Neutron Scattering to Probe Nanostructure-Property Relationships in Composite Supercapacitor Electrodes
Bibliographic record
Abstract
Supercapacitors are a promising renewable energy device for mobile power supply, due to high power density, long cycle life and high safety, but are limited by relatively poor energy density. [1] Surface area of active material and distribution of ions that bind to the electrode surface are key performance indicators. Reduced graphene oxide (rGO) with high density and surface area (2675 m 2 g -1 ), as well as high electrical conductivity and chemical stability, is an ideal candidate for high volumetric energy density devices, but sheet restacking limits pore utilization. [2] An aqueous IL–surfactant microemulsion system has been developed that facilitates spontaneous adsorption of IL-filled micelles onto rGO (IM-rGO). [3-5] The surfactant helps distribute the IL (EMImTFSI) on the rGO surface, resulting in intercalated IL which acts as spacer and electrolyte with large electrochemical window (fig.1). This novel material system has overcome major challenges, whereby tuning of the IL and surfactant content provides record high volumetric capacitances (144 F cm -3 for 60% IL). Binding and transport of EMIm + (diameter ~8 Å) is the critical mechanism of charge storage in these devices and is affected by pore size and confinement, [6] interfacial interactions and structuring, [7] and electrolyte infiltration. We have applied small angle scattering, neutron reflectivity and quasi-elastic neutron scattering (QENS) to investigate the effect of IL, surfactant and rGO composition on nanoscale pore structure and size, and ion diffusion. Structural insight of the three-phase system from solution precursors to solid electrode films of varied composition will be presented. QENS results, that distinguished ion dynamics at nanoscale time and spatial resolution, will be discussed in relation to structure and electrochemical performance to give further insight to the charging mechanism within this novel electrode system. These results not only allow optimisation of these composite electrodes and identify critical design parameters for optimised pore utilization and conductivity within carbon-based supercapacitor electrodes but importantly provide fundamental insight into nano-confined IL conduction mechanisms, which are still poorly understood. References: [1]Wang, Q., et al . Energy Environ. Sci. , 2016, 9 , 729-762. [2] Pope, M. A., et al . J. Phys. Chem. C , 2015, 119 , 20369-20378. [3] She, Z. et a l., ACS Nano , 2017, 11 , 10077-10087 [4] Lashkari, S., et al . J. Mater. Chem. A , 2023, 11 , 11222-11234 [5] Lashkari, S., et al ., Langmuir , 2022, 38 (1), 531-540. [6] Berrod, Q., et al ., Nanoscale , 2016, 8 , 7845. [7] Mezger, M., et al ., Science , 2008, 322 , 424-428. Figure 1
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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 | 0.001 |
| 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".