Poly(<i>N</i>‐Isopropylacrylamide)‐Based Microgels and Their Assemblies for Organic‐Molecule Removal from Water
Bibliographic record
Abstract
We review our recent efforts utilizing poly(N-isopropylacrylamide)-co-acrylic acid (pNIPAm-co-AAc) microgels and their assemblies for the removal of an azo-dye molecule, 4-(2-Hyrodxy-1-napthylazo) benzenesulfonic acid sodium salt (Orange II), from aqueous solutions. First, the ability of dispersed, single microgels to remove Orange II from aqueous solutions at room temperature is discussed. Uptake efficiency (i.e., the amount of Orange II removed from water) increased with AAc composition in the microgels, yielding a maximum uptake efficiency of 29.5% for pNIPAm microgels with 10% AAc. Assemblies of microgels (aggregates) were also investigated for their removal efficiency, which yielded a maximum uptake efficiency of 44.1% at room temperature. Removal efficiencies for the microgels and their aggregates were also monitored at elevated temperatures, and a maximum of 56.6% removal efficiency was achieved for unaggregated microgels, while aggregates were able to remove 73.1% Orange II. To further explore the impact of the microgel system's structure on function, we investigated the role microgel size in the aggregates plays on the uptake efficiency. Initial observations showed that the aggregates composed of microgels with large diameter yielded improved uptake efficiency over the aggregates composed of small diameter microgels. Langmuir sorption isotherms were fit to the data for the dye removal by the unaggregated and aggregated microgels, which showed good fits in all cases.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".