Quantitative assessment of dispersion stability and processing parameters in graphene‐enhanced unsaturated polyester resins: Effect of mixing techniques
Bibliographic record
Abstract
Abstract Recent advances in cost‐effective graphene mass production highlight its potential for industrial applications, along with the challenges of achieving and controlling uniform dispersion at a large scale. State‐of‐the‐art approaches such as functionalization, solvent mixing, and sonication are costly for large‐scale use. As for dispersion analysis, microscopy techniques such as scanning electron microscopy and transmission electrical microscopy are still commonly employed, while emerging quantitative methods, including electrical conductivity measurements, micro‐CT, and machine learning, remain time‐intensive and are limited to fully‐cured nanocomposites. This study aims to evaluate three mixing techniques—mechanical mixing, high shear mixing, and probe sonication—for dispersing an industrial mass‐produced graphene powder in an unsaturated polyester resin. Their impact on processing parameters is examined using thermal and rheology analysis. Furthermore, dispersion states throughout the processing window are quantified for the first time. Results indicate that graphene addition inhibits resin curing, delaying gelation and increasing peak temperatures. Mechanical mixing exhibited the lowest dispersion efficiency with dispersion indices of 62.87%, 65.15%, and 66.37% at 0.25, 0.5, and 0.75 Wt.% graphene, respectively. Probe sonication was most effective at lower concentrations (66.65% and 68.20% at 0.25 and 0.5 Wt.%), while high shear mixer excelled at 0.75 Wt.% graphene (69.11%) due to increased viscosity. The dispersion states remained stable during curing, as the resin's higher viscosity restricted nanofiller mobility. Highlights Graphene delays resin curing (5.8%–40%) by radical scavenging and hindrance. Objective method for quantitative graphene dispersion across processing window. Better dispersion obtained using high shear mixer at higher graphene content. High resin viscosity stabilizes graphene dispersion, limits mobility in curing.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".