Mechanical Characterization of Nanocelluloses/Cellulose Acetate Composite Nanofibrous Membranes
Bibliographic record
Abstract
Abstract Extensive research on membrane technologies stems from the necessity for drinking water purification, wastewater treatment, and recycling polluted water by filtration. For the successful commercialization of a filtration process, the improvement of the mechanical properties of membrane systems is an essential factor. Pressure-driven membrane filtration processes require sufficient mechanical strength to withstand operational conditions. In this study, cellulose acetate composite nanofibrous membranes, including nanocelluloses as reinforcing additives, were synthesized by the electrospinning technique. The polymer solutions were prepared from different weight percentages of Cellulose nanocrystals (CNCs) and 2,2,6,6-tetramethyl-1-piperidinyloxyl- (TEMPO) oxidized Cellulose nanofibrils (TOCNFs) from 0 to 1 wt.\% and 15 wt\% of Cellulose Acetate (CA). The impacts of heat post treatment process on the mechanical properties of composite electrospun nanofibrous membranes (ENMs) has been studied. The chemical and physical properties of the composite ENMs were investigated. The morphological structure of ultimate composite ENMs was studied by scanning electron microscopy (SEM), and the chemical interactions were demonstrated by Fourier transform infrared spectroscopy. A tensile test was performed to evaluate the mechanical properties of the composite ENMs. The highest tensile strength was achieved for heat-treated 0.25TOCNF/CA composite nanofibrous membrane. These results verify that modifying morphology and improving mechanical strength expand the application of electrospun nanofibrous membranes in water purification processes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".