Toward Universal SERS Detection of Disease Signaling Bioanalytes Using 3D Self-Assembled Nonplasmonic near-Quantum-Scale Silicon Probe
Bibliographic record
Abstract
Currently, the quantum-scale surface-enhanced Raman scattering (SERS) properties of Si materials have yet to be discovered for universal biosensing applications. In this study, a potential universal biosensing probe is generated by activating the SERS functionality of Si nanostructures through near quantum-scale (nQS) engineering. We introduce herein 3D nonplasmonic Si nanomesh structure with nQS defects for SERS biosensing applications. Through ionization of a single-crystal defect-free Si wafer, highly defect-rich Si subnano-orbs (sNOs) are fabricated and self-assemble as connective 3D Si nanomesh structures with enhanced SERS biosensing activity. By amending the laser ionization and ion–ion interactions, we observe the controlled synthesis of engineered nQS defects in the form of nQS-grain boundary disorder or surface nQS voids within the interconnected Si sNOs. To our knowledge, it is shown here for the first time that defect-rich Si nanomesh structures exhibit enhanced Raman activity, with the nQS morphological and crystallographic defects acting as the prime SERS contributors without a plasmonic contribution. The SERS biosensing sensitivity with the synthesized defect-rich Si nanomesh structures without an additional plasmonic material was evaluated using of a tripeptide biomarker l -glutathione (GSH); we observe an enhancement factor value of ∼10 2 for the GSH biomolecules with 10 –9 M sensitivity, a phenomena to our knowledge that has yet to be reported. Additionally, the SERS detection of multiple disease-signaling biomolecules (cysteine, tryptophan, and methionine) is achieved at very low analyte concentration (10 –9 M). These results indicate a potential new dimension to universal SERS biosensing applications with these unique nonplasmonic defect-rich 3D nQS-Si nanostructures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".