ADVANCEMENTS IN CELLULOSE/REDUCED GRAPHENE OXIDE COMPOSITES: SYNTHESIS, CHARACTERIZATION AND APPLICATIONS IN TRANSISTOR TECHNOLOGIES
Bibliographic record
Abstract
Cellulose and reduced graphene oxide (rGO) composites have garnered significant attention for their potential in transistor applications, combining environmental sustainability with advanced electrical functionalities.This comprehensive review delves into the recent advancements in the synthesis, characterization, and application of cellulose/rGO composites, particularly in the realm of transistors.We explore various synthesis methodologies such as in-situ reduction, chemical grafting, and physical mixing, examining their effects on the composites' structural, chemical, and morphological properties.The review highlights the deployment of these composites in diverse transistor types including field-effect transistors (FETs), organic field-effect transistors (OFETs), and biosensors, emphasizing their design, functionality, and performance enhancements.Furthermore, we discuss strategies for material optimization such as tuning composite ratios, functionalization, and the integration of additional materials to boost electrical conductivity, charge carrier mobility, and sensitivity.The review also addresses the challenges of scalability, reproducibility, and long-term stability of cellulose/rGO composites, proposing future research directions for novel composite formulations, device architectures, and broader applications in flexible and wearable electronics.This analysis not only underscores the unique properties of cellulose/rGO composites but also their transformative potential in developing sustainable, high-performance electronic devices.
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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 | 0.001 |
| 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 | 0.000 |
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".