Metal-Organic Framework Materials for Oil/Water Separation
Bibliographic record
Abstract
Considering the fast growing environmental concern of oily contaminants with a notable potential to damage the water matrices, the effective treatment method with advanced materials is of superior interest to address the environmental pollution of oily wastewater. Metal-organic frameworks (MOFs) are new type of organic-inorganic porous material that have attracted high attention for oil/water separation due to their advantageous features, such as large specific surface area, adjustable pore structure, good chemical and thermal stability. However, the MOFs are usually difficult to recycle in the industrial field because of being in the form of fine powder. In this book chapter, the features, synthesis and modification strategies of MOF were firstly summarized. In order to better illustrate the separation performance of MOFs, the research progress in field of MOFs and MOF membranes for oil/water separation is emphatically investigated. Furthermore, the oil/water separation mechanism is discussed. Finally, the research prospects are briefly discussed, aiming to provide guidance for realizing the large-scale application of MOFs and MOF membranes for application of oil/water separation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".