A Review of Different Synthesis Approaches to Nanoparticles: Bibliometric Profile
Bibliographic record
Abstract
Nanomaterials are currently one of the most popular emerging materials used in different applications such as drug delivery, water treatment, cancer treatment, electronic, food preservations, and production of pesticide. This is due to their interesting features including size-dependent properties, lightweight, biocompatibility, amphiphilicity and biodegradability. They offer wide possibilities for modification and are used in multiple functions with enormous possibilities. Some of them are medically suitable which has opened new opportunities for medical improvement especially for human health. These characteristics also make nanomaterials one of the pioneers in green materials for various needs, especially in environmental engineering and energy sectors. In this review, several synthesis approaches for nanoparticles mainly physical, chemical, and biological have been discussed extensively. Furthermore, bibliometric analysis on the synthesis of nanoparticles was evaluated. About 117,162 publications were considered, of which 92% are journal publications. RSC Advances is the most published outlet on the synthesis of nanoparticles and China has the highest number of researchers engaged in the synthesis of nanoparticles. It was noted in the evaluation of synthesis approach that biological approach is the savest method but with a low yield, while the chemical approach offers a high yield with some level of hazardous effect. Also, the bibliometric analysis revealed that the field of nanotechnology is a trending and hot ground for research.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.024 | 0.032 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".