Sustainable and Cost-Effective Synthesis of Single-Layered 2D MIL-53(Al) Nanosheets for Efficient H<sub>2</sub>S Capture
Bibliographic record
Abstract
Hydrogen sulfide (H 2 S) poses negative effects on the realm of energy storage and conversion and human health owing to its corrosivity, catalyst poisoning, and toxicity. Hence, removing H 2 S from gas streams plays a crucial role in sections of gas-derived energy production and human life. Metal–organic frameworks (MOFs) have been extensively studied for H 2 S adsorption, thanks to their large surface area and tunable structure. This work presents a sustainable and cost-effective synthesis of single-layered two-dimensional (2D) MIL-53(Al) nanosheets with excellent ability for H 2 S adsorption. The 2D MIL-53(Al) is synthesized by an organic solvent-free protocol using plastic waste as a precursor source. Synthetic conditions are optimized to obtain a high-yield synthesis process and a material with tunable properties. The MIL-53(Al) nanosheet (thickness of 6.7 Å) with a single-layered structure is confirmed by scanning transmission electron microscopy and atomic force microscopy, which has never been reported yet. The material possesses a large surface area of 1112 m 2 ·g –1 and good chemical and thermal stability. Regarding H 2 S adsorption, the 2D MIL-53(Al) exhibits a H 2 S capacity of 556 mg·g –1 under ambient conditions, and its potential regenerability is proven. These performances arise from facile exposure of H 2 S molecules to adsorptive sites in the 2D framework and reversible adsorption of the material toward H 2 S. This study opens a pathway for designing 2D MOFs in the field of H 2 S adsorption and provides a well-designed 2D MOF for further research strategies.
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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) | 0.001 | 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".