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Subsurface granitic environments are scarce and poorly investigated. A multi-disciplinary approach was used to characterize the abundant moonmilk deposits and associated microbial communities coating the granite walls of the 16th Century Paranhos spring water tunnel in Porto city (north-west Portugal). It is possible that this study is the first record of moonmilk in an urban subsurface granitic environment. The morphology and texture, mineralogical composition, stable isotope composition and microbial diversity of moonmilk deposits have been studied to infer the processes of moonmilk formation. These whitish secondary mineral deposits are composed of very fine needle fibre calcite crystals with different morphologies and density. Calcified filaments of fungal hyphae or bacteria were distinguished by field emission scanning electron microscopy. Stable isotope analysis revealed a meteoric origin of the needle fibre calcite, with an important contribution of atmospheric CO2, soil respiration and HCO3 − from weathering of Ca-bearing minerals. The DNA-based analyses revealed the presence of micro-organisms related to urban contamination, including Actinobacteria, mainly represented by Pseudonocardia hispaniensis, Thaumarchaeota and Ascomycota, dominated by Cladosporium. This microbial composition is consistent with groundwater pollution and contamination sources of the overlying urban area, including garages, petrol stations and wastewater pipeline leakage, showing that the Paranhos tunnel is greatly perturbed by anthropogenic activities. Whether the identified micro-organisms are involved in the formation of the needle fibre calcite or not is difficult to demonstrate, but this study evidenced both abiotic and biogenic genesis for the calcite moonmilk in this subsurface granitic environment.
Recent DNA-based studies have shown that the built environment is surprisingly rich in fungi. These indoor fungi – whether transient visitors or more persistent residents – may hold clues to the rising levels of human allergies and other medical and building-related health problems observed globally. The taxo¬nomic identity of these fungi is crucial in such pursuits. Molecular identification of the built mycobiome is no trivial undertaking, however, given the large number of unidentified, misidentified, and technically compromised fungal sequences in public sequence databases. In addition, the sequence metadata required to make informed taxonomic decisions – such as country and host/substrate of collection – are often lacking even from reference and ex-type sequences. Here we report on a taxonomic annotation workshop (April 10–11, 2017) organized at the James Hutton Institute/University of Aberdeen (UK) to facilitate reproducible studies of the built mycobiome. The 32 participants went through public fungal ITS bar¬code sequences related to the built mycobiome for taxonomic and nomenclatural correctness, technical quality, and metadata availability. A total of 19,508 changes – including 4,783 name changes, 14,121 metadata annotations, and the removal of 99 technically compromised sequences – were implemented in the UNITE database for molecular identification of fungi (https://unite.ut.ee/) and shared with a range of other databases and downstream resources. Among the genera that saw the largest number of changes were Penicillium, Talaromyces, Cladosporium, Acremonium, and Alternaria, all of them of significant importance in both culture-based and culture-independent surveys of the built environment.
The steel–concrete interface (SCI) is known to influence corrosion of steel in concrete. However, due to the numerous factors affecting the SCI—including steel properties, concrete properties, execution, and exposure conditions—it remains unclear which factors have the most dominant impact on the susceptibility of reinforced concrete to corrosion. In this literature review, prepared by members of RILEM technical committee 262-SCI, an attempt is made to elucidate the effect of numerous SCI characteristics on chloride-induced corrosion initiation of steel in concrete. We use a method to quantify and normalize the effect of individual SCI characteristics based on different literature results, which allows comparing them in a comprehensive context. It is found that the different SCI characteristics have received highly unbalanced research attention. Parameters such as w/b ratio and cement type have been studied most extensively. Interestingly, however, literature consistently indicates that those parameters have merely a moderate effect on the corrosion susceptibility of steel in concrete. Considerably more pronounced effects were identified for (1) steel properties, including metallurgy, presence of mill scale or rust layers, and surface roughness, and (2) the moisture state. Unfortunately, however, these aspects have received comparatively little research attention. Due to their apparently strong influence, future corrosion studies as well as developments towards predicting corrosion initiation in concrete would benefit from considering those aspects. Particularly the working mechanisms related to the moisture conditions in microscopic and macroscopic voids at the SCI is complex and presents major opportunities for further research in corrosion of steel in concrete.
Most studies about the interaction of nanoparticles (NPs) with cells have focused on how the physicochemical properties of NPs will influence their uptake by cells. However, much less is known about their potential excretion from cells. However, to control and manipulate the number of NPs in a cell, both cellular uptake and excretion must be studied quantitatively. Monitoring the intracellular and extracellular amount of NPs over time (after residual noninternalized NPs have been removed) enables one to disentangle the influences of cell proliferation and exocytosis, the major pathways for the reduction of NPs per cell. Proliferation depends on the type of cells, while exocytosis depends in addition on properties of the NPs, such as their size. Examples are given herein on the role of these two different processes for different cells and NPs.
the joints throughout the structure. The flat units are constituted by butt welded flat plates, on which longitudinal T-welded reinforcements are placed to rigidize the structure. Among the different welding technologies, Hybrid Laser Arc Welding (HLAW) is becoming a mature process, profitable and highly productive. In addition, more innovative welding equipment are being developed nowadays, offering greater work flexibility, and raising expectations of achieving better quality, and economic viability. Another key point of HLAW to keep in mind is that structural distortions are reduced, resulting in decreasing the cost and time of straightening work.
In the present contribution, the influence of HLAW parameters on the quality of fillet joints of naval steel has been analysed. Experimental HLAW tests were performed with a high power disk laser to join EH36 naval steel plates, with a T configuration. The influence of different processing parameters has been analysed, as the laser power, welding speed, wire feed rate and the configuration of the HLAW processes (including head angle and laser/arc leading process). In addition, FEM simulations were carried out in order to estimate residual stresses and distortion of welded part. The distortion values provided by FEM presented excellent agreement with the measured experimental results. To evaluate the welds, non destructive tests including X-ray tests, metallographic analysis of cross sections, and microhardness mapping tests were performed.
Full penetration 8 mm T welds were obtained for the first time at an industrially applicable 2F position with a reasonable HLAW head angle, in one single step without sealing root, and using zero gap square groove Edge preparation. The present contribution presents welding rates up to 2.2 m/min for 2F T-joints of this steel thickness, a much higher processing velocity than previously reported for industrial applications.
Supplementary comparison study - measurement capabilities for the quantification of ethanol in water
(2022)
The accurate quantification of ethanol in water is essential for forensic applications such as blood and breath alcohol testing and for commercial applications such as the assessment of alcoholic beverages.
The intercomparison EURAMET.QM-S14 is part of a capacity building project named ALCOREF “Certified forensic alcohol reference materials”
that is running within the European Metrology Programme for Innovation and Research (EMPIR). The intercomparison should allow project partners and other interested National Metrology Institutes (NMIs) and Designated Institutes (DIs) to benchmark their analytical methods for the quantification of ethanol in water. The study plan was agreed by the European Association of National Metrology Institutes (EURAMET) Subcommittee Bio- and Organic Analysis (SCBOA) and the Organic Analysis Working Group (OAWG) of the Comité Consultatif pour la Quantité de Matière (CCQM) in February and April 2019, respectively. The intercomparison was coordinated by BAM. Two concentration levels relevant for the calibration and verification of evidential breath alcohol analysers were distributed to study participants. Fifteen institutes from 15 countries registered for the intercomparison and returned results. Participants mostly applied gas chromatography with flame ionisation detection (GC-FID) or mass spectroscopy (GC-MS), one participant used titrimetry and one participant employed a test bench for breath analyser calibration (“bubble train”). Participants did either in-house purity assessment of their commercial ethanol calibrants by Karl-Fischer titration, chromatographic methods, quantitative nuclear magnetic resonance spectroscopy (qNMR) and/or density measurements; or they used ethanol/water Certified Reference Materials (CRMs) from NMIs/DIs for calibration.
CCQM OAWG agreed to use a consensus value from participants results that utilizes the reported uncertainties as Key Comparison Reference Value (KCRV). The Gaussian Random effects model with Hierarchical Bayesian solution (HB-REM) is a reasonable approach in this case. The KCRVs and Degrees of Equivalence (DoEs) were calculated with the NIST consensus builder version 1.2 Hierarchical Bayes procedure.
Successful participation in the interlaboratory comparison has demonstrated the capabilities in determining the mass fraction of ethanol in aqueous matrices in the range 0.1 mg/g to 8 mg/g. Fourteen out of 15 participants have successfully quantified both samples, one participant successfully quantified only the lower-level (0.6 mg/g) sample.
A multiproxy approach to evaluate biocidal treatments on biodeteriorated majolica glazed tiles
(2016)
The Fishing House located on the grounds of the Marquis of Pombal Palace, Oeiras, Portugal, was built in the 18th century. During this epoch, Portuguese gardens, such as the one surrounding the Fishing House, were commonly ornamented with glazed wall tile claddings. Currently, some of these outdoor tile panels are covered with dark colored biofilms, contributing to undesirable aesthetic changes and eventually inducing chemical and physical damage to the tile surfaces. Phylogenetic analyses revealed that the investigated biofilms are mainly composed of green algae, cyanobacteria and dematiaceous fungi. With the aim of mitigating biodeterioration, four different biocides (TiO2 nanoparticles, Biotin® T, Preventol® RI 80 and Albilex Biostat®) were applied in situ to the glazed wall tiles. Their efficacy was monitored by visual examination, epifluorescence microscopy and DNA-based analysis. Significant changes in the microbial community composition were observed 4 months after treatment with Preventol® RI 80 and Biotin® T. Although the original community was inactivated after these treatments, an early stage of re-colonization was detected 6 months after the biocide application. TiO2 nanoparticles showed promising results due to their self-cleaning effect, causing the detachment of the biofilm from the tile surface, which remained clean 6 and even 24 months after biocide application.
xraylib 3.1.0
(2014)
Quantitative estimate of elemental composition by spectroscopic and imaging techniques using X-ray fluorescence requires the availability of accurate data of X-ray interaction with matter. Although a wide number of computer codes and data sets are reported in literature, none of them is presented in the form of freely available library functions which can be easily included in software applications for X-ray fluorescence. This work presents a compilation of data sets from different published works and an xraylib interface in the form of callable functions. Although the target applications are on X-ray fluorescence, cross sections of interactions like photoionization, coherent scattering and Compton scattering, as well as form factors and anomalous scattering functions, are also available.
xraylib provides access to some of the most respected databases of physical data in the field of x-rays. The core of xraylib is a library, written in ANSI C, containing over 40 functions to be used to retrieve data from these databases. This C library can be directly linked with any program written in C, C++ or Objective-C. Furthermore, the xraylib package contains bindings to several popular programming languages: Fortran 2003, Perl, Python, Java, IDL, Lua, Ruby, PHP and .NET, as well as a command-line utility which can be used as a pocket-calculator. Although not officially supported, xraylib has been reported to be useable from within Matlab and LabView.
The source code is known to compile and run on the following platforms: Linux, Mac OS X, Solaris, FreeBSD and Windows.
Development occurs on Github: http://github.com/tschoonj/xraylib
Downloads are hosted by the X-ray Micro-spectroscopy and Imaging research group of Ghent University: http://lvserver.ugent.be/xraylib
Version 3.1.0 release notes:
- Database of commonly used radionuclides for X-ray sources added (new API: GetRadioNuclideDataByName, GetRadioNuclideDataByIndex, GetRadioNuclideDataList and FreeRadioNuclideData)
- numpy Python bindings added, generated with Cython. Performance basically the same as the core C library. (suggested by Matt Newville)
- docstring support added to Python bindings (suggested by Matt Newville)
- Windows SDKs now have support for Python 3.4.
- Windows 64-bit SDK now comes with IDL bindings
- Confirmed support for LabView (thanks to Dariush Hampai!)
- Universal Intel 32/64 bit Framework built for Mac OS X
- Perl support for Debian/Ubuntu
- Several bugfixes: thanks to those that reported them!
XMI-MSIM 5.0
(2014)
XMI-MSIM is an open source tool designed for predicting the spectral response of energy-dispersive X-ray fluorescence spectrometers using Monte-Carlo simulations. It comes with a fully functional graphical user interface in order to make it as user friendly as possible. Considerable effort has been taken to ensure easy installation on all major platforms.
Development of this package was part of my PhD thesis. The algorithms were inspired by the work of my promotor Prof. Laszlo Vincze of Ghent University. Links to his and my own publications can be found in our manual.
A manuscript has been published in Spectrochimica Acta Part B that covers the algorithms that power XMI-MSIM. Please include a reference to this publication in your own work if you decide to use XMI-MSIM for academic purposes.
A second manuscript was published that covers our XMI-MSIM based quantification plug-in for PyMca. Soon information on using this plug-in will be added to the manual.
XMI-MSIM is released under the terms of the GPLv3.
Development occurs at Github: http://github.com/tschoonj/xmimsim
Downloads are hosted by the X-ray Micro-spectroscopy and Imaging research group of Ghent University: http://lvserver.ugent.be/xmi-msim
Version 5.0 release notes:
Changes:
1. Custom detector response function: build a own plug-in containing your own detector response function and load it at run-time to override the builtin routines. Instructions can be found in the manual.
2. Escape peak improvements: new algorithm is used to calculate the escape peak ratios based on a combined brute-force and variance-reduction approach. Ensures high accuracy even at high incoming photon energies and thin detector crystals. Downside: it's slower…
3. Removed maximum convolution energy option. Was a bit confusing anyway.
4. Number of channels: moved from simulation controls into input-file
5. Radionuclide support added: Now you can select one or more commonly used radionuclide sources from the X-ray sources widget.
6. Advanced Compton scattering simulation: a new alternative implementation of the Compton scattering has been implemented based on the work of Fernandez and Scot (http://dx.doi.org/10.1016/j.nimb.2007.04.203), which takes into account unpopulated atomic orbitals. Provides an improved simulation of the Compton profile, as well as fluorescence contributions due to Compton effect (extremely low!), but slows the code down considerably. Advanced users only. Default: OFF
7. Plot spectra before convolution in results
8. Windows: new Inno Setup installers. Contains the headers and import libraries
9. Windows: compilers changed to GCC 4.8.1 (TDM-GCC)
10. Windows: rand_s used to generate seeds on 64-bit version (requires Vista or later)
11. Windows: new gtk runtime for the 64-bit version (see also https://github.com/tschoonj/GTK-for-Windows-Runtime-Environment-Installer)
12. Mac OS X: compilers changed to clang 5.1 (Xcode) and gfortran 4.9.1 (MacPorts)
13. Original input-files from our 2012 publication (http://dx.doi.org/10.1016/j.sab.2012.03.011) added to examples
14. Updater performs checksum verification after download
15. X-ray sources last used values stored in preferences.ini
16. xmimsimdata.h5 modified: even bigger now...
Bugfixes:
1. Windows: support for usernames with unicode characters. Fixed using customized builds of HDF5. Thanks to Takashi Omori of Techno-X for the report!
2. Spectrum import from file fixes. Was never properly tested apparently
Note:
For those that compiled XMI-MSIM from source: you will need to regenerate the xmimsimdata.h5 file with xmimsim-db. Old versions of this file will not work with XMI-MSIM 5.0.