Portable and field-deployed surface plasmon resonance and plasmonic sensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it