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 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 | 0.001 | 0.001 |
| 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".