Chemo‐rheological and quantitative dispersion analysis of <scp>mass‐produced graphene‐unsaturated</scp> polyester based nanocomposites
Bibliographic record
Abstract
Abstract Recent advancements in mass production of graphene powders utilize less energy‐intensive methods and milder chemicals compared to traditional lab‐scale techniques. This can influence the properties of the resulting graphene particles. This study investigates the effect of mass‐produced graphene powder on the curing and rheological properties of a resin transfer molding (RTM) grade unsaturated polyester resin. An objective dispersion quantification method was established to track the dispersion state of the nanocomposite throughout the curing process. The findings reveal that the graphene powder accelerated the curing evidenced by a shift in the peak temperature and gel point towards lower values. The sample containing 1 wt.% graphene exhibited remarkable dispersion stability with only 7.1% decrease by gelation. The resin matrix's low viscosity enhanced graphene particles mobility, while its fast‐curing nature allowed less time for agglomeration. Highlights Characterization of unsaturated polyester nanocomposites modified with an industrial‐grade graphene powder Real‐time in‐situ monitoring of graphene's dispersion state Quantification dispersion analysis for an objective dispersion assessment
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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".