Real Electrode Research Articles

High-fidelity reconstruction of porous cathode microstructures from FIB-SEM data with deep learning

Accurate modeling of lithium-ion battery (LIB) electrode microstructures provides essential references for understanding degradation mechanisms and optimizing materials. Traditional segmentation methods often struggle to accurately capture the complex microstructures of porous LIB electrodes in focused ion beam scanning electron microscopy (FIB-SEM) data. In this work, we develop a deep learning model based on the Swin Transformer to segment FIB-SEM data of a lithium cobalt oxide electrode, utilizing fused secondary and backscattered electron images. The proposed approach outperforms other deep learning methods, enabling the acquirement of 3D microstructure with reduced particle elongated artifacts. Analyses of the segmented microstructures reveal improved electrode tortuosity and pore connectivity crucial for ion and electron transport, emphasizing the necessity of accurate 3D modeling for reliable battery performance predictions. These results suggest a path toward voxel-level degradation analysis through more sensible battery simulation on high-fidelity microstructure models directly twinned from real porous electrodes.

Understanding the Electric Double Layer at the Electrode-Electrolyte Interface: Part I - No Ion Specific Adsorption.

We present a comprehensive mean-field model that takes into account the key components of modern electrical double layer theory at the interface between an electrode and an electrolyte solution. The model considers short-range specific interactions between different species, including electrode-ion repulsion, the hydration of ions, dielectric saturation of solvent (water), and excluded volume (steric) interactions between species. By solving a modified Poisson-Boltzmann equation and using the appropriate results of quantum chemistry calculations on the hydration of ions, we can accurately approximate the differential capacitance profiles of aqueous electrolyte solutions at the boundary with a silver electrode. The specific interactions between the ions and the electrodes in the systems under considerationare assumed to be significantly weaker than their Coulomb interactions. A novel aspect of our research is the investigation of the impact of short-range ion-water interactions on the differential capacitance, which provides new insights into the behavior of the electrical double layer. This model holds the potential to be useful for electrochemical engineers working on the development of supercapacitors and related electrochemical energy storage devices. It serves as a basis for future modeling of electrolyte systems on real electrodes, especially in scenarios where chemical ion-electrode interactions are significant.

In-Situ Liquid-Electrochemical X-Ray and Electron Microscopy for Multi-Modal Energy Materials Characterization

In-situ liquid cell electron and x-ray microscopy have enabled dynamic studies of electrochemical reactions in energy materials and revealed relationships between the performance, structure, and chemical composition of these material systems. Such fundamental relationships are critical to improving the performance of batteries, catalysts, and other energy materials. Growing research interest in energy materials systems has accelerated the development of in-situ liquid-electrochemical microscopy techniques into mature and robust characterization workflows using novel and versatile scientific hardware.Multiple characterization techniques or in-situ processing steps are often required to fully understand the mechanisms governing the behavior of energy materials for all relevant length scales and environmental conditions. A multi-modal workflow combining in-situ liquid-electrochemical transmission electron, X-ray synchrotron and scanning electron microscopy methods is presented. The breadth of research applications is discussed, including the study of chemical dynamics and structural changes to micron-scale LixFePO4 battery particles during lithium-ion insertion/extraction, electrocatalytic behavior of β-Co(OH)2 platelet particles, and electrochemical oxidation of copper nanoparticles under reductive electrolytic conditions. New insights into these materials systems provided by these experiments will directly inform the development of predictive models for material performance and guide improvement of material design and synthesis.New scientific hardware and method development has been critical to in-situ nano-scale liquid cell microscopy and spectroscopy of electrochemical systems. Therefore, best-practice hardware and method design and development for these in-situ liquid-electrochemical microscopy experiments are also discussed. The connections between potentiostat, holder, and on-chip leads must be carefully considered with respect to different ground potentials, and the incorporation of real bulk-scale reference electrodes in this hardware has yielded quantitatively higher fidelity data with less degradation from further electrochemical cycling. Heating the sample or illuminating with light during in-situ electrochemical data collection has begun to further expand the range of environmental conditions that can be incorporated into experiments.

Non-Destructive Analysis of Pb-Acid Battery Positive Plates, Based on Neutron Tomography

Lead-acid batteries (LABs) are one of the first rechargeable batteries. Historically used for backup power during the early public electricity network, LABs now hold a global market share of approximately 50%, mainly in automotive applications and energy reserves for telecommunication and data networks. Their low energy density and high surge current make them suitable for high-current, in-time applications. The LAB electrochemical cell consists of PbO2 as the positive electrode, metallic Pb as the negative electrode, and H2SO4 as the electrolyte.The composition of the positive electrode active material (PAM) involves mixtures of α- and β-PbO2 and an amorphous gel phase of Pb(IV) oxy-hydroxides, determining the PAM's functional properties. The hydrated Pb(IV) oxy-hydroxide is the electrochemically active phase, conducting ions but not electrons. The mechanical consistency of the PAM relies on α-PbO2, acting as a structural binder, while β-PbO2 provides electronic conductivity. LAB performance is influenced by the proportions of α and β crystalline zones. Jointly, α - and β -PbO2 act as a Pb(IV) reservoir for reduction, occurring through the gel phase during discharge. Together, α- and β-PbO2 function as a reservoir for Pb(IV) reduction, a process that takes place through the gel phase during discharge in lead-acid batteries (LABs). These two crystalline phases, α- and β-PbO2, reform from the gel phase during charging, and their proportions depend on the specifics of the charging process and the material state. Consequently, the relative amounts and distribution of α and β crystalline zones play a crucial role in influencing the overall performance of LABs.In the present investigation, we propose, for the first time, an ex situ NT investigation of intact PPs, endeavouring to assess the internal structure of the PAM (pore formation) and spine/PAM decohesion of real battery electrodes – fabricated with two alternative metallurgical methods (punching P and gravity casting G) – subjected to harsh electrochemical testing conditions, representative of recharge abuse. Our study is complemented by X-ray radiography - aimed at pinpointing spine and PAM cracking – and cross-sectional SEM imaging aimed at disclosing details with spine/PAM interface: grid shape variation, corrosion depth, mode and mechanism, corrosion layer nature and thickness.The synergic effect of these measurments enable a complete understanding and tracking of the PPs degradation mechanism : (i) NT offered a 3D quantitative and qualitative assessment of PAM porosity ,spine/PAM decohesions, and highly attenuating regions. Aging led to pore growth, with G electrodes displaying more extensive cracking. Variations in porosity distribution between P and G electrodes is observed. Hydrogenated gel distribution differed between electrode types and with aging. (ii) XR provided a non-destructive grid-scale overview. P electrodes exhibited limited defects, while G electrodes showed extensive spine issues. Corrosion progression differed from local SEM observations (iii) in which corrosion is explored at the spine/PAM interface, revealing no clear correlation between current density and corrosion. Representative micrographs for P and G electrodes were shared.The combination of these technique enable a complete characterization of positive plate degradation,.The methodical use of NT holds the potential to establish a comprehensive morpho chemical knowledge base for comprehending the manufacturing and aging processes of lead-acid battery electrodes directly visualization of the 3D arrangement of the hydrogen-containing electroactive amorphous phase, is a crucial metric for assessing the state of health (SOH) that is typically challenging to access experimentally This approach, coupled with in operando capabilities, relies on the quantitative examination of functionally significant features and their changes throughout the aging process. Figure 1

Mathematical Model Based on Staircase Structure for Impedance Analysis of Non-Ideal and Non-Uniform Processes in Porous Electrodes

Electrochemical impedance spectroscopy analysis for porous electrodes in energy conversion and storage, described as resistance and capacitance, is widely used to understand their electrochemical processes. These processes include: (i) electronic resistance inside the electrode (R e), (ii) ionic resistance in the electrolyte bulk and inside the porous electrode (R ion), (iii) electrical double layer formation (C dl) and charge transfer resistance at the electrode electrolyte interface (R ct) , and (iv) diffusion for charge compensation.As one of the mathematical models in electrochemical impedance spectroscopy for porous electrodes, a transmission line model is known, in which the pore structure is described as a cylinder geometry.1 We have proposed an analytical method that combines the transmission line model with electrochemical impedance spectroscopy using a symmetric cell,2 and have investigated resistance separation in lithium-ion batteries and its effect on battery performances.3-5 Due to mathematical constraints, the transmission line model is limited to a representation of an ideal, uniform process model. Here we propose a mathematical model based on a network model consisting of a staircase structure for electrochemical impedance spectroscopy at porous electrodes that can describe not only ideal and uniform processes but also non-ideal and non-uniform processes (Fig. 1), and aims to help elucidate their borderline behavior.6 The proposed staircase model (Z SCM) gives a series equivalent circuit consisting of Z 0, which is a series circuit of electrode/electrolyte interface impedance (Z int0) and electronic resistance (R e0), and electrolyte resistance (R ion0) as an initial step without pores (Z SCM0 = R ion(0) + Z 0). The one-step model (Z SCM1) is then given a series equivalent circuit consisting of the parallel circuit of Z SCM0 and Z 1 and the electrolyte resistance R ion(1) in series (Z SCM1 = R ion(1) + (Z SCM0 −1+Z1 −1)−1). The staircase model is calculated by incorporating the 1 prior step (Fig. 2). Thus, the model with n steps (Z SCM(n)) is composed of a series equivalent circuit of the parallel circuit of Z SCM(n−1) and Z n and the electrolyte resistance R ion(n) (Z SCM(n) = R ion(n) + (Z SCM(n−1) −1 +Z n −1)−1).Fig. 3 shows Nyquist plots computed for each input parameter at 50 steps for non-Faradic and Faradic processes in the staircase model. The results are in good agreement with the profiles calculated for the transmission electric model.2 The presentation will further show examples of the calculated results using the staircase model for non-ideal and inhomogeneous processes and discuss the analysis of impedance behavior using real electrodes reflecting non-uniform processes. References R. de Levie, Electrochim. Acta, 9, 1231-1245 (1964). N. Ogihara, S. Kawauchi, C. Okuda, Y. Itou, Y. Takeuchi and Y. Ukyo, J. Electrochem. Soc., 159, A1034-A1039 (2012). N. Ogihara, Y. Itou, T. Sasaki and Y. Takeuchi, J. Phys. Chem. C, 119, 4612-4619 (2015). N. Ogihara, Y. Itou and S. Kawauchi, J. Phys. Chem. Lett., 10, 5013-5018 (2019). Y. Itou, N. Ogihara and S. Kawauchi, J. Phys. Chem. C, 124, 5559-5564 (2020). N. Ogihara and Y. Itou, Phys. Chem. Chem. Phys., 24, 21863-21871 (2022). Figure 1

Impact of electrode selection on modeling tDCS in the aging brain

BackgroundPerson-specific computational models can estimate transcranial direct current stimulation (tDCS) current dose delivered to the brain and predict treatment response. Artificially created electrode models derived from virtual 10–20 EEG measurements are typically included in these models as current injection and removal sites. The present study directly compares current flow models generated via artificially placed electrodes (“artificial” electrode models) against those generated using real electrodes acquired from structural MRI scans (“real” electrode models) of older adults.MethodsA total of 16 individualized head models were derived from cognitively healthy older adults (mean age = 71.8 years) who participated in an in-scanner tDCS study with an F3-F4 montage. Visible tDCS electrodes captured within the MRI scans were segmented to create the “real” electrode model. In contrast, the “artificial” electrodes were generated in ROAST. Percentage differences in current density were computed in selected regions of interest (ROIs) as examples of stimulation targets within an F3-F4 montage.Main resultsWe found significant inverse correlations (p < 0.001) between median current density values and brain atrophy in both electrode pipelines with slightly larger correlations found in the artificial pipeline. The percent difference (PD) of the electrode distances between the two models predicted the median current density values computed in the ROIs, gray, and white matter, with significant correlation between electrode distance PDs and current density. The correlation between PD of the contact areas and the computed median current densities in the brain was found to be non-significant.ConclusionsThis study demonstrates potential discrepancies in generated current density models using real versus artificial electrode placement when applying tDCS to an older adult cohort. Our findings strongly suggest that future tDCS clinical work should consider closely monitoring and rigorously documenting electrode location during stimulation to model tDCS montages as closely as possible to actual placement. Detailed physical electrode location data may provide more precise information and thus produce more robust tDCS modeling results.

3D Heterogeneous Model for Electrodes in Lithium-Ion Batteries to Study Interfacial Detachment of Active Material Particles and Carbon-Binder Domain

Mechanics plays a crucial role in the performance and lifespan of lithium-ion battery (LIB) cells. Thus, it is important to address the interplay between electrochemistry and mechanics in LIBs, especially when aiming to enhance the energy density of electrodes. Accordingly, this work introduces a framework for a fully coupled electro-chemo-mechanical heterogeneous 3D model that allows resolving the inhomogeneities accompanied by electrochemical and mechanical responses of LIB electrodes during operation. The model is employed to numerically study the mechanical degradation of a nickel manganese cobalt (NMC) cathode electrode, assembled in a half-cell, upon cycling. As opposed to previous works, a virtual morphology for a high-energy electrode with low porosity is developed in this study, which comprises distinct domains of active material (AM) particles, the carbon-binder domain (CBD), and the pore domain to resemble real commercial electrodes. It is observed that the mechanical strain mismatch between irregularly and randomly positioned AM particles and the CBD might lead to local contact detachment. This interfacial gap, in combination with the diminishing contact strength over cell cycling, continuously deteriorates the electrode performance upon cycling by impedance rise and capacity drop. In agreement with previous experimental reports, the presented simulation results exhibit that the contact loss mostly takes place in the regions closer to the separator. Eventually, the resulting gradual capacity drop and change in impedance spectrum over cycling, as the consequence of interfacial gap formation, are discussed and indicated.

Machine Learning-Assisted Multi-Objective Optimization of Battery Manufacturing from Synthetic Data Generated by Physics-Based Simulations

Recent efforts in the ongoing energy transition showed that Lithium-Ion Batteries (LIBs) represent a suitable technology for portable devices and electric vehicles due to their high performances and relatively good cell durability. However, the optimization of the electrode manufacturing process establishes a critical step to ensure high-quality LIBs. Nowadays, such optimization is currently based on trial-and-error and costly approaches. Indeed, LIB electrode manufacturing is a complex process involving multiple interlinked steps, including numerous process parameters that unravel sparse conditions to explore. In that sense, the digitalization of the manufacturing process highlights deep insights into its impact on the electrode and cell properties to accelerate and support autonomous productions and the prototyping activities at the experimental level. [1] We have shown in previous works that 3D-resolved physics-based models constitute meaningful tools to simulate the manufacturing process chain and bring to the community a series of unique computational 3D-resolved models for unlocking the relationships between fabrication parameters and electrode properties. [2, 3] Nevertheless, the physics-based models can still have a huge computational cost for high-throughput screening and their integration in optimization loops. In this work, we reported an innovative computational approach for digital-based manufacturing process optimization. Supported by a deterministic Machine Learning (ML)-based pipeline, we raised a multi-objective optimization of LIB electrode properties and inverse design of its fabrication process using synthetic data, as Figure 1A illustrates. In the end, this gave us the possibility to manufacture the real electrode to validate its physical relevance. To do so, we first focused on the setting of a smart design of experiments based on low-discrepancy sequences, exploring our manufacturing parameters space, to then evaluate various synthetic electrode properties related to the kinetic, ionic, and electronic transport properties of 3D-generated microstructures. The latter are generated using the ARTISTIC physics-based models' chain. [2, 3] Those synthetic data constituted a relevant database, to train and validate an accurate deterministic regression approach for fitting the electrode properties as a function of the manufacturing parameters. These so-derived regression models represented a meaningful tool to bypass the manufacturing physical modeling chain for focusing on unexplored manufacturing conditions. [4] Indeed, we took the benefits of combining these models to raise a multi-objective optimization loop using the Bayesian optimization (BO) framework to pinpoint the optimal manufacturing conditions to produce a high-performance 3D microstructure (Figure 1B). [5] This objective function granted equal weights for each electrode property when optimizing to guarantee a good balance of the optimal properties with minimal tortuosity factor, maximal effective electronic conductivity, maximal active surface area between active material and pores, and maximal density. The good results were carried out by combining BO and regression functions to considerably accelerate the search for the best manufacturing parameters to adopt. In the end, we have manufactured the electrode cell using the aforementioned optimal condition predicted by the optimization loop, to experimentally validate the physical relevance of this BO prediction. As a perspective in our future research, we aim to extend this study to more manufacturing steps (e.g. electrolyte infiltration, formation, and electrochemical performance) and additional fabrication parameters (e.g. calendering speed, particle size) and show that our approach can be transferred to accelerate the manufacturing of battery technologies and the manufacturing of composite materials in general.[1] Meyer, C, Kosfeld, M., Haselrieder, W., Kwade, A. Process modeling of the electrode calendering of lithium-ion batteries regarding the variation of cathode active materials and mass loadings. Journal of Energy Storage, 18, 2018[2] Lombardo, T., Caro, F., Ngandjong, A. C., Hoock, J-B., Duquesnoy, M., Delepine, J-C., Ponchelet, A., Doison, S., Franco, A. A. The ARTISTIC online calculator: exploring the impact of lithium-ion battery electrode manufacturing parameters interactively through your browser. Batteries & Supercaps, 5(3), 2022.[3] Duquesnoy, M., Lombardo, T., Caro, F., Haudiquez, F., Ngandjong, A. C., Xu, J., Oularbi, H., Franco, A. A. Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics. npj Computational Materials, 8(161), 2022.[4] ARTISTIC Project. https://www.erc-artistic.eu/. Accessed on November 2022.[5] Gunantara, N. A review of multi-objective optimization: methods and its applications. Cogent Engineering, 5(1), 2018. Figure 1

Unraveling the Significance of Li+/e−/O2 Phase Boundaries with a 3D‐Patterned Cu Electrode for Li–O2 Batteries

AbstractThe reaction kinetics at a triple‐phase boundary (TPB) involving Li+, e−, and O2 dominate their electrochemical performances in Li–O2 batteries. Early studies on catalytic activities at Li+/e−/O2 interfaces have enabled great progress in energy efficiency; however, localized TPBs within the cathode hamper innovations in battery performance toward commercialization. Here, the effects of homogenized TPBs on the reaction kinetics in air cathodes with structurally designed pore networks in terms of pore size, interconnectivity, and orderliness are explored. The diffusion fluxes of reactants are visualized by modeling, and the simulated map reveals evenly distributed reaction areas within the periodic open structure. The 3D air cathode provides highly active, homogeneous TPBs over a real electrode scale, thus simultaneously achieving large discharge capacity, unprecedented energy efficiency, and long cyclability via mechanical/electrochemical stress relaxation. Homogeneous TPBs by cathode structural engineering provide a new strategy for improving the reaction kinetics beyond controlling the intrinsic properties of the materials.

Conditional generative adversarial network for generation of three-dimensional porous structure of solid oxide fuel cell anodes with controlled volume fractions

A structure generation model based on a generative adversarial network (GAN) is developed to synthesize artificial porous microstructures of solid oxide fuel cell (SOFC) anodes. Different from the conventional framework of GANs, additional training is performed for the generator to control statistical parameters, namely, volume fractions, of the generated structures. The developed model is validated by comparing the synthesized structures with the real electrode microstructures obtained by three-dimensional microscopy analysis. Microstructural parameters, such as volume fraction, specific surface area, and triple-phase boundary density, are used for the comparison in addition to the visual observation. The effect of the input vector size for the generator and the definition of the loss on the ability to generate realistic structures and control the volume fractions of the structures is investigated. The developed model successfully generates realistic anode microstructures with accurately controlled volume fractions, even for compositions not included in the training datasets. It is also found that the balance between the losses influences the accuracy of the volume fraction control and diversity of the generated structures. The GAN model developed is expected to be helpful in constructing a digital twin of electrode fabrication and evaluation processes.

Scale-up of Co–Ni cathodes produced from spent Ni–Cd batteries for hydrogen production in an oxy-hydrogen gas cell

Ni(II) and Co(II) ions are recovered from real leached positive electrodes of spent Ni–Cd batteries, and subsequently co-deposited on A36 carbon steel using direct and pulsed currents. The idea is to evaluate how variations in texture and composition of recycled coatings affect their catalytic activities for H2 production at microelectrolysis levels, and using an oxy-hydrogen gas (HHO) cell. The most active Ni–Co electrocatalyst in 0.1 M HClO4 or KOH towards hydrogen evolution reaction (HER) is formed using pulsed current at −50 mA cm−2, according to electrochemical measurements and gas chromatography (GC). This plating mode favors grain size refinement and pH rise during metal deposition, to prevent the formation of oxides/hydroxides with low catalytic activity arising in direct current. In the HHO cell, the most active Co–Ni coating produces 21.63 mL min−1 H2, while A36 carbon and 304 stainless steels present lower generations of 7.30 and 7.55 mL min−1, respectively.

Surface analysis insight note: X‐ray photoelectron spectroscopy analysis of battery electrodes—Challenges with nickel–manganese–cobalt and Li examples using an Al Kα x‐ray source

X‐ray photoelectron spectroscopy (XPS) has become a highly important tool for the analysis of battery materials and components. However, both anecdotal and detailed analysis of selected parts of the literature indicate that many reports of XPS on battery electrodes have significant analysis or data flaws. In this paper, we highlight several of the common challenges that analysts face when using XPS for battery materials, pointing to recent literature that addresses many of the critical issues associated with sample preparation as well as data collection and analysis. A common error for battery materials (and other materials) involves ignoring peak overlaps and interferences. Specifically, when a “minor” peak associated with a component in relatively high concentration overlaps or contributes to the primary peak (or one recommended for quantitative analysis) from a different element in the material. Overlap issues apply to many battery electrodes composed of many elements with complex photoelectron peak structures, as well as those involving peaks with seemingly simpler spectral envelopes such as Li and F. Examples of issues associated with battery systems are highlighted by a discussion of challenges associated with XPS analysis of Li and nickel–manganese–cobalt (NMC) electrodes in battery systems. Lithium analysis has challenges associated with the preparation and an often‐unrecognized peak overlap with F. In our laboratory and in the literature, NMC electrodes are often examined and new XPS users do not always recognize interference of the Auger signal from FKLL (in or on the electrode) with Ni 2p photoelectron spectrum when generated with Al Kα X‐rays. The use of simulated spectra involving both F and NiO demonstrates the extent of F Auger contributions to the Ni 2p signal strength as a function of the F/Ni atom ratio in the material and suggests spectra information that can be used to identify how significant effects will be on the resultant spectra. Our analysis demonstrates that in many cases overlap issues are significant for real electrode materials.

Crystal-Orientation-Dependent Oxygen Exchange Kinetics on Mixed Conducting Thin-Film Surfaces Investigated by In Situ Studies.

The oxygen exchange kinetics and the surface chemistry of epitaxially grown, dense La0.6Sr0.4CoO3-δ (LSC) thin films in three different orientations, (001), (110), and (111), were investigated by means of in situ impedance spectroscopy during pulsed laser deposition (i-PLD) and near-ambient-pressure X-ray photoelectron spectroscopy (NAP-XPS). i-PLD measurements showed that pristine LSC surfaces exhibit very fast surface exchange kinetics but revealed no significant differences between the specific orientations. However, as soon as the surfaces were in contact with acidic, gaseous impurities, such as S-containing compounds in nominally pure measurement atmospheres, NAP-XPS measurements revealed that the (001) orientation is substantially more susceptible to the formation of sulfate adsorbates and a concomitant performance decrease. This result is further substantiated by a stronger increase of the work function on (001)-oriented LSC surfaces upon sulfate adsorbate formation and by a faster performance degradation of these surfaces in ex situ measurement setups. This phenomenon has potentially gone unnoticed in the discussion of the interplay between the crystal orientation and the oxygen exchange kinetics and might have far-reaching implications for real solid oxide cell electrodes, where porous materials exhibit a wide variety of differently oriented and reconstructed surfaces.

Stimulation-mediated reverse engineering of silent neural networks.

Reconstructing connectivity of neuronal networks from single-cell activity is essential to understanding brain function, but the challenge of deciphering connections from populations of silent neurons has been largely unmet. We demonstrate a protocol for deriving connectivity of simulated silent neuronal networks using stimulation combined with a supervised learning algorithm, which enables inferring connection weights with high fidelity and predicting spike trains at the single-spike and single-cell levels with high accuracy. We apply our method on rat cortical recordings fed through a circuit of heterogeneously connected leaky integrate-and-fire neurons firing at typical lognormal distributions and demonstrate improved performance during stimulation for multiple subpopulations. These testable predictions about the number and protocol of the required stimulations are expected to enhance future efforts for deriving neuronal connectivity and drive new experiments to better understand brain function.NEW & NOTEWORTHY We introduce a new concept for reverse engineering silent neuronal networks using a supervised learning algorithm combined with stimulation. We quantify the performance of the algorithm and the precision of deriving synaptic weights in inhibitory and excitatory subpopulations. We then show that stimulation enables deciphering connectivity of heterogeneous circuits fed with real electrode array recordings, which could extend in the future to deciphering connectivity in broad biological and artificial neural networks.

Benchmarking electrodes modified with bi-doped polypyrrole for sensing applications

Polypyrrole-modified electrodes bi-doped with salicylate and perchlorate anions were synthetized, characterized, and benchmarked according to their ability to meet a set of performance criteria, with the aim of obtaining the best potential candidate for the development of electrochemical sensors. Bi-doped PPy films (PPy-DA) were assessed as compared to those resulting from electropolymerization in solutions providing a single doping anion, namely salicylate (PPy-Sa) or perchlorate (PPy-ClO4−) solutions. Morphological and spectroscopic characteristics of the different polymer films were determined by means of SEM and XPS and Raman spectroscopies. The electrochemical response and electrocatalytic activity of PPy-DA electrodes were characterized by chronoamperometric, voltammetric and electrochemical impedance spectroscopy (EIS) measurements. EIS appeared as a singular tool for determining the conditions where electrochemical response is either dictated by the redox process of the polymer film or governed by a redox couple in solution, and for comparing the electrocatalytic activity of the films without the requirement of knowing the real active electrode area. Comparison was also made among PPy-DA films synthesized by varying dopant concentration in the polymerization bath and electropolymerization time. The presence of salicylate ions favored the formation of films with a three-dimensional tubular structure whereas perchlorate ions contribute to increase their conductivity and electrocatalytic activity. The tubular three-dimensional structure of the electrode surface exhibits an open extended-area pattern that is highly suitable for the electrodeposition of gold nanoparticles (AuNPs), offering new possibilities for developing electrodes for voltammetric sensing. Accordingly, electrodes modified with PPy-DA films decorated with AuNPs were successfully tested for hydrazine determination.

One-dimensional transient crystals

A phase of solid state matter which exists in time dimension is investigated theoretically for a linear chain of quantum dots or atomic sites using the evolution operator technique and the Laplace transform technique.. The studies focus on the spectral density of states (DOS) function and its modifications after a sudden quench in the system. It is shown that this function reveals a very regular structure which oscillates in time and which corresponds to the stationary DOS of one-dimensional crystals. Thus, such a system stands for a transient crystal material which exists in time dimension but appears only for a short period and suddenly vanishes. Moreover, we observe different impulse propagation velocities due to the local DOS inertia, i.e., it is more difficult to rebuild a many-peaked structure of DOS related to longer chains. We also show that the transient crystal pattern in a linear chain can be observed for real electrodes characterized by the van Hove singularities or by a flat spectral density.

Electrochemical Cloud Point Temperature from Thermoamperometry: Real-Time Analysis for Phase Transition of Thermoresponsive Polymers.

Cloud point temperature (Tcp) is a thermal index used to define the phase transition of thermoresponsive polymers. In this study, we used electrochemical techniques to obtain an electrochemical cloud point temperature (Tecp) that exhibits the more accurate phase transition temperature and can replace Tcp. Thermoamperometry on an ultramicroelectrode was conducted with a poly(arylene ether sulfone) (PES10) as a model system to obtain a current-temperature (i-T) curve in real time; the Tecp of the PES10 was determined from the i-T curve. The i-T curve shows an unprecedented current decrease in the PES10 solution despite increasing temperature; on the other hand, the current increased linearly with increasing temperature in the solution without PES10. This phenomenon was analyzed by considering the characteristics of PES10 during phase transition, such as dynamic viscosity, temperature of the solution, and electrode impedance. It was confirmed that the current drops shown in the i-T curves were mainly due to the decrease of real electrode area. The comparison of Tecp and Tcp showed that both depended similarly on the concentrations of the thermoresponsive polymer and the supporting electrolyte. The results reveal that by adjusting the concentration of polymer and electrolyte in an organic solution, Tecp, as a new analytical method, can be used in electric circuit-based energy storage appliances such as Li-ion batteries and supercapacitors.

A Fast Calculation of Partially Corroded, Grounding-Resistive Electrode Electrical Parameters

The calculation of the grounding resistance of electrodes built with partially oxidized non-perfect conductors is addressed in this paper. A model based on circuit analysis to account for internal resistance is used, while the oxidation or partial coating of the conductor surface is modeled using the concept of the equivalent radius of the coated electrode. From the proposed model, the values of the electrical parameters of the grounding electrodes, such as the grounding resistance and the step and touch potentials, are calculated for real electrodes affected by coating or corrosion and compared with those values under ideal conditions. The results show significant differences in certain configurations that may compromise the safety margins established by current regulations.

Deep-learning based parameter identification enables rationalization of battery material evolution in complex electrochemical systems

High-energy density metal anodes are a key solution for next-generation mobility batteries, but the difficulty of studying materials in real-life battery context leads to a methodological gap between theory and experiments, translating into poor device control. Imaging and spectroscopy are the ultimate tools for knowledge-based battery studies, but the capability of quantitatively linking the electrical device response to the material evolution, is essentially missing, at the moment. High-throughput, Deep-Learning based parameter identification for morphochemical PDE modelling, can enable this link, in principle allowing to extract the key information from hyperspectral imaging tools and to feed it into next-generation battery management systems: this work is the first step of this process. Here we have employed a CNN, trained with the solutions of an electrochemical phase formation model we recently developed, to carry out three enabling tasks: (i) automatic partitioning of the parameter space, according to the types of patterns generated by the model; (ii) assignment of experimental patterns, derived by imaging of from real electrodes to the class patterns and (iii) identification of the model parameters for experimental electrode images.

Stable copper anode enabled by an ionic conducting sulfurized interphase

Lithium metal is one of the most promising anodes for non-aqueous battery because of its high capacity and low potential, and recently, lithium-free anode has been proposed which can further increase the real electrode capacity by utilizing lithium (Li) deposition and dissolution on copper (Cu) current collector. However, either Li or Li-free anode has been much plagued by the solid electrolyte interphase (SEI) issues, dendrite growth and dead Li formation induced by slower Li+-diffusion across the interphase and/or difficult Li nucleation on heterogeneous substrate. In this work, constructing an artificial copper sulfide (CuS) layer on Cu was proposed to address these issues. Copper sulfide has been loaded on Cu substrate through a facile vapor-deposition method. The extra layer reduced the contact between electrolyte and electrode. Additionally, a favorable interphase was generated by the in-situ conversion of CuS, which was composed of sub-micron lithium sulfide (Li2S) and Cu particles. The former optimized the SEI composition structure and improved the Li+ diffusion pathway, and the latter decreased the real local current density. With these assistances, dense and uniform Li deposition on Cu was achieved. The Li|S-Cu cell realized 300 times stable cycling and the Li@S-Cu|LiFePO4 full cell could well maintain the capacity as long as 200 cycles. This work provides an alternative strategy to regulate the electrode/electrolyte interface and sheds more lights on the Li deposition/dissolution reaction kinetics.