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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.
RGB-D sensors such as the Microsoft Kinect or the Asus Xtion are inexpensive 3D sensors. A depth image is computed by calculating the distortion of a known infrared light (IR) pattern which is projected into the scene. While these sensors are great devices they have some limitations. The distance they can measure is limited and they suffer from reflection problems on transparent, shiny, or very matte and absorbing objects. If more than one RGB-D camera is used the IR patterns interfere with each other. This results in a massive loss of depth information. In this paper, we present a simple and powerful method to overcome these problems. We propose a stereo RGB-D camera system which uses the pros of RGB-D cameras and combine them with the pros of stereo camera systems. The idea is to utilize the IR images of each two sensors as a stereo pair to generate a depth map. The IR patterns emitted by IR projectors are exploited here to enhance the dense stereo matching even if the observed objects or surfaces are texture-less or transparent. The resulting disparity map is then fused with the depth map offered by the RGB-D sensor to fill the regions and the holes that appear because of interference, or due to transparent or reflective objects. Our results show that the density of depth information is increased especially for transparent, shiny or matte objects.
Selective Laser Melting (SLM) is one of the Additive Manufacturing (AM) technologies applicable for producing complex geometries which are typically expensive or difficult to fabricate using conventional methods. This process has been extensively investigated experimentally for various metals and the fabrication process parameters have been established for different applications; however, fabricating 3D glass objects using SLM technology has remained a challenge so far although it could have many applications. This paper presents a summery on various experimental evaluations of a material database incorporating the build parameters of glass powder using the SLM process for jewelry applications.
Knowledge-based productivity in “low-tech” industries: evidence from firms in developing countries
(2014)
Using firm-level data from five developing countries—Brazil, Ecuador, South Africa, Tanzania, and Bangladesh—and three industries—food processing, textiles, and the garments and leather products—this article examines the importance of various sources of knowledge for explaining productivity and formally tests whether sector- or country-specific characteristics dominate these relationships. Knowledge sources driving productivity appear mainly sector specific. Also differences in the level of development affect the effectiveness of knowledge sources. In the food processing sector, firms with higher educated managers are more productive, and in least-developed countries, additionally those with technology licenses and imported machinery and equipment. In the capital-intensive textiles sector, productivity is higher in firms that conduct R&D. In the garments and leather products sector, higher education of the managers, licensing, and R&D raise productivity.