Performance of a novel, eco‐friendly, cellulose‐based superabsorbent polymer ( <scp>Cellulo‐SAP</scp> ): Absorbency, stability, reusability, and biodegradability
Bibliographic record
Abstract
Abstract Superabsorbent polymers (SAPs) have attracted tremendous attention recently, with researchers noting that their high water absorbability is valuable for various applications, especially in agricultural contexts. Two types of materials can be used to produce SAPs: Fossil‐based (which are harmful to the environment) and bio‐based (which are significantly more environmentally friendly, given their biodegradability and minimal toxic side effects). Although bio‐based SAPs are preferable for environmental reasons, their synthesis tends to be time consuming and labour intensive, while their absorption capacity (AC) can be far below expectations. To address these problems, a novel, eco‐friendly, cellulose‐based superabsorbent polymer (Cellulo‐SAP) was developed in this study through facile preparation via free radical synthesis using modified cellulose. Then, the absorbency, thermal/pH stability, reusability, and biodegradability of Cellulo‐SAP were evaluated. This new polymer demonstrated reusability as a water reservoir, in addition to high thermal and pH stability. More importantly, Cellulo‐SAP achieved an AC of 475 g/g and exhibited superior biodegradability compared to a commercial, fossil‐based SAP. Accordingly, these results prove that Cellulo‐SAP can be used in agriculture as an effective alternative to fossil‐based SAPs.
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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".