Tailoring the properties of a polymer nanocomposite with a magnetic field
Bibliographic record
Abstract
The properties of nanocomposites can be tailored by organizing nanoparticles within the polymer matrix, that is, for potential aerospace, automobile, and infrastructure applications. Multiwalled carbon nanotubes are coated with nickel to produce hybrid Ni‐MWNT nanoparticles. These magnetized nanoparticles are introduced into an epoxy matrix at a relatively low 0.25%–1% volume fraction and oriented along particular directions with an external magnetic field . Changing this alignment makes it possible to tailor the properties of the resulting composite material, that is, its tensile strength, microscopic elastic modulus, and electrical resistivity. The alignment imparts anisotropic properties to the bulk material, but when the nanoparticles are aligned in perpendicular directions in two sequential layers, this coupled orientation produces an overall isotropic composite material. The tensile strength of a nanocomposite containing 0.25 vol% of aligned Ni‐MWNT nanoparticles is ∼9% higher than of pure epoxy. Its AC electrical resistivity along the alignment direction is reduced by ∼20% in comparison with an epoxy composite containing a similar volume fraction of randomly dispersed nanoparticles. These outcomes reveal that, even at a relatively low nanoparticle volume fraction, the alignment of Ni‐MWNTs in epoxy with an external magnetic field induces property anisotropy, which can be tuned through multidirectional alignment. POLYM. COMPOS., 40:779–788, 2019. © 2018 Society of Plastics Engineers
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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".