Optimizing Reductive Degradation of PAHs Using Anhydrous Ethanol with Magnesium Catalyzed by Glacial Acetic Acid
Bibliographic record
Abstract
Abstract Targeted degradation of individual polycyclic aromatic hydrocarbon (PAH) constituents like anthracene, may offer cost effective and efficient cleaning of coal tar-contaminated sites. Thus, a reductive degradation procedure of anthracene using activated magnesium with anhydrous ethanol at room temperature was developed and optimized. To determine the optimum conditions for anthracene, such as effective magnesium concentrations, glacial acetic acid volumes, and exposure time for the anthracene reduction, the experiments were designed using the response surface methodology based on the central composite design. The design also minimized the number of experiments. The main product from anthracene reduction is 9,10-dihyrdoanthracene. Optimum conditions for 98% degradation capacity of anthracene (2.80 × 10–3 mmol) were 30 mg of Mg powder (1.20 mmol), 60 μL of glacial acetic acid (1.05 mmol), and 30 min exposure time. When the optimized method was tested on the coal tar specimen, twice as many reagents (i.e., Mg and glacial acetic acid) were required to obtain a 90% degradation of anthracene and fluoranthene from the coal tar. This method of using activated Mg and anhydrous ethanol selectively reduces PAHs in coal tar; in particular anthracene and fluoranthene are most efficiently removed.
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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) | 0.000 | 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".