Investigation of the size effect of graphene nano-platelets (GnPs) on the anti-corrosion performance of polyurethane/GnP composites
Bibliographic record
Abstract
planetary centrifugal mixer (PCM) and cast on polyethylene terephthalate (PET) and copper substrates. Four different grades of GnP are used to investigate the effect of GnP size on the anti-corrosion performance of the composites. Tafel, Nyquist, and Bode plots are used to quantify and compare the anti-corrosion performance of the composites, and these plots are obtained by electrochemical analysis. In addition to the anti-corrosion performance, mechanical properties and morphologies of the composites are analyzed. Various parameters indicating the anti-corrosion performance illustrate that smaller size of GnP in the composites shows higher anti-corrosion performance on copper substrate. The results show that the smaller size of GnP is not only uniformly dispersed within PU, but also offers a high surface area which helps construct an efficient filler pathway that suppresses the diffusion of a corrosive agent into the polymer matrix. Nevertheless, mechanical properties of the composites are partially improved. Essentially, this study demonstrates that the size of GnP plays a central role in determining the anti-corrosion performance of PU/GnP 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".