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Paper des Monats
- ja (2)
A thorough characterization of base materials is the prereq- uisite for further research. In this paper, the characterization data of the reference materials (CEM I 42.5 R, limestone pow- der, calcined clay and a mixture of these three components) used in the second funding phase of the priority program 2005 of the German Research Foundation (DFG SPP 2005) are presented under the aspects of chemical and min- eralogical composition as well as physical and chemical properties. The data were collected based on tests performed by up to eleven research groups involved in this cooperative program.
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.
A thorough characterization of starting materials is the precondition for further research, especially for cement, which contains various phases and presents quite a complex material for fundamental scientific investigation. In the paper at hand, the characterization data of the reference cement CEM I 42.5 R used within the priority program 2005 of the German Research Foundation (DFG SPP 2005) are presented from the aspects of chemical and mineralogical compositions as well as physical and chemical properties. The data were collected based on tests conducted by nine research groups involved in this cooperative program. For all data received, the mean values and the corresponding errors were calculated. The results shall be used for the ongoing research within the priority program.
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.
Environmental Risk Assessment of Treated Timber in Service - the Environment Focus Group Approach
(2000)
In the context of the Biocidal Products Directive (98/8/EC), and of the OECD work on wood preservatives, the Environment Focus Group (EFG), comprising 8 institutes and the European Wood Preservative Manufacturers Group, has been working on the environmental risk assessment of treated timber in service. A literature review of emissions from treated timber has revealed that very little existing data is usable for environmental risk assessment; the most relevant data are kinetic curves of emissions over time, which show clearly the non-linear emissions behaviour of treated wood over time. The EFG has suggested real exposure conditions for treated timber in the environment, and listed typical exposure scenarios. Five representative scenarios are characterised in detail, for use in the calculation of Predicted Environmental Concentrations (PECs). The existing methods to determine emissions from treated wood have been reviewed. Most existing experimental models cannot be used to predict environmental emissions. Monitoring of commodities in service has its specific constraints. Chemical analysis and ecotoxicity testing have also been reviewed and their relationship has been discussed. Principles for the design of experimental models, for the determination of emission fluxes from treated wood to the environment, have been established.
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.
Basic oxygen furnace slags (BOFS) are by-products of the steelmaking process. Several researchers have studied the production of Portland cement clinker and metallic iron from BOFS via a reductive treatment.
In this study, we applied a carbothermal reduction of BOFS in a technical-scale electric arc furnace and characterised the clinker-like products. Those clinker-like non-metallic products (NMPs) had a chemical and mineralogical composition comparable to clinker for ordinary Portland cement (OPC) and contained large elongated alite crystals as major component. The pure NMPs reacted more slowly and achieved a lower degree of hydration compared with commercial OPC.
If the reactivity of the products can be further increased by employing specific adaptations, it can be used as a full clinker substitute for OPC. Nevertheless, it is also an option to use the material without further modifications as a cement component or concrete addition, which contributes to the strength development in both cases.
Alkali-activated binders (AAB) can provide a clean alternative to conventional cement in terms of CO2 emissions. However, as yet there are no sufficiently accurate material models to effectively predict the AAB properties, thus making optimal mix design highly costly and reducing the attractiveness of such binders. This work adopts sequential learning (SL) in high-dimensional material spaces (consisting of composition and processing data) to find AABs that exhibit desired properties. The SL approach combines machine learning models and feedback from real experiments. For this purpose, 131 data points were collected from different publications. The data sources are described in detail, and the differences between the binders are discussed. The sought-after target property is the compressive strength of the binders after 28 days. The success is benchmarked in terms of the number of experiments required to find materials with the desired strength. The influence of some constraints was systematically analyzed, e.g., the possibility to parallelize the experiments, the influence of the chosen algorithm and the size of the training data set. The results show the advantage of SL, i.e., the amount of data required can potentially be reduced by at least one order of magnitude compared to traditional machine learning models, while at the same time exploiting highly complex information. This brings applications in laboratory practice within reach.
With 8% of man-made CO2 emissions, cement production is an important driver of the climate crisis. By using alkali-activated binders, part of the energy-intensive clinker production process can be dispensed. However, as numerous raw materials are involved in the manufacturing process here, the complexity of the materials increases by orders of magnitude. Finding a properly balanced binder formulation is like looking for a needle in a haystack. We have shown for the first time that artificial intelligence (AI)-based optimization of alkali-activated binder formulations can significantly accelerate research.
The "Sequential Learning App for Materials Discovery" (SLAMD) aims to accelerate practice transfer. With SLAMD, materials scientists have low-threshold access to AI through interactive and intuitive user interfaces. The value added by AI can be determined directly. For example, the CO2 emissions saved per ton of cement can be determined for each development cycle: the more efficient the AI optimization, the greater the savings.
Our material database already includes more than 120,000 data points of alternative binders and is constantly being expanded with new parameters. We are currently driving the enrichment of the data with a life cycle analysis of the building materials.
Based on a case study we show how intuitive access to AI can drive the adoption of techniques that make a real contribution to the development of resource-efficient and sustainable building materials of the future and make it easy to identify when classical experiments are more efficient.