Apprehension of Resistive Characteristics of Plasma Ionized Hybrid Nano Fibrous Silicon
Bibliographic record
Abstract
Literature review done for the study identified number of challenges with the conventional way of manufacturing nano patterned surfaces. Also, it indicated immense potential of nano structured surface as a sensing surface for numerous applications. Without using any complex and expensive conventional nano manufacturing method, synthesis of Hybrid Nano fibrous Silicon structure (HNfSi) was made possible by identifying useful laser and scanning parameters in this study.\nTo employ such structure for various sensing applications as well as new generation batteries and capacitors, understanding of its resistive behavior was quite necessary. In this study, HNfSis bulk resistance and thickness based resistivity with variation in different laser and scanning parameters was studied successfully. Methods like 4-point resistivity measurement and parallel plate electrode configuration was employed to understand resistive behavior of HNfSi. In addition, considering immense surface area available with such structure and its benefits identified with literature review, ImageJ analysis was done to comprehend change in topological constituents and its dimensions with the variation in specified parameters. To understand such change in resistive behavior, various surface and material characterization methods like, SEM (Scanning electron microscope), Raman spectroscopy, light spectroscopy and EDX (Energy-dispersive X-ray spectroscopy) was employed. Overall, with this study, important laser parameters to generate HNfSi was identified successfully and their respective resistive characteristics were understood using mentioned methods.
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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 | 0.002 | 0.001 |
| 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".