The Effect of Nano Insulating Materials on the Thermal Performance of Residential Apartments
Bibliographic record
Abstract
The environmental sustainability of residential units is related to various design principles, among which nanotechnology has emerged as a significant contributor to enhancing thermal performance.Despite their high initial cost, these materials promise to elevate the quality of residential performance and thermal comfort over time.There were numerous studies that addressed this subject, but the vast majority of them approached research and evaluation with an investigative and analytical mindset.There aren't many studies that look at the precise computer evaluation of the usage of insulating nanotechnologies in architecture, which shows that there is still a research deficit in the field.The objective of the research was to focus on computational capabilities to estimate the percentage of improvement in the thermal performance of a residential apartment in Mosul using local materials as a baseline case and compare it to expanded polystyrene second, followed by Nano insulation materials third and fourth, respectively.The results obtained show that, in each sample in the prior situations, the required thermal load decreased by rates of 45.8%, 22.8%, and 28.9% respectively, when compared to the base case.This demonstrates how crucial the nanomaterial's insulating properties are to raising thermal efficiency.Further, our findings demonstrated that the application of nanotechnology, particularly nano-vacuum insulation panels, can increase the number of days of thermal comfort in a residential apartment while concurrently reducing the peak heating and cooling requirements.
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 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) | 0.002 | 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".