Understanding Electrolyte Ion Size Effects on the Performance of Conducting Metal-Organic Framework Supercapacitors

Layered metal-organic frameworks (MOFs) have emerged as promising materials for next-generation supercapacitors. Understanding how and why electrolyte ion size impacts electrochemical performance is crucial for developing improved MOF-based devices. To address this, we investigate the energy storage performance of Cu3(HHTP)2 (HHTP = 2,3,6,7,10,11-hexahydroxytriphenylene) with a series of 1 M tetraalkylammonium tetrafluoroborate (TAABF4) electrolytes with different cation sizes. Three-electrode experiments show that Cu3(HHTP)2 exhibits an asymmetric charging response with all ion sizes, with higher energy storage upon positive charging and a greater charging asymmetry with larger TAA+ cations. The results further show that smaller TAA+ cations demonstrate superior capacitive performances upon both positive and negative charging compared to larger TAA+ cations. To gain further insights, electrochemical quartz crystal microbalance measurements were performed to probe ion electrosorption during charging and discharging. These reveal that Cu3(HHTP)2 has a cation-dominated charging mechanism, but interestingly indicate that the solvent also participates in the charging process with larger cations. Overall, the results of this study suggest that larger TAA+ cations saturate the pores of the Cu3(HHTP)2-based electrodes. This leads to more asymmetric charging behavior and forces solvent molecules to play a role in the charge storage mechanism. These findings significantly enhance our understanding of ion electrosorption in layered MOFs, and they will guide the design of improved MOF-based supercapacitors.

Microscopic Origin of Electrochemical Capacitance in Metal-Organic Frameworks

Electroconductive metal-organic frameworks (MOFs) have emerged as high-performance electrode materials for supercapacitors, but the fundamental understanding of the underlying chemical processes is limited. Here, the electrochemical interface of Cu3(HHTP)2 (HHTP = 2,3,6,7,10,11-hexahydroxytriphenylene) with an organic electrolyte is investigated using a multiscale quantum-mechanics/molecular-mechanics (QM/MM) procedure and experimental electrochemical measurements. Our simulations reproduce the observed capacitance values and reveals the polarization phenomena of the nanoporous framework. We find that excess charges mainly form on the organic ligand, and cation-dominated charging mechanisms give rise to greater capacitance. The spatially confined electric double-layer structure is further manipulated by changing the ligand from HHTP to HITP (HITP = 2,3,6,7,10,11-hexaiminotriphenylene). This minimal change to the electrode framework not only increases the capacitance but also increases the self-diffusion coefficients of in-pore electrolytes. The performance of MOF-based supercapacitors can be systematically controlled by modifying the ligating group.

Molecular Dynamics and Emerging Network Graphs of Interactions in Dinitrile-Based Li-Ion Battery Electrolytes

Advancements in battery research have shown interesting formulations of battery electrolytes that have helped improve the efficiency of Li-ion batteries over the decades. However, the quest for a safer and affordable battery electrolyte still proceeds with more unique formulations reported in the literature regularly. The dinitriles, especially adiponitrile and glutaronitrile, have caught the attention of the research community as part of this quest. In this work, we performed molecular dynamics simulations of dinitrile electrolytes with lithium bistrifluorosulfonimide (LiTFSI) as the electrolyte salt at varying concentrations and temperatures. On analysis of our simulations, we find that the densities of the mixtures follow the same trend as that of experimental values. The solvation properties were explored using the radial distribution functions. The connectivity of the Li⁺ with the dinitrile molecules and anions is established for all of the electrolyte concentrations using network graphs. We observe that the electrolytes form highly networked structures as the concentration increases without being affected by the rise in temperature. The networking of ionic interactions was quantified by calculating the average degree of each graph. Ionic conductivity calculations were computed using three methods: Nernst-Einstein relation, correlated method, and current autocorrelation function. We report the importance of accounting for the correlated motion of ions while estimating the ionic conductivity. The correlated conductivity and current autocorrelation function calculations provide a satisfactory estimation of the ionic conductivity compared to the experimental values.