In situ assembly of active surface-enhanced Raman scattering substrates via electric field-guided growth of dendritic nanoparticle structures
Bibliographic record
Abstract
Surface-enhanced Raman scattering (SERS) can provide ultrasensitive detection of chemical and biological analytes down to the level of a single molecule. The need for costly, nanostructured, noble-metal substrates, however, poses a major obstacle in the widespread application of the method. Here we present for the first time a novel type of metallic nanostructured substrates that, not only exhibit a remarkable SERS activity, but are also produced in a facile, cost-effective and nanofabrication-free manner. The substrates are formed through an electric field-guided assembly process of silver nanocolloids, which results in extended and interconnected dendritic nanoparticle structures with a high density of "hot spots". A unique and significant performance attribute of these nanostructures is their ability to also function as concentration amplification devices, thereby further enhancing their analyte detection efficiency. This major advantage against conventional SERS substrates is illustrated experimentally here with the concentration and detection of proteins from solution. Low limits of detection for illicit drugs, food contaminants and pesticides in relevant matrices are also demonstrated. The SERS-active dendrites are reusable and can be removed and replaced in a few minutes. The SERS substrates presented herein constitute a significant advance towards more effective and less expensive diagnostic tools.
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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".