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Record W2114932734 · doi:10.1021/nn201571y

Hierarchical Self-Assembly and Optical Disassembly for Controlled Switching of Magnetoferritin Nanoparticle Magnetism

2011· article· en· W2114932734 on OpenAlexaff
Mauri A. Kostiainen, Pierpaolo Ceci, Manuela Fornara, Panu Hiekkataipale, O. I. Kasyutich, Roeland J. M. Nolte, Jeroen J. L. M. Cornelissen, R. D. Desautels, J. van Lierop

Bibliographic record

VenueACS Nano · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMagnetismNanoparticleNanomagnetMaterials scienceNanotechnologyDispersitySelf-assemblyDendrimerMagnetizationMagnetic nanoparticlesChemical physicsChemistryCondensed matter physicsMagnetic field

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.231
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations77
Published2011
Admission routes1
Has abstractyes

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