MétaCan
Menu
Back to cohort
Record W2056883003 · doi:10.1021/jp0500642

Thermo-Kinetics Study of Laser-Induced Desorption of Self-Assembled Monolayers from Gold:  Case of Laser Micropatterning

2005· article· en· W2056883003 on OpenAlexafffund
Mohammad Reza Shadnam, Sean E. Kirkwood, R. Fedosejevs, Alidad Amirfazli

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2005
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLaserLaser power scalingDesorptionMicropatterningMonolayerContext (archaeology)WettingSelf-assembled monolayerMaterials scienceKineticsChemistryIrradiationAnalytical Chemistry (journal)OpticsAdsorptionNanotechnologyComposite materialPhysical chemistry

Abstract

fetched live from OpenAlex

Laser-induced desorption of self-assembled monolayers (SAMs) from gold surfaces within context of the direct laser patterning methodology was investigated through combining results of a heat diffusion thermal model with desorption kinetics of alkanethiol SAMs. It was found that contrast plots of experimental scanning electron microscopy (SEM) images, which are correlated to surface coverage of SAMs desorbed after laser irradiation, agreed with the theoretically predicted surface composition of SAMs. The surface composition of SAM was then interpreted in terms of the wetting property of the resulting surface. The effect of incident laser beam power and size on the final spatial coverage of SAMs on the surface and feature sizes was investigated both experimentally and by modeling. Theoretical modeling and experimental evidence showed that the resulting feature sizes are wider when the surface is heated by a laser of higher power. Increasing the laser beam size results in broadening of feature sizes. Considering the correlation of the theoretical and experimental results, we concluded that the feature sizes are controllable in a predictable way (using the presented thermal-kinetics model) through varying laser beam power and beam size.

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 score0.027
Threshold uncertainty score0.357

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 head0.007
GPT teacher head0.218
Teacher spread0.211 · 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

Citations20
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry BSame topicMolecular Junctions and NanostructuresFrench-language works237,207