray diffraction, and microscopic and spectroscopic analysis imply successful functionalization of wood fibers with BaTiO3 nanoparticles without altering the hydrogen bonding and crystal structure of the wood fibers. The paper has the largest piezoelectric coefficient, d33 = 4.8 ± 0.4 pC N(-1), at the highest nanoparticle loading of 48 wt % BaTiO3. This newly developed piezoelectric hybrid paper is promising as a low-cost substrate to build sensing devices.

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 score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.005
GPT teacher head0.182
Teacher spread0.177 · 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

Citations120
Published2014
Admission routes1
Has abstractyes

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