Art to use Electrospun Nanofbers/Nanofber Based Membrane in Waste Water Treatment, Chiral Separation and Desalination
Bibliographic record
Abstract
The technique to fabricate nanofibrous mat by electro-spinning has been known for a long time. But the attempts to use the electospun nanofiber mats, also known as electro-spun nanofiber membranes (ENMs), for filtration purposes began only recently. Among many membrane filtration processes, air cleaning by the removal of dust particles has already been commercialized and the product has been in the market for some time. On the other hand, the application of ENMs for liquid separation has a much shorter history and its commercialization has not yet been achieved. Since a large number of researches are reported in the open literature each year, its commercialization looks only a matter of time. For example, many papers are now available on the pressure driven membrane separation processes such as RO, NF, UF, MF by ENMs, and as many papers have been published on the other membrane separation processes including pervaporation, membrane distillation, forward osmosis and membrane adsorption. It is needless to say that EMFs have gained popularity within a short period due to the facile fabrication, interconnectivity and large area/volume ratio. Despite these advantages, ENMs’ pore sizes are intrinsically very large (fractions of micrometer to few micrometer), which makes modification of surface chemistry and especially reduction of the ENM pore size indispensable for wider applications of ENMs for membrane separation 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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".