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Record W1970046902 · doi:10.1021/ac991137e

Mapping Interfacial Chemistry Induced Variations in Protein Adsorption with Scanning Force Microscopy

2000· article· en· W1970046902 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnalytical Chemistry · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMonolayerChemistryAdsorptionCarboxylateAtomic force microscopyMicroscopyInfrared spectroscopyProtein adsorptionScanning Force MicroscopyCrystallographyChemical engineeringNanotechnologyStereochemistryOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

In this work, we demonstrate the sensitivity of scanning force microscopy (SFM), operated in friction force mode, to adsorbed protein conformation or orientation. We employ patterned films of methyl- and carboxylate-terminated alkanethiolate monolayers on gold as substrates for protein adsorption to observe the effect of each functional group in the same image. Infrared spectroscopic and SFM studies of bovine fibrinogen (BFG) adsorption to single-component monolayers indicate that complete films of BFG that are stable to imaging are formed at each functional group. After adsorption of BFG to a patterned monolayer, we observe a contrast in friction images due to differences in adsorbed BFG conformation or orientation induced by each functional group. We also observe frictional contrast in films of other proteins adsorbed on patterned monolayers. These observations lead to the conclusion that SFM-measured friction is sensitive to adsorbed protein state.

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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.047
Threshold uncertainty score0.997

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)0.0040.000

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 head0.264
Teacher spread0.255 · 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