Development of Sustainable Nanosorbcats Based Technology for Hydrocarbons and Organic Pollutants Recovery from Industrial Wastewater
Bibliographic record
Abstract
The worldwide shortage of fresh water and the huge competing demands from a variety of users stimulate an urgent need for finding innovative wastewater treatment processes. For instance, oil sand process-affected waters pose a critical energy issue and an environmental alert since these effluents are toxic to many aquatic and non-aquatic living organisms. In addition, some of these pollutants are non-biodegradable and, thus they will exist for a long time in the environment, which may cause a real challenge to the conventional wastewater treatment processes. Accordingly, economically viable and environmentally sound techniques are needed. The application of nanoparticle technology as adsorbents and catalysts (nanosorbcats), whether as a standalone or as an enabling technology, in cleaning up wastewater has recently received great attention. This is because of the unique chemical and physical properties of nanoparticles in comparison with their counterparts, which make them superior to the conventional adsorbent/catalysts. Hence, in the present study, the employment of newly in-house prepared silica-embedded nanosorbcats functionalized with active species of NiO and MgO for cleaning up produced water was investigated. A facile co-precipitation synthesis route was used to prepare those nanosorbcats, which were characterized by different characterization techniques like XRD, BET, HRTEM, CO2-TPD, and IR spectroscopy. The prepared nanosorbcats were then employed for the adsorptive removal of cationic, anionic, and organic acid model molecules. Computational modeling, DFT calculations, and MD simulations of the interaction between the model molecules and the surfaces of prepared nanoparticles were carried out to get more mechanistic insights into their adsorptive behaviors. Eventually, these nanosorbcats were successfully used to treat real SAGD produced waters within an experimental scheme including three processes, namely; oxy-cracking, packed-bed adsorption, and catalytic steam gasification.
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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".