MétaCan
Menu
Back to cohort
Record W2602159272 · doi:10.1002/adfm.201606764

Lewis Acid Induced Functionalization of Photoluminescent 2D Silicon Nanosheets for the Fabrication of Functional Hybrid Films

2017· article· en· W2602159272 on OpenAlexafffund
Tobias Helbich, Alina Lyuleeva, Philipp Marx, Lavinia M. Scherf, Tapas K. Purkait, Thomas F. Fässler, Paolo Lugli, Jonathan G. C. Veinot, Bernhard Rieger

Bibliographic record

VenueAdvanced Functional Materials · 2017
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsMaterials scienceHydrosilylationSurface modificationSiliconHydrideNanotechnologyPolymerPhotoluminescenceFabricationField-effect transistorChemical engineeringTransistorOptoelectronicsOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Various Lewis acids are found to efficiently catalyze hydrosilylation reactions of hydride‐terminated 2D silicon nanosheets at room temperature. The hydride‐terminated nanosheets can be functionalized with a variety of unsaturated functional substrates and still possess their unique characteristic (opto)electronic properties (e.g., photoluminescence). This is demonstrated by successfully implementing the readily functionalized materials into new silicon/semiconducting polymer‐based field‐effect transistors (FETs). Surface modification of the freestanding silicon nanosheets opens new possibilities to form highly homogeneous blends with the already broadly used conventional polymers poly(3‐hexylthiophene‐2,5‐diyl). The consequential combination of the different properties of the materials enables the enhancement of the sensitivity of the solution‐gated FETs and increases the transconductance of the operating device.

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 score0.002
Threshold uncertainty score0.005

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)0.0020.000

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 head0.046
GPT teacher head0.282
Teacher spread0.236 · 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

Citations29
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueAdvanced Functional MaterialsSame topicConducting polymers and applicationsFrench-language works237,207