Bibliographic record
Abstract
Background: Among biopolymers, cellulose and its derivatives are the most commonly used for hydrogel formulations. The innovation and improvement of cellulose-based hydrogels concerned the raw materials, synthesis and methods of preparation, formulations and fabrication processes, as well as applications. Objectives: This study, in the form of patent analysis, presents the state by introducing what has been innovated and patented concerning cellulose-based hydrogels. Methods: Three databases have been used in this study: the Patentscope, the Espacenet, and the Lens patent data set. A detailed analysis has been provided regarding publication dates, patent families, jurisdictions, inventors, applicants, owners, and patent classifications. Results: A total of 8053 patent documents related to cellulose-based hydrogels have been published between 1965 and 2021. The United States leads the patent race in this sector, and the Massachusetts Institute of Technology is one of the top academic applicants. Conclusion: Based on patent classifications, most patent documents are related to medicinal preparations characterized by special physical forms. More specifically, the classification concerns materials for prostheses or coating prostheses, including cellulose derivatives characterized by their function or physical properties, such as macromolecular gels, hydrogels, or hydrocolloids.
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.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".