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High-Entropy Thin Films Based on Al-Cr-Nb-Ta-Ti
Get PDFHigh-entropy alloy (HEA) thin films based on the equimolar Al-Cr-Nb-Ta-Ti system were deposited using magnetron sputtering. Without substrate heating, the films exhibited an amorphous structure with an Al0.12Cr0.21Nb0.23Ta0.28Ti0.16 composition. Partial crystallization was induced by increasing the substrate temperature, improving the hardness from 11.0 GPa to 14.0 GPa at 600 °C, while the composition is almost unchanged. Further vacuum annealing resulted in exceptionally high hardness reaching 19.1 GPa. X-ray diffraction revealed the formation of a body-centered cubic (bcc) solid-solution phase, along with peaks attributed to either a hexagonally close-packed (hcp) phase and Laves-type phases. After extended vacuum annealing at 800 °C, films deposited at lower substrate temperatures predominantly exhibited a bcc structure, whereas those synthesized at higher temperatures showed stronger contributions of hcp and Laves-type phases. The oxidation behavior of the films during annealing in ambient air was consistent with previously studied high-entropy (Al,Cr,Nb,Ta,Ti)-based carbide and nitride thin films, primarily leading to the formation of a rutile-structured oxide phase, with minor contributions from other oxide phases. Oxide scale breakdown occurred at 900 °C but was significantly improved by the addition of Si (~13 at.%). Cross-sectional imaging revealed porosity and blister formation at higher temperature as oxidation accelerates, primarily due to selective oxidation, the formation of Ta and Nb pentoxide phases, associated stresses, and oxide volatility. Elemental mapping of the oxide cross-sections further highlighted the critical role of Cr in the oxidation process
CasaMatic: Performance-Based Building Generation via Conditional Autoencoders
No full textThis study demonstrates the integration of generative artificial intelligence and building energy simulations to enhance design exploration through inverse design. Using a Conditional Autoencoder (CAE), a type of neural networks, our workflow reverses the traditional forward design process, allowing users to generate building geometries based on design conditions ranging from energy efficiency to different climate scenarios. We used a parametric model to generate the dataset, on which a CAE, comprising an encoder and a decoder, was trained and validated through the AI-extended Design (AIXD) toolbox. During inference, the decoder can be used to generate diverse design options based on requested targets (e.g. low cooling loads for a specific floor-area), while the encoder works as a surrogate model to estimate the performance gap of suggested designs. We applied the model to different case studies in Zürich, successfully generating diverse design alternatives under different boundary conditions. The trained model consistently met heating and cooling targets across different requests such as varying warming scenarios and plot configurations. In out-of-distribution and highly dimensional requests, generation capacity was degraded but could still provide insightful designs. Key challenges ahead include achieving higher degrees of control in querying inverse designs with important architectural and urban constraints, thus avoiding the generation of seemingly random designs, and expanding relevant dimensions such as material compositions and environmental impacts. This study shows the potential of generative approaches to tackle the design of energy efficient buildings in challenging and varied scenarios, underlining the potential of deep learning and AI in the field of architecture
„The best solvent is no solvent – exploring the potential of mechanochemical organic synthesis”
No full textIn recent years, mechanochemistry has revived as an important method in modern synthetic chemistry.[1] Especially en route to more sustainable and environmentally benign chemical processes, mechanochemical transformations are privileged,[2] since this approach offers important advantages: the ability to conduct conventional synthetic reactions without the need for solvents coupled with rapid reaction kinetics. These unique features make mechanochemistry highly attractive for both industrial applications and fundamental research across diverse fields, including material science, inorganic chemistry, and organic chemistry.
Hence, we started a program to investigate the possibilities for carrying out fundamental organic transformations under solvent free conditions using a mechanochemical setup. Herein we report our results on mechanochemical Tsuji-Trost allylations,[3] Wittig reactions,[4] C(sp3)-H functionalization,[5] and Diels-Alder reactions as well as multistep sequences thereof.
1 J. L. Howard, Q. Cao, D. L. Browne, Chemical Science 2018, 9, 3080.
2 E. Colacino, F. Garcia, Mechanochemistry and Emerging Technologies for Sustainable Chemical Manufacturing, CRC Press, 2023, ISBN: 1000891623, 9781000891621
3 J. Templ, M. Schnürch, Angew. Chem. Int. Ed. 2024, 63, e202314637
4 J. Templ, M. Schnürch, Angew. Chem. Int. Ed. 2024, 63, e202411536.
5 N. K. Narayanan, M. Schnürch, ChemCatChem 2024, 0, e20240161
Postcarrollian gravity
No full textWe construct postcarrollian gravity models in two, three, and four spacetime dimensions by applying algebraic expansion methods. As a byproduct, we present the most general postcarrollian 2d dilaton gravity model, construct its solutions and discuss some boundary aspects, including Schwarzian-type boundary actions. In 3d, we propose Brown-Henneaux-like boundary conditions, generalizing a corresponding Carrollian analysis, and derive the postcarrollian asymptotic symmetry algebra with its central extensions
Epoxide-functionalized hyperbranched polyethylene and its applications in functional polymer synthesis and hot lithography photopolymer toughening
No full textThe copolymerization behaviour of four epoxide-containing ene-functionalized monomers with ethene using palladium α-diimine catalyst was investigated. The linker separating oxirane and double bond played a crucial role in the process. High molar mass copolymers of ethene rich in glycidyl 10-undecenoate were obtained without a significant loss of catalyst activity. Allyl glycidyl ether was able to form copolymers with high comonomer incorporation as well, however, the copolymer molar mass and catalyst activity decreased severely. Therefore, we tuned the reaction conditions in order to achieve a better compromise between comonomer incorporation and product molar mass. Adjustments of reaction temperature, ethene pressure and used solvent were shown to impact the copolymer properties. Furthermore, the prepared copolymers were used in subsequent post-polymerization modifications. We have demonstrated that despite the steric hinderance of the polymer chain, pendent epoxide rings can be reacted with acids, O, C, N and Snucleophiles quantitatively to prepare hyperbranched molecules with a diverse range of functional groups. Finally, the prepared epoxide-functionalized hyperbranched polyethylene was tested as rubber toughening agent of brittle photopolymers revealing its potential for hot lithography 3D printing
Unifying Quantum-Well And Collector Space-Charge Dynamics In Resonant-Tunneling-Diodes: Impact On Maximum Oscillation Frequency
No full textWe present a simple and general model for a small-signal admittance of double-barrier resonant-tunneling-diodes (RTDs). The model accounts for both the effect of traveling electrons in the RTD collector spacer and the dynamics of the electron charge in the RTD quantum well. Using the model, we evaluate how the electron travel time through the collector influences the maximum oscillation frequency of state-of-the-art RTDs. The analysis shows that the impact of the traveling electrons is more pronounced in RTDs with high current density, and that a shorter collector spacer is advantageous for high-frequency RTD oscillators. However, if electrons travel ballistically through the spacer, their effect is nearly negligible