Capture of Gaseous Sulfur Dioxide Using Graphene Oxide Based Composites
Bibliographic record
Abstract
Sulfur dioxide (SO₂), a well-known pollutant emitted from fossil fuel combustion, has major adverse health and environmental impacts. It is harmful at low concentration with a permissible exposure limit of two ppm for the eight-hour time-weighted average (TWA) value. Fortunately, its atmospheric concentration, like other air pollutants, has gradually reduced in Canada in the past years. However, despite the well-established flue gas desulfurization technologies, they have the disadvantages of being energy-intensive, not very efficient to achieve very low concentrations (at ppm level) and they operate at high temperatures. Moreover, emission standards are becoming more stringent. Novel methods are therefore investigated to capture SO₂, such as adsorption processes using zeolites and metal oxides (e.g., Iron (Fe) and Vanadium (V) based) which tend to sustain wide ranges of temperatures and pressures. Graphene oxide (GO) was also shown to physisorb SO₂ at low temperatures. In this work, we propose to metal functionalize GO as a step forward on the path for efficient SO₂ capture, by promoting the SO₂ oxidation reaction into sulfur trioxide (SO₃) for increased capacity due to a possible higher affinity with the surface. The GO has a high surface area, high porosity, and controllable surface chemistry. The aim is to achieve outlet concentration of SO₂ as low as 1 ppm through combined physisorption and reaction promoted that the presence of GO and metal, at low operating temperature. Iron oxide functionalized GO was synthesized using two different techniques: a polyol process (GO-FeₓOᵧ-P) and using a hydrolysis method (GO-FeₓOᵧ-H). The characterization analysis, scanning electron microscopy (SEM) and transmission electron microscopy (TEM), performed on the materials before and after SO₂ reaction show changes on the surface due to metal adding and to the sulfur capture. The breakthrough curves and the capacity calculations of the performed experiments have shown that with the addition of FeₓOᵧ on the surface of GO, the capturing capacity increases by a factor of three to four, indicating a possible change in the capturing mechanism. The evaluation of the temperature effect (from room temperature to 100℃) showed an increasing trend in the capture capacity for SO₂ with an increase in temperature, for both functionalized and non-functionalized GO, indicating it is not driven only by surface adsorption. The presence of sulfur species captured from the gas stream has been confirmed by energy-dispersive X-ray (EDXS) analysis. The future work would be focused on the investigation of the mechanisms and capturing phenomenon and the regeneration step for the materials in order to further improve the capturing capacity and process applicability.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".