Separation of n-Butanol from Aqueous Solutions via Pervaporation Using PDMS/ZIF-8 Mixed-Matrix Membranes of Different Particle Sizes
Bibliographic record
Abstract
The use of mixed matrix membranes (MMMs) to facilitate the production of biofuels has attracted significant research interest in the field of renewable energy. In this study, the pervaporation separation of butanol from aqueous solutions was studied using a series of MMMs, including zeolitic imidazolate frameworks (ZIF-8)-polydimethylsiloxane (PDMS) and zinc oxide-PDMS mixed matrix membranes. Although several studies have reported that mixed matrix membranes incorporating ZIF-8 nanoparticles showed improved pervaporation performances attributed to their intrinsic microporosity and high specific surface area, an in-depth study on the role of ZIF-8 nanoparticle size in MMMs has not yet been reported. In this study, different average sizes of ZIF-8 nanoparticles (30, 65, and 80 nm) were synthesized, and the effects of particle size and particle loading content on the performance of butanol separation using MMMs were investigated. Furthermore, zinc oxide nanoparticles, as non-porous fillers with the same metalcore as ZIF-8 but with a very different geometric shape, were used to illustrate the importance of the particle geometry on the membrane performance. Results showed that small-sized ZIF-8 nanoparticles have better permeability and selectivity than medium and large-size ZIF-8 MMMs. While the permeation flux increased continuously with an increase in the loading of nanoparticles, the selectivity reached a maximum for MMM with 8 wt% smaller-size ZIF-8 nanoparticle loading. The flux and butanol selectivity increased by 350% and 6%, respectively, in comparison to those of neat PDMS membranes prepared in this study.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".