High Energy Density Porous RuO<sub>2 </sub>micro-Supercapacitors Using Protic Ionic Liquid Electrolytes
Bibliographic record
Abstract
The rapid development of the Internet of Things (IoT) demands reliable and long-term energy supply to microelectronic devices distributed over the network with high power performance and less maintenance required.[1] Micro-supercapacitors (MSCs) have come to the foreground as miniaturized energy storage devices showing outstanding power density and long cycle life. However, the low cell voltage and low energy density remain major bottleneck that prevents their adoption in real device applications. To this end, several studies have been devoted to the engineering of MSC electrode materials and structural architecting of current collectors within the limited available footprint. This approach could offer opportunities to enhance the electrochemically active surface and mass loading of active materials with fast ion diffusion kinetics, leading to high areal energy density performance.[2] Although several efforts have been put forth in this direction, the complex synthesis route, unfavourable interfacial and mechanical stability of the electrode, electrolyte compatibility issues, etc. remain an arduous challenge.[3] The low cell voltage is another major issue preventing from achieving high energy density values in MSCs as it is directly linked to the electrochemical stability window (ESW) of the electrolytes used.[4] In addition, liquid-state electrolytes currently employed are inappropriate for the microfabrication route as it is prone to evaporation, leakage, and potential safety issues. Hence, a lot of research attention has been given to developing solid-state electrolytes able to afford large operational windows and help promote the application of on-chip MSCs. In this work, we have demonstrated the use of protic ionic liquid (PIL)-based electrolytes able to provide pseudocapacitance in hydrous ruthenium dioxide (RuO2) electrodeposited on interdigitated MSC substrates with extended operational cell voltage. As a pseudocapacitive material, RuO2 exhibits excellent conductivity, high electrochemical reversibility, and cycling stability.[5] On the other hand, as room temperature molten salts, PILs help to overcome evaporation and encapsulation problems associated with the conventional aqueous electrolytes and flammability and safety issues linked to common organic electrolytes.[6-9] We explored pyrrolidinium-based PILs with varying alkyl substitutions, their structure-property, and electrochemical studies for RuO2 MSCs. To further expand the use of these PILs in real devices, 3D MSCs with higher active material mass loading was realised using interdigitated porous Au current collector substrates. The PIL-based porous RuO2 MSCs showed superior charge storage and higher energy density performance as compared to conventional aqueous electrolytes. To envision the practical application of RuO2 MSCs and their subsequent integration with microelectronic devices, ionogel-based solid-state electrolytes were developed. This work opens pathways to develop micro-supercapacitors exhibiting high energy and power density by combining pseudocapacitive metal oxide-based active materials and protic ionic-liquid-based non-aqueous electrolytes and help their integration with on-chip IoT devices. References [1] N. A. Kyeremateng, T. Brousse, D. Pech, Nat Nanotechnol 2017, 12, 7. [2] C. Lethien, J. Le Bideau, T. Brousse, Energ Environ Sci 2019, 12, 96. [3] Y. Li, S. Xiao, T. Qiu, X. Lang, H. Tan, Y. Wang, Y. Li, Energy Storage Mater 2022, 45, 741. [4] C. Zhong, Y. Deng, W. Hu, J. Qiao, L. Zhang, J. Zhang, Chem Soc Rev 2015, 44, 7484. [5] A. Ferris, S. Garbarino, D. Guay, D. Pech, Adv Mater 2015, 27, 6625. [6] D. Rochefort, A. L. Pont, Electrochem Commun 2006, 8, 1539. [7] M. Yoshizawa, W. Xu, C. A. Angell, J Am Chem Soc 2003, 125, 15411. [8] J. P. Belieres, C. A. Angell, J Phys Chem B 2007, 111, 4926. [9] J. S. Seenath, D. Pech, D. Rochefort, J Power Sources 2022, 548, 232040.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".