Theoretical kinetic study of the reaction between dimethyl disulfide and OH radicals

The potential energy profile of the reaction between dimethyl disulfide and OH^? radicals is explored by utilizing ab initio and hybrid meta density functional theory methods. Having the energies and structural data of the stationary points, statistical rate theories, such as transition state theory and variable reaction coordinate-transition state theory, are employed to compute the overall rate constants, and discuss the mechanism and product channels. On the basis of the calculations, the overall rate coefficient is predicted to be 2.49?×?10^?10?cm³?molecule^?1?s^?1 at 298?K. It is found that in the most favorable pathway, the reaction proceeds via formation of the relatively unstable intermediate CH₃S^?(OH)SCH₃ decomposing rapidly to yield CH₃S^?+CH₃SOH.     

Role of MgO on densification and mechanical properties in spark plasma sintered Os0.9Re0.1B2 ceramic

To promote the densification and therefore the mechanical properties of boride-based ceramics, MgO was added as sintering aid into Os_0.9Re_0.1B₂ powders for densification by using spark plasma sintering (SPS). The Os_0.9Re_0.1B₂ powders were synthesized by mechanochemical method from powder mixture of Os, Re and amorphous B. The role of MgO on densification, phase composition, microstructure and mechanical properties (hardness, fracture toughness and wear behavior) were studied by using X-ray diffraction (XRD), scanning electron microscope (SEM) with energy-dispersive spectroscopy (EDS), micro indentation and ball-on-disk tribometer. The results show that, with the introduction of MgO as sintering aid, the relative density of the Os_0.9Re_0.1B₂ ceramic samples increased. When the MgO content reached 9 wt%, the as-sintered sample is almost fully dense. No obvious regularity was found from the samples with the addition of different content of MgO. Vickers hardness values of the samples with 0, 3 wt% and 9 wt% MgO are found to be very close with each other within the experimental error (~30 GPa), while the sample with the addition of 6 wt% MgO exhibits the highest hardness of ~35 GPa. The fracture toughness of the samples is decreased slightly with the addition of MgO. The friction coefficient and wear rate of the sample with the addition of 6 wt% MgO was also found to be the lowest among all samples, which indicate best wear resistance. As a whole, with the addition content of 6 wt% MgO, the Os_0.9Re_0.1B₂ ceramic sample performs relatively excellent mechanical properties among four groups of samples.     

Separation of hematite from banded hematite jasper (BHJ) by magnetic coating

The separation of iron oxide from banded hematite jasper(BHJ) assaying 47.8% Fe, 25.6% Si O2 and 2.30%Al2O3 using selective magnetic coating was studied. Characterization studies of the low grade ore indicate that besides hematite and goethite,jasper, a microcrystalline form of quartzite, is the major impurity associated with this ore. Beneficiation by conventional magnetic separation technique could yield a magnetic concentrate containing 60.8% Fe with 51% Fe recovery. In order to enhance the recovery of the iron oxide minerals, fine magnetite, colloidal magnetite and oleate colloidal magnetite were used as the coating material. When subjected to magnetic separation, the coated ore produces an iron concentrate containing 60.2% Fe with an enhanced recovery of56%. The AFM studies indicate that the coagulation of hematite particles with the oleate colloidal magnetite facilitates the higher recovery of iron particles from the low grade BHJ iron ore under appropriate conditions.     

Magnetic Nanoparticle Arrays Self-Assembled on Perpendicular Magnetic Recording Media

We study magnetic-field directed self-assembly of magnetic nanoparticles onto templates recorded on perpendicular magnetic recording media, and quantify feature width and height as a function of assembly time. Feature widths are determined from Scanning Electron Microscope (SEM) images, while heights are obtained with Atomic Force Microscopy (AFM). For short assembly times, widths were ~150 nm, while heights were ~14 nm, a single nanoparticle on average with a 10:1 aspect ratio. For long assembly times, widths approach 550 nm, while the average height grows to 3 nanoparticles, ~35 nm; a 16:1 aspect ratio. We perform magnetometry on these self-assembled structures and observe the slope of the magnetic moment vs. field curve increases with time. This increase suggests magnetic nanoparticle interactions evolve from nanoparticle–nanoparticle interactions to cluster–cluster interactions as opposed to feature–feature interactions. We suggest the aspect ratio increase occurs because the magnetic field gradients are strongest near the transitions between recorded regions in perpendicular media. If these gradients can be optimized for assembly, strong potential exists for using perpendicular recording templates to assemble complex heterogeneous materials.     

Reducing the complexity of distance measurement methods for circular turbo codes that use structured interleavers

The knowledge of turbo code's minimum Hamming distance (d_min) and its corresponding codeword multiplicity (A_min) is of a great importance because the error correction capability of a code is strongly tied to the values of d_min and A_min. Unfortunately, the computational complexity associated with the search for d_min and A_min can be very high, especially for a turbo code that has high d_min value. This paper introduces some useful properties of turbo codes that use structured interleavers together with circular encoding. These properties allow for a significant reduction of search space and thus reduce significantly the computational complexity associated with the determination of d_min and A_min values. © 2014 The Authors. International Journal of Communication Systems published by John Wiley & Sons, Ltd.     

Study of the effect of noble metal promotion in Ni-based catalyst for the Sabatier reaction

This paper reports the preparation and the evaluation of the performance of Ni-based powder catalysts with low nickel loading on the CO2 methanation reaction, that is an integral part of the power-to-gas (PtG) technology. CeO₂, CeZrO₄ and CeO₂/SiO₂ were selected as possible supports, and the results of this first screening pointed out that 10%Ni/CeO₂ catalyst could offer the best reaction performances because of ceria's peculiar characteristics. Moreover, the promotion of this promising formulation with the addition of a small amount of noble metals (Pt, Ru, Rh) was investigated, showing that platinum in particular can enhance the catalyst performances. A further study related to the noble metal loading pointed out that platinum and ruthenium have a different optimum loading condition: this result, together with the activity tests performed on monometallic formulations with only the noble metal, suggested that the two metals are able to catalyse two different reactions, thus promoting two different reaction mechanisms.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

煤巷过断层破碎带施工工艺与支护技术实践

W1313回风顺槽在里程332 m处揭露正断层,对此采取了调整巷道顶板锚网索支护参数,采用圆木大板棚复合支护等措施,并加强了顶板、通风、掘锚机破岩、爆破作业和作业人员的安全管理,确保工作面过断层破碎带时的安全施工。     

Evolution of properties of parts during MIM and sintering of recycled oxide particles

Metal injection moulding (MIM) is an established process for high volume production of complex shaped metallic parts using commercially available feedstocks. The characteristics of parts after moulding, debinding, and sintering cannot be simply predictable from raw materials because the properties get altered with the process parameters and the corresponding levels of porosity during processing steps. In this study, physical properties, microstructure, and mechanical properties of the MIM parts have been characterised to understand the evolution of strength during various steps in MIM processing. Feedstocks with different binder loading show a considerable difference in physical as well as mechanical characteristics. During sintering of parts which have solid loading of grinding sludge, simultaneous in situ reduction and densification takes place, whereas only densification occurs in carbonyl iron parts. It is, therefore, possible to make complex shaped parts of different levels of porosity from downgraded shop floor metallic waste.     

Synthesis and characterization of a novel Ca-alginate-biochar composite as efficient zinc (Zn2+) adsorbent: Thermodynamics,process design,mass transfer and isotherm modeling

In this present work, Ca-alginate-biochar adsorbent has been synthesized, characterized and tested its effectiveness in the removal of aqueous phase Zn²⁺ metal. The removal efficiency was studied under various physicochemical process parameters. External mass transfer model, intraparticle diffusion model and pseudo-first-order and pseudo-second-order models were used to fit the experimental Zn²⁺ adoption kinetic results and to identify the mechanism of adsorption. The desorption studies indicate the possibilities of ion-exchange and physical–chemical adsorption of Zn^2+. The adsorption was best described by Langmuir isotherm model. Thermodynamic parameters suggested that the adsorption process becomes spontaneous, endothermic and irreversible in nature.