Selective Removal of Nitrophenols from Aqueous Solutions Using Acrylic Acid–Acrylamide Molecularly Imprinted Polymers
Bibliographic record
Abstract
Nitrophenols are environmentally persistent and toxic pollutants of global concern due to their harmful effects on ecosystems and human health.This study presents the synthesis of molecularly imprinted polymers (MIPs) based on poly (acrylic acid-coacrylamide) [PAA-co-PAAm], for the selective extraction of nitrophenol isomers from aqueous media for the first time.Characterization by FT-IR, TGA, and UV-Vis spectroscopy confirmed successful incorporation of cross-linking functionalities and enhanced structural integrity in MIPs compared to non-imprinted polymers (NIPs).Extraction efficiency was evaluated by determining the distribution ratio (D) of ortho-, meta-, and para-nitrophenol at pH 2, 7, and 10.D values increased with decreasing pH, with meta-nitrophenol (MNP) showing the highest selectivity.At pH 2, the D for MNP reached 4.6, a sevenfold increase over its value at pH 7.Among the three isomers studied, the MIP synthesized for MNP showed the highest binding efficiency and imprinting factor (I.F.), likely due to the formation of highly specific hydrogen-bonding interactions.Selectivity tests further confirmed the superior recognition of MNP by the MIPs compared to structurally similar compounds such as phenol and m-cresol.These results highlight the potential of PAA-co-PAAm-based MIPs as effective and selective adsorbents for the removal of nitrophenol isomers from contaminated water sources.
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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) | 0.001 | 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 | 0.001 |
| 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".