Synthesis, characterization and biological activities of NiO-cellulose nanocomposite
Bibliographic record
Abstract
NiO cellulose nanocomposite (NiO-CN) were synthesized by the precipitation method and characterized by X-Ray diffraction (XRD), Transmission Electron Microscope (TEM), Scanning Electron Microscope (SEM), Energy Dispersive X-ray (EDX) analysis, Fourier transform infrared (FTIR) measurements and UV–vis spectroscopy. The particles obtained have an average size of 20-30 nm as shown by TEM analysis. Fourier transform infrared (FTIR) measurements were carried out to identify the possible biomolecules responsible for the capping and stabilization of the nickel oxide nanoparticles synthesized by milk. The presence of elements in the nanoparticles was also analysed by Energy Dispersive X-ray (EDX) analysis. The results of EDX analysis show the weight percentages of C, O, Ni, and N-elements in the synthesized material were 41.65%, 52.49%, 3.81%, and 2.06%, respectively. Scanning Electron Microscope (SEM) has been used to assess the morphology of the nanoparticle. The effects of NiO-cellulose nanocomposite are screened for biological activities like, antibacterial activity was done by the Disc diffusion method. The bacterial organisms used in this study were Bacillus subtilis, Salmonela abony, Staphylococcus aureus and Escherichia coli. The observed inhibition zone for these microorganisms was found to be a minimum of 3.0 mm and a maximum of 22.0 mm. Moreover, This NiO-CN also decreases the 50% load of Leishmania donovani via MTT assay with 25µg/ml concentration after 72 hours incubation.
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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) | 0.001 | 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".