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Record W2042440937 · doi:10.1088/1742-6596/19/1/027

Force microscopy on insulators: imaging of organic molecules

2005· article· en· W2042440937 on OpenAlexaff
Olivier Pfeiffer, Enrico Gnecco, Lars Zimmerli, Sabine Maier, Ernst Meyer, Laurent Nony, Roland Bennewitz, F. Diederich, Han Fang, Davide Bonifazi

Bibliographic record

VenueJournal of Physics Conference Series · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsMcGill University
FundersNational Center of Competence in Research Chemical BiologyNational Science Foundation
KeywordsCantileverMaterials scienceNanotechnologyExcitationMoleculeScanning tunneling microscopeMicroscopyElectrostatic force microscopeAnnealing (glass)OptoelectronicsScanning electron microscopeSubstrate (aquarium)Conductive atomic force microscopyNon-contact atomic force microscopyAtomic force microscopyChemistryOpticsComposite materialElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

So far, most of the high resolution scanning probe microscopy studies of organic molecules were restricted to metallic substrates. Insulating substrates are mandatory when the molecules need to be electrically decoupled in a electronic circuit. In such a case, atomic force microscopy is required. In this paper we will discuss our recent studies on different organic molecules deposited on KBr surfaces in ultra-high vacuum, and then imaged by AFM at room temperature. The distance between tip and surface was controlled either by the frequency-shift of the cantilever resonance or by the excitation signal required to keep the oscillation amplitude constant. Advantages and drawbacks of both techniques are discussed. The high mobility of the molecules, due to their weak interaction with the substrate, hinders the formation of regular self assembled structures. To overcome this problem we created artificial structures on the surface by annealing and by electron irradiation, which made possible the growth of the molecules onto step edges and their confinement into rectangular pits.

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 score0.266
Threshold uncertainty score0.481

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 head0.008
GPT teacher head0.267
Teacher spread0.259 · 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

Citations12
Published2005
Admission routes1
Has abstractyes

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