Graphene Oxide-Functionalized Optical Sensor for Label-Free Detection of Breast Cancer Cells
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Accurate and noninvasive detection of cancer cells is critical for advancing early stage cancer diagnostics and monitoring tumor progression. While manual enumeration methods, such as hemocytometry, remain in use, they suffer from limited sensitivity and scalability. In this article, we report the first feasibility study demonstrating a graphene oxide (GO)-functionalized long-period fiber grating (LPG) sensor for the label-free detection of MCF-7 human breast cancer cell density via secreted cellular byproducts. The sensing mechanism is based on refractive index (RI) modulation in the culture medium, where the GO overlay serves as a functional interface to enhance light–matter interaction and mode coupling between the LPG device and the external medium. GO nanocoatings were deposited on the device surface via an in situ layer-by-layer (i-LbL) assembly method and characterized using scanning electron microscopy (SEM), atomic force microscopy (AFM), and Raman spectroscopy. Furthermore, by precisely controlling the thickness of the GO nanocoating, we experimentally investigated the impact of the GO thickness on the optical properties, revealing distinct thickness-dependent behavior. Resonance changes correlated clearly with metabolite accumulation, thus enabling indirect detection of cancer cell density. The GO-LPG sensor demonstrated detection of MCF-7 cell densities ranging from 0 to 1 × 10 5 cells/mL, achieving ultrahigh sensitivity with a limit of detection (LOD) as low as 270 cells/mL. This GO-functionalized fiber optic configuration offers significant potential as a real-time, label-free, and noninvasive bionanophotonic platform for cancer diagnostics and metabolic sensing in complex biological environments.
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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".