Protic Ionic Liquid Electrolytes to Increase Areal Energy Density of RuO<sub>2</sub> Micro-Supercapacitors
Bibliographic record
Abstract
The rising growth of smart and autonomous microelectronic devices in the IoT (Internet of Things) era urges the development of advanced microscale energy sources with tailor-made features and customized energy/power requirements [1]. Micro-supercapacitors (MSCs) emerged as potential energy storage devices complementing micro-batteries to power ubiquitous sensor networks needed to foster the development of IoT. However, the low cell voltage and low energy density remain major bottleneck that prevents their application at a large scale in real devices. To mitigate this issue, several studies have been devoted to the engineering of MSC electrode materials and structural architecting of current collectors to enhance the surface area and areal energy density by considering the limited available footprint area [2]. This approach however has associated challenges such as complex synthesis route, the deleterious interfacial and mechanical stability of the electrode, and compatibility issues with the electrolyte and the current collector underneath [3]. Another important challenge to solve for reaching high energy density values in MSCs is the limited electrochemical stability window (ESW) of the electrolytes used as energy stored is directly related to the square of the cell voltage [4]. The electrolytes play a major role in deciding the ESW and liquid-state electrolytes currently employed are troublesome for the microfabrication process due to leakage, evaporation, and safety issues. Therefore, it’s imperative to develop alternative electrolytes including solid-state electrolytes reconcilable to the target application of MSCs. To address the low energy density challenge of current MSCs, we have developed interdigitated MSCs using hydrous ruthenium dioxide (RuO2) electrodes in combination with novel protic ionic liquid (PIL)-based electrolytes able to provide pseudocapacitance while affording a higher ESWs as compared to conventional aqueous electrolytes. As a state-of-the-art pseudocapacitive electrode material, RuO2 owns the key merits of excellent conductivity, high electrochemical reversibility, and cycling stability [5], whereas PILs could help alleviate issues facing currently used electrolytes such as evaporation and encapsulation problems pertaining to aqueous-based and flammability of common organic electrolytes [6], [7], [8]. In the next step, the slow proton transport kinetics of PILs were addressed by the doping of silicotungstic acid (SiWa, H4SiW12O40) with the PIL, which further boosted the pseudocapacitive current response with enlarged cell voltage. The real MSC device was realized by the use of RuO2 deposited on interdigitated porous Au current collectors having a high area enlargement factor (AEF) in combination with triethylammonium bis(trifluoromethanesulfonyl)imide (TEAH-TFSI)-based PIL. The resultant 3D MSC rendered a cell voltage exceeding 2V with areal capacitance as high as 86 mF cm-2 at 5 mV s-1 on par with the performance of 3D MSC tested using 0.5 M H2SO4 (cell voltage of 0.9 V and areal capacitance of 85 mF cm-2 at 5 mV s-1) but higher energy density performance (more than 4 times) using similar number of RuO2 deposition cycles. To demonstrate the potential integration in real on-chip device application, ionogel-based all-solid-state MSC is developed that showed performance comparable to liquid-state electrolyte with superior long-term cycling stability. This study gives a new perspective to develop all-solid-state micro-supercapacitors using pseudocapacitive active materials that can operate in ionic-liquid-based non-aqueous electrolytes compatible with on-chip IoT-based device applications seeking high areal energy/ power performance. 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] A. Ferris, D. Bourrier, S. Garbarino, D. Guay, D. Pech, Small 2019, 15, e1901224. [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] M. Yoshizawa, W. Xu, C. A. Angell, J Am Chem Soc 2003, 125, 15411. [7] J. P. Belieres, C. A. Angell, J Phys Chem B 2007, 111, 4926. [8] D. Rochefort, A. L. Pont, Electrochem Commun 2006, 8, 1539.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".