Controllable Coating Graphene Oxide and Silanes on Cu Particles as Dual Protection for Anticorrosion
Bibliographic record
Abstract
Although two-dimensional nanosheets like graphene could be ideal atomic coatings to prevent corrosion, it is still controversial whether they are actually effective due to the presence of parasitic effects such as galvanic corrosion. Here, we reported a reduced graphene oxide (RGO) coating strategy to protect sintered Cu metal powders from corrosion by addressing the common galvanic corrosion issue of graphene. A layer of silane molecules, namely, (3-aminopropyl)triethoxysilane (APTES), is deposited between the surface of Cu particles and the graphene oxide (GO), acting as a primer to enhance adhesion and as an insulating interlayer to prevent the direct contact of the Cu with conductive RGO, mitigating the galvanic corrosion. Due to this core-shell coating, the RGO uniformly distributes in the Cu matrix after sintering, avoiding aggregation of RGO, which takes place in conventional GO-Cu composites. The dual coating of GO and silane results in bulk samples with improved anticorrosion properties, as demonstrated by galvanostatic polarization tests using Tafel analysis. Our development not only provides an efficient synthesis method to controllably coat GO on the surface of Cu but also suggests an alternative strategy to avoid the galvanic corrosion effect of graphene to improve the anticorrosion performance of metal.
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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 | 0.000 |
| 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) | 0.000 | 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".