Transport and Deposition of Colloidal Activated Carbon (CAC) in Saturated Sand Columns: Impacts of input CAC concentration, transient ionic strength, and multiple CAC injections
Bibliographic record
Abstract
Injection of colloidal activated carbon (CAC) into the subsurface is an innovative low-cost technology for remediation of legacy and emerging contaminants. It is typically used as a permeable barrier for removing contaminants via sorption and/or followed by microbial/chemical degradation. In addition, CAC has also been used as a catalyst for oxidative degradation of organic contaminants as well as a carrier for subsurface delivery of nano zerovalent iron. A growing application of CAC in the subsurface is its use for sorption and plume control of per-/polyfluorinated alkyl substances (PFAS). With a growing suite of remediation technologies for PFAS, CAC offers the advantage of not producing unknown and/or toxic intermediates while limiting further spread of PFAS in the subsurface. The performance of CAC largely depends on its ability to transport to and deposit at the desired location in the contaminated aquifer under environmentally relevant groundwater conditions. Two such conditions of utmost interest are: (1) injection of CAC with or without downgradient injection of CaCl₂ which restricts CAC mobility by aggregation and (2) multiple injections of CAC in the event of breakthrough of sorbed contaminants. Under these conditions, variations in CaCl₂ concentrations over time are expected due to its potential post-injection downstream migration as well as potential changes in hydraulic conductivity from repeated CAC injections. Thus, it is critical to understand how these conditions impact retention, release, and remobilization of not only the CAC but also of the sorbed contaminants. Our study has examined the effects of input CAC concentration, transient changes in CaCl₂ concentration, and multiple injections of CAC on its transport and deposition in 1-D saturated sand columns. The breakthrough curves and retention profiles generated for the CAC in this study are primary inputs for 1-D transport models which are necessary for prediction of CAC mobility in groundwater.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".