Effects of <scp> CO <sub>2</sub> </scp> / <scp>NO</scp> / <scp> SO <sub>2</sub> </scp> in flue gas on selenium adsorption on carbonaceous surface
Bibliographic record
Abstract
Abstract The effects of different flue gas compositions (CO 2 , NO, and SO 2 ) on selenium (Se) adsorption mechanism over carbonaceous surface (CS) were explored using density functional theory (DFT). Considering weak interaction in the adsorption process, B3LYP‐D3/6‐31G(d) was employed to conduct geometry optimization and frequency calculations, and B3LYP‐D3/6‐311+G(d, p) was used to obtain more accurate single point energy. Results show that when the Se atom was absorbed on CS, the adsorption energies were −588.86 kJ/mol and −646.56 kJ/mol, respectively. It suggests that the adsorption process between Se atom and CS belongs to chemical adsorption. CO 2 and NO have negative effect on Se adsorption on CS, while SO 2 can promote the adsorption capacity of CS for Se atom. In order to further explain how SO 2 enhances the adsorption capacity of CS for Se, the atomic dipole moment corrected Hirshfeld (ADCH) charges were calculated. Calculation results show that SO 2 enhanced the electronegativity of the active site, contributing to the Se adsorption. Mayer bond order and ADCH charge are reliable tools to analyze adsorption process. Calculation results reveal the influencing mechanism of different flue gas compositions on Se adsorption, which can lay the theoretical basis for the control of Se during coal combustion.
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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) | 0.003 | 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".