Recent Advancements in Graphene Derivative-Based Nanocomposites: Innovations in Coating and Sensing Technologies
Bibliographic record
Abstract
Graphene derivative-based nanocomposites have emerged as innovative solutions to address challenges in corrosion, marine biofouling, and environmental contamination. This review highlights recent advancements in three key areas: (1) dual-barrier and self-healing anti-corrosion materials, (2) eco-friendly anti-biofouling coatings, and (3) high-efficiency electrocatalytic films for electrochemical sensing. We emphasize the critical roles of graphene (Gr) sheets, graphene oxide (GO), and reduced graphene oxide (rGO) in enhancing nanocomposite performance through novel modifications with inorganic materials, organic polymers, and biomolecules. Key insights into advanced modification techniques and their impact on functionality and durability are presented. The review also explores graphene-enabled electrochemical sensors that showed high sensitivity to phenolic compounds in water. Mechanisms accounting for the improved performance of these materials are discussed, along with associated challenges such as scalability, cost-effectiveness, and stability. Future directions are suggested, focusing on sustainable, intelligent coatings and thin-film devices for environmental applications. This work aims to guide researchers, industry professionals, and policymakers in leveraging graphene-based technologies to tackle global issues in corrosion prevention, marine ecology, and environmental monitoring.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".