Lean methane combustion over zeolite-supported Pd catalysts: Structure-performance relationship and deactivation mechanism.

Zeolites are a promising support for Pd catalysts in lean methane (CH4) combustion. Herein, three types of zeolites (H-MOR, H-ZSM-5 and H-Y) were selected to estimate their structural effects and deactivation mechanisms in CH4 combustion. We show that variations in zeolite structure and surface acidity led to distinct changes in Pd states. Pd/H-MOR with external high-dispersing Pd nanoparticles exhibited the best apparent activity, with activation energy (Ea) at 73 kJ/mol, while Pd/H-ZSM-5 displayed the highest turnover frequency (TOF) at 19.6 × 10-3 sec-1, presumably owing to its large particles with more step sites providing active sites in one particle for CH4 activation. Pd/H-Y with dispersed PdO within pore channels and/or Pd2+ ions on ion-exchange sites yielded the lowest apparent activity and TOF. Furthermore, Pd/H-MOR and Pd/H-ZSM-5 were both stable under a dry condition, but introducing 3 vol.% H2O caused the CH4 conversion rate on Pd/H-MOR drop from 100% to 63% and that on Pd/H-ZSM-5 decreased remarkably from 82% to 36%. The former was shown to originate from zeolite structural dealumination, and the latter principally owed to Pd aggregation and the loss of active PdO.

Palladium Encapsulated by an Oxygen-Saturated TiO2 Overlayer for Low-Temperature SO2 -Tolerant Catalysis during CO Oxidation.

The development of oxidation catalysts that are resistant to sulfur poisoning is crucial for extending the lifespan of catalysts in real-working conditions. Herein, we describe the design and synthesis of oxide-metal interaction (OMI) catalyst under oxidative atmospheres. By using organic coated TiO2 , an oxide/metal inverse catalyst with non-classical oxygen-saturated TiO2 overlayers were obtained at relatively low temperature. These catalysts were found to incorporate ultra-small Pd metal and support particles with exceptional reactivity and stability for CO oxidation (under 21 vol % O2 and 10 vol % H2 O). In particular, the core (Pd)-shell (TiO2 ) structured OMI catalyst exhibited excellent resistance to SO2 poisoning, yielding robust CO oxidation performance at 120 °C for 240 h (at 100 ppm SO2 and 10 vol % H2 O). The stability of this new OMI catalyst was explained through density functional theory (DFT) calculations that interfacial oxygen atoms at Pd-O-Ti sites (of oxygen-saturated overlayers) serve as non-metal active sites for low-temperature CO oxidation, and change the SO2 adsorption from metal(d)-to-SO2 (π*) back-bonding to much weaker σ(Ti-S) bonding.

Green waste and sewage sludge feeding ratio alters co-composting performance: Emphasis on the role of bacterial community during humification.

Composting with five levels of green waste and sewage sludge was compared to examine how feeding ratios affected composting performance with special focus on humification, and the underlying mechanisms. The results showed that the raw material ratio persistently affected compost nutrients and stability. Humification and mineralization were promoted by higher proportion of sewage sludge. Bacterial community composition and within-community relationships were also significantly affected by the raw material feeding ratio. Network analysis indicated that clusters 1 and 4 which dominated by Bacteroidetes, Proteobacteria, and Acidobacteria shown significantly positive correlation with humic acid concentration. Notably, the structural equational model and variance partitioning analysis demonstrated that bacterial community structure (explained 47.82% of the variation) mediated the effect of raw material feeding ratio on humification, and exceeded the effect of environmental factors (explained 19.30% of the variation) on humic acid formation. Accordingly, optimizing the composting raw material improves the composting performance.

Semi-Supervised Unpaired Medical Image Segmentation Through Task-Affinity Consistency.

Deep learning-based semi-supervised learning (SSL) algorithms are promising in reducing the cost of manual annotation of clinicians by using unlabelled data, when developing medical image segmentation tools. However, to date, most existing semi-supervised learning (SSL) algorithms treat the labelled images and unlabelled images separately and ignore the explicit connection between them; this disregards essential shared information and thus hinders further performance improvements. To mine the shared information between the labelled and unlabelled images, we introduce a class-specific representation extraction approach, in which a task-affinity module is specifically designed for representation extraction. We further cast the representation into two different views of feature maps; one is focusing on low-level context, while the other concentrates on structural information. The two views of feature maps are incorporated into the task-affinity module, which then extracts the class-specific representations to aid the knowledge transfer from the labelled images to the unlabelled images. In particular, a task-affinity consistency loss between the labelled images and unlabelled images based on the multi-scale class-specific representations is formulated, leading to a significant performance improvement. Experimental results on three datasets show that our method consistently outperforms existing state-of-the-art methods. Our findings highlight the potential of consistency between class-specific knowledge for semi-supervised medical image segmentation. The code and models are to be made publicly available at https://github.com/jingkunchen/TAC.

Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge.

Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, compared with the other sequences LGE CMR images with gold standard labels are particularly limited. This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019. The challenge offered a data set of paired MS-CMR images, including auxiliary CMR sequences as well as LGE CMR, from 45 patients who underwent cardiomyopathy. It was aimed to develop new algorithms, as well as benchmark existing ones for LGE CMR segmentation focusing on myocardial wall of the left ventricle and blood cavity of the two ventricles. In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the ventricle segmentation of LGE CMR. Nine representative works were selected for evaluation and comparisons, among which three methods are unsupervised domain adaptation (UDA) methods and the other six are supervised. The results showed that the average performance of the nine methods was comparable to the inter-observer variations. Particularly, the top-ranking algorithms from both the supervised and UDA methods could generate reliable and robust segmentation results. The success of these methods was mainly attributed to the inclusion of the auxiliary sequences from the MS-CMR images, which provide important label information for the training of deep neural networks. The challenge continues as an ongoing resource, and the gold standard segmentation as well as the MS-CMR images of both the training and test data are available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mscmrseg/).

The positive effect of Ca2+ on cryptomelane-type octahedral molecular sieve (K-OMS-2) catalysts for chlorobenzene combustion.

Herein, K-OMS-2 catalysts with different levels of Ca2+ loading were synthesized to investigate the influence of Ca2+ deposition on the catalytic oxidation of CB. The micromorphology, redox ability, oxygen species, and surface acidity of the prepared catalysts were analyzed via SEM, HR-TEM, H2-TPR, O2-TPD, Py-IR, and GC-MS. After the Ca addition, CB conversion on the catalyst was achieved at <220 °C and the polychlorinated by-product yield decreased significantly. Although CaCO3 formation on the catalyst surface from Ca2+ caused a decline in number and fluidity of the surface lattice oxygen species, no significant impact on surface adsorbed oxygen species content was observed. Furthermore, CaCO3 reacted with dissociated chlorine species (HCl and polychlorinated products generated in situ), which inhibited chlorine poisoning of the active phase.

Elimination of chloroaromatic congeners on a commercial V2O5-WO3/TiO2 catalyst: The effect of heavy metal Pb.

Catalytic elimination of chlorinated volatile organic compounds (CVOCs) from the industrial sources of emission usually confronted with catalyst deactivation and secondary pollution. As a widely used catalyst in selective catalytic reduction (SCR) of nitric oxides, V2O5-WO3/TiO2 catalysts (denoted as VWT) have been also applied for eliminating the CVOCs, especially those from the municipal solid waste (MSW) incineration. However, the effect of heavy metals on the reaction characteristics of this catalyst is lack of exploration, which has been considered to be a main cause for catalyst deactivation. Herein, we investigated the effect of lead (Pb), a critical heavy meal in the flue gas of MSW incineration, on the catalytic elimination of chlorobenzene (CB) over the VWT catalyst. Variations of catalytic activity, CO2/HCl selectivity and chlorine adsorption/desorption behaviors were evaluated. In particular, the reaction byproducts with and without Pb loadings were qualitatively and quantitatively analyzed. It was noted that the loading of excessive Pb could change the reaction route over the VWT catalyst, leading to low CB oxidation efficiency and CO2 selectivity. However, these Pb spices likely acted as "a sink" to capture the dissociated Cl that hindered the electrophilic chlorination reaction, and avoided the formation of more toxic polychlorinated byproducts.

Enhanced cold field emission of large-area arrays of vertically aligned ZnO-nanotapers via sharpening: experiment and theory.

Large-area arrays of vertically aligned ZnO-nanotapers with tailored taper angle and height are electrodeposited on planar Zn-plate via continuously tuning the Zn(NH3)4(NO3)2 concentration in the electrolyte. Experimental measurements reveal that the field-emission performance of the ZnO-nanotaper arrays is enhanced with the sharpness and height of the ZnO-nanotapers. Theoretically, the ZnO-nanotaper is simplified to a "charge disc" model, based on which the characteristic macroscopic field enhancement factor (γC) is quantified. The theoretically calculated γC values are in good agreement with the experimental ones measured from arrays of ZnO-nanotapers with a series of geometrical parameters. The ZnO-nanotaper arrays have promising potentials in field-emission. The electrochemical synthetic strategy we developed may be extended to nanotaper arrays of other materials that are amenable to electrodeposition, and the "charge disc" model can be used for quasi-one-dimensional field emitters of other materials with nano-sized diameters.