Long-term stability of N-heterocyclic carbene (NHC) functionalized organic electrochemical transistor (OECT) for biosensing applications
Bibliographic record
Abstract
The increasing demand for the rapid identification of various pathogens and disease biomarkers makes it essential to develop selective and reliable biosensors. The three basic components of a biosensor are: (i) the bioreceptor that binds to the target analyte, (ii) the transducer that converts the signal, and (iii) a signal processing circuit. Integrating the biorecognition elements onto the transducer surface is a critical step that governs the selectivity and reliability of biosensors. Here, we present a novel approach for functionalizing aerosol jet-printed organic electrochemical transistors (OECTs) for biosensing applications. Our design utilizes a printed Au gate modified with N-heterocyclic carbene (NHC) linkers for biofunctionalization. NHC was selected due to its excellent stability and high binding affinity with transition metals, facilitating a robust biofunctionalization mechanism. Utilizing the NHC-Au surfaces, we developed OECT-based biosensors that successfully detected the biotin-streptavidin (biotin-SA) binding events as threshold voltage shift (ΔV T ) of 193 ± 64 mV, which is approximately three-fold of that for bovine serum albumin (BSA) (62 ± 41 mV), indicating the NHC functionalized OECT-based biosensor is selective towards the target analyte. In addition, the NHC-Au electrode and the printed OECT both remained functional after 24 months of storage at room temperature, with comparable performances (ΔV T = 161 ± 30 mV for SA binding) as the freshly prepared ones, demonstrating outstanding long-term stability. To the best of our knowledge, this is the first study combining NHC and OECT for biosensing and showcasing 24-month long-term stability. Given the versatility of NHCs in forming highly stable covalent bonds with most transition metals, this study is an important demonstration of their application in bioelectronics. Thus, we have shown a prominent biosensor development technology based on carbenes and organic electronics, which can be adapted to various biomolecule detection and biomedical applications. The exceptional stability of the printed OECTs and NHC functionalized gate highlights their potential for long-term biosensing applications, paving the way for reliable bioelectronics.
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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 | 0.001 |
| 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 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".