Carbon nanotubes from renewable feedstocks: A move toward sustainable nanofabrication
Bibliographic record
Abstract
ABSTRACT A recent development in the manufacturing of carbon nanotubes is the usage of renewable feedstocks as a carbon source. This new development is receiving much support and is a source of excitement among the global research communities due to the positive environmental impacts, reduced carbon footprints, and economic benefits. Various types of renewable feedstocks such as vegetable oils, plant derivatives, and other types of biomasses have been used for the green synthesis of carbon nanotubes by employing conventional fabrication techniques. As the global demand increases for green manufacturing, efforts to synthesize carbon nanotubes from renewable resources are receiving immense attention while also strengthening the concept of biorefinery. This also enables the efficient use of resources as well as improved waste management. The present review summarizes the recent developments and current status of the synthesis of carbon nanotubes using renewable feedstocks along with technical discussions, opportunities for novel precursors, and future directions. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci.2017,134, 44255.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".