<i>In Situ</i>Hybridization of Superparamagnetic Iron-Biomolecule Nanoparticles
Bibliographic record
Abstract
The increase in interest in the integration of organic-inorganic nanostructures in recent years has promoted the use of hybrid nanoparticles (HNPs) in medicine, energy conversion, and other applications. Conventional hybridization methods are, however, often long, complicated, and multistepped, and they involve biomolecules and discrete nanostructures as separate entities, all of which hinder the practical use of the resulting HNPs. Here, we present a novel, in situ approach to synthesizing size-specific HNPs using Fe-biomolecule complexes as the building blocks. We choose an anticancer peptide (p53p, MW 1.8 kDa) and an enzyme (GOx, MW 160 kDa) as model molecules to demonstrate the versatility of the method toward different types of molecules over a large size range. We show that electrostatic interaction for complex formation of metal hydroxide ion with the partially charged side of biomolecule in the solution is the key to hybridization of metal-biomolecule materials. Electrochemical deposition is then used to produce hybrid NPs from these complexes. These HNPs with controllable sizes ranging from 30 nm to 3.5 μm are found to exhibit superparamagnetic behavior, which is a big challenge for particles in this size regime. As an example of greatly improved properties and functionality of the new hybrid material, in vitro toxicity assessment of Fe-GOx HNPs shows no adverse effect, and the Fe-p53p HNPs are found to selectively bind to cancer cells. The superparamagnetic nature of these HNPs (superparamagnetic even above the size regime of 15-20 nm!), their biocompatibility, and the direct integration approach are fundamentally important to biomineralization and general synthesis strategy for bioinspired functional materials.
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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".