Logical discrimination of multiple disease-markers in an ultra-compact nano-pillar lab-in-a-photonic-crystal
Bibliographic record
Abstract
We present a theoretical prescription for a physically realizable Lab-in-a-Photonic-Crystal optical biosensor that can instantaneously detect and discriminate multiple analytes, both quantitatively and combinatorially, in a single spectroscopic measurement. Unlike other biosensors that utilize simple resonance modes, our fundamental operating principle is the analyte-induced hybridization of waveguide modes and surface modes in a photonic bandgap, leading to a complex spectral fingerprint. Our real-world liquid-infiltrated photonic crystal sensor supplants two-dimensional conceptual paradigms proposed earlier with realistic features and a path to implementation. A square-lattice photonic crystal of nanopillars with fixed height but differentiated cross sections within a narrow flow-channel is used for cascaded transmission of light through the photonic bandgap. The nanopillar array is placed on a thin layer of high-refractive-index backing material resting on a glass substrate with fluid and biomarker flow along the waveguide direction. Using finite-difference time-domain simulations of light transmission perpendicular to the waveguide, a variety of spectral fingerprints are identified as various disease-marker combinations bind to specific lines of nanopillars. Various diseases or various stages of a given disease are detected and differentiated through the interplay of central-waveguide resonances with edge modes and three-dimensional index-guided bulk modes. This offers a distinctive mechanism for instantaneous disease diagnosis using a minimal volume of fluid sample.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".