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The light-addressable potentiometric sensor (LAPS) is a semiconductor-based potentiometric sensor using a light probe with an ability of detecting the concentration of biochemical species in a spatially resolved manner. As an important biomedical sensor, research has been conducted to improve its performance, for instance, to realize high-speed measurement. In this work, the idea of facilitating the device-level simulation, instead of using an equivalent-circuit model, is presented for detailed analysis and optimization of the performance of the LAPS. Both carrier distribution and photocurrent response have been simulated to provide new insight into both amplitude-mode and phase-mode operations of the LAPS. Various device parameters can be examined to effectively design and optimize the LAPS structures and setups for enhanced performance.
Planar and three-dimensional (3D) interdigitated electrodes (IDE) with electrode digits separated by an insulating barrier of different heights were electrochemically characterized and compared in terms of their sensing properties. Due to the impact of the surface resistance, both types of IDE structures display a non-linear behavior in low-ionic strength solutions. The experimental data were fitted to an electrical equivalent circuit and interpreted taking into account the surface-charge-governed properties. The effect of a charged polyelectrolyte layer electrostatically assembled onto the sensor surface on the surface resistance in solutions with different KCl concentration is studied. In case of the same electrode footprint, 3D-IDEs show a larger cell constant and a higher sensitivity to molecular adsorption than that of planar IDEs. The obtained results demonstrate the potential of 3D-IDEs as a new transducer structure for a direct label-free sensing of charged molecules.
It is well known that biochemical and biotechnological processes are strongly dependent and affected by a variety of physico-chemical parameters such as pH value, temperature, pressure and electrolyte conductivity. Therefore, these quantities have to be monitored or controlled in order to guarantee a stable process operation, optimization and high yield. In this work, a sensor chip for the multiparameter detection of three physico-chemical parameters such as electrolyte conductivity, pH and temperature is realized using barium strontium titanate (BST) as multipurpose material. The chip integrates a capacitively coupled four-electrode electrolyte-conductivity sensor, a capacitive field-effect pH sensor and a thin-film Pt-temperature sensor. Due to the multifunctional properties of BST, it is utilized as final outermost coating layer of the processed sensor chip and serves as passivation and protection layer as well as pH-sensitive transducer material at the same time. The results of testing of the individual sensors of the developed multiparameter sensor chip are presented. In addition, a quasi-simultaneous multiparameter characterization of the sensor chip in buffer solutions with different pH value and electrolyte conductivity is performed. To study the sensor behavior and the suitability of BST as multifunctional material under harsh environmental conditions, the sensor chip was exemplarily tested in a biogas digestate.
Clostridium propionicum is the only organism known to ferment β-alanine, a constituent of coenzyme A (CoA) and the phosphopantetheinyl prosthetic group of holo-acyl carrier protein. The first step in the fermentation is a CoA-transfer to β-alanine. Subsequently, the resulting β-alanyl-CoA is deaminated by the enzyme β-alanyl-CoA:ammonia lyase (Acl) to reversibly form ammonia and acrylyl-CoA. We have determined the crystal structure of Acl in its apo-form at a resolution of 0.97 Å as well as in complex with CoA at a resolution of 1.59 Å. The structures reveal that the enyzme belongs to a superfamily of proteins exhibiting a so called “hot dog fold” which is characterized by a five-stranded antiparallel β-sheet with a long α-helix packed against it. The functional unit of all “hot dog fold” proteins is a homodimer containing two equivalent substrate binding sites which are established by the dimer interface. In the case of Acl, three functional dimers combine to a homohexamer strongly resembling the homohexamer formed by YciA-like acyl-CoA thioesterases. Here, we propose an enzymatic mechanism based on the crystal structure of the Acl·CoA complex and molecular docking. Proteins 2014; 82:2041–2053. © 2014 Wiley Periodicals, Inc.
In this work, the catalyst manganese(IV) oxide (MnO2), of calorimetric gas sensors (to monitor the sterilization agent vaporized hydrogen peroxide) has been investigated in more detail. Chemical analyses by means of X-ray-induced photoelectron spectroscopy have been performed to unravel the surface chemistry prior and after exposure to hydrogen peroxide vapor at elevated temperature, as applied in the sterilization processes of beverage cartons. The surface characterization reveals a change in oxidation states of the metal oxide catalyst after exposure to hydrogen peroxide. Additionally, a cleaning effect of the catalyst, which itself is attached to the sensor surface by means of a polymer interlayer, could be observed.
The Dry Low NOx (DLN) Micromix combustion principle with increased energy density is adapted for the industrial gas turbine APU GTCP 36-300 using hydrogen and hydrogen-rich syngas with a composition of 90%-Vol. hydrogen (H₂) and 10%-Vol. carbon-monoxide (CO). Experimental and numerical studies of several combustor geometries for hydrogen and syngas show the successful advance of the DLN Micromix combustion from pure hydrogen to hydrogen-rich syngas. The impact of the different fuel properties on the combustion principle and aerodynamic flame stabilization design laws, flow field, flame structure and emission characteristics is investigated by numerical analysis using a hybrid Eddy Break Up combustion model and validated against experimental results.
It is well known that the degradation environment can strongly influence the biodegradability and kinetics of biodegradation processes of polymers. Therefore, besides the monitoring of the degradation process, it is also necessary to control the medium in which the degradation takes place. In this work, a micromachined multi-parameter sensor chip for the control of the polymer-degradation medium has been developed. The chip combines a capacitive field-effect pH sensor, a four-electrode electrolyte-conductivity sensor and a thin-film Pt-temperature sensor. The results of characterization of individual sensors are presented. In addition, the multi-parameter sensor chip together with an impedimetric polymer-degradation sensor was simultaneously characterized in degradation solutions with different pH and electrolyte conductivity. The obtained results demonstrate the feasibility of the multi-parameter sensor chip for the control of the polymer-degradation medium.
Multimodal bioimage sensor
(2014)
To visualize the biochemical distribution two-dimensionally, we invented a solid-state-type ion image sensor that indicates the chemical activity of solutions and cells. The device, which consists of a CCD array covered with a functionalized membrane to detect charge accumulation, is highly sensitive to changes in the concentration and two-dimensional distribution of ions and biomaterials.
Background
True date palms (Phoenix dactylifera L.) are impressive trees and have served as an indispensable source of food for mankind in tropical and subtropical countries for centuries. The aim of this study is to differentiate date palm tree varieties by analysing leaflet cross sections with technical/optical methods and artificial neural networks (ANN).
Results
Fluorescence microscopy images of leaflet cross sections have been taken from a set of five date palm tree cultivars (Hewlat al Jouf, Khlas, Nabot Soltan, Shishi, Um Raheem). After features extraction from images, the obtained data have been fed in a multilayer perceptron ANN with backpropagation learning algorithm.
Conclusions
Overall, an accurate result in prediction and differentiation of date palm tree cultivars was achieved with average prediction in tenfold cross-validation is 89.1% and reached 100% in one of the best ANN.