Control of Aryldiazonium Reactions on Graphene Field-Effect Transistors
Bibliographic record
Abstract
Introduction: Graphene field-effect transistors (G-FETs) can be used as chemical sensors or biosensors, but functionalization of the graphene surface is usually necessary to ensure specificity in analyte capture. Covalent adducts are desirable to ensure stability of the functionalization during multiple flow cycles. Among functionalization strategies, aryldiazonium salts are often chosen for their high reactivity with carbon allotropes. In single-walled carbon nanotubes, many studies have demonstrated spontaneous functionalization reactions with this reagent leading to the formation of covalent bonds [1] [2]. Signatures of the covalent bonds could be observed, notably via a strong diminution of conductance in electrical measurements and increase of D/G ratio intensity in Raman spectroscopy. In graphene, signatures of such spontaneous grafting reactions have been far less consistent. The nature of the bond seems to vary between chemisorption (covalent) to physisorption (non-covalent) [3], and the experimental parameters controlling the reaction are yet to be established and fully understood [4]. Methods and Results: Here we analyze the effect of this chemistry on the electrical properties of graphene field-effect transistors. First, we conducted an extensive review of published experiments and developed a theoretical framework to compare data obtained in different conditions (channel dimensions, reagent concentration and incubation time). From the aggregated dataset, we found that the electronegativity of the para- group seems to have little impact on the electrical response, which contrasts with conclusions found in the literature. We also found that the type of graphene (exfoliated, CVD or RGO) seems to have a much more dominant impact, which could explain strong differences between previous studies. We also argue that device-to-device variations are significant, and we propose an experimental design based on multiple G-FETs arrays and statistical analysis to unambiguously characterize the effect of aryldiazonium functionalization on graphene transport properties. Second, we performed a systematic experimental study on the use of electrical gating to control the reactivity of graphene to arydiazonium reactions. We used 4-carboxylbenzene diazonium tetrafluoroborate to functionalize in G-FETs made of CVD-grown graphene and operated using an immersed electrode in saline buffer. We varied the potential applied on the immersed gate during the functionalization reaction and monitored the resulting effect of the chemistry through electrical measurements and hyperspectral Raman imaging. We report a strong variation in the rate and yield of formation of covalent adducts with gate potential, in particular between gate potentials above and below the Dirac point of the graphene. Conclusion and Significance: By incorporating past and recent experiments in a theoretical model, we were able to characterize the impact of key parameters on the formation of covalent adducts on graphene by aryldiazonium salts. We discuss a mechanism to explain the difference in graphene reactivity to the aryldiazonium chemistry with electrostatic potential, between graphene types, as well as between graphene and carbon nanotubes. These results will be instrumental for improving the functionalization of graphene FETs with stable covalent adducts for chemical and biological sensing applications. References: [1] Schmidt, G. ; Gallon, S. ; Esnouf, S.; Bourgoin, J.-P.; Chenevier, P. Mechanism of the Coupling of Diazonium to Single-Walled Carbon Nanotubes and Its Consequences. Chem. Eur. J. 2009, 15, 2101 – 211. [2] Dyke, C. A.; Stewart, M. P.; Maya, F.; Tour, J. M. Diazonium-Based Functionalization of Carbon Nanotubes: XPS and GC–MS Analysis and Mechanistic Implications. Synlett 2004, 1, 155-160. [3] Paulus, G. ; Wang, H.Q; Strano, M. Covalent Electron Transfer Chemistry of Graphene with Diazonium Salts. Acc. Chem. Res. 2013, 46, 1, 160-170. [4] Pembroke, E.; Ruan, G.; Sinitskii, A. et al. Effect of Anchor and Functional Groups in Functionalized Graphene Devices. Nano Res. 2013, 6: 138.
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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) | 0.001 | 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) | 0.001 | 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".