Mesoporous carbon xerogel material for the adsorption of model naphthenic acids: structure effect and kinetics modelling
Bibliographic record
Abstract
The study examined the preparation, characterization and the use of carbon xerogel (CX) material for the adsorption of three model naphthenic acids (NAs); such as, heptanoic acid (HPA), 5-cyclohexanepentanoic acid (CHPA), and 5-phenylvaleric acid (PVA). CX was synthesized by sol–gel method from resorcinol and formaldehyde. The characterization results showed that CX was a mesoporous material with large surface area (573 m2/g) and high pore volume (1.55 cm3/g), which was mainly composed of carbon (93.20%) and oxygen (6.71%). Adsorption studies revealed that PVA, the NA having an aromatic ring was adsorbed more easily by CX (87 mg/g) due to π–π interactions, followed by HPA (65 mg/g) and CHPA (61 mg/g). In addition, by studying the effect of solution pH, the result confirmed that repulsion greatly hindered the adsorption of HPA onto CX at pHs above that of the pHPZC and at lower pHs attractive electrostatic forces promoted adsorption. Adsorption kinetics fitted the pseudo-first-order model, which suggested that physisorption was most likely the means of adsorption. For the intraparticle diffusion model, the rate of film diffusion was higher than the rate of pore diffusion for each model compound regardless of their structure. Accordingly, this confirmed that pore diffusion was the rate-limiting step, although film diffusion still maintained a significant role in the rate of diffusion. In general, CX exhibited excellent adsorption performance due to its highly mesoporous character so it could be used as a passive treatment method in tailing ponds for removal of organic matters.
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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".