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The coupling of ligand-stabilized gold nanoparticles with field-effect devices offers new possibilities for label-free biosensing. In this work, we study the immobilization of aminooctanethiol-stabilized gold nanoparticles (AuAOTs) on the silicon dioxide surface of a capacitive field-effect sensor. The terminal amino group of the AuAOT is well suited for the functionalization with biomolecules. The attachment of the positively-charged AuAOTs on a capacitive field-effect sensor was detected by direct electrical readout using capacitance-voltage and constant capacitance measurements. With a higher particle density on the sensor surface, the measured signal change was correspondingly more pronounced. The results demonstrate the ability of capacitive field-effect sensors for the non-destructive quantitative validation of nanoparticle immobilization. In addition, the electrostatic binding of the polyanion polystyrene sulfonate to the AuAOT-modified sensor surface was studied as a model system for the label-free detection of charged macromolecules. Most likely, this approach can be transferred to the label-free detection of other charged molecules such as enzymes or antibodies.
Goal Driven Business Modelling - Supporting Decision Making within Information System Development
(1995)
To train end users how to interact with digital systems is indispensable to ensure a strong computer security. 'Competence Developing Game'-based approaches are particularly suitable for this purpose because of their motivation-and simulation-aspects. In this paper the Competence Developing Game 'GHOST' for cybersecurity awareness trainings and its underlying patterns are described. Accordingly, requirements for an 'Competence Developing Game' based training are discussed. Based on these requirements it is shown how a game can fulfill these requirements. A supplementary game interaction design and a corresponding evaluation study is shown. The combination of training requirements and interaction design is used to create a 'Competence Developing Game'-based training concept. A part of these concept is implemented into a playable prototype that serves around one hour of play respectively training time. This prototype is used to perform an evaluation of the game and training aspects of the awareness training. Thereby, the quality of the game aspect and the effectiveness of the training aspect are shown.
GHEtool is a Python package that contains all the functionalities needed to deal with borefield design. It is developed for both researchers and practitioners. The core of this package is the automated sizing of borefield under different conditions. The sizing of a borefield is typically slow due to the high complexity of the mathematical background. Because this tool has a lot of precalculated data, GHEtool can size a borefield in the order of tenths of milliseconds. This sizing typically takes the order of minutes. Therefore, this tool is suited for being implemented in typical workflows where iterations are required.
GHEtool also comes with a graphical user interface (GUI). This GUI is prebuilt as an exe-file because this provides access to all the functionalities without coding. A setup to install the GUI at the user-defined place is also implemented and available at: https://www.mech.kuleuven.be/en/tme/research/thermal_systems/tools/ghetool.