Gold nanostars as a colloidal substrate for in-solution SERS measurements using a handheld Raman spectrometer
Bibliographic record
Abstract
The evolution of Raman spectroscopy into a useful analytical technique has been due, in part, to the development of inexpensive, compact instrumentation and advancements in methodologies that enhance Raman intensities. Surface enhanced Raman scattering (SERS) is a primary methodology for quantitative and low detection limit measurements. While a broad array of applications using solid SERS substrates have been demonstrated, in-solution SERS measurements are not as widely pursued. This work seeks to optimize the synthesis of gold nanostars (AuNS) as a colloidal SERS substrate for in-solution measurements using handheld instrumentation. The types and concentrations of two buffers typically used for AuNS synthesis are examined to optimize the SERS intensity of a chemisorbed Raman probe. The observed SERS intensity primarily depends on conditions that allow higher surface coverage of the probe. Conditions that result in AuNS aggregates are found to be most optimal for SERS, similar to other nanoparticle shapes. A method to quantitate methimazole, an anti-hormone pharmaceutical, in urine is developed and reported. The primary impact of this work is the demonstration of the combination of water dispersible substrates and handheld instrumentation for rapid and sensitive analytical measurements.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".