From Structure to Function: Understanding Synthetic Conditions in Relation to Magnetic Properties of Hybrid Pd/Fe-Oxide Nanoparticles
Bibliographic record
Abstract
Heterostructured magnetic nanoparticles show great potential for numerous applications in biomedicine due to their ability to express multiple functionalities in a single structure. Magnetic properties are generally determined by the morphological characteristics of nanoparticles, such as the size/shape, and composition of the nanocrystals. These in turn are highly dependent on the synthetic conditions applied. Additionally, incorporation of a non-magnetic heterometal influences the final magnetic behavior. Therefore, construction of multifunctional hybrid nanoparticles with preserved magnetic properties represents a certain nanotechnological challenge. Here, we focus on palladium/iron oxide nanoparticles designed for combined brachytherapy, the internal form of radiotherapy, and MRI-guided hyperthermia of tumors. The choice of palladium forming the nanoparticle core is envisioned for the eventual radiolabeling with 103Pd to enable the combination of hyperthermia with brachytherapy, the latter being beyond the scope of the present study. At this stage, we investigated the synthetic mechanisms and their effects on the final magnetic properties of the hybrid nanoparticles. Thermal decomposition was applied for the synthesis of Pd/Fe-oxide nanoparticles via both, one-pot and seed-mediated processes. The latter method was found to provide better control over morphology of the nanoparticles and was therefore examined closely by varying reaction conditions. This resulted in several batches of Pd/Fe-oxide nanoparticles, whose magnetic properties were evaluated, revealing the most relevant synthetic parameters leading to promising performance in hyperthermia and MRI.
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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 | 0.001 |
| 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 | 0.001 |
| 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".