Biogenic Synthesis of Zinc Oxide (ZnO) Nanoparticles Using a Fungus (Aspargillus niger) and Their Characterization
Bibliographic record
Abstract
Nanoparticles are ultrafine structures with dimensions less than 100 nm. Nanoparticles have diverse applications. There are three important methods of fabrication of nanoparticles namely physical, chemical and biological methods. Physical method is a top down strategy for the fabrication of nanoparticles. It is energy intensive and time consuming. A chemical method is simple, but is expensive and requires expensive chemicals with high purity and also involves hazards of contaminations. Biological synthesis is very simple, cheap and environment friendly, requiring no expensive chemicals, temperature and is time saving. Plants and microorganisms are commonly used in this method. These are available everywhere. In the present work we synthesized Zinc Oxide (ZnO) nanoparticles by biological method using Aspargillus niger and zinc chloride (ZnCl2) as precursors. Biogenic synthesis of metallic nanoparticles by fungi is a safe and economical process because of formation of stable and small sized nanoparticles. Fungal biomass secretes proteins which act as reducing and stabilizing agents. The synthesized nanoparticles were characterized by XRD (X-Ray Diffraction), SEM (Scanning Electron Microscopy), UV-Vis (Ultraviolet, Visible) and EDX (Energy Dispersive X-Ray) techniques. Their size was in nm range and morphology of synthesized ZnO NPs was hexagonal. The ZnO nanoparticles are one of the most versatile materials and are used in cosmetics and in Bioenergy production, as a catalyst and as antibacterial material.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".