Fakultät Angewandte Natur- und Kulturwissenschaften
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In this study, the effect of post-synthetic ball milling on the structural and functional properties of as-synthesized nanoparticles of the copper-based metal-organic framework (MOF) HKUST-1 was investigated for the first time. Nanoparticles were synthesized using sodium formate as a capping agent. The crystalline particles were subjected to a controlled ball milling process, which induced significant structural changes. Powder X-ray diffraction (PXRD) revealed a reduction in crystallite size from 91 nm to 21 nm and the introduction of microstrain, partially disrupting the MOF's long-range crystalline order, as evidenced by peak broadening and the diminished intensity of high-angle reflections. Particle morphology pre- and post-milling was further studied with scanning electron microscopy (SEM) imaging, verifying a narrow particle size distribution of ± 22 nm after milling. Fourier-transform infrared spectroscopy (FTIR) indicated protonation of carboxylate groups in the larger pores of the framework, likely due to moisture incorporation during milling. Brunauer-Emmett-Teller (BET) surface area analysis showed a substantial decrease in specific surface area from 521 m²/g to 226 m²/g, suggesting a partial collapse of the porous framework. We discuss the applicability of ball milling as a versatile means of post-synthetic approach to reduce MOF particle sizes, a key element in the preparation of MOF-based thin films using inks or polymers.
Surface plasmon resonance (SPR) is limited by small-signal detectability and drift when subtraction occurs in software after digitization. We introduce an SPR detector that performs on-detector amplification and analog differential readout, eliminating moving parts and software-heavy correction. The hardware-native subtraction boosts the usable ADC range and suppresses illumination and environmental noise. In fixed-angle refractive-index steps (NaCl), the platform resolves Δn_min ≈ 1.8 × 10⁻⁷ RIU compared to 4.6–7.2 × 10⁻⁶ RIU on a commercial comparator and improves small-signal SNR by up to ∼5,000-fold, while remaining competitive at high signal levels. In a model IgG–BSA assay, the detector’s low noise floor clarifies early binding and equilibrium transitions. By generating inherently clean raw signals, this hardware-native approach dramatically enhances sensitivity and long-term stability for label-free biosensing and inline process analytics while rendering AI-based or complex post-processing entirely unnecessary. The concept generalizes across platforms and opens a compact route to robust, high-fidelity SPR in complex environments, with a clear path toward multi-wavelength and arrayed detectors for high-throughput chemical monitoring.
Fatigued driving has been a significant contributor to traffic accidents globally, posing serious threats to both human life and economic stability. Machine learning techniques based on electroencephalography (EEG) are showing promise for detecting driver fatigue, excelling other physiological modalities in this regard. However, it requires a lot of exertion, demands domain knowledge, and may not generalize effectively across different datasets to manually extract features from EEG signals. Therefore, investigating innovative deep-learning architectures that can effectively extract discriminative characteristics from unprocessed EEG data is necessary. This paper proposes a novel framework for driver fatigue detection from EEG signals using Continuous Wavelet Transform (CWT) and Convolutional Neural Networks (CNN). Rather than depending on hand-crafted feature extraction, we use CWT to convert EEG data into time-frequency spectrum pictures. After concatenating these spectral images from every channel, a CNN is fed to learn discriminative features for driver normal and fatigued states automatically. An average classification accuracy of 98.3% is obtained by evaluating the proposed CWT-CNN system on a publicly available EEG dataset comprising recordings from twelve subjects. The results obtained indicate that the CWT-CNN framework has enormous potential to create strong driver fatigue detection systems, thereby enhancing road safety.
In the article, a new detection system that enables observation of the electron beam signal passing through a gas layer at atmospheric pressure is presented. An experimental setup consisting of an elec-tron gun (electron emitter, extraction, and focus electrodes), silicon nitride membrane, and a CMOS image sensor is described, as well as first image of the electron beam spot after passing through 400 µm of air at atmospheric pressure is presented.
Additive manufacturing has emerged as a disruptive technology in the field of electronics, offering new possibilities for the fabrication of complex, lightweight, and customizable components. Conventional PCB manufacturing relies on multi-step processes and is limited to planar geometries, making it less suitable for rapid prototyping or integration into non-standard surfaces. In contrast, Fused Deposition Modelling (FDM) provides a cost-effective and
accessible approach to directly fabricate functional electronic structures by combining conductive and dielectric materials within a single printing process.
This thesis examines the design, simulation, fabrication, measurement and analysis of 3D-printed conductive transmission lines utilising FDM technology. A conductive filament and a dielectric filament are employed to realise fully integrated microstrip and coaxial structures. Electromagnetic simulations are performed to optimise the geometry for RF performance, and the printed prototypes are experimentally characterised using vector network analyser measurements. The analysis focuses on the transmission coefficients and the reflection coefficients, providing insights into impedance matching, insertion loss, and frequency-dependent behaviour.
These findings confirm that FDM-based additive manufacturing can produce functional RF structures with reliable performance, despite material and fabrication limitations. The work demonstrates the potential of 3D printing as a flexible and low-cost method for prototyping radio frequency components, highlighting future directions that include improved material characterisation, connector integration, and the design of more advanced components, such as
filters and antennas.
Ethische Fragen der Digitalisierung und ihre Thematisierung in Forschung und Lehre an Hochschulen
(2023)
Die Digitalisierung als fortschreitende Durchdringung aller Lebensbereiche durch Informationstechnik geht mit gesellschaftlichen Transformationsprozessen und weitreichenden ethischen Fragen einher. Diese Fragen haben einen transdisziplinären Charakter in dem Sinne, dass sie sich nur durch ein Überschreiten von fachlichen Perspektiven einzelner wissenschaftlicher Disziplinen und durch eine ganzheitliche Bezugnahme auf gesellschaftliche Auswirkungen angemessen adressieren lassen. Anhand der technischen Möglichkeiten von Big Data Analytics lässt sich aufzeigen, wie disziplinübergreifende wissenschaftliche Forschungen zu technischen Anwendungen führen können, die gewichtige, gesellschaftlich relevante Fragen nach ihrer ethischen Vertretbarkeit aufwerfen. Diese können in letzter Konsequenz auch ethische Grundprinzipien und Auffassungen des Menschseins ins Wanken bringen. Besonders prägnant lässt sich dies mit den Studien des Big-Data-Forschers Michal Kosinski und seines Forscherkreises sowie den daraus resultierenden Anwendungen von Unternehmen wie Cambridge Analytica vor Augen führen: Die Analyse von großen Mengen digitaler persönlicher Daten eröffnet umfassende Möglichkeiten zur Verhaltensbeeinflussung und stellt dabei sehr weitreichend auch ethische Grundwerte wie Privatheit in Frage. Universitäten und Hochschulen sind aufgefordert, durch transdisziplinäre Lehre und Forschung die ethischen Fragen der Digitalisierung als bestimmende Themen der Gegenwart direkt zu adressieren.
This article explores Russian and German public diplomacy efforts in Central Asia through the example of Kyrgyzstan. Due to a difficult economic and socio-political situation, Kyrgyzstan remains open to externally funded programmes and projects, including initiatives funded by foreign governments, and thus offers a unique environment for the study of public diplomacy. The article looks at transnational public diplomacy actors such as German political foundations and Russian-sponsored organisations active in the country. Overall, the article offers interesting insights into the nature and substance of the soft power policies pursued by Germany and Russia that are relevant for Kyrgyzstan and beyond.
EU’s support to civil society in Kazakhstan: A pilot evaluation of the social
capital generated
(2019)
In the last decade, several studies have been devoted to the analysis of the EU’s support to civil society abroad, but only recently has scholarly attention turned to Central Asia. Most of the research in this field has adopted a macro-level perspective, concerned with the supply side of external democratization, and has examined external assistance to civil society in terms of democratization ideal types and typologies. The main contribution of this study consists of a meso-level analysis of the outcomes of the EU’s support to civil society in terms of production of social capital, conceived as the raw material of civil society itself. More precisely, the article evaluates the first wave of grants awarded to civil society organizations in Kazakhstan as part of two EU funding schemes—the European Instrument for Democracy and Human Rights and the Non-State Actors and Local Authorities in Development. The article sheds new light on the use of social capital and its two dimensions-structural and cognitive-as heuristic tools for evaluating civil society support programs.
In the last decade, a growing body of literature has investigated the promotion of good governance, rule of law, democracy and human rights in Central Asia through funding from the European Union (EU). Most of this research has taken an EU-centric approach and is concerned with the supply side of external democratization. As a result, little is known about how EU projects are implemented on the ground and what EU-funded civil society organizations consider to be their priorities. This article investigates the agency of such organizations in Kyrgyzstan by focusing on their contribution to one particular EU instrument: the Instrument contributing to Stability and Peace (IcSP), and its programme: Strengthening Resilience to Violence and Extremism (STRIVE). The findings offer new insights into the processes of adaptation, reinterpretation and contestation through which EU-funded organizations exercise ownership while engaging in the prevention of violent extremism (PVE).
Growing food demand due to population growth, coupled with increasingly frequent and severe droughts caused by climate change make water increasingly scarce. To address this, accurate assessment of plant water demand is essential for precise drought treatment and water conservation. Hyperspectral imaging (HSI) captures hypercubes, a combination of spectral and spatial data and offers promising capabilities for detection of plant stresses. However, most reported approaches only use selected spectral bands or indices, neglecting the full hypercube information. This is assumed to limit the detection accuracy. To overcome these limitations, we aim to develop a measurement pipeline to generate a comprehensive dataset comprising hypercubes of plants under varying drought stress levels along with selected physiological, environmental, and illumination data. This dataset will be used to train suitable data-driven models that enable improved drought stress detection as well as the non-invasive determination of physiological parameters based on HSI data.