A primary SERS-active interconnected Si-nanocore network for biomolecule detection with plasmonic nanosatellites as a secondary boosting mechanism
Bibliographic record
Abstract
We report in this study, the development of a polymorphic biosensitive Si nanocore superstructure as a SERS biosensing platform. The polymorphic Si nanostructure in this study is created through ultrafast pulse laser ion-plume (UPLIP) formation and we observe enhanced detection of L-glutathione (GSH). The distinctive polymorphic nanomaterial chemistry of the interconnected network of Si nanocores and the nanonetwork architecture of this nanostructure act as a primary enhancement booster (EF similar to 6 x 10(3)). With the addition of Au/AuPd nanosatellites onto the surface of the polymorphic Si nanocore structure, a significant secondary boost in GSH enhancement is observed (EF similar to 1 x 10(5)). The addition of noble metal nanostructures results in multi-source SERS enhancement that combines linked resonance enhancement and SPR mechanisms that both contribute to the detection and boosting of the biomolecule analyte signal. With this polymorphic Si primary booster and noble metal nanosatellite secondary booster, we have demonstrated the viability of Si nanostructures for SERS biosensing 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".