Thermal characterization of different graphite polystyrene
Bibliographic record
Abstract
The development of high performance insulating materials incorporating nanotechnologies has enabled considerable decrease in the effective thermal conductivity. Besides the use of conventional insulating materials, such as mineral fibers, the adoption of new nano-technological materials such as aerogel, vacuum insulation panels, graphite expanded polystyrene, is growing. In order to reduce the thermal conductivity of polystyrene insulation materials, during the manufacturing, nano/micro-sized graphite particles are added to the melt of the polystyrene grains. The mixing of graphite flakes into the polystyrene mould further reduces the lambda value, since graphite parts significantly reflect the radiant part of the thermal energy. In this study, laboratory tests carried out on graphite insulation materials are presented. Firstly, thermal conductivity results are described, and then sorption kinetic curves at high moisture content levels are shown. The moisture up-taking behaviour of the materials was investigated with a climatic chamber where the relative humidity was 90% at 293 K temperature. Finally, calorific values of the samples are presented after combusting in a bomb calorimeter.
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 | 0.000 |
| 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) | 0.000 | 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".