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 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) | 0.001 | 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".