A Concave Nanogap for Ultrasensitive Aptamer-Based SERS Detection and <i>In Situ</i> Imaging of Heavy Metal Ions
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Heavy metal pollution can lead to irreversible damage to human health. Surface-enhanced Raman scattering (SERS) platforms are versatile for toxicity monitoring and in situ imaging, but they are often limited by false positives. In this study, we proposed the use of concave-facet nanocubes (CF NCs) to create sufficiently large nanogaps, which can be used for the deposition of probes such as aptamers. Aptamers adopt an extended configuration within these nanogaps, effectively preventing compression-induced false signals. By utilizing the robust electromagnetic field present within the nanogaps, the developed SERS platform has demonstrated a broad detection range of 0.1 to 1000 nM for Hg 2+ and a limit of detection as low as 0.1 nM. This platform was further applied to the analysis of Pb 2+ and Cd 2+ . Furthermore, this system was applied to visualize the distribution of Hg 2+ ions in zebrafish larvae, providing detailed insights into Hg 2+ bioaccumulation. This work presents a universal strategy for concave-shaped nanoparticle-assisted SERS detection of analytes with weak or no Raman signatures, such as heavy metal ions, while reducing false-positive results in analysis. It also underscores the promising potential for targeted in situ imaging in biological samples, serving as a tool for investigating the mechanisms of toxicity in various substances.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".