Magnetic field-enhanced vertical integration enables embodied intelligence in untethered soft robots.

Embodied intelligence in soft robotics offers unprecedented capabilities for operating in uncertain, confined, and fragile environments that challenge conventional technologies. However, achieving true embodied intelligence-which requires continuous environmental sensing, real-time control, and autonomous decision-making-faces challenges in energy management and system integration. We developed deformation-resilient flexible batteries with enhanced performance under magnetic fields inherently present in magnetically actuated soft robots, with capacity retention after 200 cycles improved from 31.

Effect of preparation method on the structure and properties of kaempferol-loaded zein-gum arabic nanoparticles.

Background: Kaempferol (KAE), a bioactive flavonoid, has limited solubility and stability in water. Zein-gum arabic (GA) nanoparticles (NPs) are promising carriers for KAE, but the influence of preparation methods on their structure and properties remains unclear. This study investigated the effect of preparation method on the structure and properties of KAE-loaded zein-GA NPs.

Molecular-level study of the effect of nitrogen addition levels on soil dissolved organic matter composition in a desert steppe of northwest China.

Dissolved organic matter (DOM) plays a key role in grassland carbon biogeochemistry and shows sensitivity to global climate change, particularly nitrogen (N) deposition. We investigated the soil DOM molecular composition by UV-Vis and fluorescence spectroscopy, and FT-ICR MS through a N addition experiment (CK, N5, N10, N20, and N40 [0, 5, 10, 20, and 40 g N m-2 year-1, respectively]) in a desert steppe of northwest China. Moderate N inputs (N5-N20) caused a dose-dependent increase in DOM content (9.

Molecular Motor Matrix-Based Augment Heat Dissipation Strategy Affording Ultrahigh-PCE J-Aggregates for Potentiate Cancer Photothermal Immunotherapy.

The photothermal conversion efficiency (PCE) stands as a pivotal determinant in the therapeutic efficacy of photothermal nanoagents (PTNAs) within the context of photothermal therapy (PTT). The dearth of universal strategies to greatly enhance PCE has markedly curtailed the practical deployment of PTNAs. Now this problem is addressed by proposing a universal approach founded on molecular rotors and J-aggregates, "highly efficient molecular motor matrix", to greatly elevate the PCE of traditional PTNAs.

Automated Configuration of Evolutionary Algorithms via Deep Reinforcement Learning for Constrained Multiobjective Optimization.

Learning to optimize and automated algorithm design are attracting increasing attention, but it is still in its infancy in constrained multiobjective optimization evolutionary algorithms (CMOEAs). Current learning-assisted CMOEAs are typically crafted by human experts using manually designed techniques, which tend to be overly tuned, ad hoc, and lacking versatility. To alleviate these limitations, this work proposes transforming the online configuration of CMOEA into determinations of discrete and continuous parameters, which are then solved by deep reinforcement learning (DRL) techniques.