Operando Hall Measurement of Covalent Functionalization of a Ag/AgCl Gated Graphene Field Effect Transistor in a Flow Cell
Bibliographic record
Abstract
The unique electronic properties of graphene have attracted attention for its applications in sensor technologies. To fully understand and optimize graphene-based sensing and functionalization, it is necessary to quantify the effect of these processes on graphene’s charge carrier density and mobility. Here, we introduce an integrated measurement system that combines Hall effect and field effect measurement techniques with a flow cell, enabling operando, real-time characterization of graphene’s charge carrier density and mobility. The microfluidic cell includes a Ag/AgCl gate electrode, enabling electrolyte gated Hall and field effect measurements, thereby permitting operando monitoring of dynamic processes such as gate-controlled graphene functionalization. Operando measurement of gate-controlled functionalization of graphene with 4-carboxybenzene tetrafluoroborate (CBDT) reveals a real-time decrease of charge carrier mobility with a minimal change in charge carrier density. Our observations are in qualitative agreement with a resonant short-range charge carrier scattering model, appropriate to covalent attachment of CBDT to graphene. Raman spectroscopy independently confirms the covalent attachment of functional groups. The microfluidic Hall measurement technique can be extended to the study of other low-dimensional electronics systems used for sensing liquid phase analytes.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".