Synthesis, characterization and biological activities of NiO-cellulose nanocomposite
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it