Edible Oils as Practical Phase Change Materials for Thermal Energy Storage
Bibliographic record
Abstract
Edible oils could provide more accessible alternatives to other phase change materials (PCMs) for consumers who wish to build a thermal energy storage (TES) system with sustainable materials. Edible oils have good shelf life, can be acquired easily from local stores and can be less expensive than other PCMs. In this work, we explore whether margarine, vegetable shortening, and coconut oil are feasible PCMs, by investigations of their thermal properties and thermal stability. We found that margarine and vegetable shortening are not useful for TES due to their low latent heat of fusion, ΔfusH, and poor thermal stability. In contrast, coconut oil remained thermally stable after 200 melt-freeze cycles, and has a large ΔfusH of 105 ± 11 J g−1, a low degree of supercooling and a transition temperature, Tmpt = 24.5 ± 1.5 °C, that makes it very useful for TES in buildings. We also determined coconut oil’s heat capacity and thermal conductivity as functions of temperature and used the measured properties to evaluate the feasibility of coconut oil for thermal buffering and passive heating of a residential-scale greenhouse.
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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 | 0.001 |
| 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".