Search

Li1.3Al0.3Ti1.7(PO4)3 (LATP) is one of the most promising inorganic solid electrolytes, it combines together good electrochemical stability with high ionic conductivity. Its properties are strongly dependent on the consolidation route and the relevant literature confirms the beneficial role of rapid heating. Therefore, the recently emerged Ultrafast High-temperature Sintering (UHS) was selected to prevent undesired Li loss during processing. UHS densified LATP up a relative density of 90.2% within 100s while achieving a ionic conductivity as high as 4.7 × 10−4 S/cm at 25 °C. The rapid UHS heating (≈1500 °C/min) justified the increased density and slightly improved ionic conductivity of 4.67 × 10−4 S/cm if compared to counterparts (i.e., 85.4% and 3.9 × 10−4 S/cm) produced using conventional sintering at 900 °C with a dwell time of 6 h. Additionally, the UHSed electrolyte resulted in a 20% increase in grain boundary conductivity compared to conventionally sintered bulks. We identified a narrow processing window (discharge duration of 100 s and current of 20 A) suitable for producing dense and decomposition free LATP electrolytes.

Electrochemical impedance spectroscopy (EIS) is a characterization technique widely used to evaluate the properties of electrochemical systems. The distribution of relaxation times (DRT) has emerged as a model-free alternative to equivalent circuits and physical models to circumvent the inherent challenges of EIS analysis. Deep neural networks (DNNs) can be used to deconvolve DRTs, but several issues remain, e.g., the long training time, the DNN accuracy, and the deconvolution of DRTs with negative peaks. The DNN-DRT model was developed here to address these fundamental limitations. Specifically, a pretraining step was included to decrease the computation time. A thorough error analysis was also conducted to evaluate the different components of the DRT and impedance errors to ultimately decrease them. Lastly, the training loss function was modified to handle DRTs with negative peaks. These different advances were validated with an array of synthetic EIS spectra and real EIS spectra from a lithium-ion battery, a solid oxide fuel cell, and a proton exchange membrane fuel cell. Moreover, this new model outperformed in most cases the previously developed DRTtools and deep-DRT model. Overall, we envision that this research will open the venue for more DNN-based analyses of EIS data for electrochemical systems.

Hybrid organic–inorganic perovskites are one of the promising candidates for the next‐generation semiconductors due to their superlative optoelectronic properties. However, one of the limiting factors for potential applications is their chemical and structural instability in different environments. Herein, the stability of (FAPbI3)0.85(MAPbBr3)0.15 perovskite solar cell is explored in different atmospheres using impedance spectroscopy. An equivalent circuit model and distribution of relaxation times (DRTs) are used to effectively analyze impedance spectra. DRT is further analyzed via machine learning workflow based on the non‐negative matrix factorization of reconstructed relaxation time spectra. This exploration provides the interplay of charge transport dynamics and recombination processes under environment stimuli and illumination. The results reveal that in the dark, oxygen atmosphere induces an increased hole concentration with less ionic character while ionic motion is dominant under ambient air. Under 1 Sun illumination, the environment‐dependent impedance responses show a more striking effect compared with dark conditions. In this case, the increased transport resistance observed under oxygen atmosphere in equivalent circuit analysis arises due to interruption of photogenerated hole carriers. The results not only shed light on elucidating transport mechanisms of perovskite solar cells in different environments but also offer an effective interpretation of impedance responses., The stability of (FAPbI3)0.85(MAPbBr3)0.15 perovskite solar cell is explored in different atmospheres using impedance spectroscopy. An equivalent circuit model and distribution of relaxation times are used to analyze the impedance spectra supported by an unsupervised machine learning method. The transport behavior at the interface and bulk is strongly dependent on the environmental conditions particularly under 1 Sun illumination.

Our work proposes a comparison between Spark Plasma Sintering of LiFePO4 carried out using an Alternating Current (AC) and Direct Current (DC). It quantifies the Li-ion migration using DC, and it validates such hypothesis using impedance spectroscopy, X-ray photoelectron spectroscopy and inductively coupled plasma optical emission spectroscopy. The use of an AC field seems effective to inhibit undesired Li-ion migration and achieve high ionic conductivity as high as 4.5 × 10−3 S/cm, which exceeds by one order of magnitude samples processed under a DC field. These results anticipate the possibility of fabricating a high-performance all-solid-state Li-ion battery by preventing undesired Li loss during SPS processing.

Battery management systems require efficient battery prognostics so that failures can be prevented, and efficient operation guaranteed. In this work, we develop new models based on neural networks and ordinary differential equations (ODE) to forecast the state of health (SOH) of batteries and predict their end of life (EOL). Governing differential equations are discovered using measured capacities and voltage curves. In this context, discoveries and predictions made with neural ODEs, augmented neural ODEs, predictor-corrector recurrent ODEs are compared against established recurrent neural network models, including long short-term memory and gated recurrent units. The ODE models show good performance, achieving errors of 1% in SOH and 5% in EOL estimation when predicting 30% of the remaining battery’s cycle life. Variable cycling conditions and a range of prediction horizons are analyzed to evaluate the models’ characteristics. The results obtained are extremely promising for applications in SOH and EOL predictions.

Electrochemical impedance spectroscopy (EIS) is a tool widely used to study the properties of electrochemical systems. The distribution of relaxation times (DRT) has emerged as one of the main methods for the analysis of EIS spectra. Gaussian processes can be used to regress EIS data, quantify uncertainty, and deconvolve the DRT, but current implementations do not constrain the DRT to be positive and can only use the imaginary part of EIS spectra. Herein, we overcome both issues by using a finite Gaussian process approximation to develop a new framework called the finite Gaussian process distribution of relaxation times (fGP-DRT). The analysis on artificial EIS data shows that the fGP-DRT method consistently recovers exact DRT from noise-corrupted EIS spectra while accurately regressing experimental data. Furthermore, the fGP-DRT framework is used as a machine learning tool to provide probabilistic estimates of the impedance at unmeasured frequencies. The method is further validated against experimental data from fuel cells and batteries. In short, this work develops a novel probabilistic approach for the analysis of EIS data based on Gaussian process, opening a new stream of research for the deconvolution of DRT.

BaFeO 3 − δ -derived perovskites are promising cathodes for intermediate temperature solid oxide fuel cells. The activity of these perovskites depends on the number of oxygen vacancies in their lattice, which can be tuned by cationic substitution. Our first-principle calculations show that Ag is a promising substitute for the Fe site, resulting in a reduced oxygen vacancy formation energy compared with the pristine BaFeO 3 − δ . Ag has limited solubility in perovskites, and its introduction generates an Ag metal secondary phase, which influences the cathode performances. In this work, we investigate the matter, using a Ba 0.9 La 0.1 Fe 1 − x Ag x O 3 − δ series of materials as a case study. Acknowledging the limited solubility of Ag in Ba 0.9 La 0.1 Fe 1 − x Ag x O 3 − δ , we aim to distinguish the effects of Ag substitution from those of the Ag secondary phase. We observed that Ag substitution increases the number of oxygen vacancies, confirming our calculations, and facilitates the oxygen incorporation. However, Ag substitution lowers the number of holes, in this way reducing the electronic p-type conductivity. On the other hand, Ag metal positively affects the electronic conductivity and helps the redistribution of the electronic charge at the cathode-electrolyte interface.