Nanostructured Transition Metal Oxides In article number 2418407, Hyun Ho Kim and co-workers present a universal and ultrafast method for synthesizing nanostructured transition metal oxides (NTMOs) through the induced solidification of microdroplets, enabling rapid production within a minute. Since NTMOs can be directly grown on substrates in ambient air without requiring high-purity carrier gases, this approach facilitates industrial-friendly mass production.

Nanostructured transition metal oxides (NTMOs) have consistently piqued scientific interest for several decades due to their remarkable versatility across various fields. More recently, they have gained significant attention as materials employed for energy storage/harvesting devices as well as electronic devices. However, mass production of high-quality NTMOs in a well-controlled manner still remains challenging. Here, a universal, ultrafast, and solvent-free method is presented for producing highly crystalline NTMOs directly onto target substrates. The findings reveal that the growth mechanism involves the solidification of condensed liquid-phase TMO microdroplets onto the substrate under an oxygen-rich ambient condition. This enables a continuous process under ambient air conditions, allowing for processing within just a few tens of seconds per sample. Finally, it is confirmed that the method can be extended to the synthesis of various NTMOs and their related compounds.

High-entropy alloys are emerging as highly efficient thermoelectrics, but their vast compositional spaces hinder efficient material discovery using conventional heuristics-based and advanced machine learning approaches. Here, this fundamental challenge is addressed by demonstrating an active learning framework that leverages sparse experimental data (80 out of 16206) to efficiently identify three new high-entropy chalcogenides (HECs) with remarkable thermoelectric performance (zT >2). By integrating physics-informed descriptors with uncertainty-aware sampling, this model efficiently assimilates latent structure-property relationships. This allows for systematic exclusion of unfavorable chemistries, enabling even non-experts in thermoelectrics to design unexplored systems with arbitrary components. Furthermore, novel atomic arrangements and distinctive electron and phonon transport properties are uncovered, which are responsible for the superior performance in HECs, advancing the understanding of physical phenomena in disorder-rich systems.

Design of bifunctional multimetallic alloy catalysts, which are one of the most promising candidates for water splitting, is a significant issue for the efficient production of renewable energy. Owing to large dimensions of the components and composition of multimetallic alloys, as well as the trade-off behavior in terms of the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) overpotentials for bifunctional catalysts, it is difficult to search for high-performance bifunctional catalysts with multimetallic alloys using conventional trial-and-error experiments. Here, an optimal bifunctional catalyst for water splitting is obtained by combining Pareto active learning and experiments, where 110 experimental data points out of 77946 possible points lead to effective model development. The as-obtained bifunctional catalysts for HER and OER exhibit high performance, which is revealed by model development using Pareto active learning; among the catalysts, an optimal catalyst (Pt_0.15 Pd_0.30 Ru_0.30 Cu_0.25 ) exhibits a water splitting behavior of 1.56 V at a current density of 10 mA cm^-2 . This study opens avenues for the efficient exploration of multimetallic alloys, which can be applied in multifunctional catalysts as well as in other applications.

Nanostructured Transition Metal Oxides In article number 2418407, Hyun Ho Kim and co-workers present a universal and ultrafast method for synthesizing nanostructured transition metal oxides (NTMOs) through the induced solidification of microdroplets, enabling rapid production within a minute. Since NTMOs can be directly grown on substrates in ambient air without requiring high-purity carrier gases, this approach facilitates industrial-friendly mass production.

Nanostructured transition metal oxides (NTMOs) have consistently piqued scientific interest for several decades due to their remarkable versatility across various fields. More recently, they have gained significant attention as materials employed for energy storage/harvesting devices as well as electronic devices. However, mass production of high-quality NTMOs in a well-controlled manner still remains challenging. Here, a universal, ultrafast, and solvent-free method is presented for producing highly crystalline NTMOs directly onto target substrates. The findings reveal that the growth mechanism involves the solidification of condensed liquid-phase TMO microdroplets onto the substrate under an oxygen-rich ambient condition. This enables a continuous process under ambient air conditions, allowing for processing within just a few tens of seconds per sample. Finally, it is confirmed that the method can be extended to the synthesis of various NTMOs and their related compounds.

High-entropy alloys are emerging as highly efficient thermoelectrics, but their vast compositional spaces hinder efficient material discovery using conventional heuristics-based and advanced machine learning approaches. Here, this fundamental challenge is addressed by demonstrating an active learning framework that leverages sparse experimental data (80 out of 16206) to efficiently identify three new high-entropy chalcogenides (HECs) with remarkable thermoelectric performance (zT >2). By integrating physics-informed descriptors with uncertainty-aware sampling, this model efficiently assimilates latent structure-property relationships. This allows for systematic exclusion of unfavorable chemistries, enabling even non-experts in thermoelectrics to design unexplored systems with arbitrary components. Furthermore, novel atomic arrangements and distinctive electron and phonon transport properties are uncovered, which are responsible for the superior performance in HECs, advancing the understanding of physical phenomena in disorder-rich systems.

Design of bifunctional multimetallic alloy catalysts, which are one of the most promising candidates for water splitting, is a significant issue for the efficient production of renewable energy. Owing to large dimensions of the components and composition of multimetallic alloys, as well as the trade-off behavior in terms of the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) overpotentials for bifunctional catalysts, it is difficult to search for high-performance bifunctional catalysts with multimetallic alloys using conventional trial-and-error experiments. Here, an optimal bifunctional catalyst for water splitting is obtained by combining Pareto active learning and experiments, where 110 experimental data points out of 77946 possible points lead to effective model development. The as-obtained bifunctional catalysts for HER and OER exhibit high performance, which is revealed by model development using Pareto active learning; among the catalysts, an optimal catalyst (Pt_0.15 Pd_0.30 Ru_0.30 Cu_0.25 ) exhibits a water splitting behavior of 1.56 V at a current density of 10 mA cm^-2 . This study opens avenues for the efficient exploration of multimetallic alloys, which can be applied in multifunctional catalysts as well as in other applications.

Searching for an optimal component and composition of multi-metallic alloy catalysts, comprising two or more elements, is one of the key issues in catalysis research. Due to the exhaustive data requirement of conventional machine-learning (ML) models and the high cost of experimental trials, current approaches rely mainly on the combination of density functional theory and ML techniques. In this study, a significant step is taken toward overcoming limitations by the interplay of experiment and active learning to effectively search for an optimal component and composition of multi-metallic alloy catalysts. The active-learning model is iteratively updated using by examining electrocatalytic performance of fabricated solid-solution nanoparticles for the hydrogen evolution reaction (HER). An optimal metal precursor composition of Pt_0.65 Ru_0.30 Ni_0.05 exhibits an HER overpotential of 54.2 mV, which is superior to that of the pure Pt catalyst. This result indicates the successful construction of the model by only utilizing the precursor mixture composition as input data, thereby improving the overpotential by searching for an optimal catalyst. This method appears to be widely applicable since it is able to determine an optimal component and composition of electrocatalyst without obvious restriction to the types of catalysts to which it can be applied.

Machine-learning models were developed to predict the drawbar pull of a 78-kW-class tractor for moldboard, chisel, and subsoiler plows. Four models were tested: random forest (RF), extreme gradient boosting (XGB), artificial neural network (ANN), and support vector machine (SVM). The training variables included engine speed (ES), engine torque (ET), travel speed (TS), tillage depth (TD), and slip ratio (SR). Unlike prior studies that focused mainly on engine parameters, this study incorporated nonlinear variables to improve both accuracy and practical applicability. Data were collected from three Korean paddy fields with different soil conditions, and the dataset was divided into 70% for training and 30% for testing. Five input variable combinations were used: Model A (ES, ET), Model B (ES, ET, TD), Model C (ES, ET, TS, SR), Model D (TD, TS), and Model E (ES, TD, TS). The results showed that, for the moldboard plow, RF in Model E achieved the highest performance (R² = 0.977). For the chisel plow, ANN in Models B and C provided strong predictive accuracy (R² = 0.953). The subsoiler also performed well with ANN in Models B and E (R² = 0.953). Overall, the proposed models-particularly RF and ANN-proved effective in predicting drawbar pull and outperformed XGB and SVM. This study is distinguished by its comparison of various input variable combinations for different plows (moldboard, chisel, and subsoiler) and by its proposal of a cost-effective approach using low-cost sensors.

The fundamental issues associated with Zn anodes prevent the commercialization of aqueous Zn ion batteries. To address this, a simple dip‐coating method was used to coordinate a thin layer of branched polyethyleneimine (b‐PEI) polymer onto the electrode surface. This process increases hydrophilicity and reduces interfacial resistance between the electrode and aqueous electrolyte. Consequently, electrolyte leaching from the hydrophilic polymer coating layer is prevented, charge distribution is uniform, and stable electrochemical performance is maintained over extended periods. In symmetric cell testing, the b‐PEI@Zn anode exhibits a lifespan of over 1400 h (3 mA cm −2 , 1 mAh cm −2 ). Furthermore, full‐cell tests, the b‐PEI@Zn anode demonstrates higher capacity (+26.05%) and improved stability (95.4%) compared to the bare Zn anode (0.5 A g −1 ). This study presents a practical surface modification strategy for Zn anodes and underscores the potential of innovative polymer‐based electrode coatings for aqueous battery applications.

A novel classification tool has been developed to analyze partial discharge patterns. This technique integrates features from the Peak Phase Dispersion Analysis (PPDA) Cross and the PPDA Cactus. It is intended to classify partial discharge patterns, including internal, slot, surface, and corona discharges. The PPDA Cross automatically generates key metrics, including the peak partial discharge (PD), the dispersion phase angle of the largest PD cluster, and the median phase angle of that cluster. On the other hand, the PPDA Cactus method clarifies the vertical characteristics of the phase-resolved partial discharge (PRPD) pattern in relation to the phase angle. This tool can simultaneously detect partial discharge signals from three-phase stator windings. A technique called three-phase signal discrimination (TPSD) has been introduced to improve measurement accuracy. This technique differentiates phase-related PD, external noise, and phase-to-phase PD, allowing PRPD data collection specific to each phase. Additionally, the instrument includes an innovative partial discharge sensor called the wide frequency band current transformer (WFBCT), which functions within a frequency range of 300 kHz to 60 MHz. This capability allows for acquiring PRPD patterns in voltage peak mode (VPM) and charge integration mode (CIM) under IEC60270.

Highlights Design of an electric power transmission system by analyzing measurement data from a conventional tractor of equivalent horsepower. Methodology for the selection of gear ratios and verification in electric tractors capable of both tilling and driving operations. Battery capacity selection to meet the target operating time of the electric tractor. ABSTRACT. This study was conducted to design and verify an electric power transmission system for a sub-compact tractor using measurement data from a conventional tractor. The measurement dataset was divided into two parts: 75% for designing the electric tractor system and 25% for verifying the specifications of the selected main components. Gear ratios of the electric power transmission for the front and rear axles in the first-gear stage were selected as 48.1 and 82.0, respectively, and those in the second-gear stage as 24.5 and 41.7, respectively. The battery capacity was selected as 49 kWh, allowing for continuous tillage and driving operations for up to 4 hours. Results confirmed that tillage and driving operations can be performed within a stable torque and rotational speed range using the validation dataset. Additionally, the state of charge of the battery decreased within the safe range of the battery usage cycle during agricultural operations. Keywords: Data measurement system, Electric tractor, Electrification, Measurement data, Sub-compact tractor.