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Record W2066428688 · doi:10.1021/nn901923z

Electrokinetic Assembly of Selenium and Silver Nanowires into Macroscopic Fibers

2010· article· en· W2066428688 on OpenAlexafffund
Michael C. P. Wang, Xin Zhang, Elham Majidi, Kevin Nedelec, Byron D. Gates

Bibliographic record

VenueACS Nano · 2010
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsSimon Fraser University
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of Canada
KeywordsNanowireMaterials scienceElectrokinetic phenomenaFiberNanostructureNanotechnologyElectric fieldElectrospinningNanofiberOptoelectronicsComposite materialPolymer

Abstract

fetched live from OpenAlex

Solution-phase synthesized nanowires with high aspect ratios can be a challenge to assemble into desired structures. As synthesized, these nanostructures readily bend and entangle with each other to form larger aggregates. This manuscript reports a general procedure for directing the assembly of semiconducting and metallic nanowires into fibers that can easily span distances >1 cm. Dispersions of these nanostructures in a low dielectric solution are organized by electrokinetic techniques into fibers that can be isolated from solution. Theoretical studies suggest that the assembled fibers adopt an orientation along electric field lines in the solution. The number of assembled fibers is a function of the duration of the assembly process, the magnitude of the electric potential, and the initial concentration of nanowires dispersed in solution. These findings offer a general method for the assembly of nanowires into macroscopic fibers of tunable dimensions. Fibers of selenium nanowires isolated from solution can reversibly bend in response to a source of electrostatic charges positioned in close proximity to the free-standing fiber. These flexible selenium fibers also exhibit a photoconductive response when illuminated with white light.

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 categoriesnone
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 score0.016
Threshold uncertainty score0.402

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)0.0000.000

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 head0.004
GPT teacher head0.211
Teacher spread0.207 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations36
Published2010
Admission routes2
Has abstractyes

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