ON THE FORMATION OF TITANIUM/TITANIUM OXIDE NANOFIBROUS STRUCTURES AND NANOSPHERES USING FEMTOSECOND LASER IN AIR
Bibliographic record
Abstract
In this paper, we report a unique growth of nanofibrous structures and nanospheres of titanium using femtosecond laser in air and without the need for any type of catalyst. The femtosecond laser was used to generate nanoparts on a titanium substrate. The irradiated substrate is assumed to be subjected to plane stress type of temperature variation and a new method combining finite difference and Runge–Kutta 4 transient thermal model has been developed to calculate the temperature distribution on the top surface of the substrate during laser ablation. A Matlab code has been developed and validated with the known results from the literature. Scanning electron microscopy (SEM), energy-dispersive X-ray (EDX), X-ray diffractograms (XRD) and micro-Raman analysis were conducted to characterize the microstructure and revealed metallic and oxide phases in the nanostructure analyses. Results showed that nanofibers and nanospheres were grown in the order of few hundreds nanometers or more. The effect of the laser power on the energy/pulse and hence the temperature was studied. It was found that high temperature results in the formation of nanofibers while lower temperature results in formation of nanospheres. This first time observation could have potential application in biomedical, optoelectronics and photocatalysis.
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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".