Pervaporation Separation of Butanol from Aqueous Solutions Using Polydimethylsiloxane (PDMS) Mixed Matrix Membranes
Bibliographic record
Abstract
In this study, pervaporation, a membrane-based process was studied for in-situ separation of butanol. This technique has a great potential due to its high selectivity, low energy requirement and high efficiency. The primary objective was to improve the performance of the Polydimethylsiloxane (PDMS) membrane for the pervaporation separation and the recovery of butanol by adding nanoparticles into its matrix to make mixed matrix membrane (MMM). These nanoparticles included zinc-based Metal Organic Frameworks (MOFs) and zinc oxide. Different particle sizes of zeolitic imidazolate framework (ZIF-8) were synthesized. The separation performance of MMMs incorporating different sizes of ZIF-8 nanoparticles was compared to the performance of mixed matrix membranes incorporating zinc oxide as well as pure PDMS membrane. Different characteristics of ZIF-8 and their impact on the performance of the host membrane were discussed. Result showed that the presence of nanoparticles improves the PDMS membrane performance up to a certain particle loading. Moreover, it was shown that the particle size and interfacial bond between polymer and particles have a major impact on the pervaporation membrane separation process. The best membrane for pervaporation separation of butanol from binary aqueous solutions was obtained for the 8 wt% small-size ZIF-8/PDMS MMM where the total permeation flux and butanol selectivity were increased by 350% and 6%, respectively, compared to neat PDMS membranes. In addition to the MOFs, nanotubes are considered emerging nanostructured materials for use in membrane separation applications due to their high molecular diffusivity and unique geometry. Recent progress has also been made on the modification of nanotube surface functionality, and the fabrication of nanotube mixed matrix membranes as well as the ability to align them in MMMs. Since numerous types of nanotubes are available and the process of producing well-aligned nanotube MMMs is very challenging, a theoretical model using finite difference method (FD) was used to gain a deeper understanding on the effect of nanotubes on the separation performance of mixed matrix membranes. A series of numerical simulations were performed and the effects of various structural parameters, including the tubular filler volume fraction, orientation, length-to-diameter aspect ratio, and permeability ratio, were assessed. The results showed that the relative permeability is enhanced by vertically-aligned nanotubes and further increased with an increase of the permeability ratio, filler volume fraction and the length-to-diameter aspect ratio. In addition, comparing the simulation results with existing analytical models for the prediction of the relative permeability acknowledges a need to develop a new correlation that would provide more accurate predictions of the relative permeability of MMMs with embedded nanotube fillers.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".