Enhancing CO<sub>2</sub> Adsorption via Amine-Impregnated Activated Carbon from Oil Sands Coke
Bibliographic record
Abstract
In this work, amine-impregnated activated carbon was prepared from oil sands coke, for use in CO 2 capture. Delayed oil sands coke was activated using microwave heating and KOH as activation agent. The resulting material was then impregnated with one of diethanolamine, methyl diethanolamine, or tetraethylene pentamine. Analysis of the bulk and surface composition of the impregnated samples using elemental analysis and X-ray photoelectron spectroscopy suggested that the amines were deposited on the surface of the activated carbon. Materials impregnated with diethanolamine performed best for CO 2 capture; the highest adsorption capacity achieved was 5.63 mmol CO 2 /g adsorbent for activated carbon impregnated with 1.15 mmol N/g activated carbon—nearly 75% higher than reported values for zeolite 13X. Adsorption of CO 2 on the amine-impregnated activated carbon at 40, 50, 60, and 75 °C showed that the highest adsorption capacity was obtained at 50 °C. Using oil sands delayed coke as a precursor for activated carbon transformed a petroleum waste material into an effective CO 2 adsorbent. Modifying the prepared activated carbon with amines improved CO 2 uptake capacity, creating a useful adsorbent for potential use in CO 2 capture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".