A Very Compact Metamaterial-Based Triple-Band Sensor in Terahertz Spectrum as a Perfect Absorber for Human Blood Cancer Diagnostics
Bibliographic record
Abstract
Abstract Nowadays, early cancer identification and surveillance have become vital problems. This research paper explores the development of a small, three-band sensor harnessing the potential of terahertz (THz) technology and metamaterials (MTMs) to diagnose blood cancer. The proposed sensor holds the promise of a paradigm shift in the diagnosis of blood cancer by offering a non-invasive and highly accurate approach. Terahertz radiation, occupying the unique “THz gap” in the electromagnetic spectrum, is now accessible due to recent technological breakthroughs. This work simplifies the design of multiple-band metamaterial absorbers, enhancing their effectiveness and expanding their sensing capabilities. Through the integration of THz technology, metamaterial engineering, and cancer detection, the suggested sensor seeks to launch a new phase of rapid, precise, and non-invasive blood cancer diagnosis. The proposed structure is capable of distinguishing cancer and normal cell with 1 GHz sensitivity, which would be more pronounced when we consider the THz technology devices. This work represents a significant step forward in non-invasive, accurate diagnostics for blood cancer, promising to revolutionize the way this disease is diagnosed and treated. The proposed novel strategy has a lot of promise to advance medical diagnostics and enhance patients’ outcomes.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".