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Record W1983916459 · doi:10.1039/c4cc01423e

Influence of the Debye length on the interaction of a small molecule-modified Au nanoparticle with a surface-bound bioreceptor

2014· article· en· W1983916459 on OpenAlexaff
Natalia Bukar, Sandy Shuo Zhao, David M. Charbonneau, Joelle N. Pelletier, Jean‐François Masson

Bibliographic record

VenueChemical Communications · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsDebye lengthSurface plasmon resonanceDihydrofolate reductaseNanoparticleIonic strengthDebyeChemistryColloidMoleculeLigand (biochemistry)Analytical Chemistry (journal)CrystallographyMaterials scienceIonNanotechnologyPhysical chemistryReceptorChromatographyOrganic chemistryEnzymeBiochemistry

Abstract

fetched live from OpenAlex

We report that a shorter Debye length and, as a consequence, decreased colloidal stability are required for the molecular interaction of folic acid-modified Au nanoparticles (Au NPs) to occur on a surface-bound receptor, human dihydrofolate reductase (hDHFR). The interaction measured using surface plasmon resonance (SPR) sensing was optimal in a phosphate buffer at pH 6 and ionic strength exceeding 300 mM. Under these conditions, the aggregation constant of the Au NPs was approximately 10(4) M(-1) s(-1) and the Debye length was below 1 nm, on the same length scale as the size of the folate anion (approximately 0.8 nm). Longer Debye lengths led to poorer SPR responses, revealing a reduced affinity of the folic acid-modified Au NPs for hDHFR. While high colloidal stability of Au NPs is desired in most applications, these conditions may hinder molecular interactions due to Debye lengths exceeding the size of the ligand and thus preventing close interactions with the surface-bound molecular receptor.

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 score0.001
Threshold uncertainty score0.179

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 science0.0010.000
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 head0.021
GPT teacher head0.278
Teacher spread0.257 · 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

Citations45
Published2014
Admission routes1
Has abstractyes

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