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In industrial processes there is a variety of heavy metals (e.g., copper, zinc, cadmium, and lead) in use for wires, coatings, paints, alloys, batteries, etc. Since the application of these transition metals for industry is inevitable, it is a vital task to develop proper analytical techniques for their monitoring at low activity levels, especially because most of these elements are acutely toxic for biological organisms. The determination of ions in solution by means of a simple and inexpensive sensor array is, therefore, a promising task. In this work, a sensor array with heavy metal-sensitive chalcogenide glass membranes for the simultaneous detection of the four ions Ag⁺, Cu2⁺, Cd2⁺, and Pb2⁺ in solution is realized. The results of the physical characterization by means of microscopy, profilometry, Rutherford backscattering spectroscopy (RBS), and scanning electron microscopy (SEM) as well as the electrochemical characterization by means of potentiometric measurements are presented. Additionally, the possibility to expand the sensor array by polymeric sensor membranes is discussed.
The capacitive electrolyte–insulator–semiconductor (EIS) structure is a typical device based on a field-effect sensor platform. With a simple silicon-based structure, EIS have been useful for several sensing applications, especially with incorporation of nanostructured films to modulate the ionic transport and the flat-band potential. In this paper, we report on ion transport and changes in flat-band potential in EIS sensors made with layer-by-layer films containing poly(amidoamine) (PAMAM) dendrimer and single-walled carbon nanotubes (SWNTs) adsorbed on p-Si/SiO 2 /Ta 2 O 5 chips with an Al ohmic contact. The impedance spectra were fitted using an equivalent circuit model, from which we could determine parameters such as the double-layer capacitance. This capacitance decreased with the number of bilayers owing to space charge accumulated at the electrolyte–insulator interface, up to three PAMAM/SWNTs bilayers, after which it stabilized. The charge-transfer resistance was also minimum for three bilayers, thus indicating that this is the ideal architecture for an optimized EIS performance. The understanding of the influence of nanostructures and the fine control of operation parameters pave the way for optimizing the design and performance of new EIS sensors.
Layer-by-Layer Assembly of Carbon Nanotubes Incorporated in Light-Addressable Potentiometric Sensors
(2009)
The ideal combination among biomolecules and nanomaterials is the key for reaching biosensing units with high sensitivity. The challenge, however, is to find out a stable and sensitive film architecture that can be incorporated on the sensor’s surface. In this paper, we report on the benefits of incorporating a layer-by-layer (LbL) nanofilm of polyamidoamine (PAMAM) dendrimer and carbon nanotubes (CNTs) on capacitive electrolyte-insulator-semiconductor (EIS) field-effect sensors for detecting urea. Three sensor arrangements were studied in order to investigate the adequate film architecture, involving the LbL film with the enzyme urease: (i) urease immobilized directly onto a bare EIS [EIS-urease] sensor; (ii) urease atop the LbL film over the EIS [EIS-(PAMAM/CNT)-urease] sensor; and (iii) urease sandwiched between the LbL film and another CNT layer [EIS-(PAMAM/CNT)-urease-CNT]. The surface morphology of all three urea-based EIS biosensors was investigated by atomic force microscopy (AFM), while the biosensing abilities were studied by means of capacitance–voltage (C/V) and dynamic constant-capacitance (ConCap) measureaments at urea concentrations ranging from 0.1 mM to 100 mM. The EIS-urease and EIS-(PAMAM/CNT)-urease sensors showed similar sensitivity (∼18 mV/decade) and a nonregular signal behavior as the urea concentration increased. On the other hand, the EIS-(PAMAM/CNT)-urease-CNT sensor exhibited a superior output signal performance and higher sensitivity of about 33 mV/decade. The presence of the additional CNT layer was decisive to achieve a urea based EIS sensor with enhanced properties. Such sensitive architecture demonstrates that the incorporation of an adequate hybrid enzyme-nanofilm as sensing unit opens new prospects for biosensing applications using the field-effect sensor platform.
Magnetic nanoparticles (MNP) are investigated with great interest for biomedical applications in diagnostics (e.g. imaging: magnetic particle imaging (MPI)), therapeutics (e.g. hyperthermia: magnetic fluid hyperthermia (MFH)) and multi-purpose biosensing (e.g. magnetic immunoassays (MIA)). What all of these applications have in common is that they are based on the unique magnetic relaxation mechanisms of MNP in an alternating magnetic field (AMF). While MFH and MPI are currently the most prominent examples of biomedical applications, here we present results on the relatively new biosensing application of frequency mixing magnetic detection (FMMD) from a simulation perspective. In general, we ask how the key parameters of MNP (core size and magnetic anisotropy) affect the FMMD signal: by varying the core size, we investigate the effect of the magnetic volume per MNP; and by changing the effective magnetic anisotropy, we study the MNPs’ flexibility to leave its preferred magnetization direction. From this, we predict the most effective combination of MNP core size and magnetic anisotropy for maximum signal generation.
Strategies of miniaturised reference electrodes integrated in a silicon-based „one chip“ pH sensor
(2003)
Miniaturised reference electrodes for field-effect sensors compatible to silicon chip technology
(2005)
In collaborative research projects, both researchers and practitioners work together solving business-critical challenges. These projects often deal with ETL processes, in which humans extract information from non-machine-readable documents by hand. AI-based machine learning models can help to solve this problem.
Since machine learning approaches are not deterministic, their quality of output may decrease over time. This fact leads to an overall quality loss of the application which embeds machine learning models. Hence, the software qualities in development and production may differ.
Machine learning models are black boxes. That makes practitioners skeptical and increases the inhibition threshold for early productive use of research prototypes. Continuous monitoring of software quality in production offers an early response capability on quality loss and encourages the use of machine learning approaches. Furthermore, experts have to ensure that they integrate possible new inputs into the model training as quickly as possible.
In this paper, we introduce an architecture pattern with a reference implementation that extends the concept of Metrics Driven Research Collaboration with an automated software quality monitoring in productive use and a possibility to auto-generate new test data coming from processed documents in production.
Through automated monitoring of the software quality and auto-generated test data, this approach ensures that the software quality meets and keeps requested thresholds in productive use, even during further continuous deployment and changing input data.