Engineering investigation for the size effect of graphene oxide derived from graphene nanoplatelets in polyurethane composites
Bibliographic record
Abstract
Abstract In this study, commercial polyurethane (PU) and graphene nanoplatelets (GnP) and their derivative graphene oxide (GO) were used to fabricate PU composites. The size effect of fillers on mechanical properties and anti‐corrosion performance of as‐prepared composites was thoroughly investigated. It was found that GO was more uniformly dispersed in the PU matrix than GnP due to its compatibility with PU. Furthermore, GO led to the higher mechanical properties and anti‐corrosion performance than PU/GnP composites, and the properties were strongly dependant on the size of the GO. Specifically, incorporating large sized GO (GO‐M25) in 0.5 wt% indicated the highest average synergetic tensile modulus up to 53% from the neat PU and the lowest corrosion rate of 0.001 MPY (1 MPY = 0.547 g · m−2 · d−1). This phenomenon was attributed to the fact that the larger size of GO is not only uniformly dispersed within the PU matrix but also enables interaction between PU and GO. Conversely, PU composites incorporated with the small sized GO (GO‐C750) did not show elastic behaviour from 0.1 to 0.5 wt% of the filler. This is due to the fact that the high surface area and hydrophilic functionalities of GO‐C750 resulted in hard‐segment content reduction in PU. This research can help in the design of a PU coating that is physically improved and that has a superior anti‐corrosive capacity, particularly for pipelines in the oil‐sands transportation industry.
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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".