Fast, highly sensitive and label free detection of small genetic sequence difference of DNA using novel Surface-Enhanced Raman Spectroscopy nanostructured sensor
Bibliographic record
Abstract
In this work we present a fast and label-free technique for biomolecules detection. The approach has been proved to be powerful to investigate small DNA mutation. Surface enhanced Raman spectroscopy (SERS) is an outstanding technique for DNA analyses by providing a specific fingerprint of chemical structure with a high sensitivity in a very short acquisition time. Homogeneous decoration of Silicon nanowires (SiNWs) by silver nanoparticles (Ag-NPs) was carried out using pulsed laser deposition (PLD) technique. SiNWs have been synthesized via metal-assisted chemical etching (MACE) method. We investigate in this work the effect of the Ag-NPs nanodecoration conditions through the variation of the laser ablation pulses number (NLAP). Thus, the Ag-NPs decorated SiNWs were used as sensors to detect organic and biomolecules by means of Surface Enhanced Raman Spectroscopy (SERS). By varying the NLAP, we were able to identify the optimal combination of Ag-NPs' size and surface coverage that yields the highest SERS signal. SEM images revealed well-ordered SiNWs (~2.4 μm-long and 30–60 nm diam.) with their uniform decoration by Ag-NP. High resolution-TEM analyses confirmed the effective decoration of the SiNWs by Ag-NPs of which average size is found to increase linearly from ~20 to 50 nm when the NLAP is increased from 500 to 10,000. The Ag-NPs/SiNWs matrix shows significantly higher (150 fold) Raman signal compared to their Ag-NPs-decorated-flat‑silicon counterparts. We found that SERS efficiency is sensitive to the nanoparticles size and reaches its maximum of (1.6 × 106) for the Ag-NPs having the optimal diameter of ~40 nm (obtained at NLP = 5000). The developed sensor proved to be highly sensitive to detect upto pico-molar concentrations of R6G. These Ag-NPs/SiNWs probes were demonstrated to have outstanding potential for label free detection of DNA samples with high sensitivity and reproducibility. It was found that the developed nanohybrid sensor is able to differentiate DNAs with very small genetic sequence difference.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".