Mechanically-robust electrospun nanocomposite fiber membranes for oil and water separation
Bibliographic record
Abstract
Mechanically-robust nanocomposite membranes have been developed via crosslinking chemistry and electrospinning technique based on the rational selection of dispersed phase materials with high Young's modulus (i.e., graphene and multiwalled carbon nanotubes) and Cassie-Baxter design and used for oil and water separation. Proper selection of dispersed phase materials can enhance the stiffness of nanocomposite fiber membranes while their length has to be larger than their critical length. Chemical modification of the dispersed phase materials with fluorochemcials and their induced roughness were critical to achieve superhydrophobocity. Surface analytic tools including goniometer, X-ray photoelectron spectroscopy (XPS), Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy, atomic force microscopy (AFM) and scanning electron microscope (SEM) were applied to characterize the superhydrophobic nanocomposite membranes. An AFM-based nanoindentation technique was used to measure quantitativly the stiffness of the nanocomposite membranes for local region and whole composites, compared with the results by a tensile test technique. Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) techniques were used to confirm composition and formation of nanocomposite membranes. These membranes demonstrated excellent oil/water separation. This work has potential application in the field of water purification and remediation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".