Nucleation of Ge quantum dots on the C-alloyed Si(001) surface

Carbon pre-deposition onto the bare Si(001) surface has been shown to alter the (2×1) surface structure by formation of c(4×4) reconstructed domains containing a high C-concentration. Here we studied by ultra-high vacuum scanning tunneling microscopy the effect of this restructured surface on the initial stages of Ge nucleation by molecular beam epitaxy. Ge is found to form three-dimensional (3D) islands already at sub-monolayer coverage, resulting in a Volmer–Weber growth mode. Strain effects repel Ge adatoms from the C-rich domains, leading to enhanced Ge island formation on the C-free surface regions in between the c(4×4) areas. At a low growth temperature of 350°C, very small three-dimensional islands (3–5 nm in diameter, height 3–4 ML) with a density of nearly 1×10¹² cm⁻² are obtained for only 0.5 ML of Ge. At higher substrate temperatures of approximately 500°C this three-dimensional growth mode is less pronounced, but still evident. The initially nucleated three-dimensional islands define the positions of the larger quantum dots at higher Ge coverage, that exhibit enhanced photoluminescence (PL) properties.     

Calibration-Free Chemical Process and Quality Control Units as Enablers for Modular Production

Modular chemical production is a tangible translation of the digital transformation of the process industry for specialty chemicals. In particular, it enables the speeding-up of process development and, thus, a quicker time to market by flexibly connecting and orchestrating standardized physical modules and bringing them to life (i.e., parameterizing them) with digitally accumulated process knowledge. We focus on the specific challenges of chemical process and quality control, which in its current form is not well suited for modular production and provide possible approaches and examples of the change towards direct analytical methods, analytical model transfer or machine-supported processes.     

Spatially resolved indiffusion behavior of Cu2+ and Ni2+ in polypropylene

Microplastics and their effects on the environment and food chain have become increasingly important in recent years. These polymer particles, which are only few millimeters in size or smaller, accumulate in the environment and can enter the human food chain via animals that ingest them. Moreover, they can accumulate impurities such as heavy metals. Therefore, this study focuses on the indiffusion behavior of metal ions into semicrystalline polypropylene (PP) applying time-of-flight secondary ion mass spectrometry (ToF-SIMS) at cryo-conditions. Diffusion coefficients of Cu²⁺ and Ni²⁺ in PP are determined by classical SIMS depth profiling in frozen state (T <−130°C) and subsequent data analysis according to Fick's second law of diffusion. The results show that diffusion of Cu²⁺ ions in dry PP (D_PP,Cu = [2.21 ± 0.15]·10⁻¹² cm²/s) is faster compared to Ni²⁺ ion diffusion of dry PP (D_PP,Ni = [4.43 ± 0.55]·10⁻¹³ cm²/s). Interestingly, the diffusion of Cu²⁺ ions in water-saturated PP (D_PP,H2O,Cu = [1.91 ± 0.28]·10⁻¹³ cm²/s) is slower compared to Cu²⁺ ion diffusion in dry PP. Furthermore, high-lateral resolution ToF-SIMS analysis shows that metal ions only diffuse in certain areas of PP, which are most likely amorphous.     

Authorship identification of documents with high content similarity

The goal of our work is inspired by the task of associating segments of text to their real authors. In this work, we focus on analyzing the way humans judge different writing styles. This analysis can help to better understand this process and to thus simulate/ mimic such behavior accordingly. Unlike the majority of the work done in this field (i.e. authorship attribution, plagiarism detection, etc.) which uses content features, we focus only on the stylometric, i.e. content-agnostic, characteristics of authors. Therefore, we conducted two pilot studies to determine, if humans can identify authorship among documents with high content similarity. The first was a quantitative experiment involving crowd-sourcing, while the second was a qualitative one executed by the authors of this paper. Both studies confirmed that this task is quite challenging. To gain a better understanding of how humans tackle such a problem, we conducted an exploratory data analysis on the results of the studies. In the first experiment, we compared the decisions against content features and stylometric features. While in the second, the evaluators described the process and the features on which their judgment was based. The findings of our detailed analysis could (1) help to improve algorithms such as automatic authorship attribution as well as plagiarism detection, (2) assist forensic experts or linguists to create profiles of writers, (3) support intelligence applications to analyze aggressive and threatening messages and (4) help editor conformity by adhering to, for instance, journal specific writing style.