A Green, Economic Method for Bench-Scale Activation of a MIL-101(Cr) Nanoadsorbent
Bibliographic record
Abstract
Research teams have been showing an increasing interest in using MIL-101(Cr) in a variety of applications, in recent years. Furthermore, the removal of unreacted terephthalic acid (ur-H 2 BDC) during post-synthesis purification is also among the greatest challenges in the synthesis of MIL-101(Cr), which if dealt with through simple, environmentally-friendly, and inexpensive methods can constitute a stride in the path to the commercial production and application of MIL-101(Cr). This research is focused on the development of a method based on the application of aqueous solutions of sodium bicarbonate (NaHCO 3 ) with various concentrations, as well as applying various reaction times to remove ur-H 2 BDC to evaluate the efficiency of the method. The obtained results were compared to those of conventional activation methods, indicating the high efficiency of using a 0.06 M NaHCO 3 solution for 24 h, which led to an acceptable BET surface area of 3271 m 2 ·g –1 without any damages to the framework as indicated by X-ray diffraction (XRD) analysis. The study also provides a novel approach to quantifying ur-H 2 BDC residuals (trapped inside of framework pores) using back-titration (1.67% residual ur-H 2 BDC in 1 g of the nanoadsorbent). CO 2, CH 4, and N 2 adsorption isotherms on the activated MILs were acquired using a volumetric laboratory facility at pressures ranging from 1 to 35 bar and 298 K. Data of the adsorption of CO 2 on activated MIL-101(Cr)-NaHCO 3 revealed great gas separation ability (23.1 mmol/g at 35 bar). According to the adsorbed ideal solution theory (IAST), the selectivity values of CO 2 /CH 4 and CO 2 /N 2 were around 11.60 at 1 bar and 5.85 at 35 bar and 135.44 at 1 bar and 48.96 at 35 bar, respectively. Furthermore, the procedure was associated with bench-scale MIL-101(Cr) activation with satisfactory results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".