Hollow carbon spheres in microwaves: Bio inspired absorbing coating

This is the final version of the article. Available from American Institute of Physics (AIP)] via the DOI in this record.The electromagnetic response of a heterostructure based on a monolayer of hollow glassy carbon spheres packed in 2D was experimentally surveyed with respect to its response to microwaves, namely, the Ka-band (26-37 GHz) frequency range. Such an ordered monolayer of spheres mimics the well-known "moth-eye"-like coating structures, which are widely used for designing anti-reflective surfaces, and was modelled with the long-wave approximation. Based on the experimental and modelling results, we demonstrate that carbon hollow spheres may be used for building an extremely lightweight, almost perfectly absorbing, coating for Ka-band applications.This work was supported in part by FP7-PEOPLE-2013- IRSES-610875 NAmiceMC, FP7 Twinning Grant Inconet EaP_004

A study of random resistor-capacitor-diode networks to assess the electromagnetic properties of carbon nanotube filled polymers

We determined the frequency dependent effective permittivity of a large ternary network of randomly positioned resistors, capacitors, and diodes. A linear circuit analysis of such systems is shown to match the experimental dielectric response of single-walled carbon nanotube (SWCNT) filled polymers. This modeling method is able to reproduce the two most important features of SWCNT filled composites, i.e. the low frequency dispersion and dipolar relaxation. As a result of the modeling important physical conclusion proved by the experimental data was done: the low frequency behavior of SWCNT-filled polymer composites is mostly caused by the fraction of semiconducting SWCNTs

Fully carbon metasurface: Absorbing coating in microwaves

This is the author accepted manuscript. The final version is available from AIP Publishing via the DOI in this record.The microwave-absorbing properties of a heterostructure consisting of an ordered monolayer of porous glassy carbon spheres were experimentally and theoretically investigated in the Ka-band (26–37 GHz) frequency range. The electromagnetic response of such a “moth-eye”-like all-carbon metasurface at a normal incidence angle was modelled on the basis of long-wave approximation. Modelling parameters in the Ka-band were used to estimate and predict the absorption properties of monolayers in free space in the range 1–40 GHz. Experimental and theoretical results demonstrate that a metasurface based on porous glassy carbon spheres is an inert, lightweight, compact, and perfectly absorbing material for designing new effective microwave absorbers in various practically used frequency ranges.The work was supported by Projects FP7-610875 (NAMICEMC, 2013-2017), H2020 RISE 734164 Graphene 3D, and FP7 IRSES project CANTOR (Grant No. FP7-612285). Sijin Li thanks the China Scholarship Council for the financial support under Grant No. 201406510029. Cameron Gallagher and Emma Burgess acknowledge financial support from the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom, via the EPSRC Centre for Doctoral Training in Metamaterials (Grant No. EP/L015331/1)

Cluster analysis and classification of process data by use of principal curves

Thesis (M.Ing.) -- University of Stellenbosch, 1999.ENGLISH SUMMARY: In this thesis a new method of clustering as wen as a new method of classification is proposed. Cluster analysis is a statistical method used to search for natural groups in an unstructured multivariate data set. Clusters are obtained in such. a way that the observations belonging to the same group are more alike than observations across groups. For instance, long data records are found in mineral processing plants, where the data can be reduced to clusters according to different ore types. Most of the existing clustering methods do not give reliable results when applied to engineering data, since these methods were mainly developed in the domains of psychology and biology. Classification analysis can be regarded as the natural continuation of cluster analysis. In order to classify objects, two types of observations are needed. The first are those observations whose group memberships are known a priori, which can be acquired through cluster analysis. The second kind of observations are those whose group memberships are unidentified. By means of classification these observations are allocated to one of the existing groups. Both of the proposed techniques are based on the use of a smooth one-dimensional curve, passing through the middle of the data set. To formalise such an idea, principal curves were developed by Hastie and Stuetzle (1989). A principal curve summarises the data in a non-linear fashion. For clustering, the principal curve of the entire unstructured data set is extracted. This one-dimensional representation of the data set is then used to search for different clusters. For classification, a principal curve is fitted to every known group in the data set. The observations to be assigned to one of the known groups are allocated to the group closest to the new point. Clustering with principal curves grouped engineering data better than most of the well-known clustering algorithms. Some shortcomings of this method were also established. Classification with principal curves gave similar, optimal results as compared to some existing classification methods. This classification method can be applied to data of any distribution, unlike statistical classification techniques.AFRIKAANSE OPSOMMING: In hierdie tesis word 'n nuwe metode elk vir trosanalise en klassifikasie analise voorgestel. Trosanalise is 'n statistiese tegniek waarrnee natuurlike groepe in 'n ongestruktureerde meerveranderlike datastel gevind word. Groepe word op so 'n wyse verkry dat die waamemings in dieselfde groep meer eenders is as waarnemings tussen groepe. Byvoorbeeld, in mineraalaanlegte is lang datarekords algemeen, wat deur middel van trosanalise gereduseer kan word na verskillende groepe, ooreenkomstig verskillende ertstipes. Die meerderheid bestaande groeperingsmetodes lewer nie betroubare resultate in hul toepassing op ingenieursdata nie, aangesien hierdie tegnieke meestal hul oorsprong in die sielkundige en biologiese velde het. Klassifikasie analise kan gesien word as die natuurlike opvolging van trosanalise. Om objekte te klassifiseer, word gebruik gemaak van twee soorte waarnemings. Die eerste tipe is daardie waamemings met a priori bekende groepsidentiteite, wat deur trosanalise gevind kan word. Die tweede soort is die waarnemings met onbekende groepsidentiteite. Elkeen van hierdie waarnemings kan deur middel van klassifikasie toegewys word aan een van die bestaande groepe. Beide hierdie voorgestelde tegnieke is gebaseer op die gebruik van 'n gladde, eendimensionele kromme wat deur die middel van die datastel beweeg. Om hierdie idee te formaliseer, is hoojkrommes ontwikkel deur Hastie en Stuetzle (1989). 'n Hoofkromme gee 'n nie-lineere opsomming van die data. Vir groeperingsdoeleindes word 'n hoofkromme uit die algehele ongestruktureerde datastel onttrek. Met klassifikasie word'n hootkurwe aan elke bekende groep in die datastel gepas. Die waameming wat aan een van die bestaande groepe toegewys moet word, word in die groep naaste aan die betrokke punt geplaas. Groepering met behulp van hoofkrommes, het met ingenieursdata beter resultate gelewer as meeste van die bestaande tegnieke. Deur middel van praktiese voorbeelde is sekere tekortkominge van hierdie groeperingsmetode vasgestel. Klassifikasie met behulp van hoofkrornmes lewer soortgelyke, optimale resultate as die van bekende vergelykende tegnieke. Die voorgestelde klassifikasie tegniek kan toegepas word op datastelle van enige verde ling, in teenstelling met die statistiese klassifikasietegnieke.Maste

FDM print materials applied in the microwave range

В статье рассмотрены результаты исследований, которые позволяют рассматривать углеродные микроволокна в качестве дешевой альтернативы наноуглеродным наполнителям для различных применений. Это позволит увеличить значения мнимой части эффективной комплексной диэлектрической проницаемости и, следовательно, приведет к увеличению поглощения в рассматриваемых структурах из-за омических потерь

Characterizing epoxy composites filled with carbonaceous nanoparticles from dc to microwave

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