Perovskite‐Graphene Heterostructure Biosensor Integrated with Biotunable Nanoplasmonic Ternary Logic Gate for Ultrasensitive Cytokine Detection
Bibliographic record
Abstract
Abstract The integration of 2D‐materials and optoelectronic devices has attracted great attention for advanced applications. We propose the first perovskite/graphene heterostructure‐based FET biosensor with uniquely biotunable ternary logic gating functionality. The biosensor integrates a lateral perovskite‐on‐graphene heterostructure phototransistor with a vertical bio‐nano‐photonic filter, with a decoupled construction inset. In the phototransistor, photoactive perovskite quantum dots (PQDs) serve as sensitizers to absorb light while a high mobility single‐layer graphene (SLG) acts as an expressway for carrier transport. In the bio‐nano‐photonic filter, a localized surface plasmon resonance (LSPR) is induced by gold nanoparticles (AuNPs) in conjunction with antigen‐antibody binding, tuning the delivery of light passing through the filter and facilitating biotunable functionality with ternary modes. The biosensor is set up to detect human interleukin‐6 (IL6) in order to determine and achieve ultrahigh sensitivity with a limit of detection (LOD) of 0.9 fg mL −1 (43 aM), which is 4 orders of magnitude greater than graphene‐FET biosensors. This ultrahigh sensitivity is achieved due to the synergistic effect of PQDs/SLG heterostructure, exhibiting superior electrical, optical, and physicochemical properties, consequently providing significantly high performance of the biosensor in terms of label‐free, ultrahigh sensitivity (attomolar level), rapid responsivity (5 min), excellent stability, and selectivity. This heterostructure‐based biotunable configuration could open a new avenue for 2D materials in the realm of next‐generation bio‐nano‐photonic platforms for applications in healthcare, early diagnosis, and rapid detection.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".