Karlsruhe Institute of Technology

Nonlinear optics is essential for many recent photonic technologies. Here, we introduce a novel multi‐scale approach to simulate the nonlinear optical response of molecular nanomaterials combining ab initio quantum‐chemical and classical Maxwell‐scattering computations. In this approach, the first hyperpolarizability tensor is computed with time‐dependent density‐functional theory and incorporated into a multi‐scattering formalism that considers the optical interaction between neighboring molecules. Such incorporation is achieved by a novel object: the Hyper‐Transition(T)‐matrix. With this object at hand, the nonlinear optical response from single molecules and also from entire photonic devices can be computed, including the full tensorial and dispersive nature of the optical response of the molecules, as well as the optical interaction between different molecules as, for example, in the lattice of a molecular crystal. To demonstrate the applicability of our novel approach, the generation of a second‐harmonic signal from a thin film of an Urea molecular crystal is computed and compared to more traditional simulations. Furthermore, an optical cavity is designed, which enhances the second‐harmonic response of the molecular film up to more than two orders of magnitude. Our approach is highly versatile and accurate and can be the working horse for the future exploration of nonlinear photonic molecular materials in structured photonic environments. This article is protected by copyright. All rights reserved

In this chapter, we complement the discussion of three major themes of Fourier analysis that we have studied in the previous Volumes.

The present chapter is one of the central ones of this book project. Maximal regularity provides a link between the general theory of operator-valued singular integrals and the theory of H∞-functional calculus with the regularity theory for evolution equations.

As we have seen in the preceding sections, in the context of inhomogeneous linear evolution equations, maximal regularity enables one to set up an isomorphism between the space of data (initial value and inhomogeneity) and the solution space.

The mapping properties of T will of course heavily depend on the assumptions made on the kernel K that we will discuss in more detail in this chapter.

Before addressing this question for the Calderón{Zygmund type operators of the kind studied in Chapter 11, we investigate a number of related objects in a simpler dyadic model. Besides serving as an introduction to some of the key techniques, it turns out that these dyadic operators can be, and will be, also used as building blocks of the proper singular integral operators towards the end of the chapter.

In this chapter we address a couple of topics in the theory of H∞-calculus centering around the question what can be said about an operator of the form A+B when A and B have certain “good” properties such as being (R-)sectorial or admitting a bounded H∞-calculus.

This chapter presents an in-depth study of several classes of vector-valued function spaces defined by smoothness conditions.

In this chapter we address two strongly interwoven topics: How to verify the boundedness of the H∞-calculus of an operator and how to represent and estimate its fractional powers. For concrete operators such as the Laplace operator or elliptic partial differential operators, the fractional domain spaces can often be identifed with certain function spaces considered in Chapter 14 and the imaginary powers of the operator are related to singular integral and pseudo-differential operators treated in Chapters 11 and 13.

The Powder Aerosol Deposition method (PAD) is a process to manufacture ceramic films completely at room temperature. Since the first reports by Akedo in the late 1990s, much research has been conducted to reveal the exact mechanism of the deposition process. However, it is still not fully understood. We tackled this challenge using core shell particles. Two coated oxides, Al 2 O 3 core with a SiO 2 shell and LiNi 0.6 Mn 0.2 Co 0.2 O 2 core with a LiNbO 3 shell, were investigated. Initially, the element ratios Al:Si and Ni:Nb of the powder were determined by EDX. In a second step, the change in the element ratios of Al:Si and Ni:Nb after deposition were investigated. The element ratios from powder to film strongly shift towards the shell‐elements, indicating that the particles fracture and only the outer parts of the particles are deposited. In the last step, we investigated cross‐sections of the deposited films with STEM combined with EDX and an EsB detector to unveil the element distribution within the film itself. Therefore, the following overall picture emerges: particles impact on the substrate or on previously deposited particle, fracture, and only a small part of the impacting particles that originate from the outer part of the impacting particle gets deposited. This article is protected by copyright. All rights reserved

Large‐area processing of perovskite semiconductor thin‐films is complex and evokes unexplained variance in quality, posing a major hurdle for the commercialization of perovskite photovoltaics. Advances in scalable fabrication processes are currently limited to gradual and arbitrary trial‐and‐error procedures. While the in‐situ acquisition of photoluminescence videos has the potential to reveal important variations in the thin‐film formation process, the high dimensionality of the data quickly surpasses the limits of human analysis. In response, this study leverages deep learning and explainable artificial intelligence (XAI) to discover relationships between sensor information acquired during the perovskite thin‐film formation process and the resulting solar cell performance indicators, while rendering these relationships humanly understandable. We further show how gained insights can be distilled into actionable recommendations for perovskite thin‐film processing, advancing towards industrial‐scale solar cell manufacturing. Our study demonstrates that XAI methods will play a critical role in accelerating energy materials science. This article is protected by copyright. All rights reserved

To assess the durability of concrete structures, it is necessary to understand not only how deterioration evolves over time but also how the damage progresses through the structure. To determine the evolution of the freeze‐thaw damage, different damage criteria were examined in a spatially resolved manner during the CIF test. By measuring the ultrasound pulse velocity (UPV) in different axes, depth‐dependent changes of the relative dynamic modulus of elasticity (RDME) were investigated. The RDME started to decrease in regions close to the surface, whereas the RDME in regions farther from the test surface decreased with higher speed. Changes in the water content during the CIF test were examined by nuclear magnetic resonance (NMR). The freeze‐thaw exposed specimens showed an increase in their water content that is assumed due to the micro‐ice‐lens pump as well as microstructural changes. These changes progressed from the surface into deeper layers of the specimens, following an increase of saturation also progressing into the concrete. To consider the frost damage evolution in service life design, the authors propose the use of depth‐dependent threshold values for freeze‐thaw exposed concrete structures.

Alkali-activated slag is an alternative to ordinary Portland cement that can be used for structural applications. Young’s modulus is an important property to predict stress or strains in concrete. However, the fib Model Code 2010 overestimates it for alkali-activated slag concrete. Multi-scale models allow the determination of con- crete property from the features of the microstructure. In this study, an analytical micromechanics-based multi-scale homogenization model is applied to predict Young’s modulus of alkali-activated slag. It is compared to experimental results found in the literature on paste, mortar and concrete. Better predictions of Young’s modulus are achieved from multiscale models compared to the fib MC 2010. Accu- racy could be enhanced by improving the predictions for the degree of activation of slag for the different mixes

Alkali-activated slag is an alternative to ordinary Portland cement that can be used for structural applications. Young’s modulus is an important property to predict stress or strains in concrete. However, the fib Model Code 2010 overestimates it for alkali-activated slag concrete. Multi-scale models allow the determination of con- crete property from the features of the microstructure. In this study, an analytical micromechanics-based multi-scale homogenization model is applied to predict Young’s modulus of alkali-activated slag. It is compared to experimental results found in the literature on paste, mortar and concrete. Better predictions of Young’s modulus are achieved from multiscale models compared to the fib MC 2010. Accuracy could be enhanced by improving the predictions for the degree of activation of slag for the different mixes

A large ensemble of 51 simulations with the Model for Prediction Across Scales (MPAS) has been applied to assess its ability to reproduce extreme temperatures and heat waves in the area of West Africa. With its global approach the model avoids transition errors influencing the performance of limited area climate models. The MPAS simulations were driven with sea surface temperature (SST) and sea ice extent as the only boundary condition. The results reveal moderate cold biases in the range from −0.6° to −0.9°C for the daily mean temperature and −1.2° to −2.0°C for the area mean of the daily maximum temperature. The bias in the number of tropical nights ranges from +3 to −10 days. An underestimation by up to 50% is also present regarding the number of summer days. The heat wave duration index is underestimated regionally by 10%–60%. MPAS simulations are generally closer to the reanalysis results than they are to the observational reference. The results from long term runs and from short term runs with selected SST years are similar. Shortcomings in the reproduction of the temperature and precipitation indices found in the present investigation indicate that the global MPAS approach does provide a fidelity similar to that of the regional climate models.

The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in collecting quantitative validation data. Additionally, many state-of-the-art deep learning methods are limited to 2D image data, which is insufficient for 3D digital outcrops, such as hyperclouds. To address these challenges, we present Tinto, a multi-sensor benchmark digital outcrop dataset designed to facilitate the development and validation of deep learning approaches for geological mapping, especially for non-structured 3D data like point clouds. Tinto comprises two complementary sets: 1) a real digital outcrop model from Corta Atalaya (Spain), with spectral attributes and ground-truth data, and 2) a synthetic twin that uses latent features in the original datasets to reconstruct realistic spectral data (including sensor noise and processing artifacts) from the ground-truth. The point cloud is dense and contains 3,242,964 labeled points. We used these datasets to explore the abilities of different deep learning approaches for automated geological mapping. By making Tinto publicly available, we hope to foster the development and adaptation of new deep learning tools for 3D applications in Earth sciences. The dataset can be accessed through this link: https://doi.org/10.14278/rodare.2256.

Kurzfassung Die sprunghaft zunehmende Wichtigkeit von FAIR‐ und Open‐Data für die Qualitätssicherung, aber auch für die Nachnutzbarkeit von Daten und den Erkenntnisfortschritt führt zu enormem Handlungsbedarf in Forschung und Entwicklung. Damit verbunden laufen derzeit vielfältige, ambitionierte Aktionen, z. B. bezüglich der Erstellung von Ontologien und Wissensgraphen. Das Knowhow entwickelt sich rasant, die Ansätze zur Implementation entstehen in verschiedenen Fachwelten bzw. mit unterschiedlichen Zielsetzungen parallel, so dass recht heterogene Herangehensweisen resultieren. Diese Veröffentlichung fokussiert auf Arbeiten, die derzeit als möglichst ganzheitlicher Ansatz für Materialdaten im Rahmen der Digitalisierungsinitiative „Plattform MaterialDigital“ vorangetrieben werden. Die Autoren bearbeiten baustoffbezogene Aspekte im Verbundprojekt „LeBeDigital ‐ Lebenszyklus von Beton”. Zielsetzung ist die digitale Beschreibung des Materialverhaltens von Beton über den kompletten Herstellungsprozess eines Fertigteils mit einer Integration von Daten und Modellen innerhalb eines Workflows zur probabilistischen Material‐ und Prozessoptimierung. Es wird über die Vorgehensweise und die dabei gewonnenen Erfahrungen berichtet, nicht ohne den Blick auf die oft unterschätzte Komplexität der Thematik zu lenken.

Kurzfassung Die Klimakrise führt zu einer steigenden Nachfrage nach ökologisch orientierten Konzepten. Dazu gehören im Baubereich nicht nur umweltfreundliche Materialien, sondern auch die Begrünung städtischer Gebiete. Vor diesem Hintergrund wird ein Projekt vorgestellt, dass eine Fassadenbegrünung mithilfe von mikrobiellen Organismen wie Algen statt höheren Pflanzen realisieren will. Die Entwicklung von sogenannten biorezeptiven Materialien ist eine Herausforderung, weil es aktuell noch keine standardisierten Methoden zur Bestimmung der Biorezeptivität gibt. Um ein grundlegendes Verständnis für die Prozesse und Interaktionen zwischen Organismen und Substrat zu gewinnen, werden Betonplatten unterschiedlich strukturiert und ihre Oberflächencharakteristiken dokumentiert. Anschließend werden die Platten unter verschiedensten Bedingungen bewittert und auf ihre Biorezeptivität untersucht. Von besonderem Interesse sind hierbei Einfluss der materialintrinsischen Oberflächenparameter sowie der Umwelt. Langfristig wird geplant ein geeignetes Messkonzept vorzuschlagen, das Materialwissenschaft und Biologie verbindet und verlässliche Vorhersagen zur Biorezeptivität eines Materials treffen kann. In der vorliegenden Arbeit liegt der Schwerpunkt auf dem theoretischen Ansatz und der Versuchsplanung.

The rheological properties of fresh concrete are a direct function of the interaction behaviour of the granular inventory of the concrete (i.e., gravel, sand and cement) and especially of the colloidal fractions of cement. Under low shear stresses, agglomeration of colloidal particles is observed, while at high shear stresses, dispersion of these agglomerates occurs. Besides the agglomeration state, the formation of shear banding, zones with concentrated shear flow, is another controlling mechanism of the flow behaviour of cement suspensions. Rheological creep tests in this study are focused on investigating the influence of shear history and hydration process on thixotropy of cement suspension. In this paper, the meaning of the word thixotropy is slightly extended to additionally encompass rheological aging and hydration effects. Selected samples were analyzed by coupling a rheometer to synchrotron X‐ray tomography to gain insight into the shear‐induced microstructural changes during shear start‐up tests. The observations show heterogeneities in the velocity profile in the shear gap and the development of shear banding.

The exposure of concrete structures to acid attack is a growing concern. This study employs thermodynamic modeling to investigate the changes in phase assemblage of powdered cement pastes subjected to a wide range of sulfuric and acetic acid concentrations. A modeling approach utilizing IPHREEQC implemented through Matlab is presented, and the obtained results are compared with pH measurements and compositions of equilibrated calcium and sulfate solutions. The influence of incorporating 11% silica fume (SF) as a replacement for cement predicted a 70% reduction of Portlandite content in the hardened cement paste. Consequently, the acid attack processes and subsequent pH reduction are affected. The modeling approach demonstrates good agreement with experimental data for acetic acid, across a broad range of acid concentrations, for both Portland cement and a blend with SF, without the need for any fitting parameters. However, significant discrepancies between the model and experiments are observed in the case of sulfuric acid. This discrepancy arises due to the formation of lump pieces of material in the experimental setup at higher acid concentrations. These lumps consist of a thin layer of altered hardened cement paste, primarily composed of sulfate‐rich phases, encapsulating unaltered hardened cement paste. Since the reaction was not homogeneous and the powder did not entirely react, the sulfuric attack experimental setup was not representative for validating the thermodynamic model.