Recent developments on graphene oxide and its composite materials: From fundamentals to applications in biodiesel synthesis, adsorption, photocatalysis, supercapacitors, sensors and antimicrobial activity
Bibliographic record
Abstract
Graphene oxide (GO), a 2-dimensional (2-D) monolayer of graphite, has drawn a lot of importance due to its accessibility and material compatibility. It can be produced by mechanically stirring or sonicating graphite oxide to exfoliate it into layered sheets. Due to high stability, 2-D planar structure, huge surface area, easy chemical adaptation via its functionalities, efficient surface loading with many biomolecules, and optical, electrical, and mechanical capabilities, GO and its derivatives are quickly emerging as the most intensely studied carbon-based materials in a number of fields. In this review, we provided a thorough overview of GO, synthesis of pristine and GO-based composites as well as their applications in the field of biodiesel synthesis, adsorption, photodegradation, electrochemical applications along with biological significance and their antimicrobial efficiency. Owing to their distinctive features, GO has garnered interest in the realm of catalyst. The synthesis of biodiesel via the process of transesterification using GO-based supported catalyst is highlighted herein. Similarly, the adsorption and photodegradation of several organic dyes and effluents are also discussed in this article. Finally, the electrochemical and biological importance of GO and its derived materials is discussed in relation to attractive materials trends by highlighting its future scope and commercial implications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".