Preparation and characterization of 100% bio-based polylactic acid/palmitic acid microcapsules for thermal energy storage
Bibliographic record
Abstract
Phase change materials (PCM) have gained extensive attention in thermal energy storage applications. In this work, microencapsulation of vegetable-derived palmitic acid (PA) in bio-based polylactic acid (PLA) shell by solvent evaporation and oil-in-water emulsification was investigated. Fourier transform infrared spectroscopy and scanning electron microscopy were conducted to confirm the successful encapsulation of PA in PLA shells. Differential scanning calorimetry was performed to evaluate the thermal properties, thermal reliability, and core content of the fabricated PCM microcapsules (microPCM). Through a series of parametric studies, the effects of PCM and solvent content, oil phase-to-aqueous phase ratio, as well as surfactant type and content on the morphology, particle size, and thermal properties of the PCM microcapsules were investigated. Experimental results showed that PVA was a superior emulsifier to SDS in the emulsion systems being studied. There also existed an optimal PVA concentration to reduce the average size of microPCM. When the PVA concentration was above this optimal level, the emulsifier molecules tend to form micelles among themselves. This led to the adhesion of tiny microspheres on the surface of microPCM as well as larger microPCM. In short, this work has demonstrated the possibility of using the solvent evaporation method to fabricate 100% bio-based PCM-polymer microcapsules for thermal energy storage applications.
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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) | 0.000 | 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".