Ultrafast Thermal Conduction of Phase Change Fiber Enabled by Sheath Confinement‐Induced Ordered Orientation of Hydroxylated BNNs
Bibliographic record
Abstract
Abstract Phase change fibers (PCFs) are increasingly popular in thermal storage and release applications, such as temperature management. However, a simple but effective integration of thermal conductive materials into PCFs to deliver ultrafast thermal conduction remains a big challenge. Herein, a facile one‐pot coaxial wet spinning strategy based on sheath‐confinement‐induced orientation arrangement of hydroxylated boron nitride nanosheets (BNNs‐OH) is proposed to fabricate the high‐performance PCFs. For the core‐sheath PCFs, the cellulose nanofiber (CNF)‐reinforced paraffin (CNF/PW) emulsions serve as the phase change core, while the dissolved cellulose and well‐dispersed BNNs‐OH act as the sheath precursor. By increasing the extrusion speed of the liquid core, the contraction of the fiber sheath is gradually finalized to induce the alignment of BNNs‐OH based on the confinement effect. Consequently, the highly oriented BNNs‐OH nanosheets in fiber sheath ( f = 0.8) endow PCFs with high phase transition enthalpy (125.1 J g −1 ), excellent thermal conductivity (10.15 W m −1 K −1 ), rapid heat transfer rate (1.6 cm s −1 ); moreover, the as‐prepared PCFs show good tensile strength (11.21 MPa) and leakage‐proof (0.13%). The as‐prepared PCFs are combined with thermochromic dyes for functional fabrics or applied as thermal management for a phone, demonstrating excellent thermal conduction and heat dissipation.
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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".