Tailoring Optoelectronic Properties of ZnO Nanoparticles via Incorporation of Multiwalled Carbon Nanotube Contents on of ZnO/MWCNT Nanocomposites
Bibliographic record
Abstract
The present study demonstrates the tailoring of optoelectronic properties of ZnO nanoparticles (NPs) by adding multi-walled carbon nanotubes (MWCNTs) at different weight ratios. ZnO/MWCNT nanocomposites were successfully prepared using the solution blending method, and the optical spectra of all samples were determined using a UV–vis spectrometer. An increase in the optical absorption coefficient (α) and extinction coefficient (k) was observed with an increase in MWCNT content, resulting in the significant ability to attenuate incident light in ZnO/MWCNT nanocomposites compared to pure ZnO NPs. The optical band gap of pure ZnO NPs decreased from 3.26 eV to 2.58 eV in the ZnO/50 wt% MWCNT nanocomposite. The refractive index values ranged from 1.66 to 1.61 as the wavelength varied between 400 nm and 700 nm. Furthermore, the incorporation of MWCNTs in the ZnO/MWCNT nanocomposite had an impact on various dispersion parameters related to the refractive index. With increasing MWCNT content, the dielectric constant (ε 0) decreased from 2.40 to 1.96, while the mean oscillator wavelength (λ 0) increased from 157.7 nm to 164.2 nm and the oscillator strength (S0) decreased from 5.93 × 10−5 nm−2 to 3.70 × 10−6 nm−2. The localized density of state (N/m*) increased from 7.45 × 1057 to 20.8 × 1057 (m3 Kg) −1, and the long wavelength refractive index (ε ∞) rose from 3.683 to 4.745. Moreover, the plasma frequency (ω p) of the electron exhibited an increase from 2.15 × 1031 to 6.01 × 1031 Hz. These findings highlight the potential of tailoring the optoelectronic properties of ZnO NPs through the incorporation of MWCNTs, paving the way for the development of novel materials with improved optoelectronic characteristics for a wide range of applications.
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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".