High-Throughput Fabrication of Microscale Gold Nanoparticle Superstructures with Tunable Plasmonic Coupling for Ultrasensitive SERS Detection
Bibliographic record
Abstract
Noble metal nanostructures are attractive substrates for surface-enhanced Raman scattering (SERS) but face persistent challenges in combining efficient hot-spot engineering with scalable fabrication. This paper presents a high-throughput, hot-spot-designable approach for assembling gold nanoparticles (AuNPs) onto micrometer-scale, two-dimensional polymer single-crystal templates, producing ∼10 8 uniform AuNP assemblies per milliliter. The assemblies, with planar micrometer-scale dimensions, are fully compatible with commercial confocal Raman systems. AuNP shape (spherical or anisotropic) is controlled through chemical reduction kinetics. Systematic SERS measurements reveal that precisely assembled spherical AuNPs with sub-2 nm gaps generate strong local electromagnetic field enhancement, yielding an analytical enhancement factor (AEF) of 1.43 × 10 6 . Compared with a commercial macroscopic SERS substrate of monolayer AuNPs, the proposed substrate delivers a 28-fold higher signal intensity while requiring 98% less AuNP surface coverage. Its applicability is demonstrated by the detection of biomolecules in sweat and malondialdehyde. This method provides a scalable route for high-throughput molecular sensing in real-world applications.
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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 | 0.000 | 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".