Green synthesis and physiochemical characterization of nickel oxide nanoparticles: Interaction studies with Calf thymus DNA
Bibliographic record
Abstract
One of the most promising applications of nanomaterials is that of nanobiosensors, using biomolecules such as nucleic acids as receptors. This study aimed to synthesize nickel oxide nanoparticles (NiO NPs) by an environmentally friendly green synthesis, using the extract of the herb Coriandrum sativum (coriander). The synthesized NPs were characterized using UV-Visible spectroscopy, Fourier transform infrared spectroscopy, X-ray diffraction, X-ray photon spectroscopy, field emission scanning electron microscopy coupled with energy dispersive spectroscopy, dynamic light scattering, zeta potential and transmission electron microscopy. All results confirmed the synthesis of pure, spherical, positively charged NiO NPs of around 95 nm in diameter with prominent hydroxyl groups attached to the surface. Furthermore, interaction studies of synthesized NiO NPs with calf thymus DNA (CT DNA) were performed using UV-Visible spectroscopy, UV-thermal melting, circular dichroism, and fluorescence spectroscopy. CT DNA served as a substitute for nucleic acid biosensors. All experimental studies indicated that the NiO NPs bound electrostatically with CT DNA. These studies may facilitate exploring the potential of NiO NP-nucleic acid conjugated materials to be used as nanobiosensors for various applications, especially in pharmacological, epidemiological, and environmental diagnostic applications, and in detection.
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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".