A cycling study for reliability, chemical stability and thermal durability of polyethylene glycols of molecular weight 2000 and 10000 as organic latent heat thermal energy storage materials
Bibliographic record
Abstract
In this experimental study, thermal aging test for investigating the variations in thermal and chemical properties of two selected organic phase change materials (PCMs) has been carried out. Polyethylene glycols (PEGs) of molecular weight of 2000 and 10 000 have been considered as potential latent heat thermal energy storage (LHTES) materials for investigation. The aim of this study is to identify the probable variations in thermal properties and chemical structures of PEGs after melting/freezing cycles repeated for 1500 times. The differential scanning calorimetry (DSC) technique was adopted for measuring the thermal properties such as melting point and latent heat of PCMs. In order to study the variations in PCM's chemical functional groups, Fourier transform infrared (FT-IR) spectroscopy was used. Thermal durability property after the cycling test was investigated using thermogravimetric analysis. The DSC measurements indicated that PEG2000 experienced a maximum variation of about 4°C in melting point while the latent heat of fusion reduces by 16% after the cycle test. PEG10000 shows a variation of about 0.3°C in melting point and 15% of the latent heat of melting after the entire cycle test. The spectral investigation confirms that there are no variations in the functional groups of selected materials after cycle test, which can be inferred as chemical stability of them. The thermal gravimetric analysis curves show that both PEGs are thermally quite durable as well after such large number of melt/freeze cycles. Thermal cycling results and economic analysis confirm that both PEGs are potential PCMs for passive solar thermal management purposes. Highlights Synthesis of polyethylene glycol-based binary eutectic mixtures. Accelerated thermal cycle test of eutectic mixtures. Binary mixtures were found chemically stable. Charging/discharging rates of PEG are controlled. Novelty Statement This paper presents the thermal performance of two polyethylene glycols, viz., PEG2000 and PEG10000. These materials were melted and solidified 1500 times for testing their suitability as thermal energy storage materials, and it was found that these materials are stable enough to store solar energy for five years. They found to be suitable for many solar 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) | 0.001 | 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".