Investigating the Energy Intake/Discharge Behavior of Xylitol-Based PCM with Polymer as Thermal Storage Material
Bibliographic record
Abstract
Improving xylitol (XYL) phase change behavior is essential to maximize the energy rating in positive-coefficient thermal energy storage (PC-TES).The XYL requires activation to release the stored latent heat during solidification, which necessitates some modifications to the TES unit.This work solves the issue by introducing binder material from polymer to improve the operability aspect of XYL as TES material.Various highdensity (HD) polyethylene ratios (5%, 10%, 15% and 20%) are prepared using direct mixture method to form XYL-HD mixture (XYH).Thermal properties assessment indicates that the HD content has a substantial effect on the melt behavior, particularly for ratios of 15% and 20%, resulting in a two-step melt process between 99.8/110.1℃and 98.3/113.9℃.The microscope profile indicates that the XYL and HD are physically mixed.It confirms the FT-IR profile for the mixture, which has similar chemical identity as the base material.All synthesized XYH systems exhibit a peak during the cooling process, indicating that the system can solidify within the range of 104.7-116.9℃.It contributes significantly to the improvement of energy intake/discharge behavior.The XYH system able to achieve maximum charge and discharge efficiency around 84.6% and 74.1%, which is higher than XYL (53.9% and 42.6%).Thus, using HD contributes positively to initiating crystallization and eventually improves the operability level of XYL for PC-TES system.
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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".