@phdthesis{Laroussi2022, author = {Laroussi, Arwa}, title = {Self-assembled monolayer based on 1,3-dimercaptopropan-2-ol : preparation, characterization and applications in electrochemical chemosensors}, doi = {10.26127/BTUOpen-6004}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-60041}, school = {BTU Cottbus - Senftenberg}, year = {2022}, abstract = {Die vorliegende Arbeit hatte die Entwicklung eines neuen biochemischen Sensors basierend auf einer Goldoberfl{\"a}che mit genau definierten strukturellen Nano-Motiven zum Ziel. Daf{\"u}r wurde das neue Ankermolek{\"u}l 1,3-Dimercaptopropan-2-ol synthetisiert. Aufgrund seiner symmetrischen Struktur mit 2 Thiol-Gruppen bildet es auf der Goldoberfl{\"a}che eine sehr stabile selbstanordnende Monoschicht (self-assembled monolayer SAM) aus. Die Monoschichten aus 1,3-Dimercaptopropan-2-ol wurden mit zyklischer Voltammetrie, Impedanzspektrometrie, R{\"o}ntgenfotoelektronenspektroskopie (XPS), Kinetik der Kapazit{\"a}t und Kontaktwinkelmessungen untersucht. Die Struktur der SAM wird von den Adsorptionsbedingungen bestimmt. Die vergleichende Untersuchung der Desorption der SAM zeigt, dass sich die Stabilit{\"a}t der SAM erh{\"o}ht, wenn die Molek{\"u}le zum gr{\"o}ßten Teil {\"u}ber beide Thiol-Gruppen gebunden sind. Auf dieser Monoschicht wurde elektrochemisch aktives p-Benzochinon immobilisiert. Das 1,3- Dimercaptopropan-2-ol diente als Ankermolek{\"u}l, 3-Mercaptopropions{\"a}ure fungierte als Abstandshalter (spacer) und 1,4-Benzochinon als Kopfgruppe. Die Oberfl{\"a}chenkonzentration des p-Benzochinons betrug 2.5 ± 0.2×10-10 mol•cm⁻². Das entspricht einer Funktionalisierung von 65 ± 5\% SAM-Molek{\"u}len. Die aufgebaute Schicht kann f{\"u}r die elektrisch adressierbare Immobilisierung von Biomolek{\"u}len oder die Entwicklung elektrokatalytischer Sensoren eingesetzt werden. Die SAM mit endst{\"a}ndigem Benzochinon wurde als Sensor zur quantitativen Detektion von Wasserstoffperoxid eingesetzt. Die Sensorcharakterisierung erfolgte mit zyklischer Voltammetrie im Potenzial-Bereich von -0.6 V bis +0.9 V als auch jeweils nur im anodischen oder kathodischen Bereich. Die Ergebnisse weisen auf eine oxidative elektrochemische Zersetzung des Wasserstoffperoxids bei einem Potenzial von ca. +0.4 V mit Sauerstoffbildung hin, w{\"a}hrend bei kathodischen Potenzialen eine Reduktion des gebildeten Sauerstoffs als auch des Wasserstoffperoxids stattfindet. Die Verminderung des Oxidationspotenzials f{\"u}r Wasserstoffperoxid an der Benzochinon-beschichteten Goldelektrode im Vergleich mit dem identischen Schichtaufbau ohne Benzochinon deutet auf einen elektrokatalytischen Effekt dieses Molek{\"u}ls bei der oxidativen Zersetzung von Wasserstoffperoxid. Die analytische Bewertung der Sensor-Leistungsf{\"a}higkeit erfolgte im voltammetrischen als auch im amperometrischen Modus. Im Konzentrationsbereich von 0.1 mM bis 2.5 mM Wasserstoffperoxid verl{\"a}uft die Sensor-Antwort linear, die untere Nachweisgrenze liegt bei ca. 4 µM Wasserstoffperoxid. Der amperometrische Chemosensor zeigt in Gegenwart typischer St{\"o}r-Substanzen wie Ascorbins{\"a}ure, Harns{\"a}ure oder Glukose eine gute Selektivit{\"a}t. Die hohe Empfindlichkeit legt nahe, dieses System nicht nur als Wasserstoffperoxid-Sensor einzusetzen, sondern auch als Transduktor f{\"u}r Biosensoren mit immobilisierten Oxidoreduktasen, wie z.B. als Glukose-Biosensor mit Glukoseoxidase.}, subject = {Benzochinon; Elektrochemisch aktive Schicht; Elektrokatalytische Oxidation; Wasserstoffperoxid; Benzoquinone; Electrochemically active layer; Electrocatalytic oxidation; Hydrogen peroxide; Biosensor; Chinone; Dithiolverbindungen; Bioelektrochemie; Elektrochemische Oxidation; Selbst organisierendes System}, language = {en} } @phdthesis{SoltaniZarrin2021, author = {Soltani Zarrin, Pouya}, title = {Permittivity biosensor for the recognition of saliva samples of COPD patients using neuromorphic-based machine learning}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-55016}, school = {BTU Cottbus - Senftenberg}, year = {2021}, abstract = {Chronic Obstructive Pulmonary Disease (COPD) is an inflammatory lung disease, causing breathing difficulties in patients due to obstructed airflow in lungs. COPD is one of the main leading causes of death worldwide with an annual mortality rate of three million people. Despite the absence of an effective treatment for COPD, an early-stage diagnosis plays a crucial role for the effective management of the disease. However, majority of patients with objective COPD go undiagnosed until late stages of their disease due to the lack of a reliable technology for the recognition and monitoring of COPD in Point-of-Care (PoC). Alternative diagnostic approaches such as the accurate examination of respiratory tract fluids like saliva can address this issue using a portable biosensor in a home-care environment. Nonetheless, the accurate diagnosis of COPD based on this approach is only possible by concurrent consideration of patients demographic--medical parameters. Therefore, Machine Learning (ML) tools are necessary for the comprehensive recognition of COPD in a PoC setting. On the other hand, drawbacks of cloud-based ML techniques for medical applications such as data safety, immerse energy consumption, and enormous computation requirements need to be addressed for this application. Therefore, the objective of this thesis was to develop a ML-equipped system for the management of COPD in a PoC setup. A portable permittivity biosensor was developed in this work and its in-vitro performance was evaluated throughout clinical experiments. ML techniques were applied on biosensor results, demonstrating the significant role of these algorithms for the recognition of COPD. Moreover, developed ML models were deployed on a neuromorphic platform for addressing the shortcomings of cloud-based approaches.}, subject = {BiCMOS permittivity biosensors; COPD management; Saliva characterization; Bioneuromorphics; Machine learning in medicine; BiCMOS Permittivit{\"a}ts-Biosensoren; COPD-Management; Speichelcharakterisierung; Bioneuromorphie; Maschinelles Lernen in der Medizin; Obstruktive Ventilationsst{\"o}rung; Speichel; BICMOS; Biosensor; Maschinelles Lernen}, language = {en} } @phdthesis{Scherbahn2018, author = {Scherbahn, Vitali}, title = {Towards ultrasensitive SPR-based sensing: self-referencing and detection of single nanoparticles}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-47119}, school = {BTU Cottbus - Senftenberg}, year = {2018}, abstract = {Surface plasmon resonance (SPR) and its extensions, surface plasmon resonance imaging (SPRi) and surface plasmon resonance microscopy (SPRM) both enabling visualization of the sensor surface, belong to classical, indispensable highly sensitive and robust optical (bio)analytical techniques to study affinity processes on a surface. Nevertheless, SPR and SPRi/SPRM undergo a continuous development in regard to the improvement of sensitivity. The main challenge in this direction is attributed to the separation of signals due to the binding of analytes and those due to the bulk effect. The main task of the present thesis was to apply different strategies to improve the performance of SPR sensing. Within this scope, two main objectives were pursued: (1) implementation and realization of a so-called internal referencing towards suppression of the bulk effect leading to an improvement and optimization of the signal-to-noise ratio (SNR) and (2) application of wide-field (WF)-SPRM to detect, to visualize and to characterize single nanoparticles adsorbed to modified surfaces. The first objective of this thesis comprises the realization of three different internal-referencing approaches. In the first approach, a self-referencing effect based on arbitrarily distributed micro-patterned self-assembled monolayer (SAM) containing sensing and referencing spots was realized. Measurements of classical antigen-antibody-interaction resulted in a 10-fold improvement of the SNR by suppression of the bulk effect and the corresponding microfluctuations of the bulk temperature. The application of the second internal-referencing-approach, ionic referencing, acting as an assessment of patterned SAM was realized using electrolytes with a high molar refraction of either anions or cations to micro-patterned SAM combined with WF-SPRM as detecting technology. As a result, successful, unobtrusive visualization and spatial distinction of micro-patterned surfaces was shown. Unlike visualization of micro-scaled surface areas, the application of spatio-temporal referencing in WF-SPRM, as a third type of internal referencing, enables to detect, moreover to visualize and localize, smallest changes in refractive index near/on the sensor surface. In that sense, the second objective of this thesis was dedicated to the application of the WF-SPRM technology combined with spatio-temporal referencing to detect, to visualize, to quantify and to characterize single nanoparticles adsorbed to the sensor surface; here, nanoparticles act as analyte species. Based on a sophisticated image analysis, successful detection and characterization of single nanoparticles in complex media such as wine, juice and sun cream was performed. Besides being a powerful solution for nanoparticles analytics, the WF-SPRM technology represents a base to develop novel, ultra-sensitive and fast (bio)sensing platforms. Within this scope, enzyme-assisted generation of nanoparticles was studied.}, subject = {Surface plasmon resonance microscopy; Self-referencing; Single nanoparticles; Ezymatic synthesis of nanoparticles; Surface patterning; Enzymvermittelte Synthese von Nanopartikeln; Selbstreferenzierung; Einzelne Nanopartikel; Oberfl{\"a}chenstrukturierung; Oberfl{\"a}chenplasmonenresonanzmikroskopie; Oberfl{\"a}chenplasmonenresonanz; Oberfl{\"a}chenstruktur; Biosensor; Nanopartikel}, language = {en} }