Microencapsulation of Phase Change Materials with Polystyrene/Cellulose Nanocrystal Hybrid Shell via Pickering Emulsion Polymerization
Bibliographic record
Abstract
Microcapsulation of phase change materials (PCMs) within a shell is one of the most feasible methods to explore their applications for thermal energy storage. Here, a facile method to microencapsulate PCMs within polystyrene/cellulose nanocrystal (CNC) hybrid shell via Pickering emulsion polymerization was developed. CNCs, as biobased and sustainable materials hydrolyzed from wood pulp, were employed as emulsifiers of the PCM Pickering emulsion and shell components of the PCM microcapsules as well. CNCs displayed a high efficiency in the stabilization of paraffin wax (PW) Pickering emulsion, and the heat capacity and stability of PW microcapsules with CNC shell (PW@CNC) increased dramatically with the amounts of CNCs. PW microcapsules with polystyrene and CNC hybrid shell (PW@PS/CNC) were prepared via Pickering emulsion polymerization of styrene from the CNC stabilized PW Pickering emulsion droplets. The PW@PS/CNC slurries possessed a latent heat capacity of 31.9 J/g with stability as high as 99.4% after 100 heating and cooling scans. The PW@PS/CNC powder possessed a latent heat capacity of 160.3 J/g, corresponding to a high encapsulation ratio of 83.5%. Moreover, coconut oil (CO), as an example of biobased PCMs, was also microencapsulated within polystyrene and CNC hybrid shell (CO@PS/CNC) via a similar method. Both PW@PS/CNC and CO@PS/CNC slurries displayed excellent temperature regulation ability and offered promising potentials for thermal energy storage systems.
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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) | 0.001 | 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".