Preparation and properties of novel corn straw cellulose–based superabsorbent with water‐retaining and slow‐release functions
Bibliographic record
Abstract
ABSTRACT In this study, novel semiinterpenetrating polymer networks (semi‐IPNs) superabsorbent resins with slow‐release fertilizer (CSC‐g‐AA/APP, CSC‐g‐AA/PVA‐APP), based on corn straw cellulose polymer and linear polyvinyl alcohol (PVA), were prepared by solution polymerization. The nitric acid‐aqueous solution method was adopted to extract cellulose from corn straw. Ammonium polyphosphate (APP) was introduced to supply nitrogen and phosphorus nutrients. The prepared materials were characterized by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X‐ray photoelectron spectroscopy (XPS), and thermogravimetric analysis (TGA). Moreover, the water absorbency and the slow‐release performance of CSC‐g‐AA/APP and CSC‐g‐AA/PVA‐APP were studied. The results indicated that the two superabsorbent resins exhibited excellent water absorbency of 262.8 and 303.2 g/g in distilled water, enhanced the water‐holding capacity of soil, and also released nutrients slowly. The cumulative N and P release rates of CSC‐g‐AA/PVA‐APP were 64.47 and 53.53% after 25 days in soil, which were lower than those of CSC‐g‐AA/APP. The addition of these products into soil significantly reduced the leaching losses of nutrients. Therefore, it can be concluded that the superabsorbent resins with water‐retaining and slow‐release properties, low production costs, and environment‐friendly characteristics, have great potential for applications in agricultural production. © 2020 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020, 137, 48951.
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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".