Degradation of chlorinated butenes and butadienes in granular iron columns
Bibliographic record
Abstract
Manufacturing facilities for production of chlorobutyl rubber have the potential to release a mixture of at least 5 chlorinated butenes and butadienes including trans-1,4-dichlorobutene-2 (1,4-DCB-2), 3,4-dichlorobutene-1 (3,4-DCB-1), 2,3,4-trichlorobutene-1 (TCB), 2-chlorobutadiene-1,3 (chloroprene) and 2,3-dichlorobutadiene-1,3 (DCBD) into groundwater environment. To evaluate the potential of using granular iron in the remediation of the above contaminants, a series of column experiments were conducted. Degradation of all 5 compounds followed pseudo-first-order kinetics. The three chlorinated butenes degraded much faster (surface area normalized half-lives, t(1/2)', ranged from 1.6 to 5.2 min m2/mL) than the 2 chlorinated butadienes (t(1/2)' ranged from 102 to 197 min m2/mL). All contaminants fully dechlorinated by granular iron to 1,3-butadiene as a common reaction intermediate that then degraded to a mixture of relatively non-harmful end products consisting of 1-butene, cis-2-butene, trans-2-butene and n-butane. Based on the kinetic data, product distributions, and chlorine mass balances, reaction pathways for these compounds are proposed. For the chlorinated butenes, 3,4-DCB-1 and TCB, undergo reductive beta-elimination reactions resulting in 1,3-butadiene and chloroprene intermediates. Dechlorination of 1,4-DCB-2 to 1,3-butadiene occurs through a reductive elimination similar to reductive beta-elimination. For dechlorination of the two chlorinated butadienes, chloroprene and DCBD, dechlorination occurs through a hydrogenolysis pathway. The common non-chlorinated intermediate, 1,3-butadiene, undergoes catalytic hydrogenation resulting in a mixture of butane isomers and n-butane. The results suggest that granular iron is an effective material for treatment of groundwater contaminated with these 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) | 0.000 | 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) | 0.000 | 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".