Effect of embedded activated carbon nanoparticles on the performance of polydimethylsiloxane (PDMS) membrane for pervaporation separation of butanol
Bibliographic record
Abstract
Abstract BACKGROUND The pervaporation separation method is considered to be a promising technique for biobutanol recovery from fermentation broths. In this work, activated carbon nanoparticles were embedded in polydimethylsiloxane (PDMS) membranes to improve the pervaporation performance. RESULTS Adding 6 wt% nano‐additives in PDMS membranes increased the flux and separation factor by 42.6% and 51.9%, respectively, compared with neat membranes at 37°C. Enhanced performance is due to: 1 the presence of additional sorption sites within the membrane with a high affinity for butanol; and 2 the porous structure of the nanofillers generate new pathways for facilitated mass transport through the membrane. The effect of the operating temperature and particle concentration on membrane performance was investigated. Membrane performance improved with an increase in the operating temperature. Higher temperature resulted in increased free volume in the PDMS chains leading to higher diffusion of butanol. Mechanical tensile tests showed that nanocomposite membranes have better mechanical stability in comparison with neat PDMS membranes with the best performance observed at 6 wt% of the nano‐additives. CONCLUSION The presence of activated carbon nanoparticles in the matrix of PDMS membranes leading to higher flux and separation factor can be beneficial for pervaporation separation of butanol from fermentation broths. © 2017 Society of Chemical Industry
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".