Cellulose-Based Metallogels—Part 2: Physico-Chemical Properties and Biological Stability
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 represent a class of composite materials in which a metal can be a part of the gel network as a coordinated ion, act as a cross-linker, or be incorporated as metal nanoparticles in the gel matrix. Cellulose is a natural polymer that has a set of beneficial ecological, economic, and other properties that make it sustainable: wide availability, renewability of raw materials, low-cost, biocompatibility, and biodegradability. That is why metallogels based on cellulose hydrogels and additionally enriched with new properties delivered by metals offer exciting opportunities for advanced biomaterials. Cellulosic metallogels can be either transparent or opaque, which is determined by the nature of the raw materials for the hydrogel and the metal content in the metallogel. They also exhibit a variety of colors depending on the type of metal or its compounds. Due to the introduction of metals, the mechanical strength, thermal stability, and swelling ability of cellulosic materials are improved; however, in certain conditions, metal nanoparticles can deteriorate these characteristics. The embedding of metal into the hydrogel generally does not alter the supramolecular structure of the cellulose matrix, but the crystallinity index changes after decoration with metal particles. Metallogels containing silver (0), gold (0), and Zn(II) reveal antimicrobial and antiviral properties; in some cases, promotion of cell activity and proliferation are reported. The pore system of cellulose-based metallogels allows for a prolonged biocidal effect. Thus, the incorporation of metals into cellulose-based gels introduces unique properties and functionalities of this material.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it