Cyclodextrins for Desorption and Solubilization of 2,4,6-Trinitrotoluene and Its Metabolites from Soil
Bibliographic record
Abstract
Heptakis-2,6-di-O-methyl-β-cyclodextrin (DMβCD) and hydroxypropyl-β-cyclodextrin (HPβCD) (1% w/w solutions) were investigated for their ability to desorb 2,4,6-trinitrotoluene (TNT), 4-amino-2,6-dinitrotoluene (4-ADNT), and 2,4-diamino-4-nitrotoluene (2,4-DANT) from two artificially contaminated soils (an organic rich topsoil and an illite shale). The DMβCD (which is highly surface active) was more effective than HPβCD (negligible surface activity) for desorption of the three nitroaromatic compounds (NACs) from the two soils. The efficiency of both CDs for NAC removal from topsoil decreased with increasing amino substitution (i.e. TNT > 4-ADNT > 2,4-DANT), whereas for illite the efficiency generally decreased with increasing nitro substitution (i.e. TNT < 4-ADNT. 2,4-DANT). In general, the NAC removal efficiency increased with decreases in the sorption capacity constant ( K d s ). The two CDs were also assessed for their ability to remove TNT from a highly contaminated topsoil (5265 mg/kg) obtained from a former manufacturing facility that had been aged for 10−40 yr. The estimated association constants ( K s ) were comparable to those obtained for solubilization of pure TNT into aqueous solution. This study demonstrates the effectiveness of CDs in decontaminating a variety of soils containing both high and low levels of TNT and some of its associated metabolites.
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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".