Quantum Dot and Gold Nanoparticle Immobilization for Biosensing Applications using Multidentate Imidazole Surface Ligands
Bibliographic record
Abstract
A facile approach for modification of solid substrates with multidentate imidazole ligands was developed for immobilization of high densities of quantum dots (QDs) that were capped with hydrophilic thiol-based ligands, and for immobilization of noble metal nanoparticles. Imidazole polymer was synthesized using poly(acrylic acid) as a backbone, and grafted on amine functionalized substrate in a two-step approach. The polymer-modified surface was characterized using ellipsometry, water contact angle, and X-ray photoelectron spectroscopy. Fluorescence spectroscopy and scanning electron microscopy were used to evaluate nanoparticle immobilization. Homogeneous, high density (ca. 5 × 10(11) cm(-2)) QD films formed via self-assembly were obtained within 4-6 h. Similarly, the imidazole polymer was also shown to be effective for immobilization of gold nanoparticles as a uniform film. By making use of the pH-sensitive affinity of the imidazole rings to zinc on the surface of QDs, it was possible to achieve regeneration of functional ligands suitable for subsequent immobilization of new QDs. Immobilized QDs were used as a platform for bioconjugation with oligonucleotides and peptides. The transduction of nucleic acid hybridization and enzyme activity using QDs as energy donors in interfacial fluorescence resonance energy transfer (FRET) indicated that the immobilization strategy preserved the functional properties of the QDs. The multidentate imidazole ligands used for QD immobilization offer the highest denticity of binding in comparison to the currently available approaches without compromise in their optical properties and ability to interact with biomolecules in solution.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".