Influence of Flash Graphene on the acoustic, thermal, and mechanical performance of flexible polyurethane foam
Bibliographic record
Abstract
In this study, flexible polyurethane foam (PUF) composites were prepared using three types of Flash Graphene (FG) produced from different feedstock material. The acoustic, thermal, and mechanical properties of foam composites containing 0.025 wt% FG were characterized. It was shown that PUF-1 and PUF-3 had higher sound absorption in the frequency range of 500–2000 Hz compared to neat PUF (baseline). PUF-3 experienced a 47% reduction in thermal expansion coefficient relative to the baseline. The tensile strength and compressive modulus of all composites increased by 16–26% and 33–37% respectively. Compression force deflection and tear strength did not change relative to the baseline. This may be explained by the relatively low flake diameter and aspect ratio of each FG which led to agglomeration and impacted load transfer between the filler and matrix. Overall, the addition of 0.025 wt% FG1 and FG3 improved acoustic, thermal, and tensile properties of PUF without diminishing compression force deflection and tear resistance. PUF reinforced with FG had similar or enhanced properties compared to PUF containing commercially available, exfoliated graphene nanoplatelets (GNP). This supports the use of FG as a relatively sustainable, low-cost alternative to exfoliated GNP or chemical vapor deposition (CVD)-grown graphene in porous polymer composites.
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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".