Sorption of Agrochemical Model Compounds by Sorbent Materials Containing β-cyclodextrin
Bibliographic record
Abstract
Polymeric sorbent materials that incorporate beta-cyclodextrin (CD) have been prepared and their sorption behavior toward two model agrochemical contaminant compounds, p-nitrophenol (PNP) and methyl chloride examined. The sorption of PNP was studied in aqueous solution using ultraviolet-visible (UV-Vis) spectroscopy, whereas the sorption of methyl chloride from the gas phase was studied using a Langmuir adsorption method. The sorption results for PNP in solution were compared between granular activated carbon (GAC), modified GAC, CD copolymers, and CD-based mesoporous silica hybrid materials. Nitrogen porosimetry at 77 K was used to estimate the surface area and pore structure properties of the sorbent materials. The sorbents displayed variable surface areas as follows: copolymers (36.2-157 m(2)/g), CD-silica materials (307-906 m(2)/g), surface modified GAC (657 m(2)/g), and granular activated carbon (approximately 10(3) m(2)/g). The sorption capacities for PNP and methyl chloride with the different sorbents are listed in descending order as follows: GAC > copolymers > surface modified GAC > CD-silica hybrid materials. In general, the differences in the sorption properties of the sorbents were related to the following: (i) surface area of the sorbent, (ii) CD content and accessibility, (iii) and the chemical nature of the sorbent material.
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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".