Design, Characterization, and Performance of Composite Electrospun Nanofiber Membrane Adsorption Systems for the Removal of Pharmaceutical and Personal Care Products from Water
Bibliographic record
Abstract
The contamination of water bodies by pollutants such as pharmaceutical and personal care products (PPCPs) is a globally widespread phenomenon resulting mainly from incomplete removal in wastewater treatment. Several technologies exist to remedy this problem, among them adsorption onto micro- and nanoparticles. However, a disadvantage of treatment by adsorbents such as powdered activated carbon (PAC) by dosing is the requirement to remove them in a subsequent step. A system capable of anchoring adsorbents onto a membrane to prevent leaching into treated water has the potential to present significant advantages in the field.Electrospun nanofiber membranes (ENMs) are a new generation of membrane with a large void fraction and interconnected pore structure. Although often used as size-exclusion membranes, they can also be used as adsorptive membranes. It was found that carbonaceous adsorbents can be successfully immobilized by ENMs either in sandwich membrane or co-electrospun configurations. In this study, single-walled carbon nanotubes (SWCNT) and PAC were sandwiched by two superimposed ENM sheets, which prevented the adsorbents from leaching into the permeate while achieving high removals of the 6 tested PPCPs. PAC was also successfully co-electrospun into an adsorptive ENM. To maximize their exposure, two aspects of ENM design were addressed: controlling fiber diameter via the addition of salt and controlling particle diameter by sieving the PAC particles into different size bins. Addition of salt produced PAC-ENMs with thinner fibers and enhanced surface area. This resulted in an enhancement of adsorption capacity, faster adsorption kinetics, and delayed breakthrough behaviour of a model contaminant. Electrospinnig membranes with different PAC size fractions showed that surface area, adsorption capacity, and adsorption kinetics were strongly correlated to the external surface area of PAC particles, and were thus maximized for the smallest size fraction of PAC. A model representing the initial fractional removal as a function of residence time in dynamic filtration was developed, and an exponential relationship was elucidated. In static adsorption, the correlation between the kinetic constants and the surface area of the adsorptive ENM was modelled by the predictively accurate Active Layer Diffusion Model (ALDM).
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".