Impact of TiO2 nanoparticles and nanowires on corrosion protection performance of chemically bonded phosphate ceramic coatings

The impact of the addition of TiO₂ nanoparticles and nanowires on the morphology, phase characteristics, contact angle, and electrochemical performance of chemically bonded phosphate ceramic coatings (CBPCs) was investigated. The chemical composition and surface morphology of the TiO₂ nanoparticle and nanowire modified with and without (heptadecafluoro-1,1,2,2-tetradecyl) trimethoxysilane were characterized. Results indicated that the hydrophobic –CF₂– and –CF₃ groups were successfully introduced into the TiO₂ nanoparticles and nanowires after modification. Corrosion resistance of CBPCs with TiO₂ was evidently improved compared with that without TiO₂. Such improvement was mainly due to the combined effects of low surface energy materials and micro/nano structures. In addition, CBPCs with TiO₂ nanowires exhibited higher hydrophobicity and corrosion resistance than those with TiO₂ nanoparticles because of the special columnar structure of the nanowires.     

Predictive environmental hydrogen embrittlement on fracture toughness of commercial ferritic steels with hydrogen-modified fracture strain model

In this work, a practical numerical model with few parameters was proposed for the prediction of environmental hydrogen embrittlement. The proposed method adopts hydrogen enhanced plasticity-based mechanism in a fracture strain model to describe hydrogen embrittlement. Fracture toughness degradation of three commercial steels SA372J70, AISI4130 and X80 in high pressure hydrogen environment were investigated. Firstly, governing equations for hydrogen distribution and material damage evolution was established. Hydrogen enhanced localized flow softening effect was coupled within fracture strain dependency on stress triaxiality. Then, the numerical implementation and identification process of model parameters was described. Model parameters of the investigated steels were determined based on experiment results from literatures. Finally, with the calibrated model, fracture toughness reduction of the steels was predicted in a wide range of hydrogen pressure. The prediction results were compared with experimental results. Reasonable accuracy was reached. The proposed method is an attempt to reach balance between physical accurate prediction and engineering practicality. It is promising to provide a simplified numerical tool for the design and fit for service evaluation of hydrogen storage vessels.     

Comparative study of embrittlement of quenched and tempered steels in hydrogen environments

The study of steels which guarantee safety and reliability throughout their service life in hydrogen-rich environments has increased considerably in recent years. Their mechanical behavior in terms of hydrogen embrittlement is of utmost importance. This work aims to assess the effects of hydrogen on the tensile properties of quenched and tempered 42CrMo4 steels. Tensile tests were performed on smooth and notched specimens under different conditions: pre-charged in high pressure hydrogen gas, electrochemically pre-charged, and in-situ hydrogen charged in an acid aqueous medium. The influence of the charging methodology on the corresponding embrittlement indexes was assessed. The role of other test variables, such as the applied current density, the electrolyte composition, and the displacement rate was also studied. An important reduction of the strength was detected when notched specimens were subjected to in-situ charging. When the same tests were performed on smooth tensile specimens, the deformation results were reduced. This behavior is related to significant changes in the operative failure micromechanisms, from ductile (microvoids coalescence) in absence of hydrogen or under low hydrogen contents, to brittle (decohesion of martensite lath interfaces) under the most stringent conditions.     

A Ti3C2Tx-carbon black-acrylic epoxy coating for 304SS bipolar plates with enhanced corrosion resistant and conductivity

A conducting and anticorrosive coating is crucial for the application of metal bipolar plates (BP) in proton exchange membrane fuel cell (PEMFC). In this work, a Ti₃C₂T_x (T)-carbon black (C)-acrylic epoxy (AE) coating is prepared on 304 stainless steel (SS) with enhanced corrosion resistance and conductivity. The corrosion resistance of the T-C-AE coating is investigated in a 0.5 M H₂SO₄ solution as compared to the AE, T, and T-AE coatings. The T-C-AE coated 304SS exhibits the strongest corrosion resistance with the most positive corrosion potential and the lowest corrosion current density of 0.00673 μA cm^?2 in all the samples, while retaining intact and compact surface morphology with the lowest metal ion dissolution even after immersed for 720 h. The addition of Ti₃C₂T_x and carbon black into the AE matrix greatly decreases interfacial contact resistance (ICR), and the T-C-AE coating achieves a low ICR of 15.5 mΩ cm^?2 under 140 N cm^?2 compaction force. The excellent anticorrosion performance is mainly attributed to the physical barrier and the cathodic protection provided by the stacked Ti₃C₂T_x (MXene) nanosheets in the T-C-AE coating. This eco-friendly, conducting, and anticorrosive T-C-AE coating has a good application prospect on SS BP of PEMFC.     

Ammonia mitigation and induction effects on hydrogen environment embrittlement of SCM440 low-alloy steel

The effect of ammonia (NH₃) contained in hydrogen (H₂) gas on hydrogen environment embrittlement (HEE) of SCM440 low-alloy steel was studied in association with the NH₃ concentration, loading rate, and gas pressure. NH₃ worked as both mitigator of the HEE and inducer of hydrogen embrittlement (HE) depending on the testing conditions. The mitigation of the HEE was achieved by the deactivation of the iron (Fe) surface for H₂ dissociation caused by the preferential adsorption of NH₃ on the Fe surface, which is enhanced by the increase in the NH₃ concentration and decrease in the H₂ gas pressure. NH₃ induced HE was caused due to creating hydrogen by the NH₃ decomposition. Since the NH₃ decomposition rate is low, the induction effect was observed when the loading rate was low. The effect of NH₃ was determined by the competition of the mitigation and induction effects.     

配分工艺对中碳Ti-Mo高强Q&P钢组织和性能的影响

摘要：采用盐浴实验、扫描电镜、透射电镜、拉伸实验和磨损实验等手段，研究了配分工艺对中碳Ti Mo钢组织和性能的影响，分析了不同配分工艺处理下的组织演变和性能变化。结果表明，显微组织主要由回火马氏体、渗碳体、(Ti，Mo)C粒子组成。随着配分时间的延长和配分温度的升高，板条马氏体数量减少，马氏体板条厚度增加，边界钝化。此外，随着配分温度从310℃提高至400℃，抗拉强度、硬度和低温冲击韧性同时下降，分别降低约250MPa、56HV和15J。最后，Ms以下温度配分（310℃）试样的耐磨损性能明显优于Ms以上温度配分（400℃）试样。Ms以下温度配分试样磨损表面形貌以塑性变形为主，Ms以上温度配分试样磨损表面以犁沟为主。     

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.     

A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection

A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products, largely used in many industrial sectors. However, computers used in the production line of small to medium size companies, in general, lack performance to attend real-time inspection with high processing demands. In this paper, a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed. The architecture is based on the state-of-the-art SqueezeNet approach, which was originally developed for usage with autonomous vehicles. The main features of the proposed model are: small size and low computational burden. The model is 10 to 20 times smaller when compared to other networks designed for the same task, and more than 700 times smaller than general networks. Also, the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks. Despite its small size, the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.     

基于BIM超高层密集布筋区埋入式型钢混凝土柱脚施工技术

以漳州市某超高层项目为例,针对超高层密集布筋区埋入式型钢柱脚与钢筋的深化设计、地脚螺栓的定位预埋、型钢柱脚支撑体系设计、节点钢筋穿插连接施工、埋入式型钢柱脚安装等方面,提出切实可行的施工方法,解决了埋入式型钢混凝土柱脚的地脚螺栓精准预埋、与底板密集钢筋穿插连接施工、悬空支撑稳定等一系列施工技术难题,既满足了设计方案的要求,又可为类似工程提供借鉴。     

Role of CD4+ T Cells in the Control of Viral Infections: Recent Advances and Open Questions

CD4+ T cells orchestrate adaptive immune responses through their capacity to recruit and provide help to multiple immune effectors, in addition to exerting direct effector functions. CD4+ T cells are increasingly recognized as playing an essential role in the control of chronic viral infections. In this review, we present recent advances in understanding the nature of CD4+ T cell help provided to antiviral effectors. Drawing from our studies of natural human immunodeficiency virus (HIV) control, we then focus on the role of high-affinity T cell receptor (TCR) clonotypes in mediating antiviral CD4+ T cell responses. Last, we discuss the role of TCR affinity in determining CD4+ T cell differentiation, reviewing the at times divergent studies associating TCR signal strength to the choice of a T helper 1 (Th1) or a T follicular helper (Tfh) cell fate.