Investigation of selective denitrogenation of liquid fuel with different <scp>MIL</scp>‐101 adsorbents
Bibliographic record
Abstract
Abstract In this work, three new structures of MIL‐101 with a base metal of chromium (Cr), vanadium (V), and manganese (Mn) have been synthesized to improve selective adsorbing quinoline (QUI) from a liquid fuel. Different characterization tests were used to identify the specifications of the synthesized adsorbents; namely Brunauer–Emmett–Teller (BET), Fourier transform infrared spectroscopy (FTIR), X‐ray diffraction (XRD), and temperature‐programmed desorption (TPD). Studies indicated that pseudo‐first order and Langmuir isotherm are accurate and suitable models for determining kinetic and equilibrium data of QUI adsorption on these adsorbents. Based on the results, MIL‐101 (Mn) had the highest maximum adsorption capacity of 70.08 (mg N · g −1 ads.) in comparison to MIL‐101 (Cr) and MIL‐101 (V). For different adsorbents, the QUI/dibenzothiophene (DBT) selectivity was investigated by measuring of the adsorption of these components from their mixture. Finally, the QUI/DBT selectivity was in the following order of MIL‐101 (Mn) > MIL‐101 (V) > MIL‐101 (Cr). Density functional theory (DFT) simulation was used to validate experimental results. Calculated bonding energies ratio for QUI and DBT indicated that MIL‐101 (Mn) is the most selective adsorbent.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".