Cellulose Nanofibrils Endow Phase-Change Polyethylene Glycol with Form Control and Solid-to-gel Transition for Thermal Energy Storage
Bibliographic record
Abstract
Green energy-storage materials enable the sustainable use of renewable energy and waste heat. As such, a form-stable phase-change nanohybrid (PCN) is demonstrated to solve the fluidity and leakage issues typical of phase-change materials (PCMs). Here, we introduce the advantage of solid-to-gel transition to overcome the drawbacks of typical solid-to-liquid counterparts in applications related to thermal energy storage and regulation. Polyethylene glycol (PEG) is form-stabilized with cellulose nanofibrils (CNFs) through surface interactions. The cellulosic nanofibrillar matrix is shown to act as an organogelator of highly loaded PEG melt (85 wt %) while ensuring the absence of leakage. CNFs also preserve the physical structure of the PCM and facilitate handling above its fusion temperature. The porous CNF scaffold, its crystalline structure, and the ability to hold PEG in the PCN are characterized by optical and scanning electron imaging, infrared spectroscopy, and X-ray diffraction. By the selection of the PEG molecular mass, the lightweight PCN provides a tailorable fusion temperature in the range between 18 and 65 °C for a latent heat storage of up to 146 J/g. The proposed PCN shows remarkable repeatability in latent heat storage after 100 heating/cooling cycles as assessed by differential scanning calorimetry. The thermal regulation and light-to-heat conversion of the PCN are confirmed via infrared thermal imaging under simulated sunlight and in a thermal chamber, outperforming those of a reference, commercial insulation material. Our PCN is easily processed as a structurally stable design, including three-dimensional, two-dimensional (films), and one-dimensional (filaments) materials; they are, respectively, synthesized by direct ink writing, casting/molding, and wet spinning. We demonstrate the prospects of the lightweight, green nanohybrid for smart-energy buildings and waste heat-generating electronics for thermal energy storage and management.
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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".