Bibliometric Analysis Of Aggregated Polymers From Natural Extracts And Nanoparticles With Antimicrobial And Antifungal Activity
Bibliographic record
Abstract
Research into polymers has been highlighted in recent years, due to the low cost, regulations on the use of plastics in different countries and the different applications that can be given to them, such as in medicine, as medical patches, and agro-industries, as containers for fruit storage.These materials have proven to be eco-friendly, since they are obtained mostly from organic materials that facilitate their rapid degradation and less contamination to our ecosystem.Due to the problems presented, research work on eco-friendly polymers is increasing since they are presented as a great candidate to solve them.In this research, articles, keywords, authors and countries with high-impact publications have been analyzed to show the current situation of polymer research.Using Excel and VOSViewer, the data obtained from Scopus from 2018 to March 2024 were analyzed.The purpose of carrying out this study was to perform an analysis of the development and trends of work on polymers with antimicrobial and antifungal activity, with adhesions of extracts or nanoparticles for applications in medicine, agroindustry, etc.The data showed a large number of articles in high-impact journals in the Scopus database, highlighting the group work being carried out, and also the countries in which more research is being carried out, highlighting India and China.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.103 | 0.139 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".