The antibacterial and anticorrosion activity of sodium alginate-chitosan cryogels and hydrogels loaded with Satureja montana essential oil and Monarda didyma hydrolate
Why this work is in the frame
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Bibliographic record
Abstract
This study develops chitosan-alginate (CS/SA) cryogels incorporating Satureja montana essential oil (EO) and Monarda didyma hydrolate for antibacterial and anticorrosion applications. Cryogels with a CS:SA ratio of 3:1 achieved 78.7 % encapsulation efficiency (EE), driven by electrostatic interactions between chitosan's protonated amine (-NH 3 + ) and alginate's carboxyl (-COO - ) groups, forming a dense polyelectrolyte network that entraps EO (FTIR/SEM evidence). Structural analysis revealed alginate-enhanced porosity (50–200 μm pores) and EO-induced densification, critical for controlled release. The cryogels inhibited Staphylococcus aureus (58.8 ± 2.3 %) and Pseudomonas aeruginosa (41.7 ± 3.0 %) and suppressed corrosion-associated strains: acid-producing bacteria (APB, 91.92 ± 0.16 %) and thiosulfate-reducing bacteria (BTR, 97.76 ± 0.27 %). Hydrolate-EO synergy enhanced anticorrosion performance, with CS/SA (3:1) cryogels retaining 90 % zinc on steel surfaces. This work demonstrates a sustainable strategy for dual-functional coatings, leveraging natural extracts to address industrial and environmental challenges. • Synergistic use of chitosan-alginate in cryogelsenhances bioactivity and stability. • Novel combination of hydrolate and essential oil improves antibacterial performance. • Cryogels achieve 78.7 % essential oil encapsulation efficiency with reduced leakage. • Antibacterial efficacy demonstrated against S. aureus and P. aeruginosa. • Superior anticorrosion activity minimizes biofilm formation and metal degradation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it