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Record W2317919343 · doi:10.1039/c6cp00923a

Flexible magnetic filaments under the influence of external magnetic fields in the limit of infinite dilution

2016· article· en· W2317919343 on OpenAlexaff
Joan J. Cerdà, Pedro A. Sánchez, Daniel Lüsebrink, Sofia S. Kantorovich, Tomàs Sintes

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2016
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsUniversity of British Columbia
FundersEuropean Social FundAgencia de Innovación y Desarrollo de AndalucíaConsejo Superior de Investigaciones CientíficasRussian Foundation for Basic Research
KeywordsBistabilityMagnetic fieldCondensed matter physicsDilutionPhase diagramLimit (mathematics)PhysicsMaterials sciencePhase (matter)ThermodynamicsMathematicsQuantum mechanicsMathematical analysis

Abstract

fetched live from OpenAlex

In the present work we use Langevin dynamics computer simulations to understand how the presence of a constant external magnetic field modifies the conformational phase diagram of magnetic filaments in the limit of infinite dilution. We have considered the filaments immersed in either a good (non-sticky filaments) or a poor (Stockmayer polymers) solvent. It has been found that in the presence of an applied field, filaments turn out to be much more susceptible to parameters such as temperature and solvent conditions. Filaments owe this increased susceptibility to the fact that the external magnetic field tends to level the free energy landscape as compared to the zero-field case. The field induces equalization in the free energy of competing conformational states that were separated by large energy differences in the zero-field limit. In this new scenario multistability arises, and manifests itself in the existence of broad regions in the phase diagram where two or more equilibrium configurations coexist. The existence of multistability greatly enhances the possibility of tuning the properties of the filament.

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 score0.009
Threshold uncertainty score0.277

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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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