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Angle-dependent deposition of thin amorphous hydrogenated carbon (a-C:H) layers on selected biodegradable polymer films
No full textDie aus nachwachsenden Rohstoffen hergestellten biologisch abbaubaren Polymere Polymilchsäure (polylactic acid, PLA) und Polyhydroxybuttersäure (polyhydroxybutyrate, PHB) wurden im Rahmen dieser Arbeit mit hydrierten amorphen Kohlenstoffschichten (amorphous hydrogenated carbon, a-C:H) bei unterschiedlichen Winkeleinstellungen mit verschiedenen Dicken beschichtet. Ähnlich wie herkömmliche Polymere haben Biopolymere oft ungeeignete Oberflächeneigenschaften für industrielle Zwecke, z.B. eine geringe Härte. Für manche Anwendungen ist es daher notwendig und vorteilhaft, die Oberflächeneigenschaften von Biopolymeren unter Beibehaltung der Haupteigenschaften des Trägermaterials zu modifizieren. Eine geeignete Oberflächenmodifikation ist das Aufbringen von dünnen a-C:H Schichten. Ihre Eigenschaften hängen wesentlich vom sp²- und sp³-Hybridisierungsverhältnis der Kohlenstoffatome und dem Gehalt an Wasserstoffatomen ab. Das sp²/sp³-Verhältnis sollte in der vorliegenden Arbeit durch Variation der Beschichtungsgeometrie gesteuert werden. Da Beschichtungen bei 0°, direkt vor der Plasmaquelle, einen höheren Anteil an sp³ und indirekt (180°) beschichtete ein höheren Anteil an sp² aufweisen, wird in dieser Arbeit gezeigt, dass es möglich ist, das sp²/sp³ -Verhältnis zu kontrollieren. Dazu werden die einzelnen Proben in den Winkeln 0, 30, 60, 90, 120, 150 und 180° vor der Plasmaquelle platziert und mit einer Dauer von 2.5, 5.0, 7.5 und 10.0 Minuten beschichtet. Für den Winkeln 0° ergaben sich die Schichtdicken von 25, 50, 75 und 100 nm. Die a-C:H Schichten wurden alle mit Radiofrequenzplasma-unterstützter chemischer Gasphasenabscheidung und Acetylen als C und H Quelle abgeschieden, nachdem sie 10 Minuten lang mit einem Sauerstoffplasma vorbehandelt worden waren. Nach dieser O₂-Behandlung und der a-C:H Abscheidung werden die Oberflächen mit makroskopischen und mikroskopischen Messmethoden untersucht und die Daten anschließend analysiert. Die Oberflächenmorphologie wird mit Hilfe der Rasterelektronenmikroskopie und der Rasterkraftmikroskopie erfasst. Auf diese Weise können auch Informationen über die Stabilität der Schicht und die Oberflächenrauhigkeit gesammelt werden. Mit Kontaktwinkelmessungen (contact angle, CA) wird nicht nur die Benetzbarkeit, sondern auch die Kontaktwinkelhysterese durch Auf- und Abpumpen des Tropfenvolumens bestimmt. Durch Messung des CA von verschiedenen Flüssigkeiten und deren Vergleich werden die freie Oberflächenenergie (surface free energy, SFE) und ihre polaren und dispersiven Bestandteile bestimmt. Die Veränderungen der Barriereeigenschaften werden durch Wasserdampftransmissionstests (water vapor transmission rate, WVTR) überprüft. Die chemische Analyse der Oberfläche erfolgt zum einen durch Fourier-Transformations-Infrarotspektroskopie mit spiegelnder Reflexion und zum anderen durch Synchrotron unterstützte Techniken wie der Nahkanten-Röntgenabsorptionsfeinstruktur und der Röntgen-Photoelektronenspektroskopie. Bei der Analyse der Oberflächen nach der O₂ Behandlung, von der zunächst angenommen wurde, dass sie nur der Reinigung und Aktivierung der Oberfläche für die a-C:H Beschichtung dient, wurde festgestellt, dass die Veränderungen drastischer sind als ursprünglich angenommen. Wird PLA zum Beispiel bei 0° für 10 Minuten behandelt, steigt die Rauheit um das Fünffache. Mit zunehmendem Winkel verringert sich diese wieder, bis sie bei 180° wieder dem Ausgangswert entspricht. Bei PHB hingegen wird durchgehend ein ähnlicher Wert gemessen. Für beide Polymere lässt sich zeigen, dass der polare Anteil der SFE zunimmt. In der WVTR ist bei PLA eine Abnahme der Permeabilität und bei PHB ein Anstieg des Ausgangswertes zu beobachten. Die chemische Oberflächenanalyse zeigt, dass die O₂ Behandlung kaum Auswirkungen auf die Oberflächenbindungen hat. Insgesamt kann in dieser Arbeit gezeigt werden, dass sich die O₂ Behandlung auf die Eigenschaften der Oberfläche auswirkt und nicht ausschließlich als Reinigungs- und Aktivierungsprozess betrachtet werden kann. Bei direkter a-C:H Beschichtung (bei 0°) ist sowohl bei PLA als auch bei PHB ein Schichtversagen bei 10.0 Minuten aufgrund von Eigenspannung zu beobachten. Dies ist bei PHB in geringerem Maße auch bei 30° zu erkennen. Die Durchlässigkeit der Polymere wird bei einer fünf minütigen Beschichtung um 47% reduziert und auch die Schicht bei 10.0 Minuten führt diesen Effekt trotz auftretender Risse weiter. Die Aufbringung von a-C:H Schichten zeigt für beide Polymertypen bei direkter Beschichtung eine Dominanz von sp³-Bindungen. Mit zunehmendem Winkel nimmt diese ab und bei indirekten Beschichtungen werden sp²-Bindungen dominierend. Dieses Ergebnis ist für alle Schichtdicken ähnlich, nur der Winkel, bei dem der Wechsel der dominanten Bindung stattfindet, ist unterschiedlich. Es wird gezeigt, dass es möglich ist, die Oberflächeneigenschaften durch eine winkelabhängige Beschichtung zu steuern und somit das Verhältnis sp²/sp³ zu kontrollieren.The biodegradable polymers polylactic acid (PLA) and polyhydroxybutyrate (PHB) produced from renewable raw materials were coated with hydrogenated amorphous carbon layers (a-C:H) at different deposition angles with various thicknesses as part of this thesis. Similar to conventional polymers, biopolymers often have unsuitable surface properties for industrial purposes, e.g. low hardness. For some applications, it is therefore necessary and advantageous to modify the surface properties of biopolymers while retaining the main properties of the substrate material. A suitable surface modification is the deposition of thin a-C:H layers. Their properties depend essentially on the sp² and sp³ hybridization ratio of the carbon atoms and the content of hydrogen atoms. The sp²/sp³ ratio was to be controlled in the present work by varying the coating geometry. Since coatings at 0°, directly in front of the plasma source, contain a higher percentage of sp³ and indirectly coated (180°) a higher amount of sp², it is shown in this work that it is possible to control the sp²/sp³ ratio. For this purpose, the samples are placed in front of the plasma source at angles of 0, 30, 60, 90, 120, 150 and 180° and coated for 2.5, 5.0, 7.5 and 10.0 minutes. For the angles 0°, the layer thicknesses were 25, 50, 75 and 100 nm. The a-C:H layers were all deposited using radio-frequency plasma-enhanced chemical vapor deposition and acetylene as C and H sources after being pretreated with an oxygen plasma for 10 minutes. Following the O₂ treatment and the a-C:H deposition, the surfaces are examined using macroscopic and microscopic measurement methods and the data is then analyzed. The surface morphology is recorded using scanning electron microscopy and atomic force microscopy. In addition, data on the stability of the layer and the surface roughness can be collected. Contact angle (CA) measurements are used to determine not only the wettability, but also the contact angle hysteresis by pumping the drop volume up and down. By measuring the CA with different liquids and comparing them, the surface free energy (SFE) and its polar and disperse components are determined. The changes in barrier properties are verified by water vapor transmission rate tests (WVTR). The chemical analysis of the surface is carried out on the one hand by Fourier transform infrared spectroscopy with specular reflection and on the other hand by synchrotron-supported techniques such as near-edge X-ray absorption fine structure and X-ray photoelectron spectroscopy. When analyzing the surfaces after the O₂ treatment, which was initially assumed to serve only to clean and activate the surface for the a-C:H coating, it was found that the changes were more drastic than originally assumed. For example, if PLA is treated at 0° for 10 minutes, the roughness increases fivefold. As the angle increases, it decreases again until it returns to the initial value at 180°. This can be recognized to a lesser extent with PHB at 30°. For both polymers, it can be shown that the polar fraction of the SFE increases. In the WVTR, a decrease in permeability can be observed for PLA and an increase in the initial value for PHB. The chemical surface analysis shows that the O₂ treatment has little effect on the surface bonds. Overall, it can be shown in this work that the O₂ treatment has an effect on the properties of the surface and cannot be regarded exclusively as a cleaning and activation process. With direct a-C:H coating (at 0°), a layer failure due to internal stress can be observed for both PLA and PHB. This also occurs with PHB at 30°, but to a lesser extent. Permeability of the polymers is reduced by 47% with a five-minute coating and the layer at 10.0 minutes continues to have this effect despite cracks appearing. The application of a-C:H layers shows a dominance of sp³ bonds for both polymer types with direct coating. This decreases with increasing angle and sp² bonds become dominant for indirect coatings. This result is similar for all coating thicknesses, only the angle at which the change of the dominant bond takes place is different. It is shown that it is possible to control the surface properties by an angle-dependent coating and thus to control the ratio sp²/sp³
Assessing ChatGPT’s Performance in Analyzing Students’ Sentiments: A Case Study in Course Feedback
No full textThe emergence of large language models (LLMs) like ChatGPT has impacted fields such as education, transforming natural language processing (NLP) tasks like sentiment analysis. Transformers form the foundation of LLMs, with BERT, XLNet, and GPT as key examples. ChatGPT, developed by OpenAI, is a state-of-the-art model and its ability in natural language tasks makes it a potential tool in sentiment analysis. This thesis reviews current sentiment analysis methods and examines ChatGPT’s ability to analyze sentiments across three labels (Negative, Neutral, Positive) and five labels (Very Negative, Negative, Neutral, Positive, Very Positive) on a dataset of student course reviews. Its performance is compared with fine tuned state-of-the-art models like BERT, XLNet, bart-large-mnli, and RoBERTa-large-mnli using quantitative metrics. With the help of 7 prompting techniques which are ways to instruct ChatGPT, this work also analyzed how well it understands complex linguistic nuances in the given texts using qualitative metrics. BERT and XLNet outperform ChatGPT mainly due to their bidirectional nature, which allows them to understand the full context of a sentence, not just left to right. This, combined with fine-tuning, helps them capture patterns and nuances better. ChatGPT, as a general purpose, open-domain model, processes text unidirectionally, which can limit its context understanding. Despite this, ChatGPT performed comparably to XLNet and BERT in three-label scenarios and outperformed others. Fine-tuned models excelled in five label cases. Moreover, it has shown impressive knowledge of the language. Chain-of-Thought (CoT) was the most effective technique for prompting with step by step instructions. ChatGPT showed promising performance in correctness, consistency, relevance, and robustness, except for detecting Irony. As education evolves with diverse learning environments, effective feedback analysis becomes increasingly valuable. Addressing ChatGPT’s limitations and leveraging its strengths could enhance personalized learning through better sentiment analysis
Explainable Artificial Intelligence for Site Energy Usage Intensity Prediction
No full textThe findings of this study demonstrate that the Random Forest (RF) algorithm provided the most accurate predictions in comparison with other boosting machine learning algorithms. Key drivers of energy consumption identified through XAI techniques such as SHAP and LIME include energy star rating, facility type, and floor area. These XAI methods helped enhance the interpretability of the models, making them more accessible for non-expert users, such as building managers and policymakers. By leveraging machine learning and XAI, this research provides a transparent and actionable framework for optimizing building energy efficiency and supporting sustainable energy management
Examining the role of post-event processing in test anxiety—Pilot testing in three student samples
Full text linkThis work investigates the occurrence of post-event processing (PEP) in the context of test anxiety; PEP involves rumination and self-critical thinking following an event and commonly observed in social anxiety. Three short-term longitudinal studies in student samples examined whether PEP occurs after exams and how it is associated with test anxiety. University students (N =35 in Study 1, N =146 in Study 2, and N =37 in Study 3) completed measures of trait and state test anxiety before an actual exam; PEP related to the exam was assessed at various time points afterward. Results revealed that PEP occurred to a meaningful extent after exam situations. Overall, it was positively associated with trait and state test anxiety, although some variations in the relations were found across the three studies. These findings underscore the relevance of PEP in the context of test anxiety, as PEP might contribute to maintaining test anxiety in the long term. Implications for future studies are discussed
Mitteilungsblatt der Universität Koblenz, Nr. 4/2024
No full textZweiunddreißigste Ordnung zur Änderung der Prüfungsordnung für die Prüfung im lehramtsbezogenen Bachelorstudiengang an der Universität Koblenz
Achtundzwanzigste Ordnung zur Änderung der Prüfungsordnung für die Prüfung in den Masterstudiengängen für das Lehramt an Grundschulen, das Lehramt an Realschulen plus sowie das Lehramt an Gymnasien an der Universität Koblenz
Neunundzwanzigste Ordnung zur Änderung der Ordnung für die Prüfung im lehramtsbezogenen Zertifikatsstudiengang (Erweiterungsprüfung) an der Universität Koblenz und der Hochschule Koblenz
Vierte Ordnung zur Änderung der Gemeinsamen Prüfungsordnung für die Bachelor- und Masterstudiengänge des Fachbereichs Informatik an der Universität Koblenz
Gemeinsame Prüfungsordnung für den Masterstudiengang Master of Engineering „Ceramic Science and Engineering“ an der Hochschule Koblenz und der Universität Koblenz (Kooperativer Masterstudiengang
Blickanalysen bei mentalen Rotationsaufgaben
Full text linkIn der vorliegenden Dissertation mit dem Titel "Blickanalysen bei mentalen Rotationsaufgaben" wird eine Analyse der visuellen Verarbeitungsprozesse bei mentalen Rotationsaufgaben mittels Eye-Tracking-Technologie durchgeführt, um die zugrundeliegenden kognitiven Prozesse und Strategien, die bei der Lösung dieser Aufgaben angewandt werden, zu untersuchen. Ein Anliegen dieser Arbeit ist es, die Problemstellung zu adressieren, wie individuelle Unterschiede, insbesondere geschlechtsspezifische Differenzen in den Blickmustern, die visuelle Verarbeitung und Leistung bei mentalen Rotationsaufgaben beeinflussen. Hierzu wurden drei Studien durchgeführt, die nicht nur die Identifikation von Blickmustern und die Analyse der Leistungsunterschiede in Bezug auf Geschlecht umfassen, sondern auch die Korrelation zwischen Blickverhalten und Leistung untersuchen. Die Ergebnisse dieser Forschung bieten Einblicke in die Mechanismen der visuellen und kognitiven Verarbeitung bei mentalen Rotationsaufgaben und heben die Bedeutung des Eye-Tracking als Forschungsinstrument in der kognitiven Psychologie hervor, um ein umfassendes Verständnis der Einflussfaktoren auf räumliches Denken und Problemlösungsstrategien zu erlangen
rac-1-(4-tert-Butylphenyl)-5-ethyl-4-ferrocenyl-5-hydroxy-1H-pyrrol-2(5H)-one
No full textThe title compound, [Fe(C5H5)(C21H24NO2)], which is produced by the oxidation of 1-(4-tert-butylphenyl)-2-ethyl-3-ferrocenylpyrrole, crystallizes as a racemic mixture in the centrosymmetric space group P21/n. The central heterocyclic pyrrole ring system subtends dihedral angles of 13.7 (2)° with respect to the attached cyclopentadienyl ring and of 43.6 (7)° with the major component of the disordered phenyl group bound to the N atom. The 4-tert-butylphenyl group, as well as the non-substituted Cp ring are disordered with s.o.f. values of 0.589 (16) and 0.411 (16), respectively. In the crystal, molecules with the same absolute configuration are linked into infinite chains along the b-axis direction by O—H···O hydrogen bonds between the hydroxy substituent and the carbonyl O atom of the adjacent molecule
Exploring Academic Perspectives: Sentiments and Discourse on ChatGPT Adoption in Higher Education
No full textArtificial intelligence (AI) is becoming more widely used in a number of industries, including in the field of education. Applications of artificial intelligence (AI) are becoming crucial for schools and universities, whether for automated evaluation, smart educational systems, individualized learning, or staff support. ChatGPT, anAI-based chatbot, offers coherent and helpful replies based on analyzing large volumes of data. Integrating ChatGPT, a sophisticated Natural Language Processing (NLP) tool developed by OpenAI, into higher education has sparked significant interest and debate. Since the technology is already adapted by many students and teachers, this study delves into analyzing the sentiments expressed on university websites regarding ChatGPT integration into education by creating a comprehensive sentiment analysis framework using Hierarchical Residual RSigELU Attention Network (HR-RAN). The proposed framework addresses several challenges in sentiment analysis, such as capturing fine-grained sentiment nuances, including contextual information, and handling complex language expressions in university review data. The methodology involves several steps, including data collection from various educational websites, blogs, and news platforms. The data is preprocessed to handle emoticons, URLs, and tags and then, detect and remove sarcastic text using the eXtreme Learning Hyperband Network (XLHN). Sentences are then grouped based on similarity and topics are modeled using the Non-negative Term-Document Matrix Factorization (NTDMF) approach. Features, such as lexico-semantic, lexico structural, and numerical features are extracted. Dependency parsing and coreference resolution are performed to analyze grammatical structures and understand semantic relationships. Word embedding uses the Word2Vec model to capture semantic relationships between words. The preprocessed text and extracted features are inputted into the HR-RAN classifier to categorize sentiments as positive, negative, or neutral. The sentiment analysis results indicate that 74.8% of the sentiments towards ChatGPT in higher education are neutral, 21.5% are positive, and only 3.7% are negative. This suggests a predominant neutrality among users, with a significant portion expressing positive views and a very small percentage holding negative opinions. Additionally, the analysis reveals regional variations, with Canada showing the highest number of sentiments, predominantly neutral, followed by Germany, the UK, and the USA. The sentiment analysis results are evaluated based on various metrics, such as accuracy, precision, recall, F-measure, and specificity. Results indicate that the proposed framework outperforms conventional sentiment analysis models. The HR-RAN technique achieved a precision of 98.98%, recall of 99.23%, F-measure of 99.10%, accuracy of 98.88%, and specificity of 98.31%. Additionally, word clouds are generated to visually represent the most common terms within positive, neutral, and negative sentiments, providing a clear and immediate understanding of the key themes in the data. These findings can inform educators, administrators, and developers about the benefits and challenges of integrating ChatGPT into educational
settings, guiding improvements in educational practices and AI tool development
Correlative Effects on Nanoplastic Aggregation in Model Extracellular Biofilm Substances Investigated with Fluorescence Correlation Spectroscopy
No full textRecent studies show that biofilm substances in contact with nanoplastics play an important role in the aggregation and sedimentation of nanoplastics. Consequences of these processes are changes in biofilm formation and stability and changes in the transport and fate of pollutants in the environment. Having a deeper understanding of the nanoplastics–biofilm interaction would help to evaluate the risks posed by uncontrolled nanoplastic pollution. These interactions are impacted by environmental changes due to climate change, such as, e.g., the acidification of surface waters. We apply fluorescence correlation spectroscopy (FCS) to investigate the pH-dependent aggregation tendency of non-functionalized polystyrene (PS) nanoparticles (NPs) due to intermolecular forces with model extracellular biofilm substances. Our biofilm model consists of bovine serum albumin (BSA), which serves as a representative for globular proteins, and the polysaccharide alginate, which is a main component in many biofilms, in solutions containing Na+ with an ionic strength being realistic for fresh-water conditions. Biomolecule concentrations ranging from 0.5 g/L up to at maximum 21 g/L are considered. We use non-functionalized PS NPs as representative for mostly negatively charged nanoplastics. BSA promotes NP aggregation through adsorption onto the NPs and BSA-mediated bridging. In BSA–alginate mixtures, the alginate hampers this interaction, most likely due to alginate–BSA complex formation. In most BSA–alginate mixtures as in alginate alone, NP aggregation is predominantly driven by weaker, pH-independent depletion forces. The stabilizing effect of alginate is only weakened at high BSA contents, when the electrostatic BSA–BSA attraction is not sufficiently screened by the alginate. This study clearly shows that it is crucial to consider correlative effects between multiple biofilm components to better understand the NP aggregation in the presence of complex biofilm substances. Single-component biofilm model systems based on comparing the total organic carbon (TOC) content of the extracellular biofilm substances, as usually considered, would have led to a misjudgment of the stability towards aggregation
Predictive Analytics for Early Identification of At-risk Students
Full text linkIn the realm of education, the timely identification of students who need further support to succeed in their respective courses, plays a pivotal role in fostering aca- demic success and preventing potential setbacks. This thesis thus aims to contribute to this critical area by focusing on the development of predictive models for the early detection of at-risk students in their academic journey. The primary dataset used for this thesis is provided by kaggle, encompassing diverse student informa- tion, including demographic, socio-economic factors, and academic performance categorized into three different classes, presenting an imbalanced nature that poses a significant challenge.
Thus the primary objectives of this thesis are to address the problem of imbal- anced data, explore and assess the performance of multiple classification methods such as, logistic regression, decision tress, random forests and support vector ma- chines (SVM), neural networks, and create a comprehensive end-to-end processing pipeline which includes the systematic steps of balancing the data, model training and evaluation. Additionally the developed pipeline is tested on two additional datasets to assess its generalizability and robustness. This research aims to provide a comprehensive understanding of addressing the challenges of imbalanced data and how different classification methods and regression can be optimally applied to early detection of at-risk students. The findings are expected to aid educational institutions in supporting their students and enhancing academic success through timely interventions.
Key findings demonstrates the robustness of SVM SMOTE balancing technique acro- ss the datasets used in this study, where it consistently achieved best results when combined with various models, particularly highlighting the success of the combi- nation of Random Forest model with SVM SMOTE, and Decision tree model with SVM SMOTE in achieving notable accuracy rates. This emphasizes the adaptability of the balancing techniques employed, providing a strong foundation for predictive intervention educational settings