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Record W3187984119 · doi:10.3390/ma14154300

Modulation of Crystallinity through Radiofrequency Electromagnetic Fields in PLLA/Magnetic Nanoparticles Composites: A Proof of Concept

2021· article· en· W3187984119 on OpenAlexaff
M. Multigner, Irene Morales, Marta Muñoz, V. Bonache, Fernando Giacomone, Patricia de la Presa, Rosario Benavente, B. Torres, Diego Mantovani, J. Rams

Bibliographic record

VenueMaterials · 2021
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversité Laval
FundersMinisterio de Ciencia e InnovaciónComunidad de Madrid
KeywordsCrystallinityMaterials sciencePolylactic acidNanocompositeComposite materialDifferential scanning calorimetryPolymerSonicationNanoparticleNanoindentationElastic modulusMagnetic nanoparticlesChemical engineeringNanotechnology

Abstract

fetched live from OpenAlex

To modulate the properties of degradable implants from outside of the human body represents a major challenge in the field of biomaterials. Polylactic acid is one of the most used polymers in biomedical applications, but it tends to lose its mechanical properties too quickly during degradation. In the present study, a way to reinforce poly-L lactic acid (PLLA) with magnetic nanoparticles (MNPs) that have the capacity to heat under radiofrequency electromagnetic fields (EMF) is proposed. As mechanical and degradation properties are related to the crystallinity of PLLA, the aim of the work was to explore the possibility of modifying the structure of the polymer through the heating of the reinforcing MNPs by EMF within the biological limit range f·H < 5·× 109 Am−1·s−1. Composites were prepared by dispersing MNPs under sonication in a solution of PLLA. The heat released by the MNPs was monitored by an infrared camera and changes in the polymer were analyzed with differential scanning calorimetry and nanoindentation techniques. The crystallinity, hardness, and elastic modulus of nanocomposites increase with EMF treatment.

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 categoriesInsufficient payload (model declined to judge)
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 score0.011
Threshold uncertainty score0.999

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)0.0020.000

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 head0.025
GPT teacher head0.234
Teacher spread0.209 · 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.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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