Hausmannite Mn3O4 as a positive active electrode material for rechargeable aqueous Mn‐oxide/Zn batteries

Batteries with manganese (di)oxide/zinc chemistry and aqueous‐based electrolytes have the potential to address energy storage demands of stationary applications primarily because of the abundant availability of Zn and Mn‐oxides, their intrinsic low cost, and the high specific/volumetric charge capacities. Herein, we report the use of Mn₃O₄ (hausmannite phase of manganese oxide) as the positive electrode material in a rechargeable near‐neutral Mn‐oxide/Zn battery configuration. Electrochemical investigations reveal that the hausmannite phase can activate for charge/discharge processes during the first 40 to 50 cycles and then a maximum capacity is obtained. This material shows excellent reversibility (~800 cycles) in keeping more than 65% of its maximum capacity. For the first time, the hausmannite activation mechanism was better understood under near‐neutral conditions. By using different characterization techniques (X‐ray powder diffraction [XRD], inductively coupled plasma‐optical emission spectrometry [ICP‐OES], X‐ray photoelectron spectroscopy [XPS], and energy dispersive X‐ray spectroscopy [EDS]) formation of Zn‐based compounds at the electrode surface was confirmed. One of the compounds formed is the layered double hydroxide (Zn₄SO₄[OH]₆ · 5H₂O) that forms as a side product. No direct evidence for intercalation of zinc ions was observed. Electrochemical experiments in different aqueous/organic electrolytes has shown that proton intercalation plays a significant role in the charge‐storage mechanism, while the activation process itself proceeds, most likely, through the formation of Zn‐species at the electrode surface.     

Petrological control on engineering properties of carbonate rocks in arid regions

Bulletin of Engineering Geology and the Environment - Petrological features of carbonate rocks that exert control on their mechanical and physical properties are examined in the study presented...     

Novel multiferroic La0.95Sb0.05FeO3 orthoferrite

A series of La_1−xSb_xFeO₃ was prepared using the conventional solid state method. XRD revealed the formation of the orthorhombic structure with space group Pbnm. The data showed that, the molar magnetic susceptibility and coercive field H_C were increased from 9.16 × 10⁻³ to 26.9 × 10⁻³ emu g⁻¹ mol and 1196 to 5465 Oe from for LaFeO₃ to La_0.95Sb_0.05FeO₃, respectively. The coercive field (H_C) of the sample with x = 0.05 increased 6 times than that of the parent LaFeO₃ and the saturation magnetization (M_s) was increased from 0.1614 emu g⁻¹ for the parent LaFeO₃ to 0.2654 emu g⁻¹ for the doped sample_. The dielectric constant (?′) was increased with increasing the Sb³⁺ content. The ac conductivity (σ) increases from 2.36 × 10⁻³ Ω⁻¹ m⁻¹ for the LaFeO₃ to 30 × 10⁻³ Ω⁻¹ m⁻¹ for the sample La_0.95Sb_0.05FeO₃ at T = 553 K and frequency 1 MHz. The sample La_0.95Sb_0.05FeO₃ is concluded to be a novel single phase multiferroic material.     

Comparative evaluation of 3D vs. 2D modality for automatic detection of facial action units

Automatic detection of facial expressions attracts great attention due to its potential applications in human–computer interaction as well as in human facial behavior research. Most of the research has so far been performed in 2D. However, as the limitations of 2D data are understood, expression analysis research is being pursued in 3D face modality. 3D can capture true facial surface data and is less disturbed by illumination and head pose. At this junction we have conducted a comparative evaluation of 3D and 2D face modalities. We have investigated extensively 25 action units (AU) defined in the Facial Action Coding System. For fairness we map facial surface geometry into 2D and apply totally data-driven techniques in order to avoid biases due to design. We have demonstrated that overall 3D data performs better, especially for lower face AUs and that there is room for improvement by fusion of 2D and 3D modalities. Our study involves the determination of the best feature set from 2D and 3D modalities and of the most effective classifier, both from several alternatives. Our detailed analysis puts into evidence the merits and some shortcomings of 3D modality over 2D in classifying facial expressions from single images.     

Unsupervised classification of cell images using pyramid nodelinking

In this communication we describe a segmentation technique which combines two properties in an iterative and hierarchial matter to correctly segment and classify the given cell images. The technique is applied to digital images taken from microscope slides of cultured rat liver cells, and the goal is to classify these cells into one of three possible classes. The first class cells (I) are morphologically normal and stain the darkest. The second class cells (II) are slightly damaged showing both nuclear and cytoplasmic swelling with resultant lessening of staining affinity. The third class cells (III) are markedly damaged as demonstrated by the presence of cytoplasmic vacuolization, or are completely disintegrated. First class cells are classified by taking advantage of their staining affinity; the original gray level image is segmented into four gray levels. The darkest is then classified as type I. Type III cells are classified by using high business as a characteristic; the standard deviation of the original image is segmented into four business levels. The highest level is classified as type III cell. Assuming only the three cell types are present in any given image, the remaining non-background unclassified pixels are determined to belong to type II cells.     

An asset-based approach to policymaking: revisiting the history of urban planning and neighborhood change in Cincinnati’s West End

While planners and policymakers have recognized the role of the local assets, including physical capital (housing stock, roads, and the public space) and social capital (the informal networks of trust and reciprocity) in creating healthy communities, the nexus between such assets and policymaking remains vague. This article explores this linkage by chronicling the implementation of various urban policies, including triage, urban renewal, the Model Cities Program, and the Housing Opportunities for People Everywhere (HOPE VI Program) in the West End of Cincinnati. The critical examination of such programs helps us to differentiate between an asset-based as opposed to the conventional need-based approach to policymaking, and to explore the mutual impacts of government policies and the target groups that are affected by them; policies could result in the formation or destruction of the local assets (social capital). The study offers a conceptual framework consisting of policy networks defined in terms of policy type and target groups. Two additional concepts used to evaluate the mutual impacts of policies and target groups include interconnectedness (the strength of relationship between government and the target group) and cohesion (the distribution of objectives among the actors).     

The applicability of neural networks in the determination of soil profiles

The drilling of a number of boreholes to determine the soil profile of a given area is time consuming and costly. This paper describes estimated soil profiles obtained using a model based on artificial neural networks (ANN). ANN is a powerful data-modelling tool capable of capturing and representing complex relationships between input and output. It deals with many multi-variate problems for which an exact analytical model does not exist or is very difficult and time consuming to develop. The main settlement in the Adapazari region was selected to demonstrate the capability of such model. The results obtained using ANN are promising when compared with the soil profile obtained from boreholes.      

Adaptive-Network-Based Fuzzy Inference System Analysis to Predict the Temperature and Flow Fields in a Lid-Driven Cavity

Heat transfer behavior in a 2-D square lid-driven cavity has been studied for various pertinent Reynolds and Rayleigh numbers. The lattice Boltzmann method, a numerical tool based on the particle distribution function is applied to simulate a thermal fluid flow problem. Bhatnagar-Gross-Krook (BGK) is combined with the double population thermal Lattice Boltzmann model to solve mixed convection in a square cavity. An adaptive-network-based fuzzy inference system (ANFIS) method is trained and validated using BGK Lattice Boltzmann model results. The results show that the trained ANFIS model successfully predicts the temperature and flow fields in a few seconds with acceptable accuracy.