Label-Free SERS Quantum Semiconductor Probe for Molecular-Level and in Vitro Cellular Detection: A Noble-Metal-Free Methodology
Bibliographic record
Abstract
Accurate in vitro molecular-level analysis is an essential step prior to in vivo and clinical application for early diagnosis and cancer treatment. Among the diagnostic techniques, surface-enhanced Raman scattering (SERS) biosensing has shown growing potential due to its noninvasive and real-time characterization of the biomolecules. However, the application of SERS biosensing is mostly limited to the plasmonic noble metals, in the form of either nanoparticles or tips and substrates (fixed probe), on which surface plasmon resonance (SPR) is the prominent enhancement principle. The semiconductor quantum particles have been explored in several optoelectronics applications, but have never been reported to be exploited as a means of surface-enhanced Raman scattering (SERS) for molecular-level and intracellular sensing. Here, we report on the new generation of noble-metal-free SERS probe; Si@SiO 2 quantum probe (Si@SiO 2 Q-probe) whose affinity to functional groups not only imitates a self-driven labeling attribution that enables charge transfer (CT) as an augmented enhancement principle but also its mobile nature in miniaturized scale facilitates endocytosis for in situ live cell biosensing. Moreover, a significant enhancement factor of 10 6 of rhodamine 6G (R6G) and 10 7 of glutathione (GSH) at ∼5 × 10 –12 pM concentration has been achieved that is comparable to inherently plasmonic noble metals. Our results showed a capability of the Si@SiO 2 Q-probe to unveil the “biochemical fingerprint” of substantial components of mammalian and cancerous cervical cells, which leads to diagnosis of cervical cancer. These unique attributions of the Si@SiO 2 Q-probe can provide better insight into cell mutation and malignancy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".