Quantitative structure–property relationship approach to predicting xylene separation with diverse exchanged faujasites
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Bibliographic record
Abstract
Streamlining the xylene separation process on faujasites is a promising way to design innovative adsorbents for this application. For this purpose, we present herein an original quantitative structure-property relationship (QSPR) approach. It deals with the development of a multi-linear predictive model correlating the separation properties with a set of structural descriptors for the adsorbents. The implementation of such an approach makes it necessary to (i) set an appropriate design of experiment (DOE), (ii) prepare an adsorbent database, (iii) test the adsorbent database for xylene separation and (iv) compute a set of relevant descriptors. The selected descriptors essentially characterize the nature of the confinement in the faujasite supercage, i.e., the size of the cations localized in adsorption sites II, as well as the occupancy ratio of both adsorption sites II and III. Two different statistical methods were applied to develop a structure-property relationship model linking experimental selectivity and the set of descriptors. A multiple linear regression model enables the prediction of para/meta-xylene selectivity with a correlation coefficient R2 of 0.78, while a linear discriminant analysis predicts the assignment of the adsorbents to four identified classes with a total prediction percentage of 76%.
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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 | 0.000 |
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 it