1,721,045 research outputs found
Editorial for the special issue on carbon based electronic devices
Full text linkFor more than 50 years, silicon has dominated the electronics industry [...]
Fostering research and innovation in materials manufacturing for Industry 5.0: The key role of domain intertwining between materials characterization, modelling and data science
Full text linkRecent advances in materials modelling, characterization and materials informatics suggest that deep integration of such methods can be a crucial aspect of the Industry 5.0 revolution, where the fourth industrial revolution paradigms are combined with the concepts of transition to a sustainable, human-centric and resilient industry. We pose a specific deep integration challenge beyond the ordinary multi-disciplinary modelling/characterization research approach in this short communication with research and innovation as drivers for scientific excellence. Full integration can be achieved by developing com-mon ontologies across different domains, enabling meaningful computational and experimental data integration and interoperability. On this basis, fine-tuning of adaptive materials modelling/characteriza-tion protocols can be achieved and facilitate computational and experimental efforts. Such interoperable and meaningful data combined with advanced data science tools (including machine learning and artifi-cial intelligence) become a powerful asset for materials scientists to extract complex information from the large amount of data generated by last generation characterization techniques. To achieve this ambi-tious goal, significant collaborative actions are needed to develop common, usable, and sharable digital tools that allow for effective and efficient twinning of data and workflows across the different materials modelling and characterization domains.(c) 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Innovative Data Management in advanced characterization: Implications for materials design
No full textThis paper describes a novel methodology of data documentation in materials characterization, which has as starting point the creation and usage of any Data Management Plan (DMP) for scientific data in the field of materials science and engineering, followed by the development and exploitation of ontologies for the harnessing of data created through experimental techniques. The case study that is discussed here is nanoindentation, a widely used method for the experimental assessment of mechanical properties on a small scale.The new documentation structure for characterization data (CHADA) is based on the definition of (i) sample, (ii) method, (iii) raw data and (iv) data analysis as the main component of the metadata associated to any characterization experiment. In this way, the relevant information can be stored inside the metadata associated to the experiment. The same methodology can be applicable to a large number of techniques that produce big amount of raw data, while at the same time it can be invaluable tool for big data analysis and for the creation of an open innovation environment, where data can be accessed freely and efficiently.Other fundamental aspects are reviewed in the paper, including the taxonomy and curation of data, the creation of ontology and classification of characterization techniques, the harnessing of data in open innovation environments via database construction along with the retrieval of information via algorithms. The issues of harmonization and standardization of such novel approaches are also critically discussed. Finally, the possible implications for nanomaterial design and the potential industrial impact of the new approach are described and a critical outlook is given
A novel machine learning method to exploit EBSD and nanoindentation for TRIP steels microstructures analysis
No full textThe recognition of phases and microstructures in TRIP-assisted bainitic-ferritic steels is challenging and requires sophisticated techniques to gain insights and reveal mechanical features with nanoscale precision. EBSD and nanoindentation have been employed to assess the surface composition and their properties within a reporting depth of 30 nm. Correlative mechanical microscopy and data science were used to overcome the shortcomings associated with the lack of an inclusive solution that combines the metadata from both techniques. A modular methodology is presented, which involves routines for exploiting structural and mechanical data via reproducible Machine Learning models (code and data are shared). The approach is structured to facilitate reuse by research community for correlating characterization mapping data, not limited to nanoindentation and EBSD. Gaussian mixture models are adopted to extract mechanical phases utilizing the nanomechanical properties. The K-means++ method is used for the first time to mine information from Inverse Polar Figure (IPF) mapping about anisotropy and to extract the knowledge from images for each grain, including grain coordinates and size. Moreover, k-nearest-neighbours regression was used to perform data imputation to fill in the values of descriptors related to missing coordinates relative to those of nanoindentation, grain boundary, EBSD phase, and EBSD anisotropy maps
Novel carbon fibers synthesis, plasma functionalization, and application to polymer composites
Full text linkPAN copolymers were synthesized via a novel technique, atom transfer radical polymerization (AMP), with the activator generated by electron transfer method (AGET). Carbon fibers (CF) were synthesized at low carbonization temperatures from the novel PAN precursor. Plasma treatment in an oxygen environment at a low pressure of 40 Pa was carried out for 5 minutes on the CF at 100 and 200 W plasma power. The morphology and structure of the CF changed after plasma functionalization, as evident from SEM analysis and Raman spectroscopy. The formation of functional groups like alcohols, carbonyl, and carboxylic on the surface of CF was confirmed with the aid of X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR). The wetting test confirmed the higher adhesion of the plasma functionalized CF with the epoxy matrix. Single fiber strength test revealed that plasma functionalized CF retained around 98% of their original tensile strength. Composites were fabricated from the pristine, and the plasma functionalized CF in 1 and 3% by weight with epoxy matrix. The surface-modified CF composites depicted improved tensile (23.4%), tribology (33.62%), and surface hardness (11.4%) properties compared to the composites fabricated from pristine CF
Advanced microstructural characterization in high-strength steels via machine learning-enhanced high-speed nanoindentation and EBSD mapping
Full text linkThis research investigates the nanoscopic features of Advanced High-Strength Steels (AHSS) through a bottom-up approach employing high-speed nanoindentation mapping (HSNM) to elucidate structure-property relationships. The influence of grain boundaries on nanomechanical properties was documented, highlighting the challenge of SEM-EBSD analysis in differentiating phases with identical crystal structures (BCC, FCC, etc.). Integrating SEM-EBSD with HSNM in the same region of interest is essential for detailed insights into phase/microstructure distribution and accurate grain boundary identification. A modular four-step analysis protocol, designed and validated on ferritic-bainitic TRIP steels (TBF), leverages machine learning-enhanced HSNM for significant advancements in AHSS design. The initial phase involves the application of the expectation-maximization algorithm for probability distribution fitting of HSNM data, deriving primary mechanical phase statistics. This exclusively facilitates the correlation of elastic modulus and hardness for each phase/microstructure using nanoindentation data. Further refinement of phase/microstructure to mechanical property correlations was achieved through a supervised machine learning approach, ensuring precise association between EBSD and nanoindentation data. This includes detailed image analysis and clustering of nanoindentation data, enhancing the precision in phase recognition. This methodology addresses the critical challenges in developing 3rd Generation AHSS, aiming to fill the gap in accurately identifying and quantifying phases such as martensite, austenite, bainite, and ferrite, thereby reducing classification and measurement uncertainties. The approach contributes to the fundamental understanding of AHSS microstructures and provides a scalable framework for the comprehensive characterization of structural materials
Investigation of Carbon Fibres Reclamation by Pyrolysis Process for Their Reuse Potential
Full text linkDuring Carbon Fibre Reinforced Polymers (CFRPs) manufacturing, large quantities of scrap are being produced and usually disposed to landfill or incinerated, resulting in a high environmental impact. Furthermore, CFRP parts that have been damaged or reached their end-of-life, follow the same disposal route and because of this, not only the environment is affected, but also high added-value materials, such as carbon fibres (CFs) are lost without further valorisation. Several recycling technologies have been suggested, such as pyrolysis, to retrieve the CF reinforcement from the CFRPs. However, pyrolysis produces CFs that have residual resin and pyrolytic carbon at their surface. In order to retrieve clean long fibres, oxidation treatment in high temperatures is required. The oxidation treatment, however, has a high impact on the mechanical properties of the reclaimed CFs; therefore, an optimised pyrolysis procedure of CFRPs and post-pyrolysis treatment of reclaimed fibres (rCFs) is required. In this study, CFRPs have been subjected to pyrolysis to investigate the reclamation of CF fabrics in their primal form. The temperature of 550 °C was selected as the optimum processing temperature for the investigated composites. A parametric study on the post-pyrolysis treatment was performed in order to remove the residues from the fabrics and at the same time to investigate the CFs reusability, in terms of their mechanical and surface properties
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