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This paper proposes a power‐optimized column‐parallel cyclic analog to digital converter (ADC) for complementary metal oxide semiconductors (CMOS) image sensor readout circuits. The design combines a 2.5‐bit/cycle architecture with a comparator shutdown technique based on the comparator‐based switched‐capacitor (CBSC) circuits, which results in a significant reduction in the operating time of the threshold detection comparator compared with conventional CBSC circuits. This reduction in operating time leads to power savings as the threshold detection comparator can be shut down quickly. The paper also presents a comprehensive analysis of the nonideal factors of CBSC circuits and the coarse and fine conversion allocation scheme. The 10‐bit two‐stage comparator‐based cyclic ADC is designed in a 110 nm 1P4 M CMOS technology. Simulation results show that the effective number of bit (ENOB) is 9.7 bits, with each column consuming 103 μ$$ \upmu $$W of power. The proposed cyclic ADC has a figure of merit (FOM) of 123 fJ/conv‐step. Compared with conventional structures, the proposed design reduces the power consumption of the ADC by 34.3% while maintaining the same level of performance. [ABSTRACT FROM AUTHOR]

Let k$k$ be a number field. For a variety X$X$ over k$k$ that satisfies weak approximation with Brauer–Manin obstruction, we study the same property for smooth projective models of its symmetric products. Based on the same method, we also explore the property of weak approximation with Brauer–Manin obstruction for norm varieties. [ABSTRACT FROM AUTHOR]

Nano/microparticles with hollow structures have many applications in various fields. However, engineering hollow covalent organic framework (COF) particles in a simple and efficient manner remains a significant challenge. In this study, we propose a self‐template crystallization method to prepare imine‐based COF microparticles with tailored shell thickness and high crystallinity. This method involves the post‐transformation of amorphous microparticles in a highly reversible reaction system containing monofunctional competitors. The amorphous precursors with high‐Gibbs free energy decompose and reconstruct to form highly crystalline COFs in situ on the particle surface. This process allows for the preparation of various Schiff‐base COF particles with good crystallinity and tailored hollow structures, with diameters ranging from 120 to 680 nm and shell thicknesses of 27–96 nm. These COF particles have well‐defined micropore sizes centered between 1.0 and 4.0 nm and a high‐specific surface area of up to 543 m2 g−1. The resulted COF particles could be used to support ethylene oligomerization catalysts for improving their activity and selectivity, that is, the mass fraction of α‐olefins above C10 increased from almost zero to 52.7% after COF supporting. Our method thus provides an efficient way to precisely tailor the microstructure and morphology of COF materials for advanced applications. [ABSTRACT FROM AUTHOR]

All‐optical neural networks have advantages in higher throughput, higher speed as well as lower energy consumption compared to electrical neural networks. Optical neural networks have already shown great potential in various applications; however, the operation speed of the network is limited by the 2D detector as most optical neural networks rely on space to space projection. Here, a space to time projection approach to build diffractive deep neural network (D2NN) is proposed, which can project spatial intensity distribution into time‐domain intensity variation, thus bypassing the detection speed limit of 2D imaging device. Based on this scheme, high‐speed all‐optical logic gates are theoretically analyzed and experimentally realized. In this case, the network's operation speed is only limited by the photodetector (PD), which can reach GHz levels. Moreover, the method will show great advantage when it comes to wavelengths where 2D detectors are not achievable easily such as infrared, terahertz or microwaves. [ABSTRACT FROM AUTHOR]

Neural network accelerator based on photonic‐integrated circuits is emerging as a promising technology for fast, power‐efficient, and parallel computing. Under such technology, optics is mainly used for linear transformations, e.g., through an array of cascaded switching networks. Nonlinear activation is implemented either electronically requiring extra optical–electrical conversion or via nonlinear optical materials that often suffer from high loss, large power consumption, and difficulty in integration. Herein, an optical neural chip with only one multimode waveguide, fabricated using low‐cost linear optical materials, plus seven heater electrodes to control the multimode interference, is proposed. The nonlinear networks are intrinsically integrated in the electrical‐to‐optical signal conversion through the waveguide. The linear computation, in the electronic domain, is included in the mandatory step to convert the input matrix to the intermediate current values on the seven electrodes. Though extremely simple, the proposed system can classify nonlinear datasets and images by optical readout with high accuracy and without calibration. Prospects for future development are given at the end. In this work, an alternative route is offered to exploiting the classic multimode interference for advanced optical computing applications. [ABSTRACT FROM AUTHOR]

In this work, an environmentally‐friendly deep eutectic solvent (DES) was employed as a catalyst to generate polyvinyl butyral (PVB) resins from polyvinyl alcohol (PVA) with a weakly acidic polymer (polyacrylic acid [PAA]) as an emulsifier. Finally, high viscosity PVB resins with molecular weight of 110,000–200,000 g · mol−1 and acetalization degree above 83% were successfully synthesized, which was found to satisfy the requirements for interlayer films between safety glass. Furthermore, it was well proved that the properties of PVB resins immensely depend on the molecular structures of the PVA. The molecular weight of the PVB, independent of alcoholic degrees of the PVA, indicated an upward trend with the increase of the molecular weight of the PVA. As for these four PVBs with high molecular weight, the Tg was sensitive to the content of hydroxyl and acetal groups rather than molecular weight, associated with the inter‐ or intra‐molecule hydrogen bond between OH. PVB‐1799 and PVB‐1797 had higher Ti and Tg than that of PVB‐1788 and PVB‐1795 because the former had low contents of vinyl acetate group (VAc) and alcohol hydroxyl group (VOH). The tensile strength of PVBs was all higher than 30 Mpa, and the elongation at break was about 300% due to their high molecular weights. [ABSTRACT FROM AUTHOR]

In the ongoing quest to develop lithium‐ion batteries with superior capacity and enhanced safety, the focus has shifted toward all‐solid‐state batteries (SSBs) and nickel‐rich cathode materials. Despite their promise, these technologies face significant interface challenges, notably poor contact and low ion transport efficiency, leading to substantial stability issues. This review aims to provide a comprehensive analysis of both the advantages and the challenges associated with all‐solid‐state batteries. In addition, it discusses the benefits of single‐crystal application in SSBs, in terms of their kinetic performance, mechanical properties, and stability. The review concludes by proposing various strategies to optimize single‐crystal technologies, targeting the development of efficient nickel‐rich single‐crystal materials for use in all‐solid‐state batteries. These approaches offer the potential to address the core challenges currently faced by SSBs and pave the way for the next generation of high‐performance batteries. [ABSTRACT FROM AUTHOR]

Reinforced concrete shear walls in multistory buildings often undergo the combined action of vertical and horizontal loads, with their failure primarily attributed to localized damage of the concrete at the bottom of edge elements, leading to premature loss of wall functionality. To enhance the performance of shear walls, this study explores the optimization of shear wall design from the perspective of the stress path within the wall, introducing a diagonally distributed reinforcements in shear walls (referred to as DDR shear walls). To investigate the seismic performance of DDR shear walls, we consider the effects of axial compression ratio, the inclination angle of distributed reinforcing bars, reinforcing bar spacing, and shear span ratio. We design 27 shear wall models with various parameter combinations and employ ABAQUS finite element analysis software to simulate the seismic performance. The simulation results reveal that the inclined distribution of reinforcing bars significantly enhances the seismic performance of shear walls. To achieve optimal structural performance, the inclination angle of reinforcing bars should be adjusted for different floor levels, with higher floors requiring a greater angle and lower floors a smaller one. Additionally, increasing the reinforcing bar spacing and altering the shear span ratio will have varying degrees of impact on the seismic performance of shear walls, necessitating rational design adjustments based on specific circumstances. Furthermore, the application of DDR shear walls in prefabricated construction can be considered to optimize construction processes. [ABSTRACT FROM AUTHOR]