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Although eco-friendly amidoxime-based adsorbents own an excellent uranium (U)-adsorption capacity, their U-adsorption efficiency is commonly reduced and even damaged by the biological adhesion from bacteria/microorganisms in an aqueous environment. Herein, we present an antibiofouling ultrathin poly(amidoxime) membrane (AUPM) with highly enhanced U-adsorption performance, through dispersing the quaternized chitosan (Q-CS) and poly(amidoxime) in a cross-linked sulfonated cellulose nanocrystals (S-CNC) network. The cross-linked S-CNC not only can elevate the hydrophilicity to improve the U-adsorption efficiency of AUPM but also can enhance the mechanical strength to form a self-supporting ultrathin membrane (17.21 MPa, 10 μm thickness). More importantly, this AUPM owns a good antibiofouling property, owing to the broad-spectrum antibacterial quaternary ammonium groups of the Q-CS. As a result, within the 1.00 L of low-concentration (100 ppb) U-added pure water (pH ≈ 5) and seawater (pH ≈ 8) for 48 h, 30 mg of AUPM can recover 93.7% U and 91.4% U, respectively. Furthermore, compared with the U-absorption capacity of a blank membrane without the Q-CS, that of AUPM can significantly increase 37.4% reaching from 6.39 to 8.78 mg/g after being in natural seawater for only 25 d. Additionally, this AUPM can still maintain almost constant tensile strength during 10 cycles of adsorption–desorption, which indicates the relatively long-term usability of AUPM. This AUPM will be a promising candidate for highly efficient and large-scale U-recovery from both U-containing waste freshwater/seawater and natural seawater, which will be greatly helpful to deal with the U-pollution and enrich U for the consumption of nuclear power. More importantly, the work will provide a new convenient but universal strategy to fabricate new highly enhanced low-cost U-adsorbents, through the introduction of both an antibacterial property and a high mechanical performance, which will be a good reference for the design of new highly efficient U-adsorbents.

Data-driven machine learning, as a promising approach, possesses the capability to build high-quality, exact, and robust models from ophthalmic medical data. Ophthalmic medical data, however, presently exist across disparate data silos with privacy limitations, making centralized training challenging. While ophthalmologists may not specialize in machine learning and artificial intelligence (AI), considerable impediments arise in the associated realm of research. To address these issues, we design and develop FedEYE, a scalable and flexible end-to-end ophthalmic federated learning platform. During FedEYE design, we adhere to four fundamental design principles, ensuring that ophthalmologists can effortlessly create independent and federated AI research tasks. Benefiting from the design principles and architecture of FedEYE, it encloses numerous key features, including rich and customizable capabilities, separation of concerns, scalability, and flexible deployment. We also validated the applicability of FedEYE by employing several prevalent neural networks on ophthalmic disease image classification tasks.

The photoluminescent properties of atomically precise metal nanoclusters (MCs) have garnered significant attention in the fields of chemical sensing and biological imaging. However, the limited brightness of single-component nanoclusters hinders their practical applications, and the conventional ligand engineering approaches have proven insufficient in enhancing the emission efficiency of MCs. Here, we present a DNA framework-guided strategy to prepare highly luminescent metal cluster nanoaggregates. Our approach involves an amphiphilic DNA framework comprising a hydrophobic alkyl core and a rigid DNA framework shell, serving as a nucleation site and providing well-defined nanoconfinements for the self-limiting aggregation of MCs. Through this method, we successfully produced homogeneous MC nanoaggregates (10.1 ± 1.2 nm) with remarkable nanoscale precision. Notably, this strategy proves adaptable to various MCs, leading to a substantial enhancement in emission and quantum yield, up to 3011- and 87-fold, respectively. Furthermore, our investigation using total internal reflection fluorescence microscopy at the single-particle level uncovered a more uniform photon number distribution and higher photostability for MC nanoaggregates compared to template-free counterparts. This DNA-templating strategy introduces a conceptually innovative approach for studying the photoluminescent properties of aggregates with nanoscale precision and holds promise for constructing highly luminescent MC nanoparticles for diverse applications.

The optimal energy management for a plug-in hybrid electric bus (PHEB) running along the fixed city bus route is an important technique to improve the vehicles’ fuel economy and reduce the bus emission. Considering the inherently high regularities of the fixed bus routes, the continuous state Markov decision process (MDP) is adopted to describe a cost function as total gas and electric consumption fee. Then a learning algorithm is proposed to construct such a MDP model without knowing the all parameters of the MDP. Next, fitted value iteration algorithm is given to approximate the cost function, and linear regression is used in this fitted value iteration. Simulation results show that this approach is feasible in searching for the control strategy of PHEB. Simultaneously this method has its own advantage comparing with the CDCS mode. Furthermore, a test based on a real PHEB was carried out to verify the applicable of the proposed method.