Development of metal-organic framework-based systems for H2S removal: A comprehensive review
Bibliographic record
Abstract
Biogas is recognized as a source of renewable energy that can substitute for fossil fuels, especially natural gas. Biogas is produced from various organic resources, and it contains mainly methane (CH 4 ) and carbon dioxide (CO 2 ). However, several contaminants are found in the biogas flow such as hydrogen sulfide (H 2 S), water (H 2 O), ammonia (NH 3 ), and volatile organic compounds (VOCs). Therein, due to its high corrosion, toxicity, and bad odor, H 2 S must be eliminated first and intensively to avoid equipment damage and health risks. Among H 2 S removal technologies, using the solid adsorbent is viewed as a friendly and effective way. Recently, metal-organic frameworks (MOFs) have been studied with increasing attention for H 2 S adsorption thanks to their high surface area, good thermal stability and structural tunability. Although many MOFs-based systems have been designed for H 2 S removal, an intensive study to summarize them is missing. This work aims to revise the development of MOFs-based networks for H 2 S removal in literature including pristine MOFs, functionalized MOFs, MOF composites, and mixed-metal MOFs. We focus on explaining H 2 S adsorption mechanism of MOFs, and material engineering factors that directly affect the H 2 S adsorption capacity, the selectivity over other gases, and the ability to regenerate. Furthermore, several perspectives to enhance the removal performance of MOFs are also proposed. Together, this study will provide a comprehensive document on current technologies and perspective development of MOF-derived H 2 S adsorbent. • MOF-based materials for H 2 S removal are summarized and analyzed. • Impacts of material characteristics and operating conditions on H 2 S adsorption are discussed. • The comparison among MOF systems is systematically presented. • Challenges and future studies on MOF materials for H 2 S removal are proposed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".