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Record W2979109107 · doi:10.1002/smll.201904415

Laser‐Directed Assembly of Nanorods of 2D Materials

2019· article· en· W2979109107 on OpenAlexafffund
Khaled H. Ibrahim, Inna Novodchuk, Kissan Mistry, Michael D. Singh, Chris D. Ling, Joseph Sanderson, Michal Bajcsy, Mustafa Yavuz, Kevin P. Musselman

Bibliographic record

VenueSmall · 2019
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanorodMaterials scienceTungsten disulfideLaserNanotechnologyMolybdenum disulfideFemtosecondNanoparticleBoron nitrideOptoelectronicsOpticsComposite material

Abstract

fetched live from OpenAlex

Abstract Herein, the previously unrealized ability to grow nanorods and nanotubes of 2D materials using femtosecond laser irradiation is demonstrated. In as short as 20 min, nanorods of tungsten disulfide, molybdenum disulfide, graphene, and boron nitride are grown in solutions. The technique fragments nanoparticles of the 2D materials from bulk flakes and leverages molecular scale alignment by nonresonant intense laser pulses to direct their assembly into nanorods up to several micrometers in length. The laser treatment process is found to induce phase transformations in some of the materials, and also results in the modification of the nanorods with functional groups from the solvent atoms. Notably, the WS2 nanoparticles, which are ablated from semiconducting 2H WS2 crystallographic phase flakes, reassemble into nanorods consisting of the 1T metallic phase. Due to this transition, and the 1D nature of the fabricated nanorods, the WS2 nanorods display substantial improvements in electrical conductivity and optical transparency when employed as transparent conductors.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.016
GPT teacher head0.243
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2019
Admission routes2
Has abstractyes

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