Multimodal investigation of electronic transport in PTMA and its impact on organic radical battery performance

Constructing a Janus Catholyte/Cathode Structure: A New Strategy for Stable Zn-Organic Batteries

Organic materials are promising candidates for the electrodes of aqueous zinc-ion batteries due to their nonmetallic nature, environmental friendliness, and cost-effectiveness. However, they often suffer from significant dissolution during the charge-discharge process, which poses a major hurdle to their practical applications. Inspired by membrane-less organelles in cells, a simple and versatile strategy is proposed-constructing a Janus catholyte/cathode structured electrode based on liquid-liquid phase separation, in which redox-active organic molecules are confined in the liquid state within the activated carbon, thereby eliminating the volume effect and preventing their diffusion into the electrolyte. The customization of phase separation systems by leveraging the hydrophobicity/hydrophilicity differences of various anions is successfully demonstrated. This approach allows for precise regulation of ion cluster/coordination structures, enabling the confinement of active substances while ensuring efficient ion transport. Consequently, the as-constructed Zn||Janus catholyte/cathode cells exhibit superior reversible rate capacity (186 mA h g-1 at 5.0 A g-1) and remarkable cycling performance (retention of 72.5% after 12 000 cycles). The strategy in building Janus catholyte/cathode structured electrodes breaks free from the limitations imposed by traditional solid-state electrodes, offering tremendous opportunities for exploring diverse advanced battery systems.

Machine Learning Isotropic g Values of Radical Polymers

Methods for electronic structure computations, such as density functional theory (DFT), are routinely used for the calculation of spectroscopic parameters to establish and validate structure-parameter correlations. DFT calculations, however, are computationally expensive for large systems such as polymers. This work explores the machine learning (ML) of isotropic g values, giso, obtained from electron paramagnetic resonance (EPR) experiments of an organic radical polymer. An ML model based on regression trees is trained on DFT-calculated g values of poly(2,2,6,6-tetramethylpiperidinyloxy-4-yl methacrylate) (PTMA) polymer structures extracted from different time frames of a molecular dynamics trajectory. The DFT-derived g values, gisocalc, for different radical densities of PTMA, are compared against experimentally derived g values obtained from in operando EPR measurements of a PTMA-based organic radical battery. The ML-predicted giso values, gisopred, were compared with gisocalc to evaluate the performance of the model. Mean deviations of gisopred from gisocalc were found to be on the order of 0.0001. Furthermore, a performance evaluation on test structures from a separate MD trajectory indicated that the model is sensitive to the radical density and efficiently learns to predict giso values even for radical densities that were not part of the training data set. Since our trained model can reproduce the changes in giso along the MD trajectory and is sensitive to the extent of equilibration of the polymer structure, it is a promising alternative to computationally more expensive DFT methods, particularly for large systems that cannot be easily represented by a smaller model system.

Electron transfer reaction of TEMPO-based organic radical batteries in different solvent environments: comparing quantum and classical approaches

In this study, we delve into the complex electron transfer reactions associated with the redox-active (2,2,6,6-tetramethylpiperidin-1-yl)oxyl (TEMPO), a common component in organic radical batteries (ORBs). Our approach estimates quantum electron-transfer (ET) energies using Density Functional Theory (DFT) calculations by sampling from structures simulated classically. This work presents a comparative study of reorganization energies in ET reactions across different solvents. Furthermore, we investigate how changes in the electrolyte environment can modify the reorganization energy and, consequently, impact ET dynamics. We also explore the relationship between classical and quantum vertical energies using linear regression models. Importantly, this comparison between quantum and classical vertical energies underscores the role of quantum effects, like charge delocalization, in offering added stabilization post-redox reactions. These effects are not adequately represented by the classical vertical energy distribution. Our study shows that, although we find a significant correlation between the vertical energies computed by DFT and the classical force field, the regression parameters depend on the solvent, highlighting that classical methods should be benchmarked by DFT before applying them to novel electrolyte materials.