1,721,141 research outputs found
Bare iron oxide nanoparticles: Surface tunability for biomedical, sensing and environmental applications
Full text linkSurface modification is widely assumed as a mandatory prerequisite for the real applicability of iron oxide nanoparticles. This is aimed to endow prolonged stability, electrolyte and pH tolerance as well as a desired specific surface chemistry for further functionalization to these materials. Nevertheless, coating processes have negative consequences on the sustainability of nanomaterial production contributing to high costs, heavy environmental impact and difficult scalability. In this view, bare iron oxide nanoparticles (BIONs) are arousing an increasing interest and the properties and advantages of pristine surface chemistry of iron oxide are becoming popular among the scientific community. In the authors' knowledge, rare efforts were dedicated to the use of BIONs in biomedicine, biotechnology, food industry and environmental remediation. Furthermore, literature lacks examples highlighting the potential of BIONs as platforms for the creation of more complex nanostructured architectures, and emerging properties achievable by the direct manipulation of pristine iron oxide surfaces have been little studied. Based on authors' background on BIONs, the present review is aimed at providing hints on the future expansion of these nanomaterials emphasizing the opportunities achievable by tuning their pristine surfaces
A confirmation algorithm for predictive maintenance using the Rough Set Theory
No full textThis paper presents a technique to improve the accuracy of the predictions obtained using the Rough Set
Theory (RST) in non-deterministic cases (rough cases). The RST is here applied to the data collected by the
Intelligent Field Devices for identifying predictive diagnostic algorithms for machinery, plants, subsystems,
or components. The data analysis starts from a historical data set recorded from the field instruments,
and its final result is a set of ‘‘if–then” rules identifying predictive maintenance functions.
These functions may be used to predict if a component is going to fail or not in the next future. The prediction
is obtained by applying the rules extracted with the RST algorithm on the real-time values transmitted
by the field device. It may happen that some diagnoses are uncertain, in the sense that it is not
possible to take a certain decision (device sound or close to fail) with a given set of data. In this paper,
a new algorithm for increasing the confidence in these uncertain cases is presented. To show an example,
the proposed confirmation algorithm is applied to the predictive algorithms obtained for an intelligent
pressure transmitter
Toward the specificity of bare nanomaterial surfaces for protein corona formation
Full text linkAiming at creating smart nanomaterials for biomedical applications, nanotechnology aspires to develop a new generation of nanomaterials with the ability to recognize different biological components in a complex environment. It is common opinion that nanomaterials must be coated with organic or inorganic layers as a mandatory prerequisite for applications in biological systems. Thus, it is the nanomaterial surface coating that predominantly controls the nanomaterial fate in the biological environment. In the last decades, interdisciplinary studies involving not only life sciences, but all branches of scientific research, provided hints for obtaining uncoated inorganic materials able to interact with biological systems with high complexity and selectivity. Herein, the fragmentary literature on the interactions between bare abiotic materials and biological components is reviewed. Moreover, the most relevant examples of selective binding and the conceptualization of the general principles behind recognition mechanisms were provided. Nanoparticle features, such as crystalline facets, density and distribution of surface chemical groups, and surface roughness and topography were encompassed for deepening the comprehension of the general concept of recognition patterns
Validation of ZnO surge arresters model for overvoltage studies
No full textAbstract—This paper presents an improvement of the procedure
to determine the parameters of the simplified model for
metal-oxide surge arresters, derived from the IEEE standard
model. The main innovation introduced by the paper lays in the
possibility to define an approximate dynamic model even if data
about residual voltages for steep current pulse are not defined in
the manufacturer’s data sheets. This model has a wide rangeability
and its effectiveness is good for both medium- and high-voltage
arresters. The effectiveness of the model was tested for several arresters
of different manufacturers. The residual voltages reported
in the datasheets and obtained by the manufacturers through a
discharge test are compared with the simulations performed with
Matlab. The possibility of defining a dynamic model for surge
arresters even with missing data makes the proposed model a
useful tool for insulation coordination studies involving steep front
transients
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