Real-Time, Operando Characterization of Aryl Diazonium Functionalized Bilayer Graphene
Bibliographic record
Abstract
Potentiometric sensors are widely used for ion-sensing applications. Graphene field effect transistor (GFET) based sensors enabled by wafer-scale synthesis and thin-film processing methods offer several advantages over semiconductor sensors [1]. We introduce a covalently functionalized bilayer graphene (BiG) sensor to address the outstanding challenge of achieving improved long-term stability in sensor response. This architecture combines a conductivity sensitive to surface potential and covalently immobilized selective analyte adsorption sites. To realize BiG sensors, it is required to achieve control over bilayer graphene functionalization. While various methods such as gate controlled functionalization of monolayer graphene have been demonstrated [2], research on controlled functionalization of bilayer graphene is comparatively limited [3],[4]. We present here a novel method for real-time, operando characterization of both monolayer and bilayer graphene gate-controlled functionalization. This approach integrates ac Hall instrumentation with electrochemical functionalization in a microfluidic chamber for simultaneous measurement of resistance and charge carrier density. This technique has enabled us to achieve functionalized bilayer graphene with about 50% reduction in conductivity with aryl diazonium salt chemistry. This method enables a comprehensive characterization of graphene, including the measurement of charge carrier density, Hall mobility, field effect mobility and capacitance. We will present operando measurement of charge carrier density during functionalization, with complementary Raman spectroscopy characterization of functionalization. Charge transfer from functional groups to bilayer graphene in response to environmental pH changes will be presented, giving insight into potentiometric pH sensing with bilayer graphene field effect transistors. In addition, we introduce a surface binding model to describe the charge transfer process between covalent functional groups and bilayer graphene. This model provides a framework for quantifying the dissociation constant of functional groups, such as carboxyl groups, and the density of active binding sites on the BiG surface. Future work aims to enhance the selectivity of BiG sensors by integrating ionophores into the diazonium-derived functional groups. This would enable the development of advanced sensors designed for selective ion sensing. References: [1] Fakih et al., Nature Comm. 11, 3226 (2020). [2] M. Bazan et al., Nano. Lett. 22(7), 2635-2642 (2022). [3] H. Wang et al., J. Am. Chem. Soc 135(50), 18866-18875 (2013). [4] J. Shih et al., Nano. Lett. 13(2), 809-817 (2013). Figure 1
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".