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Our ability to produce and transform engineered materials over the past 150 years is responsible for our high standards of living today, especially in the developed economies. Yet, we must carefully think of the effects our addiction to creating and using materials at this fast rate will have on the future generations. The way we currently make and use materials detrimentally affects the planet Earth, creating many severe environmental problems. It affects the next generations by putting in danger the future of economy, energy, and climate. We are at the point where something must drastically change, and it must change NOW. We must create more sustainable materials alternatives using natural raw materials and inspiration from Nature while making sure not to deplete important resources, i.e. in competition with the food chain supply. We must use less materials, eliminate the use of toxic materials and create a circular materials economy where reuse and recycle are priorities. We must develop sustainable methods for materials recycling and encourage design for disassembly. We must look across the whole materials life cycle from raw resources till end of life and apply thorough life cycle assessments based on reliable and relevant data to quantify sustainability.
Due to the extremely high specific surface area of nanoparticles and corresponding potential for adsorption, the results of surface analysis can be highly dependent on the history of the particles, particularly regarding sample preparation and storage. The sample preparation method has, therefore, the potential to have a significant influence on the results. This report describes an interlaboratory comparison (ILC) with the aim of assessing which sample preparation methods for ToF-SIMS analysis of nanoparticles provided the most intra- and interlaboratory consistency and the least amount of sample contamination. The BAM reference material BAM-P110 (TiO2 nanoparticles with a mean Feret diameter of 19 nm) was used as a sample representing typical nanoparticles. A total of 11 participants returned ToF-SIMS data,in positive and (optionally) negative polarity, using sample preparation methods of “stick-and-go” as well as optionally “drop-dry” and “spin-coat.” The results showed that the largest sources of variation within the entire data set were caused by adventitious hydrocarbon contamination or insufficient sample coverage, with the spin-coating protocol applied in this ILC showing a tendency toward insufficient sample coverage; the sample preparation method or the participant had a lesser influence on results.
Ever more stringent regulations on greenhouse gas emissions from transportation motivate efforts to revisit materials used for vehicles. High-strength aluminium alloys often used in aircrafts could help reduce the weight of automobiles, but are susceptible to environmental degradation. Hydrogen ‘embrittlement’ is often indicated as the main culprit; however, the exact mechanisms underpinning failure are not precisely known: atomic-scale analysis of H inside an alloy remains a challenge, and this prevents deploying alloy design strategies to enhance the durability of the materials. Here we performed near-atomic-scale analysis of H trapped in second-phase particles and at grain boundaries in a high-strength 7xxx Al alloy. We used these observations to guide atomistic ab initio calculations, which show that the co-segregation of alloying elements and H favours grain boundary decohesion, and the strong partitioning of H into the second-phase particles removes solute H from the matrix, hence preventing H embrittlement. Our insights further advance the mechanistic understanding of H-assisted embrittlement in Al alloys, emphasizing the role of H traps in minimizing cracking and guiding new alloy design.
Synthesis of terpene-poly(ethylene oxide)s by t-BuP4-promoted anionic ring-opening polymerization
(2012)
Terpene alcohols (menthol, retinol, cholesterol, and betulin) together with the phosphazene base t-BuP4 were used as initiating systems for anionic ring-opening polymerization of ethylene oxide. The polymerizations were conducted in a controlled manner with the initial molar ratio of t-BuP4 to hydroxyl groups of 0.01–0.2, yielding a series of biohybrid polymers comprising terpene entities and poly(ethylene oxide) (PEO) chains with low polydispersities and tunable compositions (57–87 wt% of PEO). Samples were characterized by NMR and UV/visible spectroscopy, MALDI-TOF mass spectrometry, and size exclusion chromatography; thermal properties were studied by differential scanning calorimetry. The concept of this study opens a new toolbox of terpene-based biohybrid polymers with variable properties and functions.
The present study documents the results of an
inter-disciplinary model project that was planned with the
aim of developing an innovative winter covering system
for marble statuaries located on the Schlossbru¨cke (Berlin).
Such a system would need to fulfil the various requirements
for structural stability, aesthetics, climate and practical
use. This applied research represents the first complex
scientific study of the sustainability of a winter covering
system. The study is characterised by the use of complex
scientific instruments such as special laboratory analysis
and numerical simulation tools. The interaction between
the environment and the artefacts in connection with the
innovative winter covering structures were studied by
extensive climatic monitoring.
Selective Electron Beam Melting (SEBM) is a promising powder bed Additive Manufacturing technique for near-net-shape manufacture of high-value titanium components. However without post-manufacture HIPing the fatigue life of SEBM parts is currently dominated by the presence of porosity. In this study, the size, volume fraction, and spatial distribution of the pores in model samples have been characterised in 3D, using X-ray Computed Tomography, and correlated to the process variables. The average volume fraction of the pores (b0.2%) was measured to be lower than that usually observed in competing processes, such as selective laser melting, but a strong relationship was found with the differentbeamstrategies used to contour ,and infill by hatching, a part section. The majority of pores were found to be small spherical gas pores, concentrated in the infill hatched region; this was attributed to the lower energy density and less focused beam used in the infill strategy allowing less opportunity for gas bubbles to escape the melt pool. Overall, increasing the energy density or focus of the beam was found to correlate strongly to a reduction in the level of gas porosity. Rarer irregular shaped pores were mostly located in the contour region and have been attributed to a lack of fusion between powder particles.
The RILEM TC 281–CCC "Carbonation of concrete with supplementary cementitious materials" is studying the effect of supplementary cementitious materials (SCMs) on natural and accelerated carbonation of blended cements. In this context a large database with carbonation depths over time has been set-up with currently 910 concrete and mortar mixes, which have been tested at different curing conditions. The collected data includes information on chemical composition and physical properties of the raw materials, on mix-design, compressive strength, curing and carbonation testing conditions. Data for a large variety of binders (with up to 94 % SCMs) and mix designs (with w/b ratios ranging from 0.26 to 0.75) were collected from the literature and unpublished material testing projects. Natural carbonation was recorded for several years in many cases, and indoor as well as outdoor results are included. However, in this contribution only lab conditions are considered. Carbonation kinetics are investigated considering the binder composition and mix design. Furthermore, the accuracy of accelerated carbonation testing is evaluated. As described in literature, the w/CaOreactive-ratio is a decisive factor for carbonation resistance but curing and exposure conditions influence the results as well. Even with identical test conditions, the scatter is very large.
Grain boundaries (GBs) are planar lattice defects that govern the properties of many types of polycrystalline materials. Hence, their structures have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the atomic length scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability to quantify chemical characteristics at near-atomic scale. Using APT data sets, we present here a machine-learning-based approach for the automated quantification of chemical features of GBs. We trained a convolutional neural network (CNN) using twenty thousand synthesized images of grain interiors, GBs, or triple junctions. Such a trained CNN automatically detects the locations of GBs from APT data. Those GBs are then subjected to compositional mapping and analysis, including revealing their in-plane chemical decoration patterns. We applied this approach to experimentally obtained APT data sets pertaining to three case studies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we extracted GB specific segregation features as a function of misorientation and coincidence site lattice character. Secondly, we revealed interfacial excesses and in-plane chemical features that could not have been found by standard compositional analyses. Lastly, we tracked the temporal evolution of chemical decoration from early-stage solute GB segregation in the dilute limit to interfacial phase separation, characterized by the evolution of complex composition patterns. This machine-learning-based approach provides quantitative, unbiased, and automated access to GB chemical analyses, serving as an enabling tool for new discoveries related to interface thermodynamics, kinetics, and the associated chemistry-structure-property relations.