Detection of a surrogate biological agent with a portable surface plasmon resonance sensor onboard an unmanned aircraft system
Bibliographic record
Abstract
A system was developed to perform near real-time biological threat agent (BTA) detection with a small autonomous unmanned aircraft system (UAS). Biological sensors recently reached a level of miniaturization and sensitivity that have made UAS integration a feasible task. A surface plasmon resonance (SPR) biosensor was integrated into a small UAS platform for the first time, providing the UAS with the capability to collect and then quantify the concentration of a surrogate biological agent in near realtime. The sensor operator ran the SPR unit through a ground-station laptop, viewing the sensor data in real time during flight. An aerial sampling mechanism was also developed for use with the SPR sensor. The sampling system utilized a custom impinger setup to collect and concentrate aerosolized particles. The SPR and sampling system's feasibility was demonstrated using an aerosolized sucrose solution as a mock BTA. Three field experiments were carried out to test and validate the biological sampling system. In the first field experiment, the collection system was tested by flying the UAS through a ground-based plume of water-soluble blue dye. In the second field experiment, a sucrose solution was autonomously aerosolized, collected, and then detected by the combined sampling and SPR sensor subsystems onboard the UAS. In the third field experiment, a dye was released from one UAS (the leader) and captured by another UAS (the follower). Together, these field experiments illustrate the capability of the UAS to detect and quantify the concentration of a BTA released at altitude. Our integrated SPR system sets the stage for future work to detect and track BTAs in the atmosphere and assist in localizing their sources.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".