Influence of magnetic nanoparticles on the mechano-magnetic response of wet-spun sodiumalginate-nanocellulose filaments
Bibliographic record
Abstract
Hybrid filaments are of growing interest for a wide range of applications, including those that require stimuli-responsiveness. In this study we developed magnetic filaments by combining the properties of inorganic nanoparticles with the low density, flexibility and morphological features of 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO)-oxidized cellulose nanofibrils (TOCNF). The hybrid filaments were synthesized by wet spinning of TOCNF using sodium alginate (SA) adjuvant in a hydrogel containing magnetite (Fe 3 O 4 ) nanoparticles (NPs) formed in-situ by nucleation and grow. The relationship between synthesis conditions and filament mechanical and magnetic properties were investigated at NP loading as high as 25%. Saturation magnetization of 1.60, 11.31, 19.41, and 33.25 emu/g Fe 3 O 4 were measured at 5, 10, 15, and 25% NPs with a penalty in filament tensile strength which nevertheless reached at least 118 GPa along with low magnetite crystal orientation. Such high strength is rarely reported and found to depend on cellulose crystal orientation. The magnetic filaments were found suitable to replace traditional magnetic systems but add to the opportunity to develop flexible microwave adsorption textiles, artificial muscles, and micro-sensors.
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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".