Potential of a Water-Stable Ultramicroporous Aluminum Gallate Metal Organic Framework as Sorbent for Upgradation of Natural Gas
Bibliographic record
Abstract
Developing easily accessible metal–organic framework (MOF) sorbents with industrially relevant gas separation capabilities is desirable. This can be achieved by constructing MOFs from simple ligands and ubiquitous benign metals. Aluminum–oxygen bonds are generally stable, hence crystalline aluminum MOFs from oxygen-rich compact ligands can add new sorbents with volumetric-gravimetric advantages. Here, we present a water-stable ultramicroporous Aluminum-Gallate MOF that demonstrates good CH 4 selectivity over N 2 and noticeable CO 2 /N 2 selectivity (based on IAST selectivity at 313 K, CO 2 /N 2 = 40; heat of adsorption (HOA) for CO 2 is constant over entire loading with an average value of 30 kJ/mol; CH 4 /N 2 selectivity at 20 °C ∼6.2; HOA for CH 4 = ∼23 kJ/mol). Notably, this MOF adsorbs substantially more CH 4 than other transition metal gallates. At higher pressures (1–20 bar), the MOF retains this higher uptake for CH 4 over N 2 . We have calculated the high pressure CH 4 /N 2 selectivity values at 5 bar and 20 °C for three different compositions 85%CH 4:15%N 2, selectivity = 0.9; 75%CH 4:25%N 2, selectivity = 1.7; 65%CH 4:35%N 2, selectivity = 2.7. Superior adsorption of CH 4 over N 2 is well supported by the dynamic separation studies (dynamic breakthrough capacity for CH 4 = 1.05 mmol/g). Its potential as practical natural gas purification sorbent is investigated using a 4-step PVSA process modeling. For a 0.5–5 bar pressure swing the MOF is capable of delivering 99.9% purity with greater than 80% recovery from an 85%CH 4:15%N 2 stream; the achieved purity of CH 4 meets pipeline transportation quality. The favorable composition, structure, gas separation capacity and stability make this aluminum gallate MOF an impactful candidate for natural gas purification.
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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) | 0.000 | 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 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".