Plant mediated green synthesis: modified approaches
Bibliographic record
Abstract
Plant mediated green synthesis of different metallic nanoparticles has emerged as one of the options for implementation of green chemistry principles, and successfully made an important contribution towards green nanotechnology. However, beyond the synthesis and application aspects, the science of green synthesis has carried some wrong perceptions in an unforeseen fashion. In this review, some of the key issues related to the green synthesis of metallic nanoparticles employing plants as reducing/capping agents have been addressed. Random selection of plants and its overall impact on the different aspects of green synthesis have been discussed. Emphasis is given to the setting of some standard selection criteria to be adopted for selecting a plant for use in green synthesis. How selection of a plant can positively or negatively influence both procedure and products of a green synthesis process is the prime concern of this article. In addition to selection, the key issue of biocompatibility associated with green synthesized metallic nanoparticles has been considered. Both selection of plant and biocompatibility were reconsidered for their minute details in terms of synthesis, analysis and data interpretation in the green synthesis approach. The key factors capable of fine tuning the core meaning of "green" in the synthesis of any metallic nanoparticles were taken into consideration. This article is an effort towards keeping the core meaning of green synthesis.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.007 |
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 teacher head, 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".