Programmable SERS active substrates for chemical and biosensing applications using amorphous/crystalline hybrid silicon nanomaterial
Bibliographic record
Abstract
We present the creation of a unique nanostructured amorphous/crystalline hybrid silicon material that exhibits surface enhanced Raman scattering (SERS) activity.This nanomaterial is an interconnected network of amorphous/crystalline nanospheroids which form a nanoweb structure; to our knowledge this material has not been previously observed nor has it been applied for use as a SERS sensing material.This material is formed using a femtosecond synthesis technique which facilitates a laser plume ion condensation formation mechanism. By fine-tuning the laser plume temperature and ion interaction mechanisms within the plume, we are able to precisely program the relative proportion of crystalline Si to amorphous Si content in the nanospheroids as well as the size distribution of individual nanospheroids and the size of Raman hotspot nanogaps.With the use of Rhodamine 6G (R6G) and CrystalViolet(CV) chemical dyes, we have been able to observe a maximum enhancementfactor of 5.38×106 and 3.72×106 respectively, for the hybrid nanomaterial compared to a bulk Si wafer substrate.With the creation of a silicon-based nanomaterial capable of SERS detection of analytes,this work demonstrates a redefinition ofthe role of nanostructured Si from an inactive to SERS active role in nano-Raman 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 | 0.001 |
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".