Structural analysis of IPC zeolites and related materials using positron annihilation spectroscopy and high-resolution argon adsorption
Bibliographic record
Abstract
The advanced investigation of pore networks in isoreticular zeolites and mesoporous materials related to the IPC family was performed using high-resolution argon adsorption experiments coupled with the development of a state-of-the-art non-local density functional theory approach. The optimization of a kernel for model sorption isotherms for materials possessing the same layer structure, differing only in the interlayer connectivity (e.g. oxygen bridges, single- or double-four-ring building units, mesoscale pillars etc.) revealed remarkable differences in their porous systems. Using high-resolution adsorption data, the bimodal pore size distribution consistent with crystallographic data for IPC-6, IPC-7 and UTL samples is shown for the first time. A dynamic assessment by positron annihilation lifetime spectroscopy (PALS) provided complementary insights, simply distinguishing the enhanced accessibility of the pore network in samples incorporating mesoscale pillars and revealing the presence of a certain fraction of micropores undetected by gas sorption. Nonetheless, subtle differences in the pore size could not be discriminated based on the widely-applied Tao-Eldrup model. The combination of both methods can be useful for the advanced characterization of microporous, mesoporous and hierarchical 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) | 0.000 | 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 | 0.000 |
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".