Performance of 1,8-Diazabicyclo[5.4.0]undec-7-ene-Modified SBA-15 for Selective Adsorption of CO<sub>2</sub>
Bibliographic record
Abstract
In this work, SBA-15-(DBU- X ) was prepared by dipping 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU) into the mesoporous channels of SBA-15 in an attempt to improve the CO 2 adsorption. The synthesized SBA-15-(DBU- X ) was characterized with scanning electron microscopy, thermogravimetric analysis, Brunauer–Emmett–Teller adsorption analysis, and other methods. Then the CO 2 adsorption/desorption properties were investigated using a self-designed fixed-bed adsorption system. The results indicated that DBU could be loaded on the SBA-15 mesoporous molecular sieves effectively and that SBA-15-(DBU- X ) had good adsorption capacity toward CO 2 . SBA-15-(DBU- X ) had good thermal stability at temperatures below 140 °C. At room temperature, with increasing loading of DBU and total flow rate of simulated flue gas, the adsorption of CO 2 on SBA-15-(DBU- X ) had an increasing trend followed by a decrease. When the volume fraction ratio of CO 2 and H 2 O was 1:1 in the simulated flue gas, SBA-15-(DBU- X ) had an optimum capacity of CO 2 adsorption. In addition, after six adsorption/desorption cycles, the adsorption capacity of CO 2 was stable. Thus, SBA-15-(DBU- X ) could be recycled.
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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) | 0.001 | 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 | 0.000 |
| 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".