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Nanoscale metal inclusions in or on solid-state dielectrics are an integral part of modern electrocatalysis, optoelectronics, capacitors, metamaterials and memory devices. The properties of these composite systems strongly depend on the size, dispersion of the inclusions and their chemical stability, and are usually considered constant. Here we demonstrate that nanoscale inclusions (for example, clusters) in dielectrics dynamically change their shape, size and position upon applied electric field. Through systematic in situ transmission electron microscopy studies, we show that fundamental electrochemical processes can lead to universally observed nucleation and growth of metal clusters, even for inert metals like platinum. The clusters exhibit diverse dynamic behaviours governed by kinetic factors including ion mobility and redox rates, leading to different filament growth modes and structures in memristive devices. These findings reveal the microscopic origin behind resistive switching, and also provide general guidance for the design of novel devices involving electronics and ionics. Nanoscale metal inclusions play an important role in solid-state dielectric devices. Here, the authors demonstrate that these inclusions can change their shape, size and position in response to an applied electric field, and that electrochemical processes can lead to metal cluster nucleation and growth.

Chemical short-range-order has been widely noticed to dictate the electrochemical properties of Li-excess cation-disordered rocksalt oxides, a class of cathode based on earth abundant elements for next-generation high-energy-density batteries. Existence of short-range-order is normally evidenced by a diffused intensity pattern in reciprocal space, however, derivation of local atomic arrangements of short-range-order in real space is hardly possible. Here, by a combination of aberration-corrected scanning transmission electron microscopy, electron diffraction, and cluster-expansion Monte Carlo simulations, we reveal the short-range-order is a convolution of three basic types: tetrahedron, octahedron, and cube. We discover that short-range-order directly correlates with Li percolation channels, which correspondingly affects Li transport behavior. We further demonstrate that short-range-order can be effectively manipulated by anion doping or post-synthesis thermal treatment, creating new avenues for tailoring the electrochemical properties. Our results provide fundamental insights for decoding the complex relationship between local chemical ordering and properties of crystalline compounds. Derivation of atomic arrangements of short-range-order from diffused intensity pattern in reciprocal space for Li-excess cation-disordered rocksalt cathode remains as a challenge. Here, the authors reveal the short-range-order is a convolution of three basic types: tetrahedron, octahedron, and cube.

Accurate forecasting of photovoltaic (PV) power is essential for grid scheduling and energy management. In recent years, deep learning technology has made significant progress in time-series forecasting, offering new solutions for PV power forecasting. This study provides a systematic review of deep learning models for PV power forecasting, concentrating on comparisons of the features, advantages, and limitations of different model architectures. First, we analyze the commonly used datasets for PV power forecasting. Additionally, we provide an overview of mainstream deep learning model architectures, including multilayer perceptron (MLP), recurrent neural networks (RNN), convolutional neural networks (CNN), and graph neural networks (GNN), and explain their fundamental principles and technical features. Moreover, we systematically organize the research progress of deep learning models based on different architectures for PV power forecasting. This study indicates that different deep learning model architectures have their own advantages in PV power forecasting. MLP models have strong nonlinear fitting capabilities, RNN models can capture long-term dependencies, CNN models can automatically extract local features, and GNN models have unique advantages for modeling spatiotemporal characteristics. This manuscript provides a comprehensive research survey for PV power forecasting using deep learning models, helping researchers and practitioners to gain a deeper understanding of the current applications, challenges, and opportunities of deep learning technology in this area.

Dielectric properties are crucial in understanding the behavior of water within soil, particularly the soil water content (SWC), as they measure a material’s ability to store an electric charge and are influenced by water and other minerals in the soil. However, a comprehensive review paper is needed that synthesizes the latest developments in this field, identifies the key challenges and limitations, and outlines future research directions. In addition, various factors, such as soil salinity, temperature, texture, probing space, installation gap, density, clay content, sampling volume, and environmental factors, influence the measurement of the dielectric permittivity of the soil. Therefore, this review aims to address the research gap by critically analyzing the current state-of-the-art dielectric properties-based methods for SWC measurements. The motivation for this review is the increasing importance of precise SWC data for various applications such as agriculture, environmental monitoring, and hydrological studies. We examine time domain reflectometry (TDR), frequency domain reflectometry (FDR), ground-penetrating radar (GPR), remote sensing (RS), and capacitance, which are accurate and cost-effective, enabling real-time water resource management and soil health understanding through measuring the travel time of electromagnetic waves in soil and the reflection coefficient of these waves. SWC can be estimated using various approaches, such as TDR, FDR, GPR, and microwave-based techniques. These methods are made possible by increasing the dielectric permittivity and loss factor with SWC. The available dielectric properties are further synthesized on the basis of mathematical models relating apparent permittivity to water content, providing an updated understanding of their development, applications, and monitoring. It also analyzes recent mathematical calibration models, applications, algorithms, challenges, and trends in dielectric permittivity methods for estimating SWC. By consolidating recent advances and highlighting the remaining challenges, this review article aims to guide researchers and practitioners toward more effective strategies for SWC measurements.

Implantable electrodes are crucial for stimulation safety and recording quality of neuronal activity. To enhance their electrochemical performance, electrodeposited nanostructured platinum (nanoPt) and iridium oxide (IrOx) have been proposed due to their advantages of in situ deposition and ease of processing. However, their unstable adhesion has been a challenge in practical applications. This study investigated the electrochemical performance and stability of nanoPt and IrOx coatings on hierarchical platinum-iridium (Pt-Ir) substrates prepared by femtosecond laser, compared with the coatings on smooth Pt-Ir substrates. Ultrasonic testing, agarose gel testing, and cyclic voltammetry (CV) testing were used to evaluate the coatings’ stability. Results showed that the hierarchical Pt-Ir substrate significantly enhanced the charge-storage capacity of electrodes with both coatings to more than 330 mC/cm2, which was over 75 times that of the smooth Pt-Ir electrode. The hierarchical substrate could also reduce the cracking of nanoPt coatings after ultrasonic, agarose gel and CV testing. Although some shedding was observed in the IrOx coating on the hierarchical substrate after one hour of sonication, it showed good stability in the agarose gel and CV tests. Stable nanoPt and IrOx coatings may not only improve the electrochemical performance but also benefit the function of neurobiochemical detection.

Gynecological malignancies, including ovarian, cervical, and endometrial cancers, represent a substantial global health burden, contributing significantly to morbidity and mortality among women. Despite advancements in therapeutic strategies, outcomes for many patients remain suboptimal due to challenges such as late-stage detection and resistance to standard treatments. The advent of chimeric antigen receptor (CAR)-engineered natural killer (NK) cells has introduced a cutting-edge immunotherapy option. This review provides an in-depth exploration of the development of CAR-NK cells, emphasizing their sources, design methodologies, and applications in managing gynecological cancers. It also examines current obstacles and outlines innovative strategies to improve the effectiveness and safety of CAR-NK cell-based therapies. Furthermore, we discuss prospective advancements, highlighting the importance of ongoing research and technological innovation to unlock the full potential of CAR-NK cells in the fight against gynecological cancers.

Earth-abundant cathode materials are urgently needed to enable scaling of the Li-ion industry to multiply terawatt hours of annual production, necessitating reconsideration of how good cathode materials can be obtained. Irreversible transition metal migration and phase transformations in Li-ion cathodes are typically believed to be detrimental because they may trigger voltage hysteresis, poor kinetics and capacity degradation. Here we challenge this conventional consensus by reporting an unusual phase transformation from disordered Li- and Mn-rich rock salts to a new phase (named δ ), which displays partial spinel-like ordering with short coherence length and exhibits high energy density and rate capability. Unlike other Mn-based cathodes, the δ phase exhibits almost no voltage fade upon cycling. We identify the driving force and kinetics of this in situ cathode formation and establish design guidelines for Li- and Mn-rich compositions that combine high energy density, high rate capability and good cyclability, thereby enabling Mn-based energy storage. Earth-abundant, inexpensive cathode materials are highly desirable for the sustainable development of batteries. Here the researchers report that a manganese-rich, cation-disordered rock salt material exhibits—via an in situ phase transition to a partially disordered spinel phase during cycling—potentially high energy density and rate capability.

Sustainable agricultural practices help to manage and use natural resources efficiently. Due to global climate and geospatial land design, soil texture, soil–water content (SWC), and other parameters vary greatly; thus, real time, robust, and accurate soil analytical measurements are difficult to be developed. Conventional statistical analysis tools take longer to analyze and interpret data, which may have delayed a crucial decision. Therefore, this review paper is presented to develop the researcher’s insight toward robust, accurate, and quick soil analysis using artificial intelligence (AI), deep learning (DL), and machine learning (ML) platforms to attain robustness in SWC and soil texture analysis. Machine learning algorithms, such as random forests, support vector machines, and neural networks, can be employed to develop predictive models based on available soil data and auxiliary environmental variables. Geostatistical techniques, including kriging and co-kriging, help interpolate and extrapolate soil property values to unsampled locations, improving the spatial representation of the data set. The false positivity in SWC results and bugs in advanced detection techniques are also evaluated, which may lead to wrong agricultural practices. Moreover, the advantages of AI data processing over general statistical analysis for robust and noise-free results have also been discussed in light of smart irrigation technologies. Conclusively, the conventional statistical tools for SWCs and soil texture analysis are not enough to practice and manage ergonomic land management. The broader geospatial non-numeric data are more suitable for AI processing that may soon help soil scientists develop a global SWC database.

The complex interplay between cancer progression and immune senescence is critically influenced by metabolic reprogramming in T cells. As T cells age, especially within the tumor microenvironment, they undergo significant metabolic shifts that may hinder their proliferation and functionality. This manuscript reviews how metabolic alterations contribute to T cell senescence in cancer and discusses potential therapeutic strategies aimed at reversing these metabolic changes. We explore interventions such as mitochondrial enhancement, glycolytic inhibition, and lipid metabolism adjustments that could rejuvenate senescent T cells, potentially restoring their efficacy in tumor suppression. This review also focuses on the significance of metabolic interventions in T cells with aging and further explores the future direction of the metabolism-based cancer immunotherapy in senescent T cells.

In recent decades, private labels have been the focus of great development in retail due to their price advantage and consumer-oriented production. With growing customer awareness of safety and health, private-label sustainable supplier selection has become a strategic issue for many retailers. Although there are many studies on supplier selection issues and evaluation methods, studies on the sustainability and consumption sectors are rather limited. Therefore, a novel three-phase MCDM model for private-label supplier selection compliance with sustainability criteria is proposed. First, the Delphi method is used to construct a criteria system based on a detailed literature review. Then, an integrated weight algorithm is suggested, in which objective weights are based on attributes’ entropy measurements, and subjective weights are derived from decision-makers’ preferences. Eventually, during evaluation, a fuzzy set extended in VIKOR is exploited by considering the vagueness of decision makers’ expressions. The results from a case study then show that green packaging and labelling, relationship with manufacturing brand, order flexibility, and product traceability are the most important criteria in retail private-label supplier selection. The flexibility and reliability of the proposed model are also demonstrated in a practical case of supplier evaluation.