Bibliographic record
Abstract
Polymer electrolytes are important enablers for safe, thin, future flexible electronics. Alkaline electrolytes such as KOH have been extensively used in energy storage devices such as batteries and electrochemical capacitors (ECs). Although aqueous KOH is useful, it has limitations when serving in a polymer electrolyte due to crystallization that compromises hydroxide (OH-) ion conductivity. An alternative alkaline electrolyte, tetraethylammonium hydroxide (TEAOH) was shown to be more stable with polymers compared to KOH.[1,2] Polyacrylamide (PAM) is a promising polymer host material due to its hygroscopic nature, amorphous structure, and useful functional groups that support ion conduction. When combined with TEAOH, a good ionic conductivity (> 10 mS cm-1) was achieved under ambient conditions (room temperature and 45% relative humidity (RH)). Thin, lightweight, electrochemical double layer capacitors (EDLC) were also demonstrated with TEAOH-PAM that outperformed their liquid analogues.[3] One of the important properties of TEAOH-PAM is its OH- ion-conduction dependence on hydration. As its degree of hydration changes with environmental factors such as RH and temperature, so does the conductivity of TEAOH-PAM as shown in Fig. 1. An additional consequence of this environmental vulnerability is poor dimensional stability of the polymer electrolyte which can lead to premature failure. To address these issues, the use of inert inorganic fillers as additives is explored. Inorganic fillers such as SiO2, TiO2, and Al2O3 have been commonly used to enhance the ionic conductivity, strengthen the mechanical properties, and improve the thermal stability of polymer electrolytes.[4–6] Here, we leverage different inorganic fillers to improve the environmental stability, expand the working temperature range, and provide better structural support for TEAOH-PAM polymer electrolytes. In this talk, the effects of different inorganic fillers such as SiO2 and TiO2 on OH- ion-conduction will be presented. For example, SiO2 showed both advantageous and detrimental effects on OH- ion conductivity depending on the RH condition and the size of SiO2. The possibility to enhance the OH- ion conductivity of TEAOH-PAM in low and high temperatures using SiO2 and TiO2 will be discussed. A combination of electrochemical and spectroscopy experiments is used to study the interaction of these inorganic fillers with the TEAOH-PAM alkaline polymer electrolyte. These results will help further clarify OH- ion-conduction in polymer electrolytes. [1] H. Gao, J. Li, K. Lian, Alkaline quaternary ammonium hydroxides and their polymer electrolytes for electrochemical capacitors, RSC Adv. 4 (2014) 21332–21339. doi:10.1039/C4RA01014K. [2] J. Li, K. Lian, A comparative study of tetraethylammonium hydroxide polymer electrolytes for solid electrochemical capacitors, Polymer. 99 (2016) 140–146. doi:10.1016/j.polymer.2016.07.001. [3] J. Li, J. Qiao, K. Lian, Investigation of polyacrylamide based hydroxide ion-conducting electrolyte and its application in all-solid electrochemical capacitors, Sustain. Energy Fuels. 1 (2017) 1580–1587. doi:10.1039/C7SE00266A. [4] S. Ketabi, K. Lian, Effect of SiO2 on conductivity and structural properties of PEO-EMIHSO4 polymer electrolyte and enabled solid electrochemical capacitors, Electrochim. Acta. 103 (2013) 174–178. doi:10.1016/j.electacta.2013.04.053. [5] J.E. Weston, B.C.H. Steele, Effects of inert fillers on the mechanical and electrochemical properties of lithium salt-poly(ethylene oxide) polymer electrolytes, Solid State Ionics. 7 (1982) 75–79. doi:10.1016/0167-2738(82)90072-8. [6] J. Przyluski, M. Siekierski, W. Wieczorek, Effective medium theory in studies of conductivity of composite polymeric electrolytes, Electrochim. Acta. 40 (1995) 2101–2108. doi:10.1016/0013-4686(95)00147-7. 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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".