Cellulose-Based Metallogels—Part 1: Raw Materials and Preparation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.003 |
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 it