Ultrasensitive rapid cytokine sensors based on asymmetric geometry two-dimensional MoS2 diodes
Bibliographic record
Abstract
Abstract The elevation of cytokine levels in body fluids has been associated with numerous health conditions. The detection of these cytokine biomarkers at low concentrations may help clinicians diagnose diseases at an early stage. Here, we report an asymmetric geometry MoS2 diode-based biosensor for rapid, label-free, highly sensitive, and specific detection of tumor necrosis factor-α (TNF-α), a proinflammatory cytokine. This sensor is functionalized with TNF-α binding aptamers to detect TNF-α at concentrations as low as 10 fM, well below the typical concentrations found in healthy blood. Interactions between aptamers and TNF-α at the sensor surface induce a change in surface energy that alters the current-voltage rectification behavior of the MoS2 diode, which can be read out using a two-electrode configuration. The key advantages of this diode sensor are the simple fabrication process and electrical readout, and therefore, the potential to be applied in a rapid and easy-to-use, point-of-care, diagnostic tool.
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 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".