Effect of N,N′-methylenebisacrylamide on properties of porous semi-IPN hydrogels from acrylamide, maleic acid, and its use for urea absorption
Bibliographic record
Abstract
Hydrogel is a three-dimensional polymer that can absorb large amounts of reagents while maintaining structural integrity. This material has been applied in many fields especially in smart agriculture. To improve the economic viability, the reusability of hydrogels in agricultural engineering over multiple cycles of adsorption and desorption is an urgent requirement. This can be solved if the crosslinker is used properly. Therefore, in this work, a series of porous semi-interpenetrating polymer network (IPN) hydrogels based on linear polyacrylamide, acrylamide, maleic acid, and N,N′-methylenebisacrylamide (MBA) were synthesized. The hydrogels were evaluated for the impact of MBA content on the characteristics and applicability as a urea fertilizer carrier. The chemical composition, morphology, mechanical, and rheological properties, swelling behavior, urea absorption, and desorption of hydrogels with crosslinker content in the range of 0.5%–2.0% were investigated. The porous structure was confirmed by scanning electron microscopy images. Changing the MBA content significantly affected all characteristics of the hydrogels. In particular, increasing the MBA content decreased the equilibrium swelling ratios in all investigated environments. The maximum amount of urea loaded into the hydrogel was also reduced from 435.88 to 188.50 mg/g. This increase also changed the swelling mechanism from non-Fickian to Fickian, whereas the urea release mechanism changed from Fickian to non-Fickian. Finally, the hydrogels demonstrated stability in soil over multiple cycles of water absorption and release. This study provides valuable insights into designing a semi-IPN hydrogel with desired properties that meet the application requirements of modern farming techniques.
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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) | 0.000 | 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".