Useful materials must satisfy multiple objectives, where the optimization of one objective is often at the expense of another. The Pareto front reports the optimal trade-offs between these conflicting objectives. Here we use a self-driving laboratory, Ada, to define the Pareto front of conductivities and processing temperatures for palladium films formed by combustion synthesis. Ada discovers new synthesis conditions that yield metallic films at lower processing temperatures (below 200 °C) relative to the prior art for this technique (250 °C). This temperature difference makes possible the coating of different commodity plastic materials (e.g., Nafion, polyethersulfone). These combustion synthesis conditions enable us to to spray coat uniform palladium films with moderate conductivity (1.1 × 105 S m−1) at 191 °C. Spray coating at 226 °C yields films with conductivities (2.0 × 106 S m−1) comparable to those of sputtered films (2.0 to 5.8 × 106 S m−1). This work shows how a self-driving laboratoy can discover materials that provide optimal trade-offs between conflicting objectives.

This study was conducted to determine the effects of curcumin intake on wound healing and metabolic status in patients with diabetic foot ulcer (DFU). The current randomized, double‐blind, placebo‐controlled trial was conducted among 60 patients with grade 3 DFU. Participants were randomly allocated into two groups (30 participants each group), received either 80 mg nanocurcumin daily for 12 weeks or placebo. Primary endpoints in this study were serum insulin levels and insulin resistance. Curcumin intake significantly decreased fasting plasma glucose (p = .02), insulin (p = .01), insulin resistance (p = .02), and significantly increased insulin sensitivity (p = .008) compared with the placebo. Moreover, curcumin intake led to a significant reduction in total‐ (p < .001), LDL‐cholesterol (p < .001), and a significant increase in total antioxidant capacity (TAC) (p < .001) and total glutathione (GSH) (p = .01) compared with the placebo. However, there was no significant improvement in wound healing parameters. Overall, our study demonstrated that nanocurcumin intake in patients with DFU resulted in a significant improvement of glycemic control, total‐ and LDL‐cholesterol, TAC, and GSH but did not affect the indicators of ulcer size.

The behavior of a grounding system can be predicted by using either the electrical equivalent circuit models or electromagnetic computation. Despite its advantages over the latter, the equivalent circuit model fails to accurately predict the behavior under transient conditions due to the absence of two key factors, namely: 1) the current rate-of-rise and 2) soil ionization. This paper proposes a method to enhance the performance of the equivalent circuit model by taking into consideration of both mentioned factors. It is discovered that by using the proposed method, the estimated values of and of the equivalent circuit model are improved. The computed inductance dynamically changes with the change in the lightning current parameters, thus improving its accuracy for all current rate-of-rise conditions. The soil ionization effect is implemented as recommended by CIGRE, and this further improves the accuracy of the model. As a result, the voltage response of the model becomes more accurate and comparable to the electromagnetic computation results. Another important feature of the proposed model is that it can be directly applied or connected to power system equipment. Thus, an accurate grounding system effect on the transient performance of key power equipment, such as surge arresters, can be obtained.Index Terms-Circuit modeling, current rate of rise, grounding electrode, soil ionization.

Grounding electrode resistance non-linearly changes under impulse conditions due to soil ionisation phenomenon. Several models have been proposed to model soil ionisation for grounding electrodes applications. However, to date, there is yet an attempt made to compile all these works into a comprehensive review article. Therefore, this paper is written with the objective of summarizing all related works in this field as a onestop reference. With reference to the literature, this paper is written to summarize the working principles of the soil ionisation models as well as the accuracy and performance analysis of the models. This paper, particularly highlights the deficiencies of the available models in terms of accuracy and performance. This knowledge will contribute to the development of a new accurate and efficient soil ionisation model.

Similar to advancements gained from big data in genomics, security, internet of things, and e‐commerce, the materials workflow could be made more efficient and prolific through advances in streamlining data sources, autonomous materials synthesis, rapid characterization, big data analytics, and self‐learning algorithms. In electrochemical materials science, data sets are large, unstructured/heterogeneous, and difficult to process and analyze from a single data channel or platform. Computer‐aided materials design together with advances in data mining, machine learning, and predictive analytics are expected to provide inexpensive and accelerated pathways towards tailor‐made functionally optimized energy materials. Fundamental research in the field of electrochemical energy materials focuses primarily on complex interfacial phenomena and kinetic electrocatalytic processes. This perspective article critically assesses AI‐driven modeling and computational approaches that are currently applied to those objects. An application‐driven materials intelligence platform is introduced, and its functionalities are scrutinized considering the development of electrocatalyst materials for CO2 conversion as a use case.