The effect of adsorbent textural and functional properties on model naphthenic acid adsorption
Bibliographic record
Abstract
Naphthenic acids, NAs, are a major contaminant of concern and a focus of much research around remediation of oil sand process affected waters, OSPW. Using activated carbon adsorbents are an attractive option given their low cost of fabrication and implementation. A deeper evaluation of the effect NA structural differences have on uptake affinity is warranted. Here we provide an in-depth exploration of NA adsorption including many more model NA species than have been assessed previously with evaluation of adsorption kinetics and isotherms at the relevant alkaline pH of OSPW using several different carbon adsorbents with pH buffering to simulate the behaviour of real OSPW. Uptake for the NA varied considerably regardless of the activated carbon used, ranging from 350 mg/g to near zero highlighting recalcitrant NAs. The equilibrium data was explored to identify structural features of these species and key physiochemical properties that influence adsorption. We found that certain NA will be resistant to adsorption when hydrophobic adsorbents are used. Adsorption isotherm modelling helped explore interactions occurring at the interface between NA and adsorbent surfaces. We identified the importance of NA hydrophobicity for activated carbon uptake. Evidence is also presented that indicates favorable hydrogen bonding between certain NA and surface site hydroxyl groups, demonstrating the importance of adsorbent surface functionality for NA uptake. This research highlights the challenges associated with removing NAs from OSPW through adsorption and also identifies how adsorbent surface chemistry modification can be used to increase the removal efficiency of recalcitrant NA species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".