Effect of surfactant concentration on the decomposition rate of alkaline activated persulfate
Bibliographic record
Abstract
Hydrocarbon contamination is among the most frequent sources of soil and water environmental impacts. Many remediation methods have been implemented to clean up the contaminated environment so far. In-Situ Chemical Oxidation has attracted attention as it has shown efficiency in contaminants removal and cost-effectivity. In addition, soil washing by surfactant foam has been recently proven as a promising method. The combination of these two methods can take the advantage of oxidation while eliminating the challenges regarding the poor distribution of treatment fluid in a heterogeneous porous media. The ultimate goal of this study is to use surfactant foam for delivering oxidant (persulfate) through diesel-contaminated soil in permafrost. However, the interaction between the surfactant and the oxidant needs to be studied first. A better understanding of the impact of surfactants and oxidants on each other can lead to an optimized process. At the first stage of this study, different concentrations of surfactant solutions (sodium dodecyl sulfate: cocamidopropyl betaine in a mass ratio of 1:1) were mixed with a constant persulfate concentration activated with alkali, in absence of hydrocarbon. The preliminary results showed that the initial concentration of the oxidant has no significant effect on its decomposition rate. Also, as the concentration of surfactant was increased above the Critical Micellar Concentration (CMC), the persulfate decomposition rate decreased, likely due to the formation of micelles. However, as the micelles started to be destroyed, the decomposition rate of the oxidant increased gradually and the highest rate was observed when the concentration of surfactant was close to the CMC. When no micelle was left in the solution, the decomposition rate of the oxidant waned to a low value. Thus, coupling the surfactant and the oxidant can be effective for the degradation of hydrocarbon contaminants. Micelles bring part of the hydrocarbon into the aqueous phase and then the micelles are destroyed by the oxidant that can also degrade the hydrocarbon effectively over time.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".