Probing Electrochemical Structure-Property Relationships at Non-Porous Monolayer Electrodes of Exfoliated Graphene and MoS<sub>2</sub> Single Layers
Bibliographic record
Abstract
The electrochemically available surface area and porosity of 2D materials is difficult to control due to the strong aggregation and restacking phenomena that occur when single layers are processed into thin or thick film electrodes. Furthermore, the distributed resistance, the potential for pore-size related effects and the uncertainty in relating estimated surface area and porosity data to the actual electrochemically accessible surface area make measurements of the intrinsic electrochemical performance of these materials difficult to assess. Ideally, electrochemical measurements are carried out on flat, polished or atomically flat electrode surfaces for the evaluation of intrinsic double-layer capacitance and electrochemical rate constants. In this talk, we will discuss our efforts to create large-area monolayers of solution exfoliated 2D materials for electrochemical evaluation in aqueous and non-aqueous electrolytes. Using a modified Langmuir-Blodgett technique, we demonstrate the ability to create uniform and large area monolayers of graphene oxide, thermally reduced graphene oxide as well as 1T and 2H molybdenum disulfide which can be transferred to atomically flat electrode substrates such as highly oriented pyrolytic graphite. These films are found to be stable during electrochemical cycling in various electrolytes and allow us to probe electrochemical properties as a function of 2D material structure without porosity-related artifacts. This model system has been used to probe the changes in double-layer capacitance in both aqueous and non-aqueous electrolytes for graphene-based materials as a function of defect density. It has allowed us to estimate the theoretical limits of graphene-based supercapacitors as a function of graphene “type”. More recently, this analysis has been extended to probe the intrinsic capacitance of the metallic 1T and semiconducting 2H polymorphs of MoS2 as a function of layer number to probe the charging mechanisms at play. The system also offers the ability to probe the capacitance of graphene/MoS2 heterostructures. These and future studies are expected to provide valuable insight into how we might build more energy dense supercapacitors from the optimal combination of 2D materials.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".