Selective adsorption of aromatic sulfonic acid from wastewater using a surface imprinted polymer: H‐acid as a representative contaminant
Bibliographic record
Abstract
Abstract Aromatic sulfonic acids (ASAs) play a pivotal role as essential intermediates in numerous industrial manufacturing, while a large amount wastewater with various ASAs and high concentration of inorganic salts is subsequently generated. The effective separation and removal of ASAs from wastewater is challenging due to their complex chemical composition and the limited selectivity of common adsorbents. Herein, a novel surface imprinted polymer (H‐SIP) with high selectivity and excellent salt resistance was designed with PEI/Cl‐PS‐DVB as the carrier and 1‐amino‐8‐naphthol‐3,6‐disulfonic acid (H‐acid) as the target pollutant. Compared to non‐imprinted polymer (NIP), H‐SIP exhibited superior salt resistance in the presence of Na2SO4 concentration ranging from 20 to 80 mg/L. The relative selectivity coefficients determined in the binary‐solutes experiments proved that H‐SIP demonstrated favourable selectivity towards H‐acid in binary systems of H‐acid/T‐acid or H‐acid/2‐NSA. Moreover, H‐SIP could effectively treat the simulated complex wastewater within 24 bed volume (BV) in the column adsorption, and the desorption rate exceeded 90% when eluted by NaOH solution and distilled water, respectively. Therefore, these results confirmed that surface imprinting technique was a promising method for effectively and selectively removal of ASA wastewater in the application.
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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 | 0.000 |
| 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".