The Surface Chemistry of Au Colloids and Their Interactions with Functional Amino Acids
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".