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Record W4243075214 · doi:10.26434/chemrxiv.14390516.v1

Water-Dispersible and Ferroelectric PEGylated Barium Titanate Nanoparticles

2021· preprint· en· W4243075214 on OpenAlexafffund
M. Taheri, Bryan Zance, Michelle Dolgos, Steven L. Bryant, Simon Trudel

Bibliographic record

VenueChemRxiv · 2021
Typepreprint
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Excellence Research Chairs, Government of CanadaCanada First Research Excellence FundUniversity of Calgary
KeywordsMaterials scienceBarium titanateFerroelectricityNanoparticleDielectricTetragonal crystal systemDispersantNanotechnologyNanomaterialsChemical engineeringDynamic light scatteringSurface modificationCrystal structureOptoelectronicsOpticsDispersion (optics)Organic chemistryChemistry

Abstract

fetched live from OpenAlex

Dispersions of ferroelectric nanoparticles in aqueous medium can find promising applications in electro-optical, medical, and smart fluid technologies. In this report, we show the development of highly dispersed nano-sized ferroelectric barium titanate (BaTiO3) powders with high dielectric constant prepared using a simple, one-step low temperature solution method. The surface of these tetragonal-structured nanoparticles were modified with polyethylene glycol as a stabilizer and dispersant. The crystal structure, morphology and dielectric constant of samples are discussed in detail. The colloidal stability and surface behavior of these PEGylated barium titanate nanoparticles are studied by means of z -potential and dynamic light scattering measurements. We show changing the reaction conditions allows to tune the nanoparticle size. This research promotes a pathway to develop advanced ferroelectric nanomaterials with engineered properties in a simple way.

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 categoriesInsufficient payload (model declined to judge)
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 score0.011
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 science0.0000.001
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 head0.018
GPT teacher head0.223
Teacher spread0.205 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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