Enhance cycling performance of LiMn2O4 cathode by Sr2+ and Cr3+ doping

Spinel LiSr_0·1Cr_0·1Mn_1·8O₄ was synthesised by high temperature solid state method in order to enhance the electrochemical performance. The LiSr_0·1Cr_0·1Mn_1·8O₄ (LSCMO) materials were characterised by X-ray diffraction (XRD), scanning electron microscopy (SEM) and electrochemical tests. The XRD and SEM studies confirm that LSCMO had spinel crystal structure with a space group of Fd3m, and the particle of LSCMO shows irregular shape. The cyclic voltammetry data illustrated that the heavy current charge–discharge performance of LMO was improved by Sr²⁺ and Cr³⁺ doping. The galvanostatic charge–discharge of LSCMO cathode materials was measured at 1, 5, 10 and 20 C. The results indicated that LSCMO improved the capacity retention.     

Fabrication of tungsten oxide photoanode by doctor blade technique and investigation on its photocatalytic operation mechanism

WO₃ is a potential material candidate for construction of photoanode for solar driven water splitting. In this work, μm-thick porous WO₃ photoanode is prepared by depositing a stable ink made of WO₃ nanoparticles and Aristoflex velvet polymer in water using the doctor blade technique, followed by a sintering in air. The nature of WO₃ nanoparticles, its loading mass on F-doped tin oxide electrode as well as sintering temperature are examined in order to optimize the photocatalytic activity of the resultant WO₃ photoanode. The operation of WO₃ photoanode is investigated by varying the light illumination direction and light incident intensity as well as changing the nature of the electrolyte. Dissolved tungsten in electrolyte is quantified by ICP-MS providing insights into the influences of electrolyte nature and operating conditions to the corrosion of WO₃. It is proposed that the H₂O₂ and OH^. radical generated as by-products of the photo-driven water oxidation on the photoanode surface are harmful species that accelerate the dissolution of WO₃.     

In situ observation of synthesized nanoparticles in ultra-dilute aerosols via X-ray scattering

In-air epitaxy of nanostructures (Aerotaxy) has recently emerged as a viable route for fast, large-scale production. In this study, we use small-angle X-ray scattering to perform direct in-flight characterizations of the first step of this process, i.e., the engineered formation of Au and Pt aerosol nanoparticles by spark generation in a flow of N₂ gas. This represents a particular challenge for characterization because the particle density can be extremely low in controlled production. The particles produced are examined during production at operational pressures close to atmospheric conditions and exhibit a lognormal size distribution ranging from 5–100 nm. The Au and Pt particle production and detection are compared. We observe and characterize the nanoparticles at different stages of synthesis and extract the corresponding dominant physical properties, including the average particle diameter and sphericity, as influenced by particle sintering and the presence of aggregates. We observe highly sorted and sintered spherical Au nanoparticles at ultra-dilute concentrations (< 5 × 10⁵ particles/cm³) corresponding to a volume fraction below 3 × 10^–10, which is orders of magnitude below that of previously measured aerosols. We independently confirm an average particle radius of 25 nm via Guinier and Kratky plot analysis. Our study indicates that with high-intensity synchrotron beams and careful consideration of background removal, size and shape information can be obtained for extremely low particle concentrations with industrially relevant narrow size distributions.

     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.