The antibacterial and anticorrosion activity of sodium alginate-chitosan cryogels and hydrogels loaded with Satureja montana essential oil and Monarda didyma hydrolate
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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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) | 0.000 | 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 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".