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Organisationseinheit der BAM
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- 1.1 Anorganische Spurenanalytik (1)
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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.
Interfacing artificial devices with the human brain is the central goal of neurotechnology. Yet, our imaginations are often limited by currently available paradigms and technologies. Suggestions for brain−machine interfaces have changed over time, along with the available technology.
Mechanical levers and cable winches were used to move parts of the brain during the mechanical age. Sophisticated electronic wiring and remote control have arisen during the electronic age, ultimately leading to plug-and-play computer interfaces. Nonetheless, our brains are so complex that these visions, until recently, largely remained unreachable dreams. The general problem, thus far, is that most of our technology is mechanically and/or electrically engineered, whereas the brain is a living, dynamic entity. As a result, these worlds are difficult to interface with one another. Nanotechnology, which encompasses engineered solid-state objects and integrated circuits, excels at small length scales of single to a few hundred nanometers and, thus, matches the sizes of biomolecules, biomolecular assemblies, and parts of cells. Consequently, we envision nanomaterials and nanotools as opportunities to interface with the brain in alternative ways. Here, we review the existing literature on the use of nanotechnology in brain−machine interfaces and look forward in discussing perspectives and limitations based on the authors’ expertise across a range of complementary disciplines from neuroscience, engineering, physics, and chemistry to biology and medicine, computer science and mathematics, and social science and jurisprudence. We focus on nanotechnology but also include information from related fields when useful and complementary.
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.
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.
ln den letzten Jahren wurden diverse Prüfverfahren und Methoden entwickelt, um die Leistungsfähigkeit von Beton bzw. Bindemittel hinsichtlich des Widerstands gegenüber betonaggressiven Einwirkungen beurteilen zu können. Ein wesentliches Ziel von Prüfverfahren zur Beurteilung der Dauerhaftigkeit von Baustoffen ist, die im Laufe der Nutzungsdauer zu erwartenden Einwirkungen auf den Baustoff realitätsnah abzubilden. Dazu sind die Prüfparameter auf die im jeweiligen Anwendungsbereich vorherrschenden Umgebungs- und Randbedingungen anzupassen. Daneben sind auch die Bewertungskriterien individuell unter Berücksichtigung der im Einzelfall geforderten Nutzungsdauer eines Bauwerks festzulegen. Eine systematische Zuordnung von adäquaten Prüfverfahren bzw. Prüfparametem und Bewertungskriterien für die verschiedenen Anwendungsbereiche im Betonbau, in denen betonaggressive Einwirkungen auftreten, ist mit Ausnahme von Einzelfällen bislang nicht vorhanden. Daneben stellt sich in der Praxis immer wieder das Problem, das Angriffspotenzial der Umgebung eines Bauwerks (prüftechnisch) zu erfassen und zu bewerten.
Der Hauptausschuss Baustofftechnik des DBV hat daher den Arbeitskreis „Chemischer Angriff auf Beton“ beauftragt, dieses Merkblatt zu erarbeiten, das auf Basis von einschlägigen Erfahrungen der Baupraxis und wissenschaftlichen Erkenntnissen Grundsätze und bewährte Methoden zur Beurteilung der Einwirkungs- und Widerstandsseite beim chemischen Angriff auf Beton aufzeigt und diesbezüglich Hinweise für verschiedene Anwendungsbereiche im Betonbau liefert.
Chemischer Angriff auf Betonbauwerke - Bewertung des Angriffsgrads und geeignete Schutzprinzipien
(2014)
In verschiedenen Bereichen des Betonbaus ist Beton einem chemischen Angriff von außen ausgesetzt, der die Dauerhaftigkeit des betreffenden Bauteils bzw. des gesamten Bauwerks beeinträchtigen kann. Für einen chemischen Angriff in natürlichen Böden und Grundwässern bestehen normative Vorgaben in DIN 4030-1 [R10] bzw. DIN 1045-2 [R19], um den Angriffsgrad anhand der Art und Konzentration der angreifenden Stoffe in Expositionsklassen XA1 (schwach angreifend) bis XA3 (stark angreifend) einzustufen. Hinsichtlich weiterer maßgeblicher Einflussfaktoren wird an dieser Stelle nicht näher differenziert, ob und inwieweit die Regelungen für Böden und Grundwässer auch auf andere Bereiche mit chemischem Angriff auf Beton zu übertragen sind, ist stets im Einzelfall zu überprüfen. Ebenfalls sind die zu treffenden Maßnahmen über die Vorgaben der DIN EN 206-1/DIN 1045-2 [R19] hinaus anwendungsspezifisch festzulegen. Der Hauptausschuss Baustofftechnik (HABT) des DBV hat daher den Arbeitskreis "Chemischer Angriff auf Beton" beauftragt, dieses Merkblatt zu erarbeiten, das Hilfestellung bei der Bewertung einer projektspezifischen chemischen Angriffssituation auf Beton sowie bei der Planung und Ausführung von Schutzprinzipien leisten soll.
The RILEM technical committee TC 247-DTA ‘Durability Testing of Alkali-Activated Materials’ conducted a round robin testing programme to determine the validity of various durability testing methods, originally developed for Portland cement based-concretes, for the assessment of the durability of alkali-activated concretes. The outcomes of the round robin tests evaluating sulfate resistance, alkali-silica reaction (ASR) and freeze–thaw resistance are presented in this contribution. Five different alkali-activated concretes, based on ground granulated blast furnace slag, fly ash, or metakaolin were investigated. The extent of sulfate damage to concretes based on slag or fly ash seems to be limited when exposed to an Na2SO4 solution. The mixture based on metakaolin showed an excessive, very early expansion, followed by a dimensionally stable period, which cannot be explained at present. In the slag-based concretes, MgSO4 caused more expansion and visual damage than Na2SO4; however, the expansion limits defined in the respective standards were not exceeded. Both the ASTM C1293 and RILEM AAR-3.1 test methods for the determination of ASR expansion appear to give essentially reliable identification of expansion caused by highly reactive aggregates. Alkali-activated materials in combination with an unreactive or potentially expansive aggregate were in no case seen to cause larger expansions; only the aggregates of known very high reactivity were seen to be problematic. The results of freeze–thaw testing (with/without deicing salts) of alkali-activated concretes suggest an important influence of the curing conditions and experimental conditions on the test outcomes, which need to be understood before the tests can be reliably applied and interpreted.
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model—and its qualitative and at times quantitative accuracy—on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or “foundation” model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.
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.
This study uses conventional 87Sr/86Sr and 143Nd/144Nd isotope and interelement ratios of Ca, Sr, K, Mn, Mg and Ti as fingerprints for provenancing ordinary Portland cements (OPC). Herein, the first database of Sr and Nd isotope ratios investigated in OPCs, stemming from 29 cement plants located worldwide, was created. The results show that the Sr isotope ratios of OPCs are higher than those of seawater from the observed geological period. The spread of 143Nd/144Nd in OPCs is not as large as the spread for 87Sr/86Sr isotope ratios. However, the combination of both Sr and Nd isotope ratios provides the potential for distinguishing between cements of different production sites. Most of the OPCs investigated have measurable differences in their 87Sr/86Sr and 143Nd/144Nd isotope ratios, which can be employed as a valuable analytical fingerprinting tool. In the case of equivocal results, divisive hierarchical clustering was employed to help overcome this issue. The construction of geochemical profiles allowed the computing of suitably defined distances between cements and clustering them according to their chemical similarity. By applying this methodology, successful fingerprinting was achieved in 27 out of the 29 ordinary Portland cements that were analysed.
