Nanoparticle Bioconjugates: Materials that Benefit from Chemoselective and Bioorthogonal Ligation Chemistries
Bibliographic record
Abstract
This chapter presents an overview of the preparation of nanoparticle bioconjugates, illustrated with many representative examples. Nanoparticle materials that are discussed include gold and silver nanoparticles, silica nanoparticles, iron oxide and other magnetic nanoparticles, semiconductor quantum dots, lanthanide nanoparticles, polymer and amphiphile nanoparticles (e.g., liposomes), and carbon allotropes such as carbon nanotubes, graphene and graphene oxide, carbon dots, and fullerenes. Important aspects of the synthesis and chemical functionalization of nanoparticles are summarized, as are important considerations for their bioconjugation: sites for conjugation; intricacies, challenges and opportunities associated with the physical and chemical features of nanoparticles; and the purification and characterization of nanoparticle bioconjugates. Following a brief review of the application of traditional bioconjugate reactions (e.g., carbodiimide coupling, cross-linkers, biotin-avidin) with nanoparticles, the chapter focuses on the growing utilization of chemoselective and bioorthogonal chemistries for the preparation of nanoparticle bioconjugates. These chemistries include azide-alkyne cycloaddition, Diels–Alder reactions, Staudinger ligation, hydrazone and oxime ligation, thiol-ene chemistry, native chemical ligation and intein-assisted conjugation, enzymatic ligation, and affinity and coordinate interactions. The resultant nanoparticle bioconjugates enable applications such as bioanalytical assays, cellular labeling and imaging, diagnostic imaging, and drug delivery and therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.018 | 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 teacher head, 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".