环保法规对船舶涂料企业的冲击及应对措施的探讨

随着我国环境保护法规《大气污染防治法》的修订和《涂料征收消费税通知》的公布,探讨了其对船舶涂料企业的冲击及其应对措施。     

苏通GIL运输专用机具动力电池类型探讨

GIL运输专用机具是为安全、可靠、高效地完成苏通GIL综合管廊工程GIL单元的运输任务而研制的专用机具,其中动力电池系统设计是整机设计的重点之一。通过对各种常见动力电池优缺点的分析,并结合苏通GIL运输专用机具对动力电池的需求分析,确定了动力电池系统的技术路线,为苏通GIL运输专用机具的研制提供了坚实的基础。     

Deposition of boron-containing coatings by electron-beam evaporation of boron-containing targets

Boron-containing films are promising materials for improving the hardness, wear-resistance and durability of mechanical parts used in industry. We describe a novel approach for deposition of such coatings – by using electron-beam evaporation of boron-containing target material (pure boron or boron nitride) in inert (helium) or chemically-active (oxygen, nitrogen, residual atmosphere) gases at medium vacuum. Boron-containing films deposited on titanium substrates were characterized using a nanohardness tester and a non-contact 3D profilometer. Elemental composition of the coatings was studied using raster electron microscope equipped with a device for EDX analysis. All boron-containing coatings provided significant improvement (by a factor of 4–16) in the microhardness of the substrate surface.     

减震隔声降噪地垫的研发

介绍我国分户楼板隔声及其隔声研究现状,详细阐述多层复合结构的减震隔声降噪地垫的结构、性能和安装,减震隔声降噪地垫与木地板配合使用,将其铺装于扫平层的140 mm厚现浇钢筋混凝土楼板之上,经江苏省建工建材检测中心检测,撞击声隔声为61 dB,能很好地满足住宅分户楼板撞击声隔声L'nT,W≤65 dB的要求。     

An infinite chain, {[Ni(tn)2]3[Fe(CN)4(μ-CN)2]2}n,a new catalyst for electrochemical-driven water splitting and photochemical-driven hydrogen evolution from water under blue light

A new catalyst for both water reduction and oxidation, based on an infinite chain, {[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n, is formed by the reaction of NiCl₂, 1,3-propanediamine (tn) and K₃ [Fe(CN)₆]. {[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n can electro-catalyze hydrogen evolution from a neutral aqueous buffer (pH 7.0) with a turnover frequency (TOF) of 1561 mol of hydrogen per mole of catalyst per hour (H₂/mol catalyst/h) at an overpotential (OP) of 837 mV {[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n also can electro-catalyze O₂ production from water with a TOF of ～45 mol O₂ (mol cat)^?1s^?1 at an OP of 591 mV. Under blue light (λ = 469 nm), together with CdS nanorods (CdS NRs) as a photosensitizer, and ascorbic acid (H₂A) as a sacrificial electron donor, {[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n can photo-catalyze hydrogen generation from an aqueous buffer (pH 4.0) with a turnover number (TON) of 11,450 mol H₂ per mole of catalyst (mol of H₂ (mol of cat)^?1) during 10 h irradiation. The average of apparent quantum yield (AQY) is as high as 40.96% during 10 h irradiation. Studies indicate that {[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n exists in two forms: a cyano-bridged chain ({[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n) in solid, and a salt ([Ni(tn)₂]₃ [Fe(CN)₆]₂) in aqueous media; Catalytic reaction occurs on the nickel center of [Ni(tn)₂]²⁺, and the introduction of [Fe(CN)₆]^3- can improve the catalytic efficiency of [Ni(tn)₂]²⁺ for H₂ or O₂ generation. We hope these findings can afford a new method for the design of catalysts for both water reduction and oxidation.     

Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation

Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.     

Adaptability analysis of design for additive manufacturing by using fuzzy Bayesian network approach

The rapid development of Additive Manufacturing (AM) has been conspicuous and appealing towards manufacturing end-use products and components over the past decade. The continual advancement of AM has brought many advantages such as personalization and customization, reduction of material waste, cutting off the existence of special tooling during fabrication, etc. However, the AM approach has its limitations, such as a lack of knowledge of AM process activities and the progressive industrialization of AM, which makes the design process activities unstable, unpredictable, and have a limited effect. The concept of “design for AM (DFAM)” is increasing, which means we have the opportunity to concentrate almost totally on product functioning. Therefore, the entire design paradigm must be revised to accommodate new production capabilities, geometries, and parameters to avoid molding or machine tooling technology constraints. Few studies have attempted to provide systematic and quantitative knowledge of the relationship between these elements and the feasibility of the design process, making it difficult for designers to assess and control AM industrialization. For this reason, DFAM is needed to reform AM from rapid manufacturing to a mainstream manufacturing method. This paper put forward a framework based on the Fuzzy Bayesian Network (FBN) for DFAM decision-making. Twenty impact factors were encapsulated from experts’ experience and existing literature to investigate the potential adaptability of DFAM. The proposed approach uses expert knowledge and Fuzzy Set Theory (FST) presented with Triangular Fuzzy Numbers (FFN) to perceive the uncertainties. The Bayesian Network (BN) captures the causal relationships and dependencies among the impact components and analyzes the DFAM adaptability for robust probabilistic reasoning. A robot arm claw was used to show the effectiveness of our approach. The results showed that FBN could be used to guide DFAM adaptability in the manufacturing industry.     

Scale-progressive healing mechanism dominating the ultrafast initial sintering kinetics of plasma-sprayed thermal barrier coatings

The thermal insulating performance of plasma-sprayed thermal barrier coatings (PS-TBCs) depends dominantly on inter-splat pores, which would be inevitably healed during high temperature exposure. The sintering kinetics of TBCs appears to be highly stage-sensitive. However, the ultrafast sintering kinetics during the initial sintering stage is not yet well understood. In this study, the sintering behavior of PS-TBCs was investigated in a scale-progressive (from nano- to micro-scale) way. Moreover, a novel healing mechanism suitable for lamellar TBCs was proposed based on a combined-effect of material and pore structure. Regarding the changing behavior of material, nano-scale roughening can be found at the as-deposited smooth pore surface after thermal exposure. Regarding the 2D featured inter-splat pores, the roughening behavior facilitates multiple contacts between the counter-surfaces of inter-splat pores. As a result, micro-scale bridge-connection can be observed at the healed parts. This multiple contacts mechanism caused by scale-progressive healing behavior significantly accelerated the matter transfer, resulting in ultrafast sintering kinetics at the initial sintering stage.     

Microstructure assessment of suspension plasma spraying coatings from multicomponent submicronic Y-TZP/Al2O3/SiC particles

In this research, Suspension Plasma Spraying (SPS) technique was used for the thermal deposition of a multicomponent mixture made up of an Y-TZP/Al₂O₃ matrix with SiC particles. Two suspensions of Y-TZP and Al₂O₃ with different SiC particles content (6?wt% and 12?wt%) were tested as feedstocks in the SPS process. Three stand-off distances were varied in order to assess coating microstructure and evaluate the presence of SiC in the final coatings. Coatings were characterised in terms of porosity, microstructure and phase distribution. The estimate of the amount of SiC in the coating was carried out by XRD technique.Findings showed typical cauliflower-like SPS microstructure which intensifies with stand-off distance. Coatings porosity varied significantly between 8% and 25% whereas minimum porosity was found for the intermedium stand-off distance of 40?mm.Microstructure analysis also revealed the presence of SiC particles in the coatings which was confirmed by EDX analysis, overall XRD tests as well as TG analysis. Finally, evaluation of SiC content in the final coatings by means of XRD analysis showed that most of SiC particles (c.a 80%) of the feedstocks were preserved in the final coatings.     

Enhanced UV- and visible-light driven photocatalytic performances and recycling properties of graphene oxide/ZnO hybrid layers

Light induced catalytic processes have attracted significant attention during the last years for wastewater treatment due to their efficiency in decomposition of organic contaminants. In this study we report the synthesis of graphene oxide (GO)/ZnO hybrid layers with high photocatalytic efficiency using laser radiation. The results show that the hybrid layers exhibit much improved photodecomposition efficiency as compared to pure GO or ZnO both under UV and visible-light irradiation. The enhanced photocatalytic efficiency of the hybrid as compared to the reference pure ZnO and GO layers was attributed to the contribution of GO to the separation and transport of the photogenerated charge carriers. Additionally, under visible light irradiation the organic molecules can act as first sensitizers in the degradation process. The recyclability of the layers was also investigated through repetitive photodegradation cycles under UV- or visible-light irradiation. After consecutive degradation runs, the hybrid photocatalyst layers were still stable and retained high degradation efficiency, ensuring reusability. The photocatalytic activity of the layers was correlated with the gradual change of their chemical structure during consecutive degradation cycles. Owing to the high photodegradation efficiency, reusability, and ease of recovery the synthesised hybrid layers consisting of easily available materials are suitable for environmental purification applications.