Flash Graphene Reinforced, Flexible Polyurethane Foam: Synthesis and Characterization
Bibliographic record
Abstract
Graphene nanoplatelets (GNP) are currently mass-produced by mechanical exfoliation of graphite, however, Flash Joule Heating (FJH) is a relatively sustainable, cost effective, and efficient alternative. In this work, the reinforcing abilities of industrially produced Flash Graphene (FG) in flexible polyurethane foam (FPUF) are evaluated. Three types of FG with different structure and morphology are characterized and used to produce FPUF composites. A composite containing commercial, exfoliated GNP was also prepared for comparison. It was shown that 0.025 wt.% FG improved the thermal expansion coefficient of FPUF by 47%. FG composites also demonstrated higher sound absorption at low frequency relative to neat FPUF. The tensile strength and compressive modulus of all composites also increased by 16-26% and 33-37% respectively. Furthermore, FPUF reinforced with FG showed similar or enhanced properties compared to the GNP composite. Overall, FG improved thermal and acoustic performance of FPUF without deteriorating the mechanical properties. This supports the use of FG as a sustainable, low-cost alternative to exfoliated or CVD-grown GNP in porous polymer composites.
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) | 0.001 | 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".