Characterization of molecularly imprinted and nonimprinted polymer submicron particles specifically tailored for removal of trace 17β‐estradiol in water treatment
Bibliographic record
Abstract
Abstract This study investigated the potential use of molecularly imprinted polymer (MIP) submicron particles for the selective removal of trace 17β‐estradiol (E2) in water treatment. Methacrylate‐based MIP submicron particles were synthesized, in a one‐step suspension polymerization procedure, using ethylene glycol dimethacrylate (EGDMA) as the cross‐linker. After template removal, the particles could be used as a smart material for specific binding of E2. The submicron size of MIP particles facilitated uniform dispersion in water for up to 17 days. These particles were meritorious in mass transfer behavior, allowing phase partitioning of E2 molecules in water during a short treatment time. After 1‐mL water samples of different E2 concentrations were treated with 20 mg of MIP particles for 2 min, recovery percentages as high as 97% ± 3% were achieved. The specific binding capacity of these MIP particles was determined to be 15 mg E2/g. Nonimprinted polymer nanoparticles were also evaluated for nonspecific binding of E2, using 0.5 mg in 1 mL of water, to attain 64% ± 3% efficiency in 3 min towards general water treatment. A simple capillary electrophoresis method was successfully developed for the characterization of MIP and NIP particles. Apparently the less negative the electrophoretic mobility, the higher binding efficiency and faster binding kinetics the particles would exhibit with E2 due to less hindered Brownian diffusion. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010
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) | 0.000 | 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".