Search

A new method for preparing superhydrophobic nickel coating by magnetic field induced selective scanning electrodeposition based on additive manufacturing is proposed in this paper in which ferromagnetic nano-nickel particles (Nip) are added to the plating solution and the adsorption regulation of Nip achieve by changing the presence of the magnetic field. Adjusting the magnetic field strength can change the density of the coating micro-nano hierarchical structure, thereby affecting the hydrophobicity. The forming mechanism is mainly divided into four steps: pre-coating, loose adsorption, strong adsorption, and hierarchical structure formation. The surface roughness of the superhydrophobic coating reaches 17.584 μm. The pristine coating spontaneously changed from hydrophilic to superhydrophobic after being exposing to air for 5 days (CA = 155.4°). X-ray photoelectron spectroscopy analysis shows that this phenomenon is due to the oxidation of the coating to produce NiO and adsorbing a large amount of hydrocarbons. At the same time, the electrochemical test result shows that the superhydrophobic coating has great corrosion resistance.

A magnetic field-assisted scanning electrodeposition was used to prepare stable superhydrophilic Ni-SiC coating on copper sheets in this paper. The Ni-SiC mixed powder was added to a plating solution, and the magnetic field was parallel to the direction of the electric field. The freshly prepared coating exhibited strong hydrophilicity, completely wetting the coating within 2 s. After standing for 12 weeks in the air, the coating remained superhydrophilic. Scanning electron microscopy (SEM) images showed rough hierarchical structure and micro-gaps present on the surface. The formation of the hierarchical structure is the result of the coaction of a magnetic field and an electric field. Additionally, energy dispersive spectroscopy (EDS) and X-ray diffraction (XRD) detected high surface energy SiC on the surface of the samples. X-ray photoelectron spectroscopy (XPS) results showed that the coating could maintain superhydrophilicity due to no excessive oxygen adsorption and SiC adsorption.

Ni-Co alloys and Ni-Co-SiC composite coatings were manufactured by magnetic field-induced jet electrodeposition. The microstructure and nanoparticles distribution of the coatings were characterized by field emission scanning electron microscopy, energy dispersive X-ray spectrometry, and X-ray diffraction. The surface roughness, adhesion and corrosion resistance of the coatings were measured using a three-dimensional profilometer, a scratch tester, and an electrochemical workstation. The cellular protrusions on the composite coatings decreased with the addition of nano-SiC particles. However, a large number of small spherical structures grew into large cellular protrusions with an extremely high SiC concentration under traditional jet electrodeposition. The composite coatings under magnetic field-induced jet electrodeposition showed a uniform growth and no protrusions, the nano-SiC particles were also uniformly distributed. The Ni-Co-SiC composite coating exhibited a single-phase solid solution structure, and its adhesion increased to 33.2 N, which was 2.65 times that of the Ni-Co alloy coating. The corrosion current density of the composite coating was 4.768 μAcm−2, which was sufficient to protect sintered NdFeB magnets.

In view of the corrosion failures of sintered NdFeB magnets in engineering, an Ni-Ni(S)-Ni(P) multilayer coating was prepared here via friction-assisted jet electroplating. As a comparison, Ni, Ni(S), Ni(P) single-layer, and Ni-Ni(S)-Ni(P) multilayer coatings were fabricated via jet electroplating without friction. The results revealed that the multilayer coating exhibited better physical and chemical properties compared to the single-layer coatings. After adding friction technology, the surface of the coating was smoother and the original cellular processes disappeared. In addition, the adhesion of the multilayer coating was up to 32.52 N, and the microhardness of the multilayer coatings increased from 557.43 HV to 626.5 HV, which was an increase of 12.39%. In terms of corrosion resistance, the self-corrosion potential of multilayers was up to −0.509 V, and the corrosion current density was as low as 3.564 × 10−6 A/cm2. Friction-assisted jet electroplating can effectively protect sintered NdFeB materials.

The dynamic process of the hypervelocity impact induced microjet from the grooved metal surface is studied theoretically and experimentally. Based on shock wave theory and Bernoulli's equation, a physical model is proposed to theoretically analyze the formation process of the microjet and predict the spike (head) velocity of the microjet. The hypervelocity impact experiments of the cylindrical aluminum projectile on the metal target with grooves are conducted through two-stage light gas gun, and the sequence shadowgraphy images of the microjetting formation are recorded by using Ultra-high-speed camera. Then, the numerical simulations of the hypervelocity impact-induced microjet are performed by a self-developed SPH (smoothed particle hydrodynamics) code to examine the velocity distribution, mass distribution and density distribution in detail. These numerical results are compared with those obtained by experiments and theoretical analysis, which verifies the validity of numerical model. The numerical results reveal that the front of the shock wave generated by hypervelocity impact is not strictly planar in the target due to the shock wave releasing from both sides of the impact interface. Thus, the shape of the multiple jets generated from the grooves resembles a “trident”, and the upper microjet and the lower microjet deflect upward and downward respectively. The jetting velocity and mass of the middle microjet is larger than that of the microjets on both sides in the impact area. Last, the variation of the spike velocity and jetting mass with the impact velocity and the target thickness is obtained.

Colloidal surface engineering is of particular importance to impart modular functionalities to the colloidal systems. Here, a layer of Mn/Ni layered hydroxides (Mn/Ni(OH)x LDHs) can be successfully coated on various colloidal particles, such as silica spheres, silica rods, ferrite nanocrystal supraparticles, as well as FeOOH nanorods. Such layered hydroxides have intrinsic oxidase-mimetic activities, as demonstrated by catalytic oxidation of tetramethyl benzidine in the presence of oxygen. Furthermore, Mn/Ni(OH)x LDHs structure seems to capture bacteria (both Gram positive and Gram negative) and exhibit antibacterial properties in vitro. Moreover, local delivery of Mn/Ni-LDH structure fights against infection and reverses delayed wound healing procedures in mice models. Importantly, such hierarchical structures may have strong adhesive properties to the bacteria, which may maximize the contact between Mn/Ni(OH)x LDHs and the bacteria's surface. Overall, the present versatile colloidal surface engineering strategy will bring new insights in the field of antibiotics for its high efficiency toward antibacterial activity.

The theories of impact dynamics and adiabatic temperature change were adopted to investigate the ionization degree of the plasma generated by the 2A12 aluminum target during 2A12 aluminum projectile hypervelocity impact, and the radiant temperature was also estimated during hypervelocity impact. A two-stage light gas gun combined with the plasma characteristic parameters measured by a triple Langmuir probe was applied, and then the fitting relationship between the plasma ionization degree and the theoretical impact temperature as well as the fitting relationship between the maximum electron density and impact velocity were obtained with the aid of the theoretical derivation of the plasma ionization degree and the electron temperature and electron density extracted from experiments.

To researching the damage characteristics of typical logical chip modules in spacecraft due to plasma generated by hypervelocity impacts, we have established a triple Langmuir probe diagnostic system and a logical chips measurement system, which were used to diagnose plasma characteristic parameters and the logical chip module's logical state changes due to the plasma created by a 7075 aluminum projectile hypervelocity impact on the 2A12 aluminum target. Three sets of experiments were performed with the collision speeds of 2.85 km/s, 3.1 km/s and 2.20 km/s, at the same incident angles of 30 degrees and logical chip module's positions by using a two-stage light gas gun loading system, a plasma characteristic parameters diagnostic system and a logical chip module's logical state measurement system, respectively. Electron temperature and density were measured at given position and azimuth, and damage estimation was performed for the logical chip module by using the data acquisition system. Experimental results showed that temporary damage could be induced on logical chip modules in spacecraft by plasma generated by hypervelocity impacts under the given experimental conditions and the sensors' position and azimuth.

A simple and rapid electrochemical synthesis strategy for in-situ fabrication of toluidine blue (TB)-functionalized metal organic framework (TB@MOFs) thin films via one-step reduction approach was reported. The TB@MOFs thin layer has been synthesized on the working electrode surface in different times at room temperature, and its morphology and thickness was characterized by varied instruments, including scanning electron microscope, transmission electron microscope, and scanning ion conductance microscope. The prepared TB@MOFs shows exceptional electrochemical activity and stability under mild conditions, and its electrochemical response is dependent on the amount of TB molecules which are encapsulated in the MOFs. Based on the TB@MOFs sensing platform, a sensitive label-free electrochemical immunosensor for the detection of Indole-3-acetic acid (IAA, a plant hormone) was developed with a wide linear range (2.5 pg mL−1 - 5 ng mL−1) and a low detection limit (1.4 pg mL−1). Moreover, this immune sensor shows good sensitivity and selectivity in real samples for IAA detection.

Lactofen is a chiral herbicide and widely used against broadleaf weeds in agriculture. As a pesticide, it is directly released to the environment, and easily caused contamination in soil and aquatic ecosystem. The enantioselective degradation of lactofen in the environment has been reported, but the molecular biological mechanism of this phenomenon is still unclear. In this study, strain Edaphocola flava HME-24 could degrade 96.7% of 50 mg L−1 lactofen within 72 h. Lactofen was initially hydrolyzed to desethyl lactofen and subsequently acifluorfen by strain HME-24. A novel gene lanE, involved in lactofen transformation, was obtained from Edaphocola flava HME-24. Gene lanE encoded a protein of 471 amino acids that contained the conserved GXSXG esterase motif and clustered into esterase subfamily V. LanE shared the highest identity with esterase EstD (Q9WYH1) from Thermotoga maritima MSB8 (29.14%). This esterase was also able to transform p-nitrophenyl esters (C4–C8), and the activity decreased when the carbon chain length increased. LanE showed enantioselectivity during the degradation of lactofen, diclofop-methyl, and quizalofop-ethyl, with a higher degradation efficiency of (S)-enantiomers than (R)-enantiomers. The three-dimensional structure of LanE was simulated, and molecular docking revealed that when the (S)-enantiomers of lactofen occupied the active sites, the distance between the ligand molecule and the coordination atom was shorter than that when the (R)-enantiomers occupied the active sites, which facilitated the formation of the transition state complex. The results in this study enhanced our understanding of the preferential catabolism of the (S)-enantiomers of lactofen on the molecular level and could illustrate the reported enantioselective degradation of lactofen in the environment.

The surface mechanical properties of the Selective Laser Melting (SLM) formed Ti6Al4V samples were improved by adopting a novel laser cleaning pretreatment process combined with a jet electrodeposition process. This paper aimed to investigate the effects of different laser powers on the morphologies and adhesions of the nickel coatings. The advantages of the laser cleaning process are no grinding, no contact, high efficiency and environmental protection. The morphologies, adhesion, wear resistance, and hardness of the coatings were characterized. The results indicate that when the laser energy density reached 20% (4 J/cm2), the contaminations on the substrate and the oxide layer were removed and the crystalline grain of the coating was 15.3 nm. The shallow pits generated by laser burning increased the adhesion of the coatings. In addition, when the laser energy density increased to 6 J/cm2, a yellow oxide layer was produced on the surface of the cleaned titanium alloy. Moreover, the wear resistance of the titanium alloy after the nickel plating was improved. The wear volume was only 0.046 mm3, and the hardness increased to 1967.6 N/mm2.

Given the potentially high impact of accurate financial market forecasting, there has been considerable research on time series analysis for financial markets. We present a new Intelligent Hybrid Weighted Fuzzy (IHWF) time series model to improve forecasting accuracy in financial markets, which are complex nonlinear time-sensitive systems, influenced by many factors. The IHWF model uniquely combines Empirical Mode Decomposition (EMD) with a novel weighted fuzzy time series method. The model is enhanced by an Adaptive Sine-Cosine Human Learning Optimization (ASCHLO) algorithm to help find optimal parameters that further improve forecasting performance. EMD is a time series processing technique to extract the possible modes of various kinds of institutional and individual investors and traders, embedded in a given time series. Subsequently, the proposed weighted fuzzy time series method with chronological order based frequency and Neighborhood Volatility Direction (NVD) is analyzed and integrated with ASCHLO to determine the effective universe discourse, intervals and weights. In order to evaluate the performance of proposed model, we evaluate actual trading data of Taiwan Capitalization Weighted Stock Index (TAIEX) from 1990 to 2004 and the findings are compared with other well-known forecasting models. The results show that the proposed method outperforms the listing models in terms of accuracy.

Online reviews often contain user’s specific opinions on aspects (features) of items. These opinions are very useful to merchants and customers, but manually extracting them is time-consuming. Several topic models have been proposed to simultaneously extract item aspects and user’s opinions on the aspects, as well as to detect sentiment associated with the opinions. However, existing models tend to find poor aspect-opinion associations when limited examples of the required word co-occurrences are available in corpus. These models often also assign incorrect sentiment to words. In this paper, we propose a Latent embedding structured Opinion mining Topic model, called the LOT, which can simultaneously discover relevant aspect-level specific opinions from small or large numbers of reviews and to assign accurate sentiment to words. Experimental results for topic coherence, document sentiment classification, and a human evaluation all show that our proposed model achieves significant improvements over several state-of-the-art baselines.

Topic modeling has been widely used for extracting the major topics from a corpus. Each discovered topic contains a set of related individual words that describe the topic itself. The discovered topics summarize the major themes of the corpus. Recently, a few phrase-based topic models have been proposed, which simultaneously model phrases and topics. The topics discovered by these models consist of phrases besides individual words, as phrases are typically more meaningful. However, these models typically require large amounts of data to provide reliable statistics for phrase-based topic modeling, thus limiting their performance in scenarios with limited data. To address this limitation, we propose a knowledge-based topic model that incorporates two types of pre-identified external knowledge for topical phrase discovery: Phrase knowledge, and phrase correlation knowledge. Phrase knowledge guides the discovery of meaningful phrases by leveraging a set of pre-identified exemplary phrases; Phrase correlation knowledge guides the discovery of meaningful topics by exploiting a set of pre-identified pairs of related phrases. Experimental results show that our method outperforms the state-of-the-art baseline on both small and large datasets, extracting more meaningful phrases and coherent topics.

Recommender Systems (RS) in e-commerce are typically used to suggest products to online shopping customers, and now play a key role in product marketing strategies for major online retailers, such as Walmart and Amazon. The main goal of such systems is to predict likely future customer desires and to trigger purchases through the timely provision of product recommendations. Therefore, RS have become indispensable tools for both customers and retailers. However, most existing RS recommend products from the point view of customers (i.e. likelihood of customer purchase) but ignore one of the most important business goals: the optimization of revenue. Consequently, there is an increasing need to learn utility patterns online and provide near real-time utility-based recommendations. To address these challenges, we first define the utility of recommendation sets and formulate the problem of real time utility-based recommendation. Next, we consider that online transaction streams are usually accompanied with flow fluctuation, and propose an Adaptive Online Top-K (RAOTK) high utility itemsets mining model to guide the utility-based recommendations. Additionally, three variants of this algorithm are described and we provide a structural comparison of the four algorithms with discussions on their advantages and limitations. Moreover, to make our model more personalized, we also take the buying power of customers into account and propose a simple but effective method to estimate the consumers' willingness to pay. Finally, extensive empirical results on real-world datasets show that the proposed model works effectively and outperforms several baselines.

We propose a latent-embedding-structured lifelong learning topic model, called the LLT model, to discover coherent topics from a corpus. Specifically, we exploit latent word embeddings to structure our model and mine word correlation knowledge to assist in topic modeling. During each learning iteration, our model learns new word embeddings based on the topics generated in the previous learning iteration. Experimental results demonstrate that our LLT model is able to generate more coherent topics than state-of-the-art methods.

Financial forecasting is an extremely challenging task given the complex, nonlinear nature of financial market systems. To overcome this challenge, we present an intelligent weighted fuzzy time series model for financial forecasting, which uses a sine-cosine adaptive human learning optimization (SCHLO) algorithm to search for the optimal parameters for forecasting. New weighted operators that consider frequency based chronological order and stock volume are analyzed, and SCHLO is integrated to determine the effective intervals and weighting factors. Furthermore, a novel short-term trend repair operation is developed to complement the final forecasting process. Finally, the proposed model is applied to four world major trading markets: the Dow Jones Index (DJI), the German Stock Index (DAX), the Japanese Stock Index (NIKKEI), and Taiwan Stock Index (TAIEX). Experimental results show that our model is consistently more accurate than the state-of-the-art baseline methods. The easy implementation and effective forecasting performance suggest our proposed model could be a favorable market application prospect.

To improve the composite coating surface quality and performance, a magnetic-field-assisted method was used to manufacture Ni–SiC composite coating based on jet electrodeposition. The effect of different concentrations of SiC and the magnetic field on the surface morphology, microhardness, wear resistance, and corrosion resistance of the coating were investigated by (1) changing the content of nano-SiC particles in the bath, and (2) whether magnetic-field-assisted device was used. Results showed that adding nano-SiC can refine the coating grain size and reduce the surface roughness. The microhardness of the coating increased significantly from 442.65 HV to 514.03 HV for pure nickel coating, an increase of 16.12%; the microhardness increased to 542.76 HV with the introduction of a magnetic field. Magnetic field assistance can reduce cellular protrusions and improve flatness. The Ni–SiC coating wear resistance was strengthened with Magnetic-field-assisted, the wear mechanism changed from the fatigue wear to abrasive wear and adhesive wear. The corrosion resistance of Ni–SiC composite coating was also improved compared with pure Ni coating.