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 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".