Ultrasound induced destruction of emerging contaminants
Bibliographic record
Abstract
There are many reports indicating the presence of emerging contaminants such as: estrogen hormones, 1,4-dioxane and perfluoro-octanoic acids in the natural environment. Estrogen hormones are considered important emerging class of contaminants due to their endocrine disrupting effects. These compounds are invariably found in the environment originating mostly from natural sources. Trace concentrations of estrogen hormones (low µg/L concentrations) have been detected in municipal wastewater treatment plants and observed in receiving water bodies. 1,4-Dioxane (C4H8O2) is used as an organic solvent and solvent stabilizer numerous in chemical processes. The United States Environmental Protection Agency (US-EPA) has recognized 1,4-dioxane as a toxic chemical and a possible human carcinogen. 1,4-dioxane has been detected as a contaminant in the natural environment, drinking water supplies, superfund sites, public groundwater sources in the United States, Canada and Japan at concentrations greater than the permissible standards. Perfluorinated chemicals such as perfluoro-octanoic acid (PFOA) and perfluorooctane-sulfonate (PFOS) have been manufactured for use in a variety of industrial and consumer applications. Due to their environmental persistence, PFOAs have been detected in surface waters at a number of locations at concentrations ranging from pg/L to ng/L. Elevated concentrations of PFOAs have been measured in surface and ground waters near specific point sources. Through this project, successful attempts have been made for the destruction of emerging contaminants using ultrasound. This study deals with the optimization of various process parameters for the destruction of estrogen hormones. The influence of process parameters such as power density, reactor geometry, power intensity, ultrasound amplitude, and external mixing was investigated. Artificial neural network (ANN) approach was used to describe the interactions between optimized parameters. The important findings obtained in the present work for the optimized estrogen degradation can help tackle the challenges of scale up such as operational optimization and energy consumption. The effect of process conditions such as pH and presence of oxidizing agents on the ultrasonic destruction of 1,4-dioxane and PFOA was studied. Acidic conditions favored the destruction of both the compounds. The presence of activated sulfate radicals enhanced the reaction rate kinetics. An innovative technology using electric potential and ultrasound for the removal organic contaminants was developed. The existence of organic contaminants in ionic form under certain process conditions has led to the development of this technology. Applying a low electric potential across the probe enhances the mass transfer of the contaminants into effective reaction zone, thereby enhancing the total destruction. A two-fold increase in the reaction rates was observed. This study shows ultrasound as an efficient and effective treatment technology for the destruction of emerging contaminants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 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".