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A multi-spot (4 × 4 spots) light-addressable potentiometric sensor (MLAPS) consisting of an Al–p-Si–SiO2 structure has been applied for the label-free electrical detection of DNA (deoxyribonucleic acid) immobilization and hybridization by the intrinsic molecular charge for the first time. Single-stranded probe ssDNA molecules (20 bases) were covalently immobilized onto the silanized SiO2 gate surface. The unspecific adsorption of mismatch ssDNA on the MLAPS gate surface was blocked by bovine serum albumin molecules. To reduce the screening effect and to achieve a high sensor signal, the measurements were performed in a low ionic-strength solution. The photocurrent–voltage (I–V) curves were simultaneously recorded on all 16 spots after each surface functionalization step. Large shifts of I–V curves of 25 mV were registered after the DNA immobilization and hybridization event. In contrast, a small potential shift (∼5 mV) was observed in case of mismatch ssDNA, revealing good specificity of the sensor. The obtained results demonstrate the potential of the MLAPS as promising transducer platform for the multi-spot label-free electrical detection of DNA molecules by their intrinsic molecular charge.
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.
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.
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.