Preparation and Application of Polymer Grafted Nanopyroxene for the Removal of Naphthenic Acids from Wastewater
Bibliographic record
Abstract
Pyroxene nanoparticles were prepared and grafted with an environmentally friendly monomer for the removal of naphthenic acids from oil sand process-affected water (OSPW) which contributes to its toxicity. Grafting was utilized to achieve high affinity towards the removal of naphthenic acids (NA) in the OSPW. The prepared grafted nanopyroxenes were fully characterized using FTIR, TGA, BET, XRD, HRTEM and AFM to study and confirm their textural surface properties and morphology. Computational modeling was conducted to provide deep insight on the grafting technique as well as the NA removal mechanism. An OSPW sample was characterized using FTIR, NMR, TGA, XRD, SimDist and GC-MS to understand the nature of the contaminants in the wastewater. This was followed by the preparation of a synthetic wastewater solution by dissolving two model molecules and commercial NA in water. Macroscopic batch adsorption experiments were conducted to carefully study the removal mechanism analyzed using GC-MS and compare it with the computational modeling study. The prepared grafted nanopyroxenes were found to interact with the contaminants in the water removing essentially all the two model molecules and about 50% of the commercial NA. The size, shape and structure of the contaminant played a key role in the interaction. The bigger molecules were found to have a stronger interaction with the grafted nanopyroxenes over the smaller ones. The present work holds great promise for the OSPW remediation and the thesis falls within our efforts to reduce the environmental impact of the oil and gas industry in Alberta.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".