Citric acid - crosslinked cellulose derivatives superabsorbent hydrogels (SAH) as sustainable alternatives for personal hygiene applications
Bibliographic record
Abstract
This study explores the development of citric acid (CA)-crosslinked superabsorbent hydrogels (SAHs) based on cellulose derivatives as sustainable alternatives to conventional petroleum-based superabsorbent polymers (SAPs) used in personal hygiene products. A biodegradable and biocompatible hydrogel formulation was synthesized using CA as a green crosslinking agent, with sodium carboxymethyl cellulose (Na-CMC) as the primary polymer and hydroxyethyl cellulose (HEC) as the secondary polymer, in varying ratios. The effects of polymer composition, swelling medium, pH, and water/saline solution absorption under load (AUL) were systematically evaluated by employing thermal analysis, rheology, water absorption capacity, swelling/deswelling kinetics, and soil biodegradation tests. Biocompatibility was also assessed via indirect contact cytotoxicity assays using mouse fibroblast cells. The optimized formulation, particularly the CMC—1.5 % CA hydrogel, exhibited high equilibrium water absorption (Q eq ) of 110 g/g in saline media at 38 °C, favorable swelling/deswelling behavior, rheological robustness, significant biodegradability (72 % within 7 weeks under composting conditions), and non-cytotoxicity (>80 % cell viability). These results highlight the potential of these hydrogels as biodegradable, biocompatible materials aligned with the Sustainable Development Goals (SDGs), especially for eco-friendly hygiene applications. • Citric acid–crosslinked cellulose hydrogels showed superior water absorption, exceeding commercial superabsorbents. • Hydrogels were up to 72 % biodegradable and non-cytotoxic, meeting key safety requirements. • Green-crosslinked cellulose hydrogels offer an eco-friendly alternative to synthetic absorbent materials. • The hydrogel process is simple, high-yield (>85 %), and scalable for industrial hygiene applications.
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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".