The front cover artwork is provided by Wei‐Chen Chen and Rudolph A. Marcus. The image shows an electron undergoing backscattering in a disordered solid, a phenomenon commonly described using the ...Drude‐Smith equation represented by the damping curve. Read the full text of the Article at 10.1002/cphc.202100299.
“We show how theories such as Drude‐Smith and Cocker et al. are examples of a broader class of theories by showing how they also arise as particular cases of a memory function formalism that divides the interactions into short and long range…” This and more about the story behind the front cover can be found in the Article at 10.1002/cphc.202100299.
For structural health monitoring, electrical resistivity measurement (ERM) method is commonly employed for the detection of concrete's durability, as indicated by the chloride permeability and the ...corrosion of steel reinforcement. However, according to previous experimental studies, ERM results are susceptible to significant uncertainties due to multiple influencing factors such as concrete water/cement ratio and structure curing environment as well as their complex interrelationships. The present study therefore proposes an XGBoost algorithm-based prediction model which considers all potential influential factors simultaneously. A database containing 800 experimental instances composed of 16 input attributes is constructed according to existing reported studies and utilized for training and testing the XGBoost model. Statistical scores (RMSE, MAE and R2) and the GridsearchCV feature are applied to evaluate and optimize the established model respectively. Results show that the proposed XGBoost model achieves satisfactory predictive performance as demonstrated by high coefficients of regression fitting lines (0.991 and 0.943) and comparatively low RMSE values (4.6 and 11.3 kΩ·cm) for both training and testing sets respectively. The analyses of the attribute importance ranking also reveal that curing age and cement content have the greatest influence on ERM results.
•An XGBoost model is proposed for predicting concrete electrical resistivity based on the experimental database.•The proposed XGBoost model can predict concrete electrical resistivity with multiple factors.•The influence of factors on the concrete electrical resistivity is investigated through the proposed XGBoost model.
The inverse problem of electrical resistivity surveys (ERSs) is difficult because of its nonlinear and ill-posed nature. For this task, traditional linear inversion methods still face challenges such ...as suboptimal approximation and initial model selection. Inspired by the remarkable nonlinear mapping ability of deep learning approaches, in this article, we propose to build the mapping from apparent resistivity data (input) to resistivity model (output) directly by convolutional neural networks (CNNs). However, the vertically varying characteristic of patterns in the apparent resistivity data may cause ambiguity when using CNNs with the weight sharing and effective receptive field properties. To address the potential issue, we supply an additional tier feature map to CNNs to help those aware of the relationship between input and output. Based on the prevalent U-Net architecture, we design our network (ERSInvNet) that can be trained end-to-end and can reach a very fast inference speed during testing. We further introduce a depth weighting function and a smooth constraint into loss function to improve inversion accuracy for the deep region and suppress false anomalies. Six groups of experiments are considered to demonstrate the feasibility and efficiency of the proposed methods. According to the comprehensive qualitative analysis and quantitative comparison, ERSInvNet with tier feature map, smooth constraints, and depth weighting function together achieve the best performance.
Triphenylene ligands hexasubstituted with amino or phenol groups afford two phases of electrically conductive layered two-dimensional metal–organic frameworks upon reaction with various metals. ...Regardless of the identity of the metal or chelating atom, π-stacking within the MOF layers is essential to achieve high electrical conductivity, redox activity, and catalytic activity.
Oxides with the nominal chemical formula Li6ALa2Ta2O12 (A = Sr, Ba) have been prepared via a solid‐state reaction in air using high purity La2O3, LiOH·H2O, Sr(NO3)2, Ba(NO3)2, and Ta2O5 and are ...characterized by powder X‐ray diffraction (XRD) in order to identify the phase formation and AC impedance to determine the lithium ion conductivity. The powder XRD data of Li6ALa2Ta2O12 show that they are isostructural with the parent garnet‐like compound Li5La3Ta2O12. The cubic lattice parameter was found to increase with increasing ionic size of the alkaline earth ions (Li6SrLa2Ta2O12: 12.808(2) Å; Li6BaLa2Ta2O12: 12.946(3) Å). AC impedance results show that both the strontium and barium members exhibit mainly a bulk contribution with a rather small grain‐boundary contribution. The ionic conductivity increases with increasing ionic radius of the alkaline earth elements. The barium compound, Li6BaLa2Ta2O12, shows the highest ionic conductivity, 4.0×10–5 S cm–1 at 22 °C with an activation energy of 0.40 eV, which is comparable to other lithium ion conductors, especially with the presently employed solid electrolyte lithium phosphorus oxynitride (Lipon) for all‐solid‐state lithium ion batteries. DC electrical measurements using lithium‐ion‐blocking and reversible electrodes revealed that the electronic conductivity is very small, and a high electrochemical stability (> 6 V/Li) was exhibited at room temperature. Interestingly, Li6ALa2Ta2O12 was found to be chemically stable with molten metallic lithium.
A novel garnet‐like structure Li6ALa2Ta2O12 (A = Sr, Ba) exhibits fast, lithium ion conductivity and is found to be stable against chemical reactions with molten metallic lithium. Li6BaLa2Ta2O12 shows the highest ionic conductivity of 4.0 × 10–5 S cm–1 at 22 °C (see Figure). The direct current electrical measurements using lithium‐ion blocking and reversible electrodes reveal that the electronic conductivity is very small and exhibits high electrochemical stability (≥6 V/Li) at room temperature.
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