Energy Efficiency in Buildings Through the Application of Phase Change Materials: An In-Depth Analysis of the Integration of Spent Coffee Grounds (SCGs)
Bibliographic record
Abstract
Energy demand in the building sector has drastically increased due to rising occupant comfort requirements, accounting for 30% of the world’s final energy consumption and 26% of global carbon emissions. Thus, to improve building efficiency in heating and cooling applications, phase change material (PCM)-based passive thermal management techniques have been considered due to their energy storage capabilities. This study provides a comprehensive review of the research on PCM applications, types, and encapsulation forms. Various solutions have been proposed to enhance PCM performance. In this review, the authors suggest new methods to improve PCM efficiency by using the multilayered wall technique, which involves employing two layers of a hybrid bio-composite—specifically, the hybrid hemp/wood fiber-reinforced composite with a polypropylene (PP) matrix—along with a layer of PCM made from spent coffee grounds (SCGs). Previous studies have shown that oil extracted from SCGs demonstrates good thermal and chemical stability, as it contains approximately 60–80% fatty acids, with a phase transition temperature of approximately 4.5 ± 0.72 °C and latent heat values of 51.15 ± 1.46 kJ/kg.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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 teacher head, 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".