Synthesis of Superabsorbent Polymer (SAP) via Industrially Preferred Route
Bibliographic record
Abstract
The assigned study is dedicated to the synthesis, improvement and characterization of acrylic-based superabsorbent polymers (SAPs) which can be used in versatile applications notably in disposable diapers and pharmaceutics. The industrially preferred solution polymerization route and low cost monomers were used to synthesize SAPs. Homopolymer and copolymer based SAPs were prepared with varying amount of cross-linker and initiator concentrations and compared for swelling rate with a commercially available SAP sample. Swelling capacity linearly decreases with increase in cross-linker content for both the synthesized SAPs samples whereas it first increases and then decreases with initiator content for the synthesized copolymer SAP. Swelling kinetics of the synthesized and commercial SAPs were modelled using model equation proposed by Omidian et al. Both the synthesized SAPs showed substantial increase in swelling capacity whereas copolymer SAP exhibited the highest swelling rate (rate parameter 2.78 min) when compared to homopolymer SAP and the commercially available SAP samples. Accordingly, the copolymer SAP may find its application in disposable diapers or pharmaceutics where the higher swelling rate is of prime importance. Copolymer and commercially available SAPs depicted significant decrease in swelling capacity even at very low saline solution concentration (0.01 %).
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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 | 0.001 |
| 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".