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Record W2052300140 · doi:10.1021/la0518804

Gold Film Surface Preparation for Self-Assembled Monolayer Studies

2006· article· en· W2052300140 on OpenAlexaff
Jing Kang, P. Rowntree

Bibliographic record

VenueLangmuir · 2006
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMonolayerSelf-assembled monolayerNanotechnologyMaterials scienceChemical engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

Evaporated gold films are frequently used as substrates for the study of biomolecular adsorbates, nanoparticle systems, amd partial and full monolayer films. These studies often benefit from a predeposition cleaning of the surface that removes adventitiously adsorbed material from laboratory contaminants. Scanning tunneling microscopy (STM) is used in this study to explore the microscopic consequences of two pretreatment protocols used in literature reports of self-assembled monolayers, based on sulfochromic and piranha acid solutions. These measurements show that treatment of the Au/mica surface with piranha acid can lead to extensive and uncontrolled etching of the surface and severe disruption of the surface topography; extended exposure causes the precipitation of crystallites on the surface that are highly mobile during STM imaging processes. Exposure of Au/mica surfaces to sulfochromic acid leads to the formation of permanent etch pits of the surface that are exclusively one Au layer deep; extended exposure leads to progressive etching and oxidation of the surface, ultimately leading to the formation of 0.33-0.36 nm high islands on the otherwise flat Au/mica surface. The piranha acid solutions are significantly more likely to cause the Au film to delaminate from the mica support than are the sulfochromic acid solutions. These results show that sulfochromic surface preparation is a direct and reliable method for the elimination of organic residues from Au(111)-textured surfaces, while causing a minimum of structural and chemical surface damage.

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 score0.051
Threshold uncertainty score0.302

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 head0.009
GPT teacher head0.242
Teacher spread0.234 · 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

Citations63
Published2006
Admission routes1
Has abstractyes

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