Adsorption of C<sub>7</sub> Hydrocarbons on Biporous SBA-15 Mesoporous Silica
Classification
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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In our recent studies (Vinh-Thang, H.; Huang, Q.; Eic, M.; Trong-On, D.; Kaliaguine, S. Langmuir 2005, 21, 2051-2057; Vinh-Thang, H.; Huang, Q.; Eic, M.; Trong-On, D.; Kaliaguine, S. Stud. Surf. Sci. Catal. 2005, in press), a series of synthesized SBA-15 materials were characterized using nitrogen adsorption/desorption isotherms at 77 K and SEM images. In the present paper, four of them (MMS-1-RT, MMS-1-60, MMS-1-80, and MMS-5-80) were further investigated with regard to their equilibrium characteristics using n-heptane and toluene as sorbates by the standard gravimetric technique. SBA-15 materials proved to have a broad pore size distribution within the micropore/small-mesopore range in the walls of their main mesoporous channels. The adsorption capacities for toluene were found to be higher than for n-heptane. The isosteric heats of adsorption, estimated by the Clausius-Clapeyron equation, are also higher for toluene compared to n-heptane. They were found to depend on framework microporosity of the relevant SBA-15 samples. The isosteric heats of adsorption for all sorbates decrease with increased loading and approach the heats of evaporation of the respective sorbate. The adsorption capacities of SBA-15 samples are significantly higher than those of silicalite, i.e., the MFI zeolite silica analogue. In contrast to that, the isosteric heats of adsorption in the mesopore channels of SBA-15 were found to be much smaller. This result also suggests that SBA-15 can potentially be a good candidate for separation of C(7) hydrocarbons.
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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) | 0.001 | 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 it