A Conductive 2D Conjugated Tetrathia[8]circulene-Based Nickel Metal–Organic Framework for Energy Storage

Herein, a novel D₄ symmetrical redox-active ligand tetrathia[8]circulene-2,3,5,6,8,9,11,12-octaol (8OH-TTC) is designed and synthesized, which coordinates with Ni²⁺ ions to construct a 2D conductive metal-organic framework (2D c-MOF) named Ni-TTC. Ni-TTC exhibits typical semiconducting properties with electrical conductivity up to ≈1.0 S m⁻¹ at 298 K. Furthermore, magnetism measurements show the paramagnetic property of Ni-TTC with strong antiferromagnetic coupling due to the presence of semiquinone ligand radicals and Ni²⁺ sites. In virtue of its decent electrical conductivity and good redox activity, the gravimetric capacitance of Ni-TTC is up to 249 F g⁻¹ at a discharge rate of 0.2 A g⁻¹, which demonstrates the potential of tetrathia[8]circulene-based redox-active 2D c-MOFs in energy storage applications.     

Effective identification of dominant fully absorbing sets for Raptor-like LDPC codes

The error-rate floor of low-density parity-check (LDPC) codes is attributed to the trapping sets of their Tanner graphs. Among them, fully absorbing sets dominantly affect the error-rate performance, especially for short blocklengths. Efficient methods to identify the dominant trapping sets of LDPC codes were thoroughly researched as exhaustively searching them is NP-hard. However, the existing methods are ineffective for Raptor-like LDPC codes, which have many types of trapping sets. An effective method to identify dominant fully absorbing sets of Raptor-like LDPC codes is proposed. The search space of the proposed algorithm is optimized into the Tanner subgraphs of the codes to afford time-efficiency and search-effectiveness. For 5G New Radio (NR) base graph (BG) 2 LDPC codes for short blocklengths, the proposed algorithm finds more dominant fully absorbing sets within one seventh of the computation time of the existing search algorithm, and its search-effectiveness is verified using importance sampling. The proposed method is also applied to 5G NR BG1 LDPC code and Advanced Television Systems Committee 3.0 type A LDPC code for large blocklengths.     

Fast Li+ Transport via Silica Network-Driven Nanochannels in Ionomer-in-Framework for Lithium Metal Batteries

For the development of all-solid-state lithium metal batteries (LMBs), a high-porous silica aerogel (SA)-reinforced single-Li⁺ conducting nanocomposite polymer electrolyte (NPE) is prepared via two-step selective functionalization. The mesoporous SA is introduced as a mechanical framework for NPE as well as a channel for fast lithium cation migration. Two types of monomers containing weak-binding imide anions and Li⁺ cations are synthesized and used to prepare NPEs, where these monomers are grafted in SA to produce SA-based NPEs (SANPEs) as ionomer-in-framework. This hybrid SANPE exhibits high ionic conductivities (≈10⁻³ S cm⁻¹), high modulus (≈10⁵ Pa), high lithium transference number (0.84), and wide electrochemical window (>4.8 V). The resultant SANPE in the lithium symmetric cell possesses long-term cyclic stability without short-circuiting over 800 h under 0.2 mA cm⁻². Furthermore, the LiFePO₄|SANPE|Li solid-state batteries present a high discharge capacity of 167 mAh g⁻¹ at 0.1 C, good rate capability up to 1 C, wide operating temperatures (from −10 to 40 °C), and a stable cycling performance with 97% capacity retention and 100% coulombic efficiency after 75 cycles at 1 C and 25 °C. The SANPE demonstrates a new design principle for solid-state electrolytes, allowing for a perfect complex between inorganic silica and organic polymer, for high-energy-density LMBs.     

Pre-Buried ETL with Bottom-Up Strategy Toward Flexible Perovskite Solar Cells with Efficiency Over 23%

With rapid development of photovoltaic technology, flexible perovskite solar cells (f-PSCs) have attracted much attention for their light weight, high flexibility and portability. However, the power conversion efficiency (PCE) achieved so far is not yet comparable to that of rigid devices. This is mainly due to the great challenge of depositing homogeneous and high-quality perovskite films on flexible substrate. In this study, the pre-buried 3-aminopropionic acid hydroiodide (3AAH) additives into the electron transport layer (ETL) and modified the ETL/perovskite (PVK) interface by a bottom-up strategy. 3AAH treatment induced a templated perovskite grain growth and improved the quality of the ETL. By this, the residual stresses generated in PVK during the annealing-cooling process are released and converted into micro-compressive stresses. As a result, the defect density of f-PSCs with pre-buried 3AAH is reduced and the photovoltaic performance is greatly improved, reaching an exceptional PCE of 23.36%. This strategy provides a new idea to bridge the gap between flexible and rigid devices.     

Ultra-Stable and Sensitive Ultraviolet Photodetectors Based on Monocrystalline Perovskite Thin Films

The detection of ultraviolet (UV) radiation with effective performance and robust stability is essential to practical applications. Metal halide single-crystal perovskites (ABX₃) are promising next-generation materials for UV detection. The device performance of all-inorganic CsPbCl₃ photodetectors (PDs) is still limited by inner imperfection of crystals grown in solution. Here wafer-scale single-crystal CsPbCl₃ thin films are successfully grown by vapor-phase epitaxy method, and the as-constructed PDs under UV light illumination exhibit an ultralow dark current of 7.18 pA, ultrahigh ON/OFF ratio of ≈5.22 × 10⁵, competitive responsivity of 32.8 A W⁻¹, external quantum efficiency of 10867% and specific detectivity of 4.22 × 10¹² Jones. More importantly, they feature superb long-term stability toward moisture and oxygen within twenty-one months, good temperature tolerances at low and high temperatures. The ability of the photodetector arrays for excellent UV light imaging is further demonstrated.     

Room-Temperature-Processable Highly Reliable Resistive Switching Memory with Reconfigurability for Neuromorphic Computing and Ultrasonic Tissue Classification

Reversible metal-filamentary mechanism has been widely investigated to design an analog resistive switching memory (RSM) for neuromorphic hardware-implementation. However, uncontrollable filament-formation, inducing its reliability issues, has been a fundamental challenge. Here, an analog RSM with 3D ion transport channels that can provide unprecedentedly high reliability and robustness is demonstrated. This architecture is realized by a laser-assisted photo-thermochemical process, compatible with the back-end-of-line process and even applicable to a flexible format. These superior characteristics also lead to the proposal of a practical adaptive learning rule for hardware neural networks that can significantly simplify the voltage pulse application methodology even with high computing accuracy. A neural network, which can perform the biological tissue classification task using the ultrasound signals, is designed, and the simulation results confirm that this practical adaptive learning rule is efficient enough to classify these weak and complicated signals with high accuracy (97%). Furthermore, the proposed RSM can work as a diffusive-memristor at the opposite voltage polarity, exhibiting extremely stable threshold switching characteristics. In this mode, several crucial operations in biological nervous systems, such as Ca²⁺ dynamics and nonlinear integrate-and-fire functions of neurons, are successfully emulated. This reconfigurability is also exceedingly beneficial for decreasing the complexity of systems—requiring both drift- and diffusive-memristors.     

Energy Transfer Induced by TADF Polymer Enables the Recycling of Excitons in Perovskite Solar Cells

Formamidinium lead triiodide (FAPbI₃) has been demonstrated as the most efficient perovskite system to date, due to its excellent thermal stability and an ideal bandgap approaching the Shockley-Queisser limit. Whereas, there are intrinsic quantum confinement effects in FAPbI₃, which lead to unwanted non-radiative recombination. Additionally, the black α-phase of FAPbI₃ is unstable under room temperature due to the significant residual tensile stress in the film. To simultaneously address the above issues, a thermally-activated delayed fluorescence polymer P1 is designed in the study to modify the FAPbI₃ film. Owing to the spectral overlap between the photoluminescence of P1 and absorption of the above-bandgap quantum wells of FAPbI₃, the Förster energy transfer occurs at the P1/FAPbI₃ interface, which further triggers the Dexter energy transfer within FAPbI₃. The exciton “recycling” can thus be realized, which reduces the non-radiative recombination losses in perovskite solar cells (PSCs). Moreover, P1 is found to introduce compressive stress into FAPbI₃, which relieves the tensile stress in perovskite. Consequently, the PSCs with P1 treatment achieve an outstanding power conversion efficiency (PCE) of 23.51%. Moreover, with the alleviation of stress in the perovskite film, flexible PSCs (f-PSCs) also deliver a high PCE of 21.40%.     

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work proposed a deep learning-based (DL) solution for the early detection of this deadly disease from histopathology images. To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized. The proposed automatic diagnosis of BC detection and classification mainly involves three steps. Initially, a DL model is proposed for feature extraction. Secondly, the extracted feature vector (FV) is passed to the proposed novel feature selection (FS) framework for the best FS. Finally, for the classification of BC into invasive ductal carcinoma (IDC) and normal class different machine learning (ML) algorithms are used. Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7% which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.     

Analysis of issues in gate recess etching in the InAlAs/InGaAs HEMT manufacturing process

We have developed an InAlAs/InGaAs metamorphic high electron mobility transistor device fabrication process where the gate length can be tuned within the range of 0.13 μm–0.16 μm to suit the intended application. The core processes are a two-step electron-beam lithography process using a three-layer resist and gate recess etching process using citric acid. An electron-beam lithography process was developed to fabricate a T-shaped gate electrode with a fine gate foot and a relatively large gate head. This was realized through the use of three-layered resist and two-step electron beam exposure and development. Citric acid-based gate recess etching is a wet etching, so it is very important to secure etching uniformity and process reproducibility. The device layout was designed by considering the electrochemical reaction involved in recess etching, and a reproducible gate recess etching process was developed by finding optimized etching conditions. Using the developed gate electrode process technology, we were able to successfully manufacture various monolithic microwave integrated circuits, including low noise amplifiers that can be used in the 28 GHz to 94 GHz frequency range.     

软件库依赖图谱的复杂性度量方法及其潜在应用

在软件开发过程中,软件库可以减少开发时间和节约成本而被广泛使用,因此现代软件项目包含多种不同来源的代码而使得系统具有更高的复杂性和多样性.软件库在使用的过程中常常伴随着各种风险,如低质量或安全漏洞,从而严重影响软件项目的质量.通过分析与软件库的耦合强度,来量化由软件库的依赖关系而引入客户代码的复杂性和多样性.首先,根据客户代码与软件库之间方法的调用关系建立软件边界图模型,区分开客户代码和软件库的代码边界;进而基于此提出一套软件库依赖图谱的复杂性度量指标RMS,用以量化不同来源软件之间的耦合强度.在实验过程中,挖掘Apache开源社区中10个流行软件所有历史版本数据,最终收集到7 857个真实项目间依赖缺陷问题.在上述真实数据基础上,结合所提出的复杂性度量指标RMS,利用假设验证方法开展实证调查研究来探讨:H1:风险因子更高的边界节点是否更容易引入更多数量的项目间依赖缺陷; H2:风险因子更高的边界节点会是否更容易引入严重等级高的项目间依赖缺陷; H3:RMS度量指标数值多大程度地影响了引入项目间依赖缺陷数量和严重等级.实验结果表明,根据RMS度量指标评估,与软件库耦合度更高的边界节点容...