Advanced Nano-biotechnology for Chlorinated Volatile Compound Pollutants Control
Bibliographic record
Abstract
Volatile organic compounds (VOCs) include different organic chemicals that can be easily vaporized and transported long distances via the environment. VOCs and health effects are dependent on the type, concentrations and duration of exposure. Chlorinated volatile compounds (CVOCs) are the most toxic VOCs because of their potential to cause cancer in humans. Many CVOCs are present in significant amounts in our ecosystems, including air, water and soil, and are resistant to degrade, despite the fact that their use has recently been more carefully managed and restricted. These chlorinated compounds are highly toxic and numerous have been banned from commercial utilization because they are persistent in the environment and accumulate in biological systems. Although these chemicals have been banned for decades, they are still being measured in the environment and the food chain. This paper provides a comprehensive review of the recent applications of biotechnology and nanotechnology in CVOCs remediation in various environmental systems. It is divided into many sections; each focuses on specific subtopics, covering diverse perspectives on the principal topic. Sections presented in the paper include; occurrence of CVOCs in the environment, sources, potential human health effects, recent biotechnology and nanotechnology used for CVOCs remediation, advantages and disadvantages of each strategy of treatment and future perspectives in this aspect are also provided. Finally, this paper presents advanced technologies available, to remind CVOCs emissions with their relative merits and demerits, better understand this integrated technology, and to effectively apply them in air, soil, and groundwater remediation. Consequently, we hope that this paper will guide and inspire the application of biotechnology and nanotechnology to the remediation of CVOCs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".