Combined Application of Tracer Zero Length Column Technique and Pulsed Field Gradient Nuclear Magnetic Resonance for Studies of Diffusion of Small Sorbate Molecules in Mesoporous Silica SBA-15
Bibliographic record
Abstract
Tracer zero length column (TZLC) and pulsed field gradient (PFG) NMR techniques were used to study self-diffusion of toluene in two samples of SBA-15 silica. Analysis of the diffusion data allowed us to assign evaluated diffusivities to diffusion of toluene in pore systems of SBA-15 particles. It was observed that there is a large discrepancy between the values of the diffusivities obtained by the two techniques under very similar experimental conditions. The most likely reason of this discrepancy is related to the particular morphology of the SBA-15 particles, which form stringlike aggregates with lengths close to 20−30 μm. As a consequence of the formation of such aggregate structures some mesoporous channels can be as long as the length of these structures. These long channels are expected to have transport barriers at the points of intergrowth of primary particles. Other channels are much shorter and are not expected to exhibit any significant transport barriers. Under the experimental conditions used TZLC measurements are most sensitive to sorbate diffusion in the channels of the former type. At the same time, PFG NMR measurements are expected to be more sensitive to diffusion in the channels of the latter type where T 2 NMR relaxation times are not shortened by the presence of multiple transport resistances. As a result, these two techniques can provide complementary diffusion data on sorbate transport in SBA-15 materials.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".