Rate equation theory for the hydrogenation kinetics of Mg-based materials

A uniform solid product layer normally assumed in the shrinking-core model cannot predict the kinetic transition behavior of the H₂ adsorption reactions. In this study, the concept of a uniform solid product layer has been replaced by that of the inward growth of solid products on the solid surface. A rate equation is established to calculate the inward growth of the solid product and was implemented into the shrinking-core model to calculate the H₂ adsorption kinetics for various shapes of Mg-based materials. The prediction accuracy of the developed model is verified from the detailed experimental data. To account for the external gas diffusion around the particle and the intraparticle gas diffusion, an analytical equation is derived using the Thiele modulus method. This model can be used to analyze various kinetic aspects and to analyze the effect of change in the particle microstructure on intraparticle diffusion.     

Modeling the effect of deep traps on the capacitance–voltage characteristics of p-type Si-doped GaAs Schottky diodes grown on high index GaAs substrates

Numerical simulation, using SILVACO-TCAD, is carried out to explain experimentally observed effects of different types of deep levels on the capacitance–voltage characteristics of p-type Si-doped GaAs Schottky diodes grown on high index GaAs substrates. Two diodes were grown on (311)A and (211)A oriented GaAs substrates using Molecular Beam Epitaxy (MBE). Although, deep levels were observed in both structures, the measured capacitance–voltage characteristics show a negative differential capacitance (NDC) for the (311)A diodes, while the (211)A devices display a usual behaviour. The NDC is related to the nature and spatial distribution of the deep levels, which are characterized by the Deep Level Transient Spectroscopy (DLTS) technique. In the (311)A structure only majority deep levels (hole traps) were observed while both majority and minority deep levels were present in the (211)A diodes. The simulation, which calculates the capacitance–voltage characteristics in the absence and presence of different types of deep levels, agrees well with the experimentally observed behaviour.     

改进HSR-FCN的服装图像识别分类算法研究

目前网络上的服装图像数量增长迅猛，对于大量服装图像实现智能分类的需求日益增加。将基于区域的全卷积网络（Region-Based Fully Convolutional Networks，R-FCN）引入到服装图像识别中，针对服装图像分类中网络训练时间长、形变服装图像识别率低的问题，提出一种新颖的改进框架HSR-FCN。新框架将R-FCN中的区域建议网络和HyperNet网络相融合，改变图片特征学习方式，使得HSR-FCN可以在更短的训练时间内达到更高的准确率。在模型中引入了空间转换网络，对输入服装图像和特征图进行了空间变换及对齐，加强了对多角度服装和形变服装的特征学习。实验结果表明，改进后的HSR-FCN模型有效地加强了对形变服装图像的学习，且在训练时间更短的情况下，比原来的网络模型R-FCN平均准确率提高了大约3个百分点，达到96.69%。     

Prompting structure stability of O3–NaNi0.5Mn0.5O2 via effective surface regulation based on atomic layer deposition

Layered O3 type oxides exhibit promising prospects as high-performance cathodes for sodium-ion batteries (SIBs) due to their low cost and high theoretical capacities. Nevertheless, the intrinsic surface composition and bulk structure degradation upon cycling presents a huge obstacle to stable sodium-ion storage/transportation. Besides, the effective surface decoration on layered O3 oxides is still challenging through conventional wet chemical route owing to their extraordinarily high surface sensitivities. Herein, a typical O3 type layered oxide of NaNi_0.5Mn_0.5O₂ (NNMO) was selected and successfully encapsulated by precisely controlled Al₂O₃ layers via atomic layer deposition (ALD) technology. With the optimally controlled Al₂O₃ thickness of 3 nm, the surface regulated NNMO delivers a highly reversible capacity of 73.6 mA h g^-1, with a significantly improved capacity retention of 68.0% after 300 cycles at 0.5 C, and a superior rate capability of 65.8 mA h g^-1 at 10 C. Further air sensitivity tests demonstrate that the protective layer could effectively mitigate the generation of sodium-based impurities on NNMO, and reduce the surface sensitivities. Both chemical and electrochemical aging tests confirm the contribution of Al₂O₃ coating layer in alleviating ion dissolution as well as stabilizing the structure and morphology of NNMO. Based on regulating the surface of O3 type layered oxides utilizing ALD technique, this work supplies an effective and facile strategy to overcome the challenges from fast structure degradation and electrochemical property decay, which not only highlights the significance and effectiveness of surface engineering in secondary batteries, but also sheds light on accurate interface construction and regulation for active electrode materials, particularly for those sensitive to ambient atmosphere.     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

黄沙坪铅锌多金属矿的成矿规律及深部找矿远景

众所周知,矿物质的成分是多种多样的,社会的发展和科学的进步需要运用到多重金属矿物质,在专业人员的勘探与挖掘中,发现了黄沙坪铅锌多金属矿,这个矿区中有丰富的有色金属,这个矿的发掘为研究成矿规律提供了物质基础,同时也为深部找矿提供了可靠的依据。本文主要分析黄沙坪铅锌多金属矿的成矿规律及深部找矿远景。     

Charge injection in metal/organic/metal structures with ZnO:Al/organic interface modified by Zn1−xMgxO:Al layer

Aluminum-doped zinc oxide (ZnO:Al, AZO) electrodes were covered with very thin (∼6 nm) Zn_1−xMg_xO:Al (AMZO) layers grown by atomic layer deposition. They were tested as hole blocking/electron injecting contacts to organic semiconductors. Depending on the ALD growth conditions, the magnesium content at the film surface varied from x = 0 to x = 0.6. Magnesium was present only at the ZnO:Al surface and subsurface regions and did not diffuse into deeper parts of the layer. The work function of the AZO/AMZO (x = 0.3) film was 3.4 eV (based on the ultraviolet photoelectron spectroscopy). To investigate carrier injection properties of such contacts, single layer organic structures with either pentacene or 2,4-bis[4-(N,N-diisobutylamino)-2,6-dihydroxyphenyl] squaraine layers were prepared. Deposition of the AMZO layers with x = 0.3 resulted in a decrease of the reverse currents by 1–2 orders of magnitude and an improvement of the diode rectification. The AMZO layer improved hole blocking/electron injecting properties of the AZO electrodes. The analysis of the current-voltage characteristics by a differential approach revealed a richer injection and recombination mechanisms in the structures containing the additional AMZO layer. Among those mechanisms, monomolecular, bimolecular and superhigh injection were identified.     

Enhanced thermoelectric properties of atomic-layer-deposited Hf:Zn16O/18O superlattice films by interface-engineering

This study examines three novel approaches for enhancing the thermoelectric (TE) properties of atomic-layer-deposited (ALD) ZnO thin films: 1) Hf-doping, which preserved the crystallinity of ZnO and provided effective phonon scattering owing to Hf's similar atomic radius to and large mass difference with Zn, leading to high power factor (PF) and low thermal conductivity (κ); 2) controlling the distribution of Hf into an alternating scattered phase/clustered phase superlattice, which balanced the high PF of the scattered phases with the low κ of the clustered phases, while providing significant energy-filtering effect to raise the Seebeck coefficient; 3) introducing ¹⁸O/¹⁶O periodicity into the Hf:ZnO films—by alternately using H₂¹⁶O and H₂¹⁸O as oxidants in the ALD processes, which further suppressed κ without compromising PF. The combination of the three approaches resulted in a maximum improvement in ZT of ~1600% over that of the undoped ZnO.     

Fully solution-processed and multilayer blue organic light-emitting diodes based on efficient small molecule emissive layer and intergrated interlayer optimization

Efficient and fully solution-processed blue organic light-emitting diodes (OLEDs) based on fluorescent small-molecule and methanol/water soluble conjugated polymer as electron-injection material are reported. The emitting layer is 3,6-bis(9,9,9′,9′-tetrakis (6-(9H-carbazol-9-yl)hexyl)-9H,9′H-[2,2′-bifluoren]-7-yl)dib-nzo[b, d]thiophene 5, 5-dioxide (OCSoC) with a blue-fluorescent small-molecule, and a methanol/water soluble polymer poly[(9,9-bis(30-(N,N-dimethylamino)propyl)-2,7-fluorene)-alt-2,7-(9,9-dioctyl-fluorene)] (PFN) acted as electron-injection layer (EIL). All the organic layers are spin-coated from solution. The multilayer device structure with emitting layer/electron-injection layer is achieved by solution-processed method without the dissolution problem between layers. The performances of the devices show that the maximum luminous efficiency of the multilayer device is increased about 43%, compared to the single-layer device. PFN acting as the EIL material plays a key role in the improvement of the device performance when used in solution-processed small-molecule OLEDs.     

基于深度学习的人体姿态估计方法综述

基于深度学习的人体姿态估计方法旨在通过构建合适的神经网络，直接从二维的图像特征中回归出人体姿态信息。主要按照2D人体姿态估计到3D人体姿态估计的顺序，并从单人检测与多人检测、稀疏的关节点检测与密集的模型构建等方面，对近年来基于深度学习的人体姿态估计方法进行系统介绍，从而初步了解如何通过深度学习的方法得到人体姿态的各个要素，包括肢体部件的相对朝向和比例尺度、骨骼关节点的位置坐标和连接关系，甚至更为复杂的人体蒙皮模型信息。最后,对当前研究面临的挑战以及未来的热点动向进行概述，清晰地呈现出该领域的发展脉络。