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Black phosphorus (BP) is a promising two-dimensional layered semiconductor material for next-generation electronics and optoelectronics, with a thickness-dependent tunable direct bandgap and high carrier mobility. Though great research advantages have been achieved on BP, lateral synthesis of high quality BP films still remains a great challenge. Here, we report the direct growth of large-scale crystalline BP films on insulating silicon substrates by a gas-phase growth strategy with an epitaxial nucleation design and a further lateral growth control. The optimized lateral size of the achieved BP films can reach up to millimeters, with the ability to modulate thickness from a few to hundreds of nanometers. The as-grown BP films exhibit excellent electrical properties, with a field-effect and Hall mobility of over 1200 cm 2 V −1 s −1 and 1400 cm 2 V −1 s −1 at room temperature, respectively, comparable to those exfoliated from BP bulk crystals. Our work opens the door for broad applications with BP in scalable electronic and optoelectronic devices. Direct synthesis of large area crystalline black phosphorus films is still challenging. Here, the authors report growth of high-quality black phosphorus films on insulating silicon substrates through a gas-phase epitaxial growth strategy with field-effect and Hall mobilities of over 1200 and 1400 cm2 /Vs at room temperature, respectively and a current on/off ratio of up to 10 6 , comparable to the exfoliated flakes.

Stimuli-responsive microgels, smart polymeric particles at a micro- to sub-micro scale that are capable of undergoing reversible changes in response to external triggers, have emerged as versatile tools in the field of biomedical research. The review begins by emphasizing the importance of precise control over microgel properties, such as size, composition, and responsiveness, to harness their full potential. It delves into various synthetic methodologies, including precipitation polymerization, emulsion polymerization, and microfluidic techniques. The versatility of microgels, combined with their ability to respond to specific stimuli, holds great promise for tailored biomedical applications. By dissecting the responsive attributes of microgels and unraveling the intricate structure-property relationships they embody, this review elucidates the invaluable contributions of these remarkable smart materials to diverse biomedical applications, paving the way for future advancements in the field.

Given that bromine possesses similar properties but extra merits of easily synthesizing and polarizing comparing to homomorphic fluorine and chlorine, it is quite surprising very rare high-performance brominated small molecule acceptors have been reported. This may be caused by undesirable film morphologies stemming from relatively larger steric hindrance and excessive crystallinity of bromides. To maximize the advantages of bromides while circumventing weaknesses, three acceptors (CH20, CH21 and CH22) are constructed with stepwise brominating on central units rather than conventional end groups, thus enhancing intermolecular packing, crystallinity and dielectric constant of them without damaging the favorable intermolecular packing through end groups. Consequently, PM6:CH22-based binary organic solar cells render the highest efficiency of 19.06% for brominated acceptors, more excitingly, a record-breaking efficiency of 15.70% when further thickening active layers to ~500 nm. By exhibiting such a rare high-performance brominated acceptor, our work highlights the great potential for achieving record-breaking organic solar cells through delicately brominating. The relatively larger steric hindrance and excessive crystallinity of bromides could lead to undesirable film morphologies. Here, the authors take advantage of bromides and construct small molecule acceptors with stepwise bromination and realize maximum efficiency of 19% in organic solar cells.

Modern adaptive radars can switch work modes to perform various missions and simultaneously use pulse parameter agility in each mode to improve survivability, which leads to a multiplicative increase in the decision-making complexity and declining performance of the existing jamming methods. In this paper, a two-level jamming decision-making framework is developed, based on which a dual Q-learning (DQL) model is proposed to optimize the jamming strategy and a dynamic method for jamming effectiveness evaluation is designed to update the model. Specifically, the jamming procedure is modeled as a finite Markov decision process. On this basis, the high-dimensional jamming action space is disassembled into two low-dimensional subspaces containing jamming mode and pulse parameters respectively, then two specialized Q-learning models with interaction are built to obtain the optimal solution. Moreover, the jamming effectiveness is evaluated through indicator vector distance measuring to acquire the feedback for the DQL model, where indicators are dynamically weighted to adapt to the environment. The experiments demonstrate the advantage of the proposed method in learning radar joint strategy of mode switching and parameter agility, shown as improving the average jamming-to-signal radio (JSR) by 4.05% while reducing the convergence time by 34.94% compared with the normal Q-learning method.

Taking square concrete-filled steel tubular (CFST) members under axial tension as the research object, a three-dimensional mesoscopic finite element model was established based on the experimental results of six specimens. Ten parametric models were further developed to investigate the effects of section size, confinement coefficient, and slenderness ratio on tensile performance. In addition, code-based comparisons and machine learning predictions were carried out. The results indicate that the finite element simulations agree well with the test results, with the ratios of simulation results to test results all being below 0.95, indicating that the simulation predictions are within a reasonable range of the experimental data, which reflects good agreement. The parametric analysis shows that when the confinement coefficient increases from 0 to 0.99, the maximum load rises from 182 kN to 895 kN; when the slenderness ratio increases from 8 to 20, the maximum load exhibits an overall decreasing trend. The code comparison shows that the predictions from the Chinese code are closer to the finite element results, with an average error of approximately 4.57%. To improve prediction accuracy with limited data, a Generative Adversarial Network (GAN)-based data augmentation method was employed. Using both original and WGAN-GP-augmented data, predictive models were developed. Among these models, the Random Forest model achieved the best overall performance. On the augmented test set, the coefficients of determination (R2) for ultimate load and displacement prediction reached 0.997 and 0.9855, respectively. The findings provide a reference for tensile performance analysis and rapid assessment of this type of member, demonstrating the effectiveness of GAN-based data augmentation in enhancing predictive accuracy.

The diagnosis of brain pathologies usually involves imaging to analyze the condition of the brain. Magnetic resonance imaging (MRI) technology is widely used in brain disorder diagnosis. The image quality of MRI depends on the magnetostatic field strength and scanning time. Scanners with lower field strengths have the disadvantages of a low resolution and high imaging cost, and scanning takes a long time. The traditional super-resolution reconstruction method based on MRI generally states an optimization problem in terms of prior information. It solves the problem using an iterative approach with a large time cost. Many methods based on deep learning have emerged to replace traditional methods. MRI super-resolution technology based on deep learning can effectively improve MRI resolution through a three-dimensional convolutional neural network; however, the training costs are relatively high. In this paper, we propose the use of two-dimensional super-resolution technology for the super-resolution reconstruction of MRI images. In the first reconstruction, we choose a scale factor of 2 and simulate half the volume of MRI slices as input. We utilize a receiving field block enhanced super-resolution generative adversarial network (RFB-ESRGAN), which is superior to other super-resolution technologies in terms of texture and frequency information. We then rebuild the super-resolution reconstructed slices in the MRI. In the second reconstruction, the image after the first reconstruction is composed of only half of the slices, and there are still missing values. In our previous work, we adopted the traditional interpolation method, and there was still a gap in the visual effect of the reconstructed images. Therefore, we propose a noise-based super-resolution network (nESRGAN). The noise addition to the network can provide additional texture restoration possibilities. We use nESRGAN to further restore MRI resolution and high-frequency information. Finally, we achieve the 3D reconstruction of brain MRI images through two super-resolution reconstructions. Our proposed method is superior to 3D super-resolution technology based on deep learning in terms of perception range and image quality evaluation standards.

Rare earth elements (REEs) play indispensable roles in various advanced technologies, from electronics to renewable energy. However, the heavy global REEs supply and the environmental impact of traditional mining practices have spurred the search for sustainable REEs recovery methods. Polymeric materials have emerged as promising candidates due to their selective adsorption capabilities, versatility, scalability, and regenerability. This paper provides an extensive overview of polymeric materials for REEs recovery, including polymeric resins, polymer membranes, cross-linked polymer networks, and nanocomposite polymers. Each category is examined for its advantages, challenges, and notable developments. Furthermore, we highlight the potential of polymeric materials to contribute to eco-friendly and efficient REEs recovery, while acknowledging the need to address challenges such as selectivity, stability, and scalability. The research in this field actively seeks innovative solutions to reduce reliance on hazardous chemicals and minimize waste generation. As the demand for REEs continues to rise, the development of sustainable REEs recovery technologies remains a critical area of investigation, with the collaboration between researchers and industry experts driving progress in this evolving field.

This paper studies the incentive for vertical information sharing in competing supply chains with production technologies that exhibit diseconomies of scale. We consider a model of two supply chains each consisting of one manufacturer selling to one retailer, with the retailers engaging in Cournot or Bertrand competition. For Cournot retail competition, we show that information sharing benefits a supply chain when (1) the production diseconomy is large and (2) either competition is less intense or at least one retailer's information is less accurate. A supply chain may become worse off when making its information more accurate or production diseconomy smaller, if such an improvement induces the firms in the rival supply chain to cease sharing information. For Bertrand retail competition, we show that information sharing benefits a supply chain when (1) the production diseconomy is large and (2) either competition is less intense or information is more accurate. Under Bertrand competition a manufacturer may be worse off by receiving information, which is never the case under Cournot competition. Information sharing in one supply chain triggers a competitive reaction from the other supply chain and this reaction is damaging to the first supply chain under Cournot competition but may be beneficial under Bertrand competition. This paper was accepted by Martin Lariviere, operations management.

The Fe2+-peroxydisulfate (PDS) process is an effective method to enhance paper sludge dewaterability. However, the generated waste of iron-rich sludge cake needs to be disposed of properly. Converting sludge cake into magnetic biochar catalysts can help to shorten its environmental risks, recover resources, and reduce catalyst costs. In this work, the magnetic sludge biochar (Fe-SBC) was prepared and applied as a PDS activator to remove tetracycline (TC). Results showed that the Fe-SBC pyrolyzed at 600 °C presented excellent catalytic activity for PDS to degrade TC (rate of 74.89%) under the optimum conditions (intrinsic pH, 10 mM PDS, 0.5 g/L Fe-SBC, and 50 mg/L TC). It was found for the first time that the addition sequences of the biochar catalyst and PDS were insignificant on TC removal. Furthermore, the TC removal mechanism was proposed to be free radicals (·OH, SO4·−, and O2−·) and non-radical (1O2) pathways working together for TC removal, for which 1O2 was dominated. In addition, coexisting ions (Cl− and CO32−) had an inhibitory effect on TC degradation. This work will provide a new recycling approach for waste paper sludge in wastewater treatment.

The analogy between the brain and deep neural networks (DNNs) has sparked interest in neuroscience. Although DNNs have limitations, they remain valuable for modeling specific brain characteristics. This study used Skye’s Oblique Grating illusion to assess DNNs’ relevance to brain neural networks. We collected data on human perceptual responses to a series of visual illusions. This data was then used to assess how DNN responses to these illusions paralleled or differed from human behavior. We performed two analyses:(1) We trained DNNs to perform horizontal vs. non-horizontal classification on images with bars tilted different degrees (non-illusory images) and tested them on images with horizontal bars with different illusory strengths measured by human behavior (illusory images), finding that DNNs showed human-like illusions; (2) We performed representational similarity analysis to assess whether illusory representation existed in different layers within DNNs, finding that DNNs showed illusion-like responses to illusory images. The representational similarity between real tilted images and illusory images was calculated, which showed the highest values in the early layers and decreased layer-by-layer. Our findings suggest that DNNs could serve as potential models for explaining the mechanism of visual illusions in human brain, particularly those that may originate in early visual areas like the primary visual cortex (V1). While promising, further research is necessary to understand the nuanced differences between DNNs and human visual pathways.