Detection of Buried Explosives Using a Surface-Enhanced Raman Scattering (SERS) Substrate Tailored for Miniaturized Spectrometers
Why this work is in the frame
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Bibliographic record
Abstract
The advent of miniaturized, fiber-based, Raman spectrometers provides a clear path for the wide implementation of surface-enhanced Raman scattering (SERS) in analytical chemistry. For instance, miniaturized systems are especially useful in field applications due to their simplicity and low cost. However, traditional SERS substrates are generally developed and optimized using expensive Raman microscope systems equipped with high numerical aperture (NA) objective lenses. Here, we introduced a new type of SERS substrate with intrinsic Raman photon directing capability that compensates the relatively low signal collection power of fiber-based Raman spectrometers. The substrate was tested for the detection of buried 2,4-dinitrotoluene in simulated field conditions. A linear calibration curve (R2 = 0.98) for 2,4-dinitrotoluene spanning 3 orders of magnitude (from μg kg–1 to mg kg–1) was obtained with a limit of detection of 10 μg kg–1 within a total volume of 10 μL. This detection level is 2 orders of magnitude lower than that possible with the current state-of-the-art technologies, such as ion mobility spectrometry–mass spectrometry. The approach reported here demonstrated a high-performance detection of 2,4-dinitrotoluene in field conditions by a SERS platform optimized for miniaturized Raman systems that can be deployed for a routine inspection of landmine-contaminated sites and homeland security applications.
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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 | 0.000 | 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 it