Nanofibers for textile waste water management
Bibliographic record
Abstract
Abstract Currently, textile wastewater management focuses on dye removal efficiency and operating costs. Dual responsive polymers are choice materials because they can extract diverse organic compounds from water at their phase transition point. They are copolymers of the acrylamide class, and have been fully characterized by FT-IR, 1H-NMR, DSC, GPC and surface area analysis. Of the five dual responsive polymers, the copolymer of NIPAAM and DMAEMA (CoP-1) offers the best extraction of acidic and basic dyes from wastewater. All copolymers investigated can achieve better than 90% dye removal when used at 4 mg/ml concentration. This dye-scavenging efficiency increases to almost 99% at 3 mg/ml, on conversion of the copolymers to nanofibers in 300 to 500 nm size. Langmuir and Freundlich isotherms were constructed to study the mechanism of dye adsorption. The nanofibers have been shown to be reusable for removal of dyes from water, suggesting that such systems may add benefit to current dye removal methods from textile industry wastewater.
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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".