Coastal wetland soil organic carbon (CW-SOC) is crucial for both “blue carbon” and carbon sequestration. It is of great significance to understand the content of soil organic carbon (SOC) in soil resource management. A total of 133 soil samples were evaluated using an indoor spectral curve and were categorized into silty soil and sandy soil. The prediction model of CW-SOC was established using optimized support vector machine regression (OSVR) and optimized random forest regression (ORFR). The Leave-One-Out Cross-Validation (LOO-CV) method was used to verify the model, and the performance of the two prediction models, as well as the models’ stability and uncertainty, was examined. The results show that (1) The SOC content of different coastal wetlands is significantly different, and the SOC content of silty soils is about 1.8 times that of sandy soils. Moreover, the characteristic wavelengths associated with SOC in silty soils are mainly concentrated in the spectral range of 500–1000 nm and 1900–2400 nm, while the spectral range of sandy soils is concentrated in the spectral range of 600–1400 nm and 1700–2400 nm. (2) The organic carbon prediction model of silty soil based on the OSVR method under the first-order differential of reflectance (R′) is the best, with the Adjusted-R2 value as high as 0.78, the RPD value is much greater than 2.0 and 5.07, and the RMSE value as low as 0.07. (3) The performance of the OSVR model is about 15~30% higher than that of the support vector machine regression (SVR) model, and the performance of the ORFR model is about 3~5% higher than that of the random forest regression (RFR) model. OSVR and ORFR are better methods of accurately predicting the CW-SOC content and provide data support for the carbon cycle, soil conservation, plant growth, and environmental protection of coastal wetlands.

/pdf/estimation-of-soil-organic-carbon-content-in-coastal-k43g36tt.pdf

Estimation of Soil Organic Carbon Content in Coastal Wetlands with Measured VIS-NIR Spectroscopy Using Optimized Support Vector Machines and Random Forests


 Analysis of electrochemical impedance spectroscopy (EIS) data for electrochemical systems often consists of defining an equivalent circuit model (ECM) using expert knowledge and then optimizing the model parameters to deconvolute various resistance, capacitive, inductive, or diffusion responses. For small data sets, this procedure can be conducted manually; however, it is not feasible to manually define a proper ECM for extensive data sets with a wide range of EIS responses. Automatic identification of an ECM would substantially accelerate the analysis of large sets of EIS data. We showcase machine learning methods to classify the ECMs of 9300 impedance spectra provided by QuantumScape for the BatteryDEV hackathon. The best-performing approach is a gradient-boosted tree model utilizing a library to automatically generate features, followed by a random forest model using the raw spectral data. A convolutional neural network using boolean images of Nyquist representations is presented as an alternative, although it achieves a lower accuracy. We publish the data and open source the associated code. The approaches described in this article can serve as benchmarks for further studies. A key remaining challenge is the identifiability of the labels, underlined by the model performances and the comparison of misclassified spectra.

pdf/machine-learning-benchmarks-for-the-classification-of-3qs8rox0.pdf

Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra

The binary piezoelectric synergistic effect of KNbO3/MoS2 heterojunction for improving photocatalytic performance

Protection of lithium (Li) metal electrode is a core challenge for all-solid-state Li metal batteries (ASSLMBs). Carbon materials with variant structures have shown great effect of Li protection in liquid electrolytes, however, can accelerate the solid-state electrolyte (SE) decomposition owing to the high electronic conductivity, seriously limiting their application in ASSLMBs. Here, a novel strategy is proposed to tailor the carbon materials for efficient Li protection in ASSLMBs, by in-situ forming a rational niobium-based Li-rich disordered rock salt (DRS) shell on the carbon materials, providing a favorable percolating Li+ diffusion network for speeding the carbon lithiation, and enabling simultaneously improved lithiophilicity and reduced electronic conductivity of the carbon structure at deep lithiation state. Using the proposed strategy, different carbon materials, such as graphitic carbon paper and carbon nanotubes, are tailored with great ability to speed the interfacial kinetics, homogenize the Li plating/stripping processes, and suppress the SE decompositions, enabling much improved performances of ASSLMBs under various conditions approaching the practical application. This strategy is expected to create a novel roadmap of Li protection for developing reliable high-energy-density ASSLMBs. This article is protected by copyright. All rights reserved.

Deeply Lithiated Carbonaceous Materials for Great Lithium Metal Protection in all-solid-state Batteries.

Estimating the parameters of lithium-ion (Li-ion) batteries under dynamic working conditions is a critical challenge in the health management of electrical energy storage systems. This paper estimates the equivalent circuit model (ECM) parameters and analyzes the influence of different factors on the Li-ion batteries impedance using the electrochemical impedance spectroscopy (EIS) technique. Firstly, the influence of the temperature, state of charge (SOC) and number of charging/discharging cycles on the impedance spectrum was studied. Nyquist plots were used to perform this analysis. Subsequently, ECMs were used to characterize the Li-ion batteries under study at different operating conditions. Specifically, the ECM parameters were estimated using the gaining-sharing knowledge (GSK) metaheuristic algorithm, allowing the consequent estimation of the impedance spectra obtained by the EIS technique. Experimental results included 15 datasets measured experimentally at three temperature levels for five SOCs and different number of charging/discharging cycles. To investigate the variation of Li-ion battery parameters with cyclic aging 350 datasets from the literature were also used. The results showed that the impedance and model parameters depend considerably on the considered factors. The estimation of the ECM parameters demonstrated high accuracy and reliability (mean RMSE values of 4.32E-02 and 2.33E-02 and mean STD values of 3.38E-16 and 5.82E-13 with measured and literature datasets, respectively), allowing the robust determination of the numerous impedance spectra under study.

Impedance Analysis and Parameter Estimation of Lithium-Ion Batteries Using the EIS Technique

EIS equivalent circuit model prediction using interpretable machine learning and parameter identification using global optimization algorithms

An Automated Test Platform for High-Throughput Micro-Electrochemical Characterization of Metallic Materials and Its Application on a Fe–Cr–Ni Combinatorial Materials Chip

IrOx electrodes were fabricated by cyclic thermal heating and water quenching (CHQ) process and high temperature carbonate oxidation (HCO), respectively. By examining the E-pH relationship, response rate, potential drift behavior of the fabricated electrodes, the electrodes prepared by CHQ process seemed to show better comprehensive performance. Characterization tests such as scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and electrochemical impedance spectroscopy (EIS) were used to characterize the fabricated IrOx electrodes and find out the reason for the better performance of the electrodes prepared by CHQ process. Morphology tests indicate that the CHQ electrode shows a multi-layer structure with more ion channels, which could provide larger surface area for the H+ response process. Furthermore, combining the XPS, Raman and EIS tests etc., more effective response composition, better crystal quality, and smaller response reaction resistance of surface IrOx film could account for the better performance of the CHQ-fabricated IrOx electrode. The film formation process, H+ response mechanism, as well as the response behavior difference between the two kinds of the electrodes are further elaborated.

A Comparative Study of Fabricating IrOx Electrodes by High Temperature Carbonate Oxidation and Cyclic Thermal Oxidation and Quenching Process

Automated workflow of EIS data validation and quality improvement based on the definition, detection, and removal of outliers

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Papers

EIS equivalent circuit model prediction using interpretable machine learning and parameter identification using global optimization algorithms

An Automated Test Platform for High-Throughput Micro-Electrochemical Characterization of Metallic Materials and Its Application on a Fe–Cr–Ni Combinatorial Materials Chip

A Comparative Study of Fabricating IrOx Electrodes by High Temperature Carbonate Oxidation and Cyclic Thermal Oxidation and Quenching Process

Automated workflow of EIS data validation and quality improvement based on the definition, detection, and removal of outliers