MétaCan
Menu
Back to cohort
Record W2082078647 · doi:10.1021/jp037056a

The Surface Chemistry of Au Colloids and Their Interactions with Functional Amino Acids

2004· article· en· W2082078647 on OpenAlexaff
Ziyi Zhong, Sergiy Patskovskyy, Pierre Bouvrette, John H. T. Luong, Aharon Gedanken

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2004
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsPolytechnique MontréalNational Research Council CanadaBiotechnology Research Institute
Fundersnot available
KeywordsChemistryAmino acidAmine gas treatingCarboxylateColloidDispersityReactivity (psychology)AlkylNanoparticleSurface chargeFunctional groupMoleculeAbsorption (acoustics)Particle sizeOrganic chemistryPhysical chemistryPolymerNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

The work reported here describes interactions between nanoscale Au colloids and two main types of organic functional groups, viz., alkanethiols and amino acids. The surface chemistry of particulate Au is dominated by electrodynamic factors related to its (negative) surface charge. Generalized multiparticle Mie calculations were used to model the optical absorption characteristics of Au particles, existing either singly or in varying degrees of aggregation. Experiments with standard (monodisperse) Au colloids confirm the theoretical prediction of a new peak appearing at longer wavelength that intensifies and shifts further from the original peak with increasing particle size, increasing aggregate size, or shorter interparticle spacing. Control of aggregation degree in alkanethiols is achieved by judicious selection of terminal group composition (single- or double-ended), alkyl chain length, and the presence of pH sensitive groups such as carboxylates. In amino acids, the reactivity of the α-amine (adjacent to −COOH) is found to be pH-dependent. Linking via the α-amine is activated at low pH but suppressed at intermediate and high pH due to electrostatic repulsive forces between the Au surface and the charged carboxylate group or even the (formally neutral) polar carbonyl group in amides. However, dibasic amino acids can still be used to cross-link Au colloids at high pH. The pH insensitive (remote) amine binds amino acids to each particle, leaving protruding pairs of α-amines that can be bridged by a symmetrical linker molecule like glutaraldehyde (via its electrophilic centers). This offers a new way to organize Au nanoparticles into extended architectures and functional materials over a wide range of pH. The potential of Au colloids to recognize and determine dibasic amino acids based on optical absorption changes is briefly assessed. A higher detection limit for cysteine (1.2 μg/mL) was found for larger (40 nm) Au particles.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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

Citations433
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry BSame topicGold and Silver Nanoparticles Synthesis and ApplicationsFrench-language works237,207