Investigating Performance Properties of Three New Types of <scp>UF</scp> Membranes for Carwash Sector Applications
Bibliographic record
Abstract
ABSTRACT Carwash facilities generate considerable amounts of wastewater, which presents both an environmental challenge and a potential alternative water source. To facilitate reuse, this wastewater must meet stringent quality standards. In this study, three novel ultrafiltration (UF) membranes were developed for the first time to treat carwash wastewater (CWW). These membranes were fabricated from postmodified (PM) polyvinyl chloride (PVC) derivatives: PVC modified with 4‐tert‐octylthiophenol (PP1), 4‐tert‐butylthiophenol (PP2), and thiophenol (PP3) using a mechanosynthesis approach. The membranes were synthesized by incorporating the respective modified polymers into a solvent mixture of tetrahydrofuran (THF) and N‐methyl‐2‐pyrrolidone (NMP) via the casting solution technique. Structural characterization was performed using Fourier‐transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The membranes exhibited thicknesses of 143 μm (PP1), 178 μm (PP2), and 139 μm (PP3), porosity ranging from 64.67% to 73.33%, and average pore sizes between 23.41 and 28.81 nm. Performance testing revealed that PP1 demonstrated the highest rejection rates for suspended solids (99%), oil products (82.4%), and surfactants (79%), with a flux of 40.24 L/m2 h. Although PP3 achieved the highest water flux (61.82 L/m2 h), its rejection performance was slightly lower. Retentate underwent biological treatment, removing 85%–90% of organic matter, supporting sustainable wastewater reuse.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".