Laser Patterned N-doped Carbon: Preparation, Functionalization and Selective Chemical Sensors

Die kürzliche globale COVID-19-Pandemie hat deutlich gezeigt, dass hohe medizinische Kosten eine große Herausforderung für unser Gesundheitssystem darstellen. Daher besteht eine wachsende Nachfrage nach personalisierten tragbaren Geräten zur kontinuierlichen Überwachung des Gesundheitszustands von Menschen durch nicht-invasive Erfassung physiologischer Signale. Diese Dissertation fasst die Forschung zur Laserkarbonisierung als Werkzeug für die Synthese flexibler Gassensoren zusammen und präsentiert die Arbeit in vier Teilen. Der erste Teil stellt ein integriertes zweistufiges Verfahren zur Herstellung von laserstrukturiertem (Stickstoff-dotiertem) Kohlenstoff (LP-NC) ausgehend von molekularen Vorstufen vor. Der zweite Teil demonstriert die Herstellung eines flexiblen Sensors für die Kohlendioxid Erfassung basierend auf der Laserumwandlung einer Adenin-basierten Primärtinte. Die unidirektionale Energieeinwirkung kombiniert mit der tiefenabhängigen Abschwächung des Laserstrahls ergibt eine neuartige geschichtete Sensorheterostruktur mit porösen Transducer- und aktiven Sensorschichten. Dieser auf molekularen Vorläufern basierende Laserkarbonisierungsprozess ermöglicht eine selektive Modifikation der Eigenschaften von gedruckten Kohlenstoffmaterialien. Im dritten Teil wird gezeigt, dass die Imprägnierung von LP-NC mit Molybdäncarbid Nanopartikeln die Ladungsträgerdichte verändert, was wiederum die Empfindlichkeit von LP-NC gegenüber gasförmigen Analyten erhöht. Der letzte Teil erläutert, dass die Leitfähigkeit und die Oberflächeneigenschaften von LP-NC verändert werden können, indem der Originaltinte unterschiedliche Konzentrationen von Zinknitrat zugesetzt werden, um die selektiven Elemente des Sensormaterials zu verändern. Basierend auf diesen Faktoren zeigte die hergestellte LP-NC-basierte Sensorplattform in dieser Studie eine hohe Empfindlichkeit und Selektivität für verschiedene flüchtige organische Verbindungen.The recent global COVID-19 pandemic clearly displayed that the high costs of medical care on top of an aging population bring great challenges to our health systems. As a result, the demand for personalized wearable devices to continuously monitor the health status of individuals by non-invasive detection of physiological signals, thereby providing sufficient information for health monitoring and even preliminary medical diagnosis, is growing. This dissertation summarizes my research on laser-carbonization as a tool for the synthesis of functional materials for flexible gas sensors. The whole work is divided into four parts. The first part presents an integrated two-step approach starting from molecular precursor to prepare laser-patterned (nitrogen-doped) carbon (LP-NC). The second part shows the fabrication of a flexible LP-NC sensor architecture for room-temperature sensing of carbon dioxide via laser conversion of an adenine-based primary ink. By the unidirectional energy impact in conjunction with depth-dependent attenuation of the laser beam, a novel layered sensor heterostructure with a porous transducer and an active sensor layer is formed. This molecular precursor-based laser carbonization method enables the modification of printed carbon materials. In the third part, it is shown that impregnation of LP-NC with molybdenum carbide nanoparticle alters the charge carrier density, which, in turn, increases the sensitivity of LP-NC towards gaseous analytes. The last part explains that the electrical conductivity and surface properties of LP-NC can be modified by adding different concentrations of zinc nitrate into the primary ink to add selectivity elements to the sensor materials. Based on these factors, the LP-NC-based sensor platforms prepared in this study exhibited high sensitivity and selectivity for different volatile organic compounds

Impact of load ramping on power transformer dissolved gas analysis

Dissolved gas in oil analysis (DGA) is one of the most reliable condition monitoring techniques, which is currently used by the industry to detect incipient faults within the power transformers. While the technique is well matured since the development of various offline and online measurement techniques along with various interpretation methods, no much attention was given so far to the oil sampling time and its correlation with the transformer loading. A power transformer loading is subject to continuous daily and seasonal variations, which is expected to increase with the increased penetration level of renewable energy sources of intermittent characteristics, such as photovoltaic (PV) and wind energy into the current electricity grids. Generating unit transformers also undergoes similar loading variations to follow the demand, particularly in the new electricity market. As such, the insulation system within the power transformers is expected to exhibit operating temperature variations due to the continuous ramping up and down of the generation and load. If the oil is sampled for the DGA measurement during such ramping cycles, results will not be accurate, and a fault may be reported due to a gas evolution resulting from such temporarily loading variation. This paper is aimed at correlating the generation and load ramping with the DGA measurements through extensive experimental analyses. The results reveal a strong correlation between the sampling time and the generation/load ramping. The experimental results show the effect of load variations on the gas generation and demonstrate the vulnerabilities of misinterpretation of transformer faults resulting from temporary gas evolution. To achieve accurate DGA, transformer loading profile during oil sampling for the DGA measurement should be available. Based on the initial investigation in this paper, the more accurate DGA results can be achieved after a ramping down cycle of the load. This sampling time could be defined as an optimum oil sampling time for transformer DGA

Corticosteroids effects on LPS-induced rat inflammatory keratocyte cell model.

PURPOSE:Corticosteroids are efficient anti-inflammation treatments. However, there are still arguments on whether it should be used in keratitis. This study was to observe the effect of corticosteroids on keratocytes both in normal condition and inflammation status in vitro. METHODS:Rat keratocytes were cultured and used for examination. 10 μg/ml lipopolysaccharide (LPS) was used to establish the inflammatory keratocyte cell model, and prednisolone acetate (PA), dexamethasone (Dex) and fluorometholone (Flu) were used as corticosteroids treatments. 5 d-growth curve and cell viabilities were assayed by CCK8, and cell morphologies and migration rate were studied. TNF-α, IL-6 and IL-1β levels were examined by ELISA. Western blotting was used to quantified type VI collagen (Col VI) and matrix metalloproteinase 9 (MMP9) expressions, and immunofluorescence staining assays of Col I and Col VI were carried out. RESULTS:In normal condition, proliferation and migration of keratocytes were slightly influenced in PA, Dex and Flu groups. The secretion of Col I and Col VI was suppressed and MMP9 expression increased in corticosteroids groups. But no significant difference was seen in TNF-α, IL-6 and IL-1β expression levels. In inflammatory status, TNF-α, IL-6 and MMP9 levels increased in LPS group, while they significantly decreased in corticosteroids groups. Although keratocytes viabilities and migration were slightly affected in 24 h, no significant differences were seen between LPS group and corticosteroids groups in 5-d proliferation. Col I and Col VI secretion in LPS-keratocytes was maintained with corticosteroids treatments. CONCLUSIONS:Corticosteroids showed lightly effects on keratocytes proliferation and migration, but it successfully decreased TNF-α, IL-6 level and maintained the secretion of and Col I and Col VI, while suppressed the expression of MMP9 in LPS-induced keratocytes. PA was suggested to use in early stage of keratitis clinical treatment

Effects of Thermal Aging on Moisture Diffusion in Insulation Paper Immersed with Mineral Oil

Thermal aging of insulation paper has obvious effects on its moisture absorption property. The presented studies aim to investigating the effects of aging on moisture diffusion in oil-immersed insulation paper. Based on the experimental designs and preparation of experimental materials, the transient moisture contents of insulation paper with different aging status were obtained at different conditions. Then, the experimental results were discussed. Furthermore, the diffusion factors and equilibrium time constants were extracted based on the model of moisture diffusion in oil-immersed insulation paper, which are applicable to our studies. Finally, the effects of thermal aging on the diffusion factor and equilibrium time constant were analyzed. The results show that: 1) With the aging of insulation paper, the rate of moisture absorption increases, and the equilibrium moisture content of insulation paper decreases at the same temperature. Besides, the equilibrium time constant decreases with aging at the same temperature. 2) Considering the effects of aging, thickness of insulation paper and temperature, the empirical expression of diffusion factor was obtained and verified

Effects of thermal aging on moisture equilibrium in oil-paper insulation

Recent studies have reported that thermal aging of insulation paper affects its moisture absorption property, which can affect moisture equilibrium in oil-paper insulation. In engineering practice, Fessler's model is one of the most widely used methods to estimate the moisture content of paper through measurement of moisture content of oil. However, this model lacks consideration of the thermal aging effect on equilibrium moisture. This study comprehensively investigated the effect of thermal aging on moisture equilibrium in oil-paper insulation. Based on the accelerated ageing and moisture measurements, the equilibrium moisture content of insulation paper with different aging and temperatures were obtained. The effects of aging condition and the temperature on equilibrium moisture are discussed and Fessler's model was modified accordingly. The results show that: 1) the decreasing of specific surface area is the dominant factor that affects the moisture equilibrium in oil-paper insulation, which is caused by thermal aging; 2) the modified Fessler model is reasonable and suitable to estimate the equilibrium moisture content of insulation paper with different aging

Laser-Induced Nitrogen Fixation

Industrial ammonia production is currently performed at 400–500 °C and 100–200 bar with fossil–fuel–involved power and hydrogen feedstock by the Haber-Bosch method, which enabled the growth of humanity beyond previous limits but demands larger infrastructure, capital investments and causes substantial emissions of carbon dioxide. For distributed ammonia production and decarbonization of this process by exploiting renewable energy sources, alternative methods, such as the electrochemical approach or using plasma on a small–scale, have been explored. Nonetheless, they still lack yield and efficiency to be industrially relevant. Here, we demonstrate a new approach of nitrogen fixation to synthesize ammonia at ambient conditions via laser–induced multiphoton dissociation of lithium oxide. Lithium oxide is dissociated under non–equilibrium multiphoton absorption and high temperatures under focused infrared light, and the generated zero–valent metal spontaneously fixes nitrogen and forms a lithium nitride, which upon subsequent hydrolysis generates ammonia. The highest ammonia yield rate of 30.9 micromoles per second per square centimeter is achieved at 25 °C and 1.0 bar nitrogen. This is two orders of magnitude higher than state–of–the–art ammonia synthesis at ambient conditions. The focused infrared light here is produced by a commercial simple CO2 laser, serving as a demonstration of potentially solar pumped lasers for nitrogen fixation and other high excitation chemistry. We anticipate such solar-laser-involved technology will bring unprecedented opportunities to realize not only local ammonia production but also other new chemistry

Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy

Abstract Objectives This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Parkinson’s disease (PD) and multiple system atrophy (MSA). Methods A total of 201 participants, including 57 patients with PD, 74 with MSA, and 70 healthy control (HCs) individuals, underwent T1WI and SWI scans. From the 12 subcortical nuclei (e.g. red nucleus, substantia nigra, subthalamic nucleus, putamen, globus pallidus, and caudate nucleus), 2640 radiomic features were extracted from both T1WI and SWI scans. Three classification models - logistic regression (LR), support vector machine (SVM), and light gradient boosting machine (LGBM) - were used to distinguish between MSA and PD, as well as among MSA, PD, and HC. These classifications were based on features extracted from T1WI, SWI, and a combination of T1WI and SWI. Five-fold cross-validation was used to evaluate the performance of the models with metrics such as sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC). During each fold, the ANOVA and least absolute shrinkage and selection operator (LASSO) methods were used to identify the most relevant subset of features for the model training process. Results The LGBM model trained by the features combination of T1WI and SWI exhibited the most outstanding differential performance in both the three-class classification task of MSA vs. PD vs. HC and the binary classification task of MSA vs. PD, with an accuracy of 0.814 and 0.854, and an AUC of 0.904 and 0.881, respectively. The texture-based differences (GLCM) of the SN and the shape-based differences of the GP were highly effective in discriminating between the three classes and two classes, respectively. Conclusions Radiomic features combining T1WI and SWI can achieve a satisfactory differential diagnosis for PD, MSA, and HC groups, as well as for PD and MSA groups, thus providing a useful tool for clinical decision-making based on routine MRI sequences