Cellulose-Based Metallogels—Part 1: Raw Materials and Preparation
Bibliographic record
Abstract
Metallogels are a class of materials produced by the complexation of polymer gels with metal ions that can form coordination bonds with the functional groups of the gel. Hydrogels with metal phases attract special attention due to the numerous possibilities for functionalization. Cellulose is preferable for the production of hydrogels from economic, ecological, physical, chemical, and biological points of view since it is inexpensive, renewable, versatile, non-toxic, reveals high mechanical and thermal stability, has a porous structure, an imposing number of reactive OH groups, and good biocompatibility. Due to the poor solubility of natural cellulose, the hydrogels are commonly produced from cellulose derivatives that require multiple chemical manipulations. However, there is a number of techniques of hydrogel preparation via dissolution and regeneration of non-derivatized cellulose of various origins. Thus, hydrogels can be produced from plant-derived cellulose, lignocellulose and cellulose wastes, including agricultural, food and paper wastes. The advantages and limitations of using solvents are discussed in this review with regard to the possibility of industrial scaling up. Metallogels are often formed on the basis of ready-made hydrogels, which is why the choice of an adequate solvent is important for obtaining desirable results. The methods of the preparation of cellulose metallogels with d-transition metals in the present state of the art are reviewed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".