Tailoring of Photoluminescence Properties in All-Vacuum Deposited Perovskite via Ruddlesden–Popper Faults

Ruddlesden–Popper (RP) faults are well known in oxide perovskites, and are also observed in promising metal halide perovskites. However, the effect of RP faults on optical properties of perovskite has not been systematically investigated. In this study, it is found that RP faults are common planar faults in all-vacuum deposited CsPbBr₃-based perovskite polycrystal thin films, and the density of RP planar faults can be greatly increased by non-stoichiometric composition (Cs-rich) as well as reduced dimensionality (quasi-2D) strategies. The photoluminescence (PL) measurement reveals monotonically increasing peak intensities with higher densities of RP planar faults from Cs-rich, quasi-2D to Cs-rich & quasi-2D samples. The corresponding atomic-scale differential phase contrast maps indicate strongly confined charges within the RP planar fault network, which explains well the relationship between PL enhancement and the density of RP planar faults, and offers an alternative pathway for tailoring the optoelectronic properties of perovskite.     

High-rate Decoupled Water Electrolysis System Integrated with α-MoO3 as a Redox Mediator with Fast Anhydrous Proton Kinetics

Hydrogen is a promising alternative to fossil fuels that can reduce greenhouse gas emissions. Decoupled water electrolysis system using a reversible proton storage redox mediator, where the oxygen evolution reaction and hydrogen evolution reaction are separated in time and space, is an effective approach to producing hydrogen gas with high purity, high flexibility, and low cost. To realize fast hydrogen production in such a system, a redox mediator capable of releasing protons rapidly is required. Herein, α-MoO₃, with an ultrafast proton transfer property that can be explained by a dense hydrogen bond network in the lattice oxygen arrays of HxMoO₃, is examined as a high-rate redox mediator for fast hydrogen production in acidic electrolytes. The α-MoO₃ redox mediator shows both a large capacity of 204 mAh g⁻¹ and fast hydrogen production at a current rate of 10 A cm⁻²(≈153 A g⁻¹), outperforming most of the previously reported solid-state redox mediators.     

A Stable Polymer-based Solid-State Lithium Metal Battery and its Interfacial Characteristics Revealed by Cryogenic Transmission Electron Microscopy

Solid-state lithium metal batteries (SSLMBs) are a promising candidate for next-generation energy storage systems due to their intrinsic safety and high energy density. However, they still suffer from poor interfacial stability, which can incur high interfacial resistance and insufficient cycle lifespan. Herein, a novel poly(vinylidene fluoride‑hexafuoropropylene)-based polymer electrolyte (PPE) with LiBF₄ and propylene carbonate plasticizer is developed, which has a high room-temperature ionic conductivity up to 1.15 × 10⁻³ S cm⁻¹ and excellent interfacial stability. Benefitting from the stable interphase, the PPE-based symmetric cell can operate for over 1000 h. By virtue of cryogenic transmission electron microscopy (Cryo-TEM) characterization, the high interfacial compatibility between Li metal anode and PPE is revealed. The solid electrolyte interphase is made up of an amorphous outer layer that can keep intimate contact with PPE and an inner Li₂O-dominated layer that can protect Li from continuous side reactions during battery cycling. A LiF-rich transition layer is also discovered in the region of PPE close to Li metal anode. The feasibility of investigating interphases in polymer-based solid-state batteries via Cryo-TEM techniques is demonstrated, which can be widely employed in future to rationalize the correlation between solid-state electrolytes and battery performance from ultrafine interfacial structures.     

Solid Ink Laser Patterning for High-Resolution Information Labels with Supervised Learning Readout

Tagging, tracking, or validation of products are often facilitated by inkjet-printed optical information labels. However, this requires thorough substrate pretreatment, ink optimization, and often lacks in printing precision/resolution. Herein, a printing method based on laser-driven deposition of solid polymer ink that allows for printing on various substrates without pretreatment is demonstrated. Since the deposition process has a precision of <1 µm, it can introduce the concept of sub-positions with overlapping spots. This enables high-resolution fluorescent labels with comparable spot-to-spot distance of down to 15 µm (444,444 spots cm⁻²) and rapid machine learning-supported readout based on low-resolution fluorescence imaging. Furthermore, the defined thickness of the printed polymer ink spots can be used to fabricate multi-channel information labels. Additional information can be stored in different fluorescence channels or in a hidden topography channel of the label that is independent of the fluorescence.     

CuI Encapsulated within Single-Walled Carbon Nanotube Networks with High Current Carrying Capacity and Excellent Conductivity

High current carrying capacity and high conductivity are two important indicators for materials used in microscale electronics and inverters. However, it is challenging to obtain high conductivity and high current carrying capacity at the same time since high conductivity requires a weakly bonded system to provide free electrons, while high current carrying capacity requires a strongly bonded system. In this paper, CuI@SWCNT networks by filling the single-walled carbon nanotubes (SWCNTs) with CuI is ingeniously prepared. CuI@SWCNT shows good stability due to the confinement protection of SWCNTs. Through the host-guest hybridization, CuI@SWCNT networks exhibit a current carrying capacity of 2.04 × 10⁷ A cm⁻² and a conductivity of 31.67 kS m⁻¹. Their current carrying capacity and conductivity are significantly improved compared with SWCNT. The Kelvin probe force microscopy measurements show a drop of surface potential energy after SWCNT filled with CuI, indicating that the CuI guest molecules regulate the position of the Fermi level of SWCNTs, increasing carrier concentration, achieving high conductivity and high current carrying capacity. This study offers ideas and solutions for the regulation of high-performance carbon tube networks, which hold great promise for future applications in carbon-based electronic devices.     

Direct Observation of Oxygen Ion Dynamics in a WO3-x based Second-Order Memristor with Dendritic Integration Functions

Direct observation of oxygen dynamics in an oxide-based second-order memristor can provide the valid evidence to clarify the memristive mechanism, however, which is still limited for now. In this study, the migration and diffusion of oxygen ions in the region of Pt/WO_3-x Schottky interface are observed in the WO_3-x second-order memristor by using the technique of in situ transmission electron microscopy (TEM) and the electron energy loss spectroscopy. Interestingly, the coexistence of memristive and memcapacitive switching can be implemented in this memristor. Combined with the analysis of depth-profile X-ray photoelectron spectroscopy (XPS), an interface-barrier-modulation second-order memristive model is proposed based on the above results. Notably, temporally correlative oxygen dynamics in the memristor offers the platform to integrate signals from multiple inputs, enabling the realization of the dendritic functions of synchronous and asynchronous integration for the application of logic operations with fault-tolerance capability and associative learning. These findings provide the experimental evidence to in-depth understanding of oxygen dynamics and switching mechanism in second-order memristor, which can support the optimization of memristive performance and the achievement of biorealistic synaptic functions.     

Detection of COVID-19 from chest X-ray images: Boosting the performance with convolutional neural network and transfer learning

Coronavirus disease (COVID-19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID-19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID-19 can protect people from severe infection. Unfortunately, COVID-19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID-19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID-19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID-19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID-19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre-trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X-rays (1200 cases of COVID-19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X-ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID-19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1-score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID-19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening.     

SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

(Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due to its simplicity of usage, quick learning speed, and great generalization performances. The number of hidden neurons in ELM is set to 2000. Ten runs of 10-fold cross-validation are implemented to generate impartial results. (Result) For the 296-image dataset, our SNELM model attains a sensitivity of 96.35 ± 1.50%, a specificity of 96.08 ± 1.05%, a precision of 96.10 ± 1.00%, and an accuracy of 96.22 ± 0.94%. For the 640-image dataset, the SNELM attains a sensitivity of 96.00 ± 1.25%, a specificity of 96.28 ± 1.16%, a precision of 96.28 ± 1.13%, and an accuracy of 96.14 ± 0.96%. (Conclusion) The proposed SNELM model is successful in diagnosing COVID-19. The performances of our model are higher than seven state-of-the-art COVID-19 recognition models.     

基于模型预测—中枢模式发生器的六足机器人轨迹跟踪控制

为了模仿动物卓越的运动能力和环境适应能力,提出了六足仿生机器人的轨迹跟踪控制方法。首先建立了机器人的运动学模型,接着通过转向参数将机器人的速度和角速度与中枢模式发生器（CPG）参数结合起来,设计了转换函数。然后通过转换函数将模型预测控制器和CPG网络结合起来,提出了基于CPG的模型预测控制器（MPC-CPG）,并证明了其稳定性。最后对机器人跟踪圆周轨迹和直线轨迹进行了仿真和实验。实验表明,在有初始误差的条件下,机器人在MPC-CPG控制器的作用下能够快速地消除位置误差和航向角误差,跟踪上参考轨迹。轨迹跟踪的位置误差始终保持在-0.1～0.1 m,航向角误差保持在-27?～20?。在MPC-CPG控制器的作用下,机器人不仅具有较高的轨迹跟踪精度,同时还表现出良好的运动平滑性和协调性,进一步验证了所提出的MPC-CPG控制器的有效性。     

面向样本扩充的新型风格迁移网络研究

全监督语义分割网络在训练时需要耗费大量的人力与时间成本来标注样本。所以减少人工标注样本的时间,同时提升语义分割效果,对于深度学习网络的快速部署和应用推广具有重要意义。提出一种基于改进图像风格迁移网络（CycleGAN-AD）的样本扩充方法。以CycleGAN为基础,在生成器中引入注意力机制并将深度残差网络改为密集连接卷积网络。利用计算机批量产生自带标签的模拟样本,使用CycleGAN-AD网络将模拟样本风格迁移成为真实样本风格（标签不变）,并用于扩充训练样本。对石墨电极的钢印字符进行语义分割的实验结果表明,采用CycleGAN-AD网络进行样本扩充后,其分割效果得到显著提升,MIoU值最高升至0.826 0。可见,提出的样本扩充方法有希望在显著减少人工标注工作量的同时,获得高质量的训练样本。