Naphthalene Sorption to Organic Additives in Cement-Treated Soils
Bibliographic record
Abstract
Abstract Cement-based solidification/stabilization (S/S) is a widely used source-controlled treatment method for contaminated sediments. Increasingly, the technology is being used to remediate sites that contain high molecular weight organic compounds. The low level of organic content in cement-based S/S mixtures often creates a need for organic additives in the mixtures in order to improve the level of sorption in the treatment process. Very little work has been published related to the quantification of the sorption of organic contaminants to cement-based S/S mixtures and the level of improvement afforded by additives such as fly ash and organoclays. The objective of this study is to examine the sorption levels of naphthalene to several cement-treated soil mixtures with and without organic additives (i.e., fly ash and organoclay) using batch testing. It is found that the sorption values of naphthalene vary but appear to be dependent on the amount of organic carbon present in the mixture. In order to assess the potential benefit of this improved sorption for field applications, contaminant migration modeling is performed using the results obtained. It is shown that cement-based S/S remediation systems can provide long-term protection against naphthalene contaminant migration, especially cement-based S/S mixtures with organoclay additives, for the assumptions considered in the modeling.
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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) | 0.001 | 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".