MétaCan
Menu
Back to cohort
Record W2167094006 · doi:10.1002/cphc.200200475

Electrosurface Phenomena at Polymer Films for Biosensor Applications

2003· article· en· W2167094006 on OpenAlexaff
Ralf Zimmermann, Oliver Birkert, Günter Gauglitz, Carsten Werner

Bibliographic record

VenueChemPhysChem · 2003
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of TorontoUniversity of New Brunswick
Fundersnot available
KeywordsEthylene glycolZeta potentialPolymerElectrokinetic phenomenaSurface chargeAqueous solutionIsoelectric pointBiosensorBiotinylationChemical engineeringElectrolyteChemistryStreptavidinMaterials sciencePolymer chemistryAnalytical Chemistry (journal)ChromatographyNanotechnologyOrganic chemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Electrosurface phenomena at thin polymer films utilized in the preparation of biosensors have been studied by use of the Microslit Electrokinetic set-up. For the investigated polymer layers (aminodextran, carboxylated dextran, diaminopoly(ethylene glycol), dicarboxypoly(ethylene glycol), biotinylated poly(ethylene glycol), and streptavidin on biotinylated poly(ethylene glycol), the charge formation in aqueous electrolyte solutions was found to depend on the pH value, that is, OH- and H3O+ are the charge determining ions. The isoelectric points obtained from zeta potential versus pH plots could be utilized to draw conclusions on the introduction of acidic or basic groups and on the degree of molecular surface coverage, respectively. The hydrodynamically mobile charge reflected by the magnitude of the zeta potential contributed to only about 6% or less of the total surface conductivity of the polymer layers. The experimental determination of the total surface conductivity was found to provide valuable information on structural features of biosensor interfaces in aqueous environments.

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 score0.336
Threshold uncertainty score0.791

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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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

Citations17
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueChemPhysChemSame topicMicrofluidic and Capillary Electrophoresis ApplicationsFrench-language works237,207