A field-deployed surface plasmon resonance (SPR) sensor for RDX quantification in environmental waters
Bibliographic record
Abstract
A field-deployable surface plasmon resonance (SPR) sensor is reported for the detection of the energetic material (commonly termed explosives) 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) at ppb concentration in environmental samples. The SPR sensor was first validated under laboratory conditions with uncontaminated natural water samples spiked with known concentrations of RDX near the EPA limit of 2 ppb, which was then applied to monitor environmental samples collected in different downgradient wells near a grenade training range. The SPR sensor was finally tested on the field, where environmental samples were analysed on location in less than 90 minutes per well, which included the time to setup the equipment, sample the well and analyse the sample. The SPR analysis time was less than 45 minutes for equilibration, recalibration and measuring the water sample. Results obtained with the SPR sensors were cross-validated with the standard HPLC method (EPA method 8330b), and they showed good agreement with an accuracy within less than 1.6 ppb for analysis at the sampling sites, and with the relative standard deviation (RSD) better than 20% for field and laboratory measurements. The SPR sensor worked in a range of environmental conditions, including operation from about 0 °C to nearly 30 °C. The instrument was easily deployed near the sampling site using motor vehicles under summer conditions (Lab-in-a-Jeep) and using a sled under winter conditions (Lab-on-a-sled), showcasing the field deployability of the RDX SPR sensor and the possibility of continuously monitoring RDX in the environment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".