Biodegradation of α-Pinene in Model Biofilms in Biofilters
Bibliographic record
Abstract
Treatment of air pollutants in a biofilter requires that the compound be effectively transported from the gas phase to the organisms that reside in a biofilm that forms upon a packing material. Models of biofiltration generally treat the biofilm like water by using a Henry's law constant to predict mass transfer rates into the biofilm where degradation occurs and, hence, predict low rates for hydrophobic compounds. However, some compounds that are virtually insoluble in water are also treated unusually well. The objective of this study was to develop a fundamental understanding of the apparent enhanced degradation of hydrophobic pollutants in biofilms. Specifically, the goals of this study were to experimentally determine transport and reaction rates of hydrophobic pollutants in artificial biofilms. We studied the transport and reaction rates of alpha-pinene (as a model hydrophobic pollutant) in a headspace in contact with a well-defined biofilm made up of biomass immobilized in low melting point agarose and found that reaction rates were similar in order of magnitude to biofilter rates. The transport rates through these films once deactivated were found to be the same as through agar (diffusion coefficient between 2.6 and 3.4 x 10(-6) cm2/s). The degradation rates through model biofilms ranged from 2 to 4 x 10(-7) (g/(cm2 min)). A new explanation of high degradation rates was put forth whereby a biologically mediated transformation is taking place in which alpha-pinene is oxidized into a more soluble, less volatile compound that can then penetrate deeper into the biofilm. The formation of this more soluble byproduct was confirmed with batch kinetics experiments using filtered samples, and its proposed identity is cis-2,8-p-menthadien-1-ol, a menthadienol, a novel metabolite of alpha-pinene degradation. A simple conceptual model based on these results is also presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".