Cross-reactivities in conjugation reactions involving iron oxide nanoparticles
Bibliographic record
Abstract
The preparation of multimodal nanoparticles by capping magnetic iron oxide nanoparticles (IONPs) with functional organic molecules is a major area of research for biomedical applications. Conjugation reactions, such as carbodiimide coupling and the highly selective class of reactions known as "click chemistry", have been instrumental in tailoring the ligand layers of IONPs to produce functional biomedical nanomaterials. However, few studies report the controls performed to determine if the loading of molecules onto IONPs is due to the proposed coupling reaction(s) employed, or some other unknown interaction with the IONP surface. Herein, we use 3,4-dihydroxybenzoic acid-functionalized IONPs (IONP-3,4-DHBA) as a platform upon which carbodiimide coupling can be used to conjugate clickable small molecules for further functionalization using two common click reactions, namely, the copper-catalyzed azide-alkyne cycloaddition (CuAAC), and the thiol-maleimide Michael addition reactions. Through the judicious use of controls, we demonstrate significant cross-reactivities of amines, thiols, maleimides, and common disulfide reducing agents with surface Fe of IONPs and show how these unwanted interactions can produce false positive results. Without proper controls, these can lead to erroneous conclusions about the efficacy of conjugation reactions, which can have detrimental impacts on the functionality and safety of IONPs in biomedical applications.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 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".