Direct CO2 hydrogenation to higher alcohols (HAs) is a promising way to achieve the conversion of CO2 to high-value chemicals. Alkali metals as promoters are generally crucial for Cu–Fe-based ...catalysts, but their critical role in higher alcohol synthesis (HAS) is still far from clear. Here, we report the regulating effect of a potassium (K) promoter from a reactant activation perspective on Cu–Fe-based catalysts for HAS from CO2 hydrogenation using in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and chemisorption methods. The optimized catalyst denoted as 4.6K-CMZF with a moderate K content exhibits the highest HA space time yield (STY) in a fixed-bed reactor. It is found that the K can promote reverse water gas shift (RWGS) reaction and tailor the ratio of nondissociated CO to dissociated CO by strengthening linear CO adsorption and weakening bridging CO adsorption. A proper amount of K can balance the nondissociated and dissociated activation of CO, thus providing an adequate *CH x and *CO species to take part in *CH x –*CO coupling reaction. The K promoter can also suppress H2 activation, thereby inhibiting alkylation reaction. The promoting effect of K can be attributed to the balance of surface *CH x , *CO, and *H species by regulating CO activation and H2 activation, thus favoring HA synthesis via *CH x –*CO coupling and hydrogenation reactions.
Although considerable efforts have been made in the selective conversion of syngas carbon monoxide (CO) and hydrogen to olefins through Fischer-Tropsch synthesis (FTS), ~50% of the converted CO is ...transformed into the undesired one-carbon molecule (C1) by-products carbon dioxide (CO
) and methane (CH
). In this study, a core-shell FeMn@Si catalyst with excellent hydrophobicity was designed to hinder the formation of CO
and CH
The hydrophobic shell protected the iron carbide core from oxidation by water generated during FTS and shortened the retention of water on the catalyst surface, restraining the side reactions related to water. Furthermore, the electron transfer from manganese to iron atoms boosted olefin production and inhibited CH
formation. The multifunctional catalyst could suppress the total selectivity of CO
and CH
to less than 22.5% with an olefin yield of up to 36.6% at a CO conversion of 56.1%.
This paper presents a hybrid differential evolution (DE) with quantum-behaved particle swarm optimization (QPSO) for the unmanned aerial vehicle (UAV) route planning on the sea. The proposed method, ...denoted as DEQPSO, combines the DE algorithm with the QPSO algorithm in an attempt to further enhance the performance of both algorithms. The route planning for UAV on the sea is formulated as an optimization problem. A simple method of pretreatment to the terrain environment is proposed. A novel route planner for UAV is designed to generate a safe and flyable path in the presence of different threat environments based on the DEQPSO algorithm. To show the high performance of the proposed method, the DEQPSO algorithm is compared with the real-valued genetic algorithm, DE, standard particle swarm optimization (PSO), hybrid particle swarm with differential evolution operator, and QPSO in terms of the solution quality, robustness, and the convergence property. Experimental results demonstrate that the proposed method is capable of generating higher quality paths efficiently for UAV than any other tested optimization algorithms.
Medical image classification plays an important role in disease diagnosis since it can provide important reference information for doctors. The supervised convolutional neural networks (CNNs) such as ...DenseNet provide the versatile and effective method for medical image classification tasks, but they require large amounts of data with labels and involve complex and time-consuming training process. The unsupervised CNNs such as principal component analysis network (PCANet) need no labels for training but cannot provide desirable classification accuracy. To realize the accurate medical image classification in the case of a small training dataset, we have proposed a light-weighted hybrid neural network which consists of a modified PCANet cascaded with a simplified DenseNet. The modified PCANet has two stages, in which the network produces the effective feature maps at each stage by convoluting inputs with various learned kernels. The following simplified DenseNet with a small number of weights will take all feature maps produced by the PCANet as inputs and employ the dense shortcut connections to realize accurate medical image classification. To appreciate the performance of the proposed method, some experiments have been done on mammography and osteosarcoma histology images. Experimental results show that the proposed hybrid neural network is easy to train and it outperforms such popular CNN models as PCANet, ResNet and DenseNet in terms of classification accuracy, sensitivity and specificity.
Converting the greenhouse gas CO2 into higher alcohols (HAs) via hydrogenation reaction requires more attention in C1 chemistry because the C2+ alcoholic products are value-added chemicals as fuel ...additives, reaction solvents, and intermediates. However, the chemical inertness of CO2, complexity in various reaction routes, and uncontrollability of C–C coupling from untamed surface moieties in higher alcohol synthesis (HAS) make this approach very challenging to achieve. In this review, we summarize and analyze the recent advances in catalytic HAS from direct CO2 hydrogenation. The first section highlights the potential promising catalyst families, including a noble-metal class of catalysts, modified Co-based catalysts, modified Cu-based catalysts, and Mo-based catalysts with the roles of promoters and supports specified in each case. The second section reviews the possible reaction mechanisms based on previous experimental results. The rational design of ideal catalyst systems for this reaction is discussed in the third section.
Display omitted
New catalytic technologies to convert the greenhouse gas CO2 into useful products by renewable energy is becoming more important than ever due to recent alarming events attributable to climate change. Among various products from C1 chemistry, more attention should be paid to the hydrogenation of CO2 to higher alcohols since they are value-added chemicals as fuel additives, reaction solvents, and intermediates. However, higher alcohol synthesis is severely impeded by the difficulties in the chemical inertness of CO2 and complexity in various reaction routes and the uncontrollability of C–C coupling from untamed surface moieties. Development of highly effective and selective catalysts remains a great challenge to the production of higher alcohols. Moreover, further in-depth comprehension of the reaction mechanisms offers practical guidance to new design of catalyst systems. This review provides a new prospect for future research on catalytic CO2 hydrogenation to higher alcohols.
Higher alcohol synthesis from CO2 hydrogenation is a promising and challenging way to realize the efficient utilization of CO2 resources. Despite recent progress, there is still a lack of deeper understanding in this field. This review focuses on the recent advances in heterogeneous catalytic hydrogenation of CO2 to higher alcohols, in terms of catalyst families, reaction mechanisms, and the rational design of ideal catalysts. This will providea new prospect for future research on catalytic CO2 hydrogenation to higher alcohols.
Although considerable efforts have been made toward converting syngas to liquid fuels and value-added chemicals, selectively converting syngas to aromatics remains a big challenge because of severe ...deactivation and low selectivity. Here, we reported a bifunctional catalyst composed of Fe3O4@MnO2 and hollow HZSM-5, which could synthesize aromatics from syngas with a high selectivity of 57% at CO conversion >90%. The catalyst retained good stability for 180 h under industrially relevant conditions. The electron transfer from the Mn to Fe species in the core–shell Fe3O4@MnO2 catalyst promoted the formation of olefins intermediates, which were subsequently diffused onto the acid sites of HZSM-5, further converting to aromatics. Shortened channels and cavity structures of hollow HZSM-5 strengthened the diffusion of reactants and products, enhancing the catalyst stability via the suppression of carbon deposition. The present research provides insight into developing a potential bifunctional catalyst candidate for selectively converting syngas to aromatics.
Aqueous zinc metal batteries benefit from the high volumetric energy density and rich abundance of zinc metal, but suffer from the uncontrollable dendrites, passivation and corrosion which severely ...hinder their development. Developing Zn-containing cathodes to couple with Zn-free anodes is an effect approach to overcome the above challenges, however, such robust hosts that can afford reversible and stable Zn2+ storage have been rarely reported. Herein, we reported a novel low-strain Zn3V4(PO4)6 cathode for zinc-ion battery which delivers a specific capacity of 105.2 mAh g−1, outstanding cycling stability (100 % capacity retention over 250 cycles) and superior rate capability (62.9 mAh g−1 at 40 C). Both density functional theory (DFT) calculation and in-situ characterization reveals the small volume change (2.4 %) of Zn3V4(PO4)6 upon Zn2+ storage. Note that a "rocking-chair" zinc-ion battery is established based on the Zn3V4(PO4)6 cathode and layered TiS2 anode, which demonstrates remarkable electrochemical reversibility and favorable cycling stability.
Display omitted
•Zn3V4(PO4)6 is firstly reported as a low strain cathode for zinc-ion battery.•Zn3V4(PO4)6 exhibits outstanding Zn2+-storage performances.•"Rocking-Chair" full battery is established by Zn3V4(PO4)6 and TiS2.
Speckle reduction remains a critical issue for ultrasound image processing and analysis. The nonlocal means (NLM) filter has recently attached much attention due to its competitive despeckling ...performance. However, the existing NLM methods usually determine the similarity between two patches by directly utilizing the gray-level information of the noisy image, which renders it difficult to represent the structural similarity of ultrasound images effectively. To address this problem, the NLM method based on the simple deep learning baseline named PCANet is proposed by introducing the intrinsic features of image patches extracted by this network rather than the pixel intensities into the pixel similarity computation. In this approach, the improved two-stage PCANet is proposed by using Parametric Rectified Linear Unit (PReLU) activation function instead of the binary hashing and block histograms in the original PCANet. This model is firstly trained on the ultrasound database to learn the convolution kernels. Then, the trained PCANet is utilized to extract the intrinsic features from the image patches in the pre-denoised version of the noisy image to be despeckled. These obtained features are concatenated together to determine the structural similarity between image patches in the NLM method, based on which the weighted mean of all pixels in a search window is computed to produce the final despeckled image. Extensive experiments have been conducted on a variety of images to demonstrate the superiority of the proposed method over several well-known despeckling algorithm and the PCANet based NLM method using ReLU function and sigmoid function. Visual inspection indicates that the proposed method outperforms the compared methods in reducing speckle noise and preserving image details. The quantitative comparisons show that among all the evaluated methods, our method produces the best structural similarity index metrics (SSIM) values for the synthetic image, as well as the highest equivalent number of looks (ENL) value for the simulated image and the clinical ultrasound images.
Display omitted
•Phase transformation during reduction is following as α-Fe2O3→Fe3O4→FeO→α-Fe.•Carburization ability of reduced iron phases is following as α-Fe>FeO>Fe3O4.•Iron carbides are formed on ...the Fe(II) oxide species.•Hydrocarbons species are formed gradually on the surface of iron carbides.
Reduction and carburization behaviors of iron phases over a precipitated iron-based Fischer–Tropsch synthesis (FTS) catalyst were investigated by some techniques of Mössbauer effect spectroscopy (MES), X-ray photoelectron spectroscopy (XPS) and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) as well as H2&CO temperature-programmed desorption (H2&CO-TPD). It was found that in H2 atmosphere phase transformation of iron phases involved α-Fe2O3→Fe3O4→FeO→α-Fe, both occurring in the bulk and on the surface layers. All of reduced iron species took place the carburization reaction, whereas carburizing ability was following the order α-Fe>FeO>Fe3O4. During FTS both iron carbides and Fe(II) oxide species reached a balance state without appearing the intermediate α-Fe. The conversion of reduced iron phases to iron carbides (especially for χ-Fe5C2) on the surface layers played a positive role in promoting the formation of hydrocarbons species.
Magnetic resonance (MR) images are often corrupted by Rician noise which degrades the accuracy of image-based diagnosis tasks. The nonlocal means (NLM) method is a representative filter in denoising ...MR images due to its competitive denoising performance. However, the existing NLM methods usually exploit the gray-level information or hand-crafted features to evaluate the similarity between image patches, which is disadvantageous for preserving the image details while smoothing out noise. In this paper, an improved nonlocal means method is proposed for removing Rician noise in MR images by using the refined similarity measures. The proposed method firstly extracts the intrinsic features from the pre-denoised image using a shallow convolutional neural network named Laplacian eigenmaps network (LEPNet). Then, the extracted features are used for computing the similarity in the NLM method to produce the denoised image. Finally, the method noise of the denoised image is utilized to further improve the denoising performance. Specifically, the LEPNet model is composed of two cascaded convolutional layers and a nonlinear output layer, in which the Laplacian eigenmaps are employed to learn the filter bank in the convolutional layers and the Leaky Rectified Linear Unit activation function is used in the final output layer to output the nonlinear features. Due to the advantage of LEPNet in recovering the geometric structure of the manifold in the low-dimension space, the features extracted by this network can facilitate characterizing the self-similarity better than the existing NLM methods. Experiments have been performed on the BrainWeb phantom and the real images. Experimental results demonstrate that among several compared denoising methods, the proposed method can provide more effective noise removal and better details preservation in terms of human vision and such objective indexes as peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).
PDF
