Diffusion in nanoporous materials with special consideration of the measurement of determining parameters (IUPAC Technical Report)
Bibliographic record
Abstract
Abstract The random motion (the diffusion) of guest molecules in nanoporous host materials is key to their manifold technological applications and, simultaneously, a ubiquitous phenomenon in nature quite in general. Based on a specification of the different conditions under which molecular diffusion in nanoporous materials may occur and of the thus resulting relevant parameters, a survey of the various ways of the measurement of the determining parameters is given. Starting with a condensed introduction to the respective measuring principles, the survey notably includes a summary of the various parameters accessible by each individual technique, jointly with an overview of their strengths and weaknesses as well as of the respective ranges of observation. The presentation is complemented by basic relations of diffusion theory and molecular modeling in nanoporous materials, illustrating their significance for enhancing the informative value of each measuring technique and the added value attainable by their combination. By providing guidelines for the measurement and reporting of diffusion properties of chemical compounds in nanopores, the document aims to contribute to the clarification and standardization of the presentation, nomenclature, and methodology associated with the documentation of diffusion phenomena in nanoporous materials serving for catalytic, mass separation, and other relevant purposes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".