Carbon injection to support in‐situ smoldering remediation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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 it