Monitoring the Remediation of Organic Contaminants by Colloidal Activated Carbon: a Spectral Induced Polarization Study
Bibliographic record
Abstract
Summary Colloidal activated carbon (CAC) filters are becoming a popular option to remediate groundwater contaminated by DNAPLs and a real-time monitoring approach is desirable to assess its effectiveness. In this study, the spectral induced polarization (SIP) technique is evaluated for its applicability as a monitoring tool for DNAPL adsorption within CAC-filters. The adsorption of low-concentration (50 mg/L) tetrachloroethylene (PCE) in a CAC-filter was examined using a set of dynamic column experiments combined with SIP monitoring. The initial flushing of CAC into inert porous media was tracked by SIP monitoring, with an increase in both real and imaginary components of the complex conductivity. The CAC was then flushed out of the column via groundwater, leaving behind only carbon particles that will later adsorb the PCE. The process of flushing the CAC by groundwater indicated a decrease in both the SIP real and imaginary conductivities, with the imaginary holding a small amount of polarizability, which is likely associated with the remaining carbon particles. Finally, dissolved phase PCE was injected through the column, with insignificant changes in the simultaneous SIP response. This study suggests that SIP can monitor CAC within porous media but is insensitive to the low concentrations of dissolved phase PCE.
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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) | 0.000 | 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".