The effect of foaming additives on acrylic acid/acrylamide hydrogels
Bibliographic record
Abstract
The physical and chemical properties of hydrogels are greatly dictated by their composition. In this study, modifications were brought to the macroscopic structure of hydrogels using foaming additives, and their effects on the swelling capacity and heavy metal adsorption were investigated. Significant differences in swelling capacities were found for hydrogels synthesized with a foaming agent and a foam stabilizer (257 g/g), with a foaming agent without foam stabilizer (195 g/g), or without any additives (182 g/g). The study compared the ion removal capacity for copper (II), cadmium (II), and nickel (II) under competitive and non-competitive conditions. The use of a foaming agent significantly increased the ion removal capacity of the hydrogels, from 54 to 93 mg/g cadmium, from 75 mg/g to 104 mg/g copper, and from 48 mg/g to 80 mg/g nickel. Under competitive conditions, the hydrogels preferentially removed cadmium > copper > nickel. However, maximum removal decreased for individual heavy metals under competitive conditions.
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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 | 0.000 | 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".