Hollow Filaments Synthesized by Dry-Jet Wet Spinning of Cellulose Nanofibrils: Structural Properties and Thermoregulation with Phase-Change Infills
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide We use dry-jet wet spinning in a coaxial configuration by extruding an aqueous colloidal suspension of oxidized nanocellulose (hydrogel shell) combined with airflow in the core. The coagulation of the hydrogel in a water bath results in hollow filaments (HF) that are drawn continuously at relatively high rates. Small-angle and wide-angle X-ray scattering (SAXS/WAXS) reveals the orientation and order of the cellulose sheath, depending on the applied shear flow and drying method (free-drying and drying under tension). The obtained dry HF show Young’s modulus and tensile strength of up to 9 GPa and 66 MPa, respectively. Two types of phase-change materials (PCM), polyethylene glycol (PEG) and paraffin (PA), are used as infills to enable filaments for energy regulation. An increased strain (9%) is observed in the PCM-filled filaments (HF-PEG and HF-PA). The filaments display similar thermal behavior (dynamic scanning calorimetry) compared to the neat infill, PEG, or paraffin, reaching a maximum latent heat capacity of 170 J·g –1 (48–55 °C) and 169 J·g –1 (52–54 °C), respectively. Overall, this study demonstrates the facile and scalable production of two-component core-shell filaments that combine structural integrity, heat storage, and thermoregulation properties.
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 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) | 0.000 | 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".