Intra-nanogap controllable Au plates as efficient, robust, and reproducible surface-enhanced Raman scattering-active platforms
Bibliographic record
Abstract
Practical application of surface-enhanced Raman scattering (SERS)-active platforms requires that they provide highly uniform and reproducible SERS signals. Moreover, to achieve highly stable and consistent SERS signals, it is important to control the nanostructured gaps of SERS-active platforms precisely. Herein, we report the synthesis of gap-controllable nanoporous plates and their application to efficient, robust, uniform, and reproducible SERS-active platforms. To prepare well-defined nanoporous plates, ultraflat, ultraclean, and single-crystalline Au nanoplates were employed. The Au nanoplates were transformed to AuAg alloy nanoplates by reacting with AgI in the vapor phase. The Ag in the alloy nanoplates was then chemically etched, thus forming well-defined SERS-active nanoporous plates. For the precise control of gaps in the nanoporous plates, we investigated the alloy forming mechanism based on X-ray photoelectron spectroscopy and transmission electron microscopy analyses. According to the mechanism, the composition of Ag was tunable by varying the reaction temperature, thus making the nanostructured gaps of the porous plates adjustable. We optimized the nanoporous plates to exhibit the strongest SERS signals as well as excellent uniformity and reproducibility. The computational simulation also supports the experimental SERS signals of nanoporous plates. Furthermore, we successfully performed label-free detection of a biocide mixture (5-chloro-2-methyl-4-isothiazolin-3-one/2-methyl-4-isothiazol-3-one) up to 10 ppm using Au nanoporous plates. The adoption of single-crystalline Au nanoplates, the novel synthesis method for alloy nanoplates in the vapor phase, and the investigation of alloy forming mechanisms synergistically contributed to the formation of well-defined nanoporous plates. We anticipate that the nanoporous plates will be useful for the practical sensing of trace chemical and biological analytes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".