An innovative method on synthesis of carbon‐zeolite porous composite from tailings solvent recovery units and its utilization in the <scp> CO <sub>2</sub> </scp> adsorption process
Bibliographic record
Abstract
Abstract Millions of metric tonnes of tailings solvent recovery units (TSRU) stream are produced annually by the oil sands mining industries and stored in the tailing ponds. This study was focused on implementation of a physiochemical methodology for the conversion of TSRU into the activated carbon‐zeolite composite materials (ACZ). ACZ composites were fabricated via physiochemical activation approach using CO 2 stream along with the use of NaOH and KOH in different ratios. The activation step was followed by the hydrothermal treatment for the crystallization of zeolite. All the composites have been characterized by X‐ray diffraction (XRD)—X‐ray fluorescence (XRF), N 2 adsorption–desorption, Barrett–Joyner–Halenda (BJH) desorption pore size distributions, and scanning electron microscopy (SEM) techniques. CO 2 isotherm is also generated for all the composites and CO 2 capture capacity is also tested for multiple cycles for all the composites using TGA at 1 atm pressure and 30°C. It was observed that the composite synthesized using a higher ratio of KOH and NaOH showed more porosity. It was also recorded that ACZ‐Na‐KOH‐1 had more CO 2 adsorption capacity as compared to other composites. Results indicated that all the ACZ composites were stable for multiple CO 2 adsorption cycles.
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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".