Synthesis of Electrical Conductive Silica Nanofiber/Gold Nanoparticle Composite by Laser Pulses and Sputtering Technique
Bibliographic record
Abstract
Biocompatible-sensing materials hold an important role in biomedical applications where there is a need to translate biological responses into electrical signals. Increasing the biocompatibility of these sensing devices generally causes a reduction in the overall conductivity due to the processing techniques. Silicon is becoming a more feasible and available option for use in these applications due to its semiconductor properties and availability. When processed to be porous, it has shown promising biocompatibility; however, a reduction in its conductivity is caused by its oxidization. To overcome this, gold embedding through sputtering techniques are proposed in this research as a means of controlling and further imparting electrical properties to laser induced silicon oxide nanofibers. Single crystalline silicon wafers were laser processed using an Nd:YAG pulsed nanosecond laser system at different laser parameters before undergoing gold sputtering. Controlling the scanning parameters (e.g., smaller line spacings) was found to induce the formation of nanofibrous structures, whose diameters grew with increasing overlaps (number of laser beam scanning through the same path). At larger line spacings, nano and microparticle formation was observed. Overlap (OL) increases led to higher light absorbance's by the wafers. The gold sputtered samples resulted in greater conductivities at higher gold concentrations, especially in samples with smaller fiber sizes. Overall, these findings show promising results for the future of silicon as a semiconductor and a biocompatible material for its use and development in the improvement of sensing 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) | 0.001 | 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".