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Chemical Modifications of Atomic Force Microscopy Tips

2011· review· en· W179307736 on OpenAlexaff
Régis Barattin, Normand Voyer

Bibliographic record

VenueMethods in molecular biology · 2011
Typereview
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAtomic force microscopyNanotechnologyScanning probe microscopyMicroscopyChemical force microscopyMoleculeMaterials scienceScanning Force MicroscopyForce spectroscopyConductive atomic force microscopyChemistryNon-contact atomic force microscopyOpticsPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Atomic force microscopy (AFM) works by scanning a very tiny tip over a surface with great precision. The microscope tips can be chemically functionalized to improve the images obtained. Well-defined chemical functionalization of AFM tips is especially important for experiments, such as chemical force microscopy and single molecule recognition force microscopy, to examine specific interactions at the single molecular level. In this chapter, we present an overview of chemical modifications of tips that have been reported to date with regards to the proper fixation of probe molecules, focusing particularly on chemical procedures developed to anchor biological molecules on AFM tips.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.051
GPT teacher head0.471
Teacher spread0.420 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations24
Published2011
Admission routes1
Has abstractyes

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