Three-Dimensional Hierarchical Plasmonic Nano-Architecture Enhanced Surface-Enhanced Raman Scattering Immunosensor for Cancer Biomarker Detection in Blood Plasma
Bibliographic record
Abstract
A three-dimensional (3D) hierarchical plasmonic nano-architecture has been designed for a sensitive surface-enhanced Raman scattering (SERS) immunosensor for protein biomarker detection. The capture antibody molecules are immobilized on a plasmonic gold triangle nanoarray pattern. On the other hand, the detection antibody molecules are linked to the gold nanostar@Raman reporter@silica sandwich nanoparticles. When protein biomarkers are present, the sandwich nanoparticles are captured over the gold triangle nanoarray, forming a confined 3D plasmonic field, leading to the enhanced electromagnetic field in intensity and in 3D space. As a result, the Raman reporter molecules are exposed to a high density of "hot spots", which amplifies the Raman signal remarkably, improving the sensitivity of the SERS immunosensor. This SERS immunosensor exhibits a wide linear range (0.1 pg/mL to 10 ng/mL) and a low limit of detection (7 fg/mL) toward human immunoglobulin G protein in the buffer solution. This biosensor has been successfully used for detection of the vascular endothelial growth factor in the human blood plasma from clinical breast cancer patient samples.
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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) | 0.000 | 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 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".