Energy Recovery in Pressurized Hydraulic Networks

Water Resources Management - This paper aims to evaluate the possibility of using non-utilized hydraulic energy in urban water distribution systems. For this purpose, the viability and possible...     

Controlled Solidification of Liquids Within the SFC Primary Phase Field of the “Fe2O3”-CaO-SiO2 System in Air

Metallurgical and Materials Transactions B - To provide fundamental information on the phases and microstructures formed during sintering, a liquid with a bulk composition within the silico ferrite...     

Sensory analysis integrated by palynological and physicochemical determinations plays a key role in differentiating unifloral honeys of similar botanical origins (Myrtaceae honeys from southern Spain)

Myrtaceae honeys produced in Sierra Morena (southern Spain, Andalusia) are obtained from both Eucalyptus camaldulensis and Myrtus communis, and honeys can be easily confused when classified as they come from the same botanical family. The characterisation was intended on the basis of their physicochemical, sensory and palynological properties. Cluster analysis (CA) and principal component analysis (PCA) allowed us to make a distinction between two honey profiles. The first one corresponds to the European sensory profile described for Eucalyptus unifloral honeys. The second profile, which sensory characteristics are absolutely different, has not been previously described. Classification of samples is proposed to be based on their pollen type and content and sensory attributes. Therefore, pollen type and content in E. camaldulensis and M. communis are required to distinguish Eucalyptus honey type from other honeys coming from Myrtaceae family. Sensory analysis identifies honey botanical source as well as palynological analysis: Eucalyptus sensory profile requires over 50% of E. camaldulensis and under 30% of M. communis pollen. Previously undefined sensory profile of myrtle honey is showed under 50% of E. camaldulensis and over 30% of M. communis pollen.     

Fourier transform ion cyclotron resonance mass spectrometry at the true cyclotron frequency

Ion cyclotron resonance (ICR) cells provide stability and coherence of ion oscillations in crossed electric and magnetic fields over extended periods of time. Using the Fourier transform enables precise measurements of ion oscillation frequencies. These precisely measured frequencies are converted into highly accurate mass-to-charge ratios of the analyte ions by calibration procedures. In terms of resolution and mass accuracy, Fourier transform ICR mass spectrometry (FT-ICR MS) offers the highest performance of any MS technology. This is reflected in its wide range of applications. However, in the most challenging MS application, for example, imaging, enhancements in the mass accuracy of fluctuating ion fluxes are required to continue advancing the field. One approach is to shift the ion signal power into the peak corresponding to the true cyclotron frequency instead of the reduced cyclotron frequency peak. The benefits of measuring the true cyclotron frequency include increased tolerance to electric fields within the ICR cell, which enhances frequency measurement precision. As a result, many attempts to implement this mode of FT-ICR MS operation have occurred. Examples of true cyclotron frequency measurements include detection of magnetron inter-harmonics of the reduced cyclotron frequency (i.e., the sidebands), trapping field-free (i.e., screened) ICR cells, and hyperbolic ICR cells with quadrupolar ion detection. More recently, ICR cells with spatially distributed ion clouds have demonstrated attractive performance characteristics for true cyclotron frequency ion detection. Here, we review the corresponding developments in FT-ICR MS over the past 40 years.     

Assessment of the austemperability of high-silicon cast steels through Jominy hardenability tests

The austemperability of seven high silicon cast steels with different alloy contents was characteri sed. The maximum round bar diameter that can be fully austempered changed from about 10?mm for an unalloyed steel to more than 70?mm for a low-alloy steel. The austemperability was calculated by applying a procedure based on a standard Jominy test and the characterisation of the microstructure along the Jominy sample. The method, which was validated experimentally, creates a relatively simple procedure to measure austemperability. Processing factors such as the ability of the salt bath to extract heat and the austempering temperature are accounted for the method. The metallographic study revealed the influence of microsegregation on hardenability, which is particularly important for cast steels.     

Mechanosynthesis of the multiferroic cubic spinel Co2MnO4: Influence of the calcination temperature

Multiferroic materials showing magnetoelectric coupling are required in various technological applications. Many synthetical approaches can be used to improve the magnetic and/or electrical properties, in particular when the materials exhibit cationic valence fluctuations, as in the Co₂MnO₄ cubic spinel. In this compound, Co and Mn ions are in competition at the tetrahedral and octahedral positions, depending on their various oxidation states. The Co₂MnO₄ was prepared following two techniques: by a soft chemical route based on a modified polymer precursor method, and by a mechanoactivation route. Both approaches yield polycrystalline powders, but their crystallites sizes and particles morphologies differ as a function of the calcination conditions. The magnetic characterization (ZFC/FC cycles, ordering temperatures, ferromagnetic coercive fields and saturation magnetizations) showed that the synthesis procedure influenced the physical properties of Co₂MnO₄ mainly through the size of the magnetic domains, which play an important role on the magnetic interactions between the Co/Mn cations.     

Boron-doped diamond anodic treatment of landfill leachate: evaluation of operating variables and formation of oxidation by-products

Landfill leachate with a low BOD/COD ratio was electrochemically oxidized by means of a boron-doped diamond anode. In addition to organic matter removal, this study addressed the issue of formation of both chlorinated organic compounds and nitrate ions as a result of organic matter and ammonia and/or organic nitrogen electro-oxidation in the presence of chloride ions. A factorial design methodology was implemented to evaluate the statistically important operating variables: treatment time (1-4 h), pH (5-8), current intensity (6.3-8.4 A) and addition of chloride (2500-4500 mg L⁻¹). The process was evaluated on COD, total nitrogen (TN) and colour removal, as well as on the formation of nitrate, nitrite and chlorinated organics. Of the four variables studied, treatment time and pH had a considerable influence on COD and colour removal. On the contrary, none of the variables had a significant effect on the elimination of TN for which an average removal of 61 mg L⁻¹ was obtained. The studied variables exhibited different effects on the four groups of organo-chlorinated compounds considered in this study, namely trihalomethanes (THMs), haloacetonitriles (HANs), haloketons (HKs) and 1,2-dichloroethane (DCA). Further analysis at more intense conditions, i.e. current intensity up to 18 A and reaction time up to 8 h revealed that high levels of decolourization (84%) could be achieved followed by low COD (51%) and ammonia (32%) removals. Apart from DCA, the concentration of chlorinated organics increased continuously with treatment time reaching values as high as 1.9 mg L⁻¹, 753 μg L⁻¹ and 431 μg L⁻¹ of THMs, HANs and HKs, respectively.     

Multivariate Analysis of Multiple Datasets: a Practical Guide for Chemical Ecology

Chemical ecology has strong links with metabolomics, the large-scale study of all metabolites detectable in a biological sample. Consequently, chemical ecologists are often challenged by the statistical analyses of such large datasets. This holds especially true when the purpose is to integrate multiple datasets to obtain a holistic view and a better understanding of a biological system under study. The present article provides a comprehensive resource to analyze such complex datasets using multivariate methods. It starts from the necessary pre-treatment of data including data transformations and distance calculations, to the application of both gold standard and novel multivariate methods for the integration of different omics data. We illustrate the process of analysis along with detailed results interpretations for six issues representative of the different types of biological questions encountered by chemical ecologists. We provide the necessary knowledge and tools with reproducible R codes and chemical-ecological datasets to practice and teach multivariate methods.