Cellulose-Based Metallogels—Part 3: Multifunctional Materials
Bibliographic record
Abstract
The incorporation of the metal phase into cellulose hydrogels, resulting in the formation of metallogels, greatly expands their application potential by introducing new functionalities and improving their performance in various fields. The unique antiviral, antibacterial, antifungal, and anticancer properties of metal and metal oxide nanoparticles (Ag, Au, Cu, CuxOy, ZnO, Al2O3, TiO2, etc.), coupled with the biocompatibility of cellulose, allow the development of composite hydrogels with multifunctional therapeutic potential. These materials can serve as efficient carriers for controlled drug delivery, targeting specific cells or pathogens, as well as for the design of artificial tissues or wound and burn dressings. Cellulose-based metallogels can be used in the food packaging industry to provide biodegradable and biocidal materials to extend the shelf life of the goods. Metal and bimetallic nanoparticles (Au, Cu, Ni, AuAg, and AuPt) can catalyze chemical reactions, enabling composite cellulose hydrogels to be used as efficient catalysts in organic synthesis. In addition, metal-loaded hydrogels (with ZnO, TiO2, Ag, and Fe3O4 nanoparticles) can exhibit enhanced adsorption capacities for pollutants, such as dyes, heavy metal ions, and pharmaceuticals, making them valuable materials for water purification and environmental remediation. Magnetic properties imparted to metallogels by iron oxides (Fe2O3 and Fe3O4) simplify the wastewater treatment process, making it more cost-effective and environmentally friendly. The conductivity of metallogels due to Ag, TiO2, ZnO, and Al2O3 is useful for the design of various sensors. The integration of metal nanoparticles also allows the development of responsive materials, where changes in metal properties can be exploited for stimuli-responsive applications, such as controlled release systems. Overall, the introduction of metal phases augments the functionality of cellulose hydrogels, expanding their versatility for diverse applications across a broad spectrum of industries not envisaged during the initial research stages.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 0.001 |
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".