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Optimization of 1D Photonic Crystal Biosensor for Hemoglobin Based Anemia Detection

2025· article· W7116770113 on OpenAlexfundno aff
Smruti M, Swastika S, B Divya, N Venkateswaran, A. Rajamani

Bibliographic record

Venuenot available
Typearticle
Language
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMultiphysicsBiosensorHemoglobinFinite element methodTransmittanceAnemiaRefractive indexPhotonic crystal

Abstract

fetched live from OpenAlex

Conventional anemia diagnostics such as complete blood count tests are invasive, time-consuming, and costly. This study proposes a one-dimensional photonic crystal biosensor for real-time, non-invasive anemia detection by monitoring refractive index shifts caused by hemoglobin level changes. The sensor is modeled using the Finite Element Method (FEM) in COMSOL Multiphysics and the Transfer Matrix Method in MATLAB to analyze transmittance spectra. FEM provides higher precision for complex geometries. The important design parameters include the material of choice, the thickness of layer and the defect layers features that makes the sensor suitable for diagnostic applications.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.254
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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