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This paper presents the results of an interlaboratory study of the rheological properties of cement paste and ultrasound gel as reference substance. The goal was to quantify the comparability and reproducibility of measurements of the Bingham parameters yield stress and plastic viscosity when measured on one specific paste composition and one particular ultrasound gel in different laboratories using different rheometers and measurement geometries.
The procedures for both in preparing the cement paste and carrying out the rheological measurements on cement paste and ultrasound gel were carefully defined for all of the study’s participants. Different conversion schemes for comparing the results obtained with the different measurement setups are presented here and critically discussed. The procedure proposed in this paper ensured a reasonable comparability of the results with a coefficient of variation for the yield stress of 27% and for the plastic viscosity of 24%, despite the individual measurement series’ having been performed in different labs with different rheometers and measurement geometries.
Two types of cements were selected as the reference cement in the priority program 2005 of the German Research Foundation (DFG SPP 2005). A thorough characterization of CEM I 42.5 R has been made in a recent publication. In this paper, the characterization data of the other reference cement CEM III/A 42.5 N are presented from the aspects of chemical and mineralogical compositions as well as physical and chemical properties. The characterization data of the slag, which is the second main constituent of this specific cement besides the clinker, are presented independently. For all data received, the mean values and the corresponding errors were calculated. The data shall be used for the ongoing research within the priority program. Also, researchers from outside this priority program can benefit from these data if the same materials are used.
The present work shows results on elemental distribution analyses in Cu(In,Ga)Se2 thin films for solar cells performed by use of wavelength-dispersive and energy-dispersive X-ray spectrometry (EDX) in a scanning electron microscope, EDX in a transmission electron microscope, X-ray photoelectron, angle-dependent soft X-ray emission, secondary ion-mass (SIMS), time-of-flight SIMS, sputtered neutral mass, glow-discharge optical emission and glow-discharge mass, Auger electron, and Rutherford backscattering spectrometry, by use of scanning Auger electron microscopy, Raman depth profiling, and Raman mapping, as well as by use of elastic recoil detection analysis, grazing-incidence X-ray and electron backscatter diffraction, and grazing-incidence X-ray fluorescence analysis. The Cu(In,Ga)Se2 thin films used for the present comparison were produced during the same identical deposition run and exhibit thicknesses of about 2 µm. The analysis techniques were compared with respect to their spatial and depth resolutions, measuring speeds, availabilities, and detection limits.
Whereas the characterization of nanomaterials using different analytical techniques is often highly automated and standardized, the sample preparation that precedes it causes a bottleneck in nanomaterial analysis as it is performed manually. Usually, this pretreatment depends on the skills and experience of the analysts. Furthermore, adequate reporting of the sample preparation is often missing. In this overview, some solutions for techniques widely used in nano-analytics to overcome this problem are discussed. Two examples of sample preparation optimization by au-tomation are presented, which demonstrate that this approach is leading to increased analytical confidence. Our first example is motivated by the need to exclude human bias and focuses on the development of automation in sample introduction. To this end, a robotic system has been de-veloped, which can prepare stable and homogeneous nanomaterial suspensions amenable to a variety of well-established analytical methods, such as dynamic light scattering (DLS), small-angle X-ray scattering (SAXS), field-flow fractionation (FFF) or single-particle inductively coupled mass spectrometry (sp-ICP-MS). Our second example addresses biological samples, such as cells exposed to nanomaterials, which are still challenging for reliable analysis. An air–liquid interface has been developed for the exposure of biological samples to nanomaterial-containing aerosols. The system exposes transmission electron microscopy (TEM) grids under reproducible conditions, whilst also allowing characterization of aerosol composition with mass spectrometry. Such an approach enables correlative measurements combining biological with physicochemical analysis. These case studies demonstrate that standardization and automation of sample preparation setups, combined with appropriate measurement processes and data reduction are crucial steps towards more reliable and reproducible data.
ACEnano is an EU-funded project which aims at developing, optimising and validating methods for the detection and characterisation of nanomaterials (NMs) in increasingly complex matrices to improve confidence in the results and support their use in regulation. Within this project, several interlaboratory comparisons (ILCs) for the determination of particle size and concentration have been organised to benchmark existing analytical methods. In this paper the results of a number of these ILCs for the characterisation of NMs are presented and discussed. The results of the analyses of pristine well-defined particles such as 60 nm Au NMs in a simple aqueous suspension showed that laboratories are well capable of determining the sizes of these particles. The analysis of particles in complex matrices or formulations such as consumer products resulted in larger variations in particle sizes within technologies and clear differences in capability between techniques. Sunscreen lotion sample analysis by laboratories using spICP-MS and TEM/SEM identified and confirmed the TiO2 particles as being nanoscale and compliant with the EU definition of an NM for regulatory purposes. In a toothpaste sample orthogonal results by PTA, spICP-MS and TEM/SEM agreed and stated the TiO2 particles as not fitting the EU definition of an NM. In general, from the results of these ILCs we conclude that laboratories are well capable of determining particle sizes of NM, even in fairly complex formulations.
As part of the development of a library of accurate and efficient methods for measurement of nanoparticle properties, we develop and optimize a method for the efficient analysis of nanoparticle size distribution from suspensions via microprinting and digital analysis of electron microscopy (SEM and TEM) images, with the ultimate aim of automated quantitative concentration analysis (calculated from drop volume). A series of different nanoparticle suspensions (gold, latex, and SiO2 in varying sizes and concentrations) were printed onto TEM grids in a 4 x 4 array in the concentration range 7x10^8 to 1x10^11 nanoparticles/mL and imaged with SEM. Concentrations and printing conditions (temperature, relative humidity) were varied in order to minimize the coffee-ring effect.
This overview article provides insight into how to apply Raman spectroscopy in combination with a confocal, optical microscope setup on polycrystalline material systems, in order to obtain quantitative information on phase distribution, grain sizes, crystal orientations and microstrain. Although the present work uses Cu(In,Ga)(S,Se)₂ absorber layers in corresponding thin-film solar cells as a model system to demonstrate the capabilities of Raman microspectroscopy, the approaches discussed may be applied to any organic or inorganic, polycrystalline materials system.
The improved Monte-Carlo (MC) method for standard-less analysis in laser induced breakdown spectroscopy (LIBS) is presented. Concentrations in MC LIBS are found by fitting model-generated synthetic spectra to experimental spectra. The current version of MC LIBS is based on the graphic processing unit (GPU) computation and reduces the analysis time down to several seconds per spectrum/sample. The previous version of MC LIBS which was based on the central processing unit (CPU) computation requested unacceptably long analysis times of 10's minutes per spectrum/sample. The reduction of the computational time is achieved through the massively parallel computing on the GPU which embeds thousands of co-processors. It is shown that the number of iterations on the GPU exceeds that on the CPU by a factor > 1000 for the 5-dimentional parameter space and yet requires > 10-fold shorter computational time. The improved GPU-MC LIBS outperforms the CPU-MS LIBS in terms of accuracy, precision, and analysis time. The performance is tested on LIBS-spectra obtained from pelletized powders of metal oxides consisting of CaO, Fe2O3, MgO, and TiO2 that simulated by-products of steel industry, steel slags. It is demonstrated that GPU-based MC LIBS is capable of rapid multi-element analysis with relative error between 1 and 10's percent that is sufficient for industrial applications (e.g. steel slag analysis). The results of the improved GPU-based MC LIBS are positively compared to that of the CPU-based MC LIBS as well as to the results of the standard calibration-free (CF) LIBS based on the Boltzmann plot method.
Laser-induced breakdown spectroscopy (LIBS) is a fast and versatile technique for (semi) quantitative element analysis of solids, liquids, gases, and particulate matter. The LIBS method is used for optical sensing in various branches of industrial production. In the contribution we review some of our recent results on LIBS analysis of slags from secondary metallurgy in industrial steel making. Major oxides in steel slags are measured at-line and after homogenization using a calibration-free (CF) method. Two approaches for CF analysis based on the Boltzmann plot method and on the calculation of synthetic spectra are compared for the analysis of quaternary oxides. We also present the research in cooperation with our industrial partners in the process-analytical chemistry network PAC.