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Record W2006498831 · doi:10.1039/b314058j

Protein microarray scanning in label-free format by Kelvin nanoprobe

2004· article· en· W2006498831 on OpenAlexafffund
Larisa‐Emilia Cheran, Melissa Chacko, Mingquan Zhang, Michael Thompson

Bibliographic record

VenueThe Analyst · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Biosensing Techniques and Applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanoprobeChemistryAnalytical Chemistry (journal)Kelvin probe force microscopeSecondary ion mass spectrometryBiosensorMass spectrometryMicroscopyChromatographyNanotechnologyMaterials scienceNanoparticleBiochemistryAtomic force microscopy

Abstract

fetched live from OpenAlex

Surface-immobilized protein species deposited in the microarray format have been detected by time-of-flight secondary ion mass spectrometry and by scanning Kelvin nanoprobe. The former method was used to examine the nature of protein deposition on amine-coated glass slides and gold substrates in preparation for Kelvin measurements. Both gallium and SF(5)(+) ion sources were employed to produce positive and negative ion spectra of amino acids and polypeptides. Scanning Kelvin technology has been used to detect antibody-antigen interactions in a label-free protocol through measurement of the surface potential of the biochemical pair on indium tin oxide, amine-treated slides and gold substrates. The results show that good inter-spot reproducibility can be achieved and that deposited areas can be examined for homogeneity at 100 nm resolution. This work represents the first report on surface potential detection in protein microarray technology.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.007
GPT teacher head0.250
Teacher spread0.243 · 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 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

Citations27
Published2004
Admission routes2
Has abstractyes

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