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Abstract Radical polymers are actively being investigated in different applications such as batteries. In order to store higher energy, radical polymers with high redox potential are searched. This work describes the synthesis of 2,2,5,5‐tetramethylpyrrolin−N−oxyl methacrylate monomer (PROXYL−methacrylate) and its polymerization to get a PolyPROXYL−methacrylate (PPMA) by free radical polymerization. The structures of the monomer and the percentage of redox active groups of the polymer were determined by mean of NMR and UV‐Vis spectroscopy, respectively. Cyclic voltammetry of the novel redox polymer revealed a half‐wave potential (E1/2) of 3.7 V vs Li+/Li stable at different scan rates, which was ascribed to the efficient swelling and insoluble properties of PPMA. The polyPROXYL−methacrylate polymer exhibits higher nominal redox potential than the reference radical polymer polyTEMPO−methacrylate (PTMA). Li−metal lab‐scale cells fabricated with the PPMA cathode active material performed at different C‐rates (up to 50 C) with reversible charge/discharge and a specific capacity output of 80 mAh g−1 at 1 C, along with 96 % capacity retention after more than 100 cycles. Finally, the comparison between PPMA and PTMA redox polymers evidenced the superior performances of the PROXYL with respect to TEMPO in terms of energy density and half‐wave potential.

This article explores the novel application of a trained artificial neural network (ANN) in the prediction of vanadium redox flow battery behaviour and compares its performance with that of a two-dimensional numerical model. The aim is to evaluate the capability of two ANNs, one for predicting the cell potential and one for the overpotential under various operating conditions. The two-dimensional model, previously validated with experimental data, was used to generate data to train and test the ANNs. The results show that the first ANN precisely predicts the cell voltage under different states of charge and current density conditions in both the charge and discharge modes. The second ANN, which is responsible for the overpotential calculation, can accurately predict the overpotential across the cell domains, with the lowest confidence near high-gradient areas such as the electrode membrane and domain boundaries. Furthermore, the computational time is substantially reduced, making ANNs a suitable option for the fast understanding and optimisation of VRFBs.

Abstract The use of symmetrical cells is becoming popular for the search of new electroactive materials in redox flow batteries. Unfortunately, low‐cost battery cyclers, commonly used for electrochemical battery testing, are not compatible with symmetrical cells since they usually cannot apply negative bias voltages needed for symmetrical cells. The insertion of a Ni−Cd battery in the voltage sensing path is a simple and effective methodology to overcome this limitation for certain battery cyclers. Herein, the validity of this useful method is evaluated for other battery cyclers, realizing that the strategy is not universal. A modified methodology is developed for a battery cycler in which the previous method is not valid. The new strategy is based on inserting a Ni‐MH battery in the current path, and enables using a low‐cost Neware CT‐4008T‐5V6A‐S1 cycler for ferro‐ /ferricyanide symmetrical cells demonstrating proper operation for >19 days. This new method possesses advantages, e. g. direct reading of the cell voltage, and disadvantages, e. g. the Ni‐MH battery is charged/discharged during operation, which are discussed. The four battery cyclers evaluated show that, despite neither method is universal, both methods are complementary to each other. Thus, the decision of using either one method or the other must be reached on a case‐by‐case basis.