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Record W1964557912 · doi:10.1021/am100348f

Building Upon Patterned Organic Monolayers Produced via Catalytic Stamp Lithography

2010· article· en· W1964557912 on OpenAlexafffund
Hidenori Mizuno, Jillian M. Buriak

Bibliographic record

VenueACS Applied Materials & Interfaces · 2010
Typearticle
Languageen
FieldEngineering
TopicNanofabrication and Lithography Techniques
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMonolayerLithographyCatalysisNanotechnologyMicrocontact printingOptoelectronicsOrganic chemistry

Abstract

fetched live from OpenAlex

Soft lithographic sub-100 nm chemical patterning was demonstrated on organic monolayer surfaces using poly(dimethylsiloxane)-based stamps decorated with Pd nanostructures, structures termed "catalytic stamps". Chemically reactive azide or alkene functionalities were incorporated on oxide-capped silicon surfaces and utilized for patterning via Pd-catalyzed hydrogenation or Heck reactions. The catalytic stamps were soft lithographic stamps based on PDMS with embedded nanoscale palladium catalysts, prepared via block copolymer-based templating. Nanoscale chemical patterns were readily generated on the azide or alkene precursor surfaces simply by applying the Pd catalytic stamps and the reactive molecule, the molecular ink, to the surface, thanks to the highly localized catalytic transformations induced by the patterned, immobilized solid Pd catalysts. A series of successful postfunctionalization reactions on the resulting patterned surfaces further demonstrated the utility of this approach to construct novel designs of nanoarchitectures, with potentially unique and innovative properties.

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 score0.005
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)0.0000.000

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 head0.005
GPT teacher head0.212
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.

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

Citations15
Published2010
Admission routes2
Has abstractyes

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