ssDNA Aptamer-Based Column for Simultaneous Removal of Nanogram Per Liter Level of Illicit and Analgesic Pharmaceuticals in Drinking Water
Bibliographic record
Abstract
Aptamers are a new class of single-stranded DNA/RNA molecules selected from synthetic nucleic acid libraries for molecular recognition. Our group reports a novel aptamer column for the removal of trace (ng/L) pharmaceuticals in drinking water. In this study, cocaine and diclofenac were chosen as model molecules to test the aptamer column which presented high removal capacity, selectivity, and stability. The removal of pharmaceuticals was as high as 88-95%. The data of adsorption were fitted with Langmuir isotherm and a pseudo-second-order kinetic model. A thermodynamic experiment proved the adsorption processes were exothermic in spontaneity. The kinetics of aptamer was composed of three steps: activation, binding, and hybridization. The first step was the rate-controlling step. The adsorption system was divided into three parts: kinetic, mixed, and thermodynamic zones from 0% to 100% binding fraction of aptamer. Furthermore, the aptamer column was reusable and achieved strong removal efficiency from 4 to 30 °C at normal cation ion concentration (5-100 mg/L) for multipollutants without cross effects and secondary pollution. This work indicates that aptamer, as a new sorbent, can be used in the removal of persistent organic pollutants, biological toxins, and pathogenic bacteria from surface, drinking, and ground water.
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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) | 0.000 | 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 | 0.001 |
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".