Anaerobic Biodegradation of Trichloroethylene Sorbed by a Surrogate Soil Organic Matter
Bibliographic record
Abstract
Abstract The effects of aging trichloroethylene (TCE) with a surrogate soil organic matter (SSOM) on its anaerobic biodegradation and desorption characteristics were studied. Surrogate soil organic matter comprised of composted sphagnum moss was used as a model sorbent, and Desulfomonile tiedjei was used as a model member of an anaerobic consortium capable of reductive dechlorination. After TCE was aged with the sterile SSOM for periods of 2, 14, and 30 d, the completely mixed batch reactors were inoculated with D. tiedjei cells and incubated for 5 d. The mass of TCE dechlorinated to cis ‐1,2‐dichloroethylene (DCE) by D. tiedjei in this period decreased by 72% from 2 to 30 d of aging. Although there was an increase in the equilibrium desorption coefficient ( K d ) with aging, sorption was completely reversible. Hence, the reduced availability of TCE for degradation was not coupled with irreversible sorption. To examine the longer‐term fate of TCE, the unaged TCE‐SSOM slurries were inoculated with D. tiedjei and then incubated for longer times (15, 24, and 29 d vs. 5 d). The conversion of TCE to cis ‐1,2‐DCE was up to eight times greater than what was observed in control experiments without SSOM. Therefore, although aging resulted in reduced TCE biodegradation for short‐term incubation (5 d), longer‐term incubation (29 d) with the unaged SSOM resulted in extensive TCE dechlorination (up to 40%) to cis ‐1,2‐DCE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.077 | 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; both teacher heads agree on what is shown here.
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".