Treatment of recycled cigarette butts (man-made pollutants) to prepare electrically conducting material
Bibliographic record
Abstract
Department of Chemical Engineering, Calcutta Institute of Technology, West Bengal University of Technology, Uluberia, Howrah-711 316, West Bengal, India Department of Chemistry, West Bengal State University, Barasat, Kolkata-700 126, India E-mail : mukutchem@yahoo.co.in Department of Polymer Science and Technology, University of Calcutta, 92, Acharya Prafulla Chandra Road, Kolkata-700 009, India E-mail : dipankar.chattopadhyay@gmail.com Department of Chemical and Biological Engineering, Industrial Membrane Research Institute, University of Ottawa, 161 Louis Pasteur St., Ottawa, ON, KIN 6N5, Canada Department of Electronic Science, University of Calcutta, 92, Acharya Prafulla Chandra Road, Kolkata-700 009, India Manuscript received 28 March 2017, accepted 13 April 2017 Recycling of waste is a major thrust area for decreasing environmental pollution. Disposed cigarette butts, a common waste material in our livelihood consisting of the cigarette filters, are toxic to aquatic life and degrade soil porosity. We report a simple process to prepare a conducting material by heat treatment of these used cigarette filters, which are composed largely of cellulose acetate. As they are non-biodegradable they therefore pose a serious threat to the environment after disposal. In keeping with the concept of recycled renewable resources, using the process of heat treatment (in Muffle furnane), we have prepared an active material from used cigarette filters. The pyrolyzed product is characterized by X-ray diffraction, Fourier transform infrared spectroscopy, Raman spectroscopic analysis, field emission scanning electron microscopy, ultra-violet visible spectroscopy, dynamic light scattering and zeta potential measurements. The electrical characteristics of the product have been measured to check its electrochemical behavior and results are found to be promising for further application as electronic materials.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".