Electrospun polymeric nanofibrous membranes for water treatment
Bibliographic record
Abstract
A necessity for water filtration technology, due to global pollution and population growth, has led to an increase in attention to advanced nanomaterials that can aid in the purification of air and water. This size-dependent filtration is possible via nanofibrous membranes as they contain high porosity and this pore size is tunable through the fabrication process. Because of this tunability in the nanofibril membrane composition and structure, they possess promising straining abilities, such as high permeability and selectivity, as well as low fouling. There are a variety of polymer blends or organic/inorganic nanofillers that can be used depending on filtration needs. The production of nanofibers consists of various avenues such as synthetic templates, separation by different phases, nanoparticle self-assembly, and most widespread, electrospinning. Electrospinning is prevalent owing to its ease of use and low cost compared to template and self- assembly processes. This chapter describes the multifaceted progression governing electrospinning and its working factors as well as the environmental settings that form nanofibers and their resultant membranes. Additionally, the various designs of electrospinning apparatuses' and review of the methods used to prepare multifunctional composite electrospun nanofibrous membranes will be discussed. Past achievements and current challenges will be provided. Conclusions and perspectives are specified fitting to studied progress so far as well as future needs with regards to water treatment, with a particular focus on industrial applications.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".