Tunable Hydroxyapatite/Magnetite Nanohybrids with Preserved Magnetic Properties
Bibliographic record
Abstract
Abstract Magnetic nanoparticles (MNPs) have been extensively investigated in a wide range of biomedical applications. Controlled coating of the MNPs is commonly utilized to protect and maintain their magnetic properties and to improve their biocompatibility, hydrophilicity, colloidal stability and overall biodistribution. Hydroxyapatite (HAp), a highly biocompatible material, is considered for the functionalization of MNPs. In this study, two simple chemical approaches are used to prepare nanohybrid MNPs‐on‐HAp and HAp‐on‐MNPs composites. The effect of heat treatment on the phase composition, morphology, and magnetic properties of both types of magnetic composites is extensively evaluated. In the presence of HAp, MNPs are segregated onto their surfaces and their transformation to hematite upon heat treatment is delayed. On the other hand, needle‐shaped HAp nanocrystallites preferentially grow onto the hydroxylated MNPs surfaces, leading to a synergistic enhancement in the magnetic properties of the produced nanocomposites, with preserved magnetic properties. Compared with a saturation magnetization (Ms) value of 80 emu g−1 of pure MNPs, a MNPs‐on HAp nanohybrid shows a maximum of 14 emu g−1, while nanohybrids based on HAp‐on‐MNPs show Ms values in the range of 43–78 emu g−1. These findings demonstrate the ability to fine‐tune the magnetic properties of the HAp/MNPs nanohybrids via optimizing their processing conditions.
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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 | 0.001 | 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".