Upcycling of Plastic Wastes and Biomass for Sustainable Graphitic Carbon Production: A Critical Review
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Upcycling of waste plastics diverts plastics from landfill, which helps in reducing greenhouse gas emissions. Graphitic carbon is an interesting material with a wide range of applications in electronics, energy storage, fuel cells, and even as advanced fillers for polymer composites. It is a very strong and highly conductive material consisting of weakly bound graphene layers arranged in a hexagonal structure. There are different ways of synthesizing graphitic carbons, of which the co-pyrolysis of biomass and plastic wastes is a promising approach for large-scale production. Highly graphitized carbon with surface areas in the range of 201 m 2 /g was produced from the co-pyrolysis of polyethylene and pinewood at 600 °C. Similarly, porous carbon having a superior discharge capacity (290 mAh/g) was developed from the co-pyrolysis of sugar cane and plastic polymers with catalysts. The addition of plastic wastes including polyethylene and high-density polyethylene to the pyrolysis of biomass tends to increase the surface area and improve the discharge capacity of the produced graphitic carbons. Likewise, temperature plays an important role in enhancing the carbon content and thereby the quality of the graphitic carbon during the co-pyrolysis process. The application of metal catalysts can reduce the graphitization temperature while at the same time improve the quality of the graphitic carbon by increasing the carbon contents. This work reports some typical graphitic carbon preparation methods from the co-pyrolysis of biomass and plastic wastes for the first time including thermochemical methods, exfoliation methods, template-based production methods, and salt-based methods. The factors affecting the graphitic char quality during the conversion processes are reviewed critically. Moreover, the current state-of-the-art characterization technologies such as Raman, scanning electron microscopy, high-resolution transmission electron microscopy, and X-ray photoelectron spectroscopy are discussed in detail, and finally, an overview on the applications, scalability, and future trends of graphitic-like carbons is highlighted.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".