Biotechnology as an alternative for carbon disulfide treatment in air pollution control
Bibliographic record
Abstract
Biotechnology has emerged as an affordable, effective, and eco-friendly alternative to treat carbon disulfide (CS2) containing waste gases. Carbon disulfide is a chemical of widespread use both in the past and in the present. Its industry demand has dramatically changed over the last two decades and is expected to grow in future in step with those industries involving fibre, mining, rubber products, and the agro-chemical sector. This compound, classified as a hazardous air pollutant about 20 years ago, has been re-appraised, as increasingly restrictive pollution standards are expected to be introduced. An array of physical–chemical technologies for treating CS2 containing air are already in use, but they have a series of drawbacks, such as high energy consumption (incineration, thermal oxidation), the immobilization of the contaminant solely from one phase to another (adsorption), and the generation of secondary by-products that require additional treatment (hydrolysis). Thus, technological research on affordable processing alternatives is of considerable interest for related industries using or producing CS2. The biological technologies whose efficiency is based on the activity of microorganisms to biodegrade air pollutants overcome many of the disadvantages of conventional techniques and are especially useful for the removal of relatively low concentrations of pollutants. In this paper, the main properties, sources, and uses of CS2 are summarized, and alternative biotreatments for CS2 are reviewed. Several applications of the technical and economical feasibility of biofilters and biotrickling filters are shown, although their widespread industrial application still requires further research.
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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 | 0.001 | 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) | 0.002 | 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".