Portable and field-deployed surface plasmon resonance and plasmonic sensors
Bibliographic record
Abstract
Plasmonic sensors are ideally suited for the design of small, integrated, and portable devices that can be employed in situ for the detection of analytes relevant to environmental sciences, clinical diagnostics, infectious diseases, food, and industrial applications. To successfully deploy plasmonic sensors, scaled-down analytical devices based on surface plasmon resonance (SPR) and localized surface plasmon resonance (LSPR) must integrate optics, plasmonic materials, surface chemistry, fluidics, detectors and data processing in a functional instrument with a small footprint. The field has significantly progressed from the implementation of the various components in specifically designed prism-based instruments to the use of nanomaterials, optical fibers and smartphones to yield increasingly portable devices, which have been shown for a number of applications in the laboratory and deployed on site for environmental, biomedical/clinical, and food applications. A roadmap to deploy plasmonic sensors is provided by reviewing the current successes and by laying out the directions the field is currently taking to increase the use of field-deployed plasmonic sensors at the point-of-care, in the environment and in industries.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".