Cation‐assisted adsorption of chlorophenols by nano‐xerogels
Bibliographic record
Abstract
With water quality deteriorating globally, identification of novel adsorbents for removal of chloroaromatic compounds for wastewater treatment is of growing interest. Classic xerogels produced by drying sol‐gels of tetraalkoxy silanes, such as tetramethoxy orthosilane (TMOS) or tetraethoxy orthosilane (TEOS), carry a negative surface charge in water when the pH is higher than the Point of Zero Charge (PZC). This feature was employed in this study to load divalent metal cations on the surfaces of these materials to facilitate chlorophenol (CP) adsorption. Although classic xerogels do not show a high affinity for the adsorption of chlorophenols such as 2‐chlorophenol (2CP) in aqueous media, TEOS xerogels loaded with the cations Ni2+, Cd2+, and Zn2+ showed 225, 249, and 306 % increases, respectively, in the adsorption of 2CP compared to the unloaded TEOS xerogel. Adsorption of the divalent cations on the xerogels exhibited the order of Zn2+ > Cd2+ > Ni2+, with maximum adsorption capacities (Q°) of 15.9 and 13 mg/g for Zn2+ on TEOS and TMOS xerogels, respectively. Using TEOS xerogels loaded with Zn2+ (TEOS‐Zn), adsorption of 2CP, 2,4‐dichlorophenol (DCP), and 2,4,6‐trichlorophenol (TCP) in aqueous media was studied. The adsorption was found to be spontaneous and obeyed pseudo‐second order kinetics, suggesting a chemisorption mechanism between CP and TEOS‐Zn. The equilibrium isothermal adsorption data were most closely fitted with a Langmuir equation resulting in Q° values of 13.9, 9.17, and 8.5 mg/g for 2CP, DCP, and TCP, respectively.
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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".