Iron Oxide Surface Chemistry: Effect of Chemical Structure on Binding in Benzoic Acid and Catechol Derivatives
Bibliographic record
Abstract
The excellent performance of functionalized iron oxide nanoparticles (IONPs) in nanomaterial and biomedical applications often relies on achieving the attachment of ligands to the iron oxide surface both in sufficient number and with proper orientation. Toward this end, we determine relationships between the ligand chemical structure and surface binding on magnetic IONPs for a series of related benzoic acid and catechol derivatives. Ligand exchange was used to introduce the model ligands, and the resultant nanoparticles were characterized using Fourier transform infrared-attenuated internal reflectance spectroscopy, transmission electron microscopy, and nanoparticle solubility behavior. An in-depth analysis of ligand electronic effects and reaction conditions reveals that the nature of ligand binding does not solely depend on the presence of functional groups known to bind to IONPs. The structure of the resultant ligand-surface complex was primarily influenced by the relative positioning of hydroxyl and carboxylic acid groups within the ligand and whether or not HCl(aq) was added to the ligand-exchange reaction. Overall, this study will help guide future ligand-design and ligand-exchange strategies toward realizing truly custom-built IONPs.
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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) | 0.001 | 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".