Carbon injection to support in‐situ smoldering remediation
Bibliographic record
Abstract
Abstract Per‐ and polyfluoroalkyl substances (PFAS) are a group of anthropogenic contaminants that are receiving increasing concern due to their associated negative health effects. The properties of PFAS result in their persistence and stability, which present challenges for remediation. Activated carbon is currently the most widely used method for PFAS treatment since carbon microparticle injection can be used for in‐situ treatment; however, this method does not result in PFAS destruction. Thermal treatment is a promising posttreatment method that can be used with activated carbon as long as sufficient PFAS‐destroying temperatures are achieved (>900°C). A promising in‐situ thermal treatment technology is Self‐Sustaining Treatment for Active Remediation (STAR), which uses smoldering combustion to destroy organic contaminants embedded within a porous matrix. This study investigates carbon injection to support STAR for the treatment of PFAS. Four solutions were used (1) 17% colloidal activated carbon (CAC); (2) 23% CAC; (3) 17% powdered activated carbon (PAC); and, (4) 23% PAC. Smoldering temperatures greater than the required PFAS destruction temperature were reached if 50 g carbon/kg sand was achieved for injection and soil‐mixing delivery methods. Moreover, emulsified vegetable oil (EVO) was a successful secondary surrogate fuel to enhance smoldering temperatures when supplied at a quantity less than or equal to carbon microparticles. These findings present the necessary intermediate laboratory work to evaluate methods that will achieve PFAS treatment through STAR when applied in the field.
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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".