The use of used automobile tyres in a partitioning bioreactor for the biodegradation of xenobiotic mixtures
Bibliographic record
Abstract
Waste tyres were utilized as the sorption phase in a two-phase partitioning bioreactor (TPPB) for the biodegradation of a binary mixture of 2,4-dichlorophenol (DCP) and 4-nitrophenol (4NP). These compounds are extensively used in the chemical industry and are found in many industrial effluents. Although both compounds are toxic and are on the EPA list of priority pollutants, a higher inhibitory effect on microorganisms is exerted by DCP, and our experimental tests were focused on strategies to reduce its negative impact on microbial activity. Sorption/desorption tests for the DCP-4NP mixture were first performed to verify the related uptake/release rates by the tyres, which showed that the tyres had a higher capacity for DCP uptake and practically no affinity for 4NP. An acclimatized mixed culture was then utilized in a sequencing batch reactor (SBR) operated in conventional and two-phase mode. For the binary DCP-4NP mixture a significant reduction in DCP toxicity, and a concomitant enhancement in substrate removal efficiency (up to 83%for DCP and approximate 100% for 4NP) were clearly seen for the TPPB operated with 10% and 15% v/v tyres, for influent concentrations up to 180 mg/L, with practically negligible biodegradation in the conventional single phase reactor. The long-term utilization of tyres was confirmed at an influent loading of 180 mg/L with a test performed over 20 work cycles showing an improvement of the removal performance for both compounds.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".