Sour Gas Adsorption on Silica Gels
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide One of the essential factors for water adsorption on silica gels is the concentration of silanol groups on the silica surface. However, no systematic investigation on the adsorption of sour gas components, methane (CH 4 ), carbon dioxide (CO 2 ), and hydrogen sulfide (H 2 S) on silica gels with different textural properties and surface silanol concentrations, has been conducted. Three silica gels of 22, 30, and 60 Å pore sizes, with silanol concentrations of α total = 2.516, 2.340, and 2.152 OH nm –2, respectively, were studied in this work. The adsorption data for CH 4, CO 2, H 2 S, and H 2 O at T = 0, 25, and 50 °C on the 22 and 30 Å pore size silica gels were presented, and a comparison of the data for the 60 Å pore size silica gel on the same adsorbates was conducted. All three silica gels showed an adsorption affinity in the order of H 2 O > H 2 S > CO 2 > CH 4 . The isosteric heats of adsorption of H 2 O and H 2 S had a greater dependence on the silanol concentration than CO 2 and CH 4 . At p < 10 bar, there was no difference in the adsorption per m 2 of CH 4 between the silica gels ( n ads = 1.7 mmol m –2, for all silicas at p = 10 bar), while higher pressures resulted in greater adsorption capacity in the larger pore volume silica gels (at p = 20 bar: n ads = 3.0, 3.3, and 3.4 mmol m –2 for the 22, 30, and 60 Å pore size silicas, respectively). H 2 S adsorption at low pressures ( p < 4 bar) was larger on the samples with larger silanol concentrations (at p = 3 bar: n ads = 6.1, 4.7, and 4.5 mmol m –2 for the 22, 30, and 60 Å pore size silicas, respectively), but above p = 4 bar, the 60 Å pore size silica had a greater adsorption capacity than the 30 Å pore size (at p = 5 bar: n ads = 8.0, 6.0, and 6.2 mmol m –2 for the 22, 30, and 60 Å pore size silicas, respectively).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.001 |
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 teacher head, 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".