Hierarchical Self-Assembly and Optical Disassembly for Controlled Switching of Magnetoferritin Nanoparticle Magnetism
Bibliographic record
Abstract
Protein cages such as ferritin and viral capsids are interesting building blocks for nanotechnology due to their monodisperse structure and ability to encapsulate various functional moieties. Here we show that recombinant ferritin protein cages encapsulating Fe(3)O(4)-γ-Fe(2)O(3) iron oxide (magnetoferritin) nanoparticles and photodegradable Newkome-type dendrons self-assemble into micrometer-sized complexes with a face-centered-cubic (fcc) superstructure and a lattice constant of 13.1 nm. The magnetic properties of the magnetoferritin particles are affected directly by the hierarchical organization. Magnetoferritin nanoparticles dispersed in water exhibit typical magnetism of single domain noninteracting nanoparticles; however, the same nanoparticles organized into fcc superstructures show clearly the effects of the altered magnetostatic (e.g., dipole-dipole) interactions by exhibiting, for example, different hysteresis of the field-dependent magnetization. The magnetoferritin-dendron assemblies can be efficiently disassembled by a short optical stimulus resulting in release of free magnetoferritin particles. After the triggered release the nanomagnetic properties of the pristine magnetoferritin nanoparticles are regained.
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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) | 0.000 | 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".