Deep Neural Network for Prediction of Adsorbent Selectivity on Hydrogen Purification
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Bibliographic record
Abstract
Abstract With emergence of new materials, more and more materials are available for adsorption and separation processes. The adsorption selectivity of adsorbent to adsorbate is one of the important indicators in choosing materials. Because the adsorption experiment of the mixture is time-consuming and difficult, the selectivity of the adsorbent is generally calculated by the ideal adsorbed solution theory (IAST). Taking the CO 2 /H 2 gas mixture as an example, this paper proposes a new adsorption selectivity calculation method based on a deep neural network (DNN) with 5 hidden layers, which takes the molar fraction of CO 2 , adsorption pressure and Langmuir adsorption isotherm parameters as the inputs of DNN. Combining the DNN and the NIST/ARPA-E database to quickly and accurately calculate the adsorption selectivity, the hydrogen purification and carbon dioxide storage materials can be quickly screened.
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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 | 0.001 |
| 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 it