Explore the vast range of titles available.

Materials capable of extracting gold from complex sources, especially electronic waste (e-waste), are needed for gold resource sustainability and effective e-waste recycling. However, it remains challenging to achieve high extraction capacity and precise selectivity if only a trace amount of gold is present along with other metallic elements . Here we report an approach based on reduced graphene oxide (rGO) which provides an ultrahigh capacity and selective extraction of gold ions present in ppm concentrations (>1000 mg of gold per gram of rGO at 1 ppm). The excellent gold extraction performance is accounted to the graphene areas and oxidized regions of rGO. The graphene areas spontaneously reduce gold ions to metallic gold, and the oxidized regions allow good dispersibility of the rGO material so that efficient adsorption and reduction of gold ions at the graphene areas can be realized. By controlling the protonation of the oxidized regions of rGO, gold can be extracted exclusively, without contamination by the other 14 co-existing elements typically present in e-waste. These findings are further exploited to demonstrate recycling gold from real-world e-waste with good scalability and economic viability, as exemplified by using rGO membranes in a continuous flow-through process. High extraction capacity with precise selectivity to trace amounts of gold over a wide range of co-existing elements remains a challenge for effective e-waste recycling. Here, authors demonstrate the excellent performance of rGO for gold extraction from e-waste leachate, even at minute concentrations.

HighlightsThe engineering design principles for enhancing interfacial contact between the electrodes (Li anodes and S cathode) and solid-state electrolytes in solid-state Li–S batteries are classified and discussed.Research progresses of experimental strategies for reducing interfacial impedance in solid-state Li–S batteries are summarized.Challenges and future perspectives of rational interfacial strategies in solid-state Li–S batteries are highlighted.The utilization of solid-state electrolytes (SSEs) presents a promising solution to the issues of safety concern and shuttle effect in Li–S batteries, which has garnered significant interest recently. However, the high interfacial impedances existing between the SSEs and the electrodes (both lithium anodes and sulfur cathodes) hinder the charge transfer and intensify the uneven deposition of lithium, which ultimately result in insufficient capacity utilization and poor cycling stability. Hence, the reduction of interfacial resistance between SSEs and electrodes is of paramount importance in the pursuit of efficacious solid-state batteries. In this review, we focus on the experimental strategies employed to enhance the interfacial contact between SSEs and electrodes, and summarize recent progresses of their applications in solid-state Li–S batteries. Moreover, the challenges and perspectives of rational interfacial design in practical solid-state Li–S batteries are outlined as well. We expect that this review will provide new insights into the further technique development and practical applications of solid-state lithium batteries.

Given the advent of the digital era, digital transformation has become necessary for enterprise development. Political connections are the most important resources for enterprise development in most countries. However, the impact of political connections on corporate digital transformation has yet to be verified. This study uses ERNIE, a large language model, to construct a measurement of corporate digital transformation from the perspective of digital technology application through a textual analysis of the annual reports of A-share privately listed companies from 2008 to 2020 and analyzes the impact of political connections on corporate digital transformation and its mechanism of action. The findings demonstrate that political connections have a significant inhibitory effect on corporate digital transformation. This conclusion still holds after a series of robustness and endogeneity tests. The mechanism analyses demonstrate that political connections primarily affect corporate digital transformation through three mechanisms: weakening risk, inhibiting innovation, and enhancing resource crowding. We theoretically expand the understanding of the economic impact of political connections and provide new ideas for accelerating enterprise digital transformation from the perspective of policy makers.

Vehicular sensor networks (VSNs) consist of vehicles equipped with various sensing devices, such as LiDAR. In a VSN, vehicles and/or roadside units (RSUs) can be organized into a vehicular cloud (VC) to enable the sharing of sensing and computational resources among participants, thereby supporting crowd-sensing applications. However, the highly dynamic nature of vehicular mobility poses significant challenges in terms of establishing secure and scalable group communication within the VC. To address these challenges, we first introduce a lightweight extension of the continuous group key agreement (CGKA) scheme by incorporating an administrator mechanism. The resulting scheme, referred to as CGKAwAM, supports the designation of multiple administrators within a single group for flexible member management. Building upon CGKAwAM, we propose a blockchain-based dynamic key management scheme, termed BcDKM. This scheme supports asynchronous join and leave operations while achieving communication round optimality. Furthermore, RSUs are leveraged as blockchain nodes to enable decentralized VC discovery and management, ensuring scalability without relying on a centralized server. We formally analyze the security of both CGKAwAM and BcDKM. The results demonstrate that the proposed scheme satisfies several critical security properties, including known-key security, forward secrecy, post-compromise security, and vehicle privacy. Experimental evaluations further confirm that BcDKM is practical and achieves a well-balanced tradeoff between security and performance.

Text Correction [...]

Musculoskeletal ultrasound has gained increasing attention as a noninvasive imaging modality in pain clinics. Its advantages include the lack of radiation exposure, cost-effectiveness, and efficiency, allowing for rapid provision of dynamic examination results. Notably, it has demonstrated significant effectiveness in diagnosing common joint injuries. This review synthesizes the advancements in the application of musculoskeletal ultrasound across various domains, including muscles, joints, bones, tendons, ligaments, and bursae. It explores the critical role of ultrasound in pain management, highlighting both its importance and limitations. Moreover, the review analyzes the latest research findings and the potential for clinical applications, underscoring the evolving landscape of musculoskeletal ultrasound in enhancing diagnostic accuracy in pain management.

Grid security and system dispatch can be compromised by pronounced volatility in power load under extreme meteorological conditions. However, the dynamic and nonlinear interactions between power load and meteorological variables across diverse weather scenarios are not well captured by existing methods, resulting in limited accuracy and robustness. To address this gap, a short-term power load forecasting model with a dual-channel architecture is proposed. Features are extracted in parallel via dual-channel feature extraction (DCFE): the first channel employs an improved Cascaded Multiscale 2D Convolutional Network (CMCNN) to model local fluctuations and global periodicity in the load time series. The second channel derives scenario-aware variable weights using the Maximal Information Coefficient (MIC); meteorological variables are then gated and weighted before being processed by a multi-layer self-attention network to learn global dependencies. Subsequently, dynamic feature-level fusion is achieved through cross-attention, strengthening key interactions between power load and meteorological factors. The fused representation is fed into an Attention-Enhanced Bidirectional Gated Recurrent Unit (AE-BiGRU) to precisely model temporal dependencies across multiple weather scenarios. Experiments on five years of power load and meteorological data from a region in Australia indicate that the proposed method outperforms the best baseline across multiple weather conditions: RMSE, MAE, MAPE, and sMAPE decrease on average by 32.44%, 31.42%, 30.73%, and 31.05%, respectively, while R2 increases by 0.034 on average, demonstrating strong adaptability and robustness.

Accurate prediction of electric vehicle charging profiles and durations is critical for adoption and optimising infrastructure. Direct current fast charging presents complex behaviours shaped by many factors. This work introduces a deep learning framework trained on 909,135 real-world sessions, capable of predicting charging profiles and durations from minimal input with uncertainty estimates. The model initiates predictions from a single point on the power and state-of-charge profile and incrementally refines them as new observations arrive, enabling real-time updates. The model generalises across vehicle types and charging scenarios. It achieves 90% accuracy in predicting charging duration from a single point, and 95% accuracy with an absolute error under one minute using six points within five minutes. This work shows that using readily available input data at charge time enables accurate prediction of charging behaviour and offers a practical, scalable solution for deployment, energy planning, and infrastructure reliability. Here, the authors present a deep learning framework trained on nearly one million direct current fast charging sessions that accurately predicts electric vehicle charging profiles and the remaining driving time. The model provides the predictions in real time from minimal input, improving user experience, energy planning, and infrastructure reliability.

The application of terahertz time-domain spectroscopy (THz-TDS) in the quantitative analysis of major minerals in Bayan Obo magnetite ore was explored. The positive correlation between the optical parameters of the original ore and its iron content is confirmed. The detections of three main iron containing minerals, including magnetite, pyrite, and hematite, were simulated using corresponding reagents. The random forest algorithm is used for quantitative analysis, and FeS 2 is detected with precision of R 2  = 0.7686 and MAE = 0.6307% in ternary mixtures. The experimental results demonstrate that THz-TDS can distinguish specific iron containing minerals and reveal the potential application value of this testing method in exploration and mineral processing fields.

Photovoltaic (PV) string exhibits complex multiple-peak characteristics under various partial shading conditions (PSC). If the maximum power point tracking cannot be achieved quickly and accurately, it will lead to a large amount of energy loss. Therefore, it has become a hot topic to study a reliable maximum power tracking control algorithm to ensure the PV system can still output maximum power under PSC. This paper proposes an immune firefly algorithm (IFA), which utilizes vaccine data-base to shorten the convergence time, eliminates the influence of bad individuals in time by immune replenishment operation, and reduces the steady-state oscillation by the improving iteration formula. The simulations in static and dynamic environments verify that the immune firefly algorithm can track the maximum power point under various partial shading conditions. Compared with conventional firefly algorithm (FA), IFA has faster convergence speed, and can effectively restrain the oscillation of voltage and power.