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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".