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Georgios Konstantopoulos
Research Unit of Advanced, Composite, Nano Materials & Nanotechnology, School of Chemical Engineering, National Technical University of Athens, Department III, 9 Heroon Polytechniou str., Zografou Campus,157 73 Athens, Greece

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Journal article
Published: 17 June 2020 in Materials
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A methodology for designing the oxidative stabilization process of polyacrylonitrile (PAN) fibers is examined. In its core, this methodology is based on a model that describes the characteristic fiber length variation during thermal processing, through the de-convolution of three main contributors (i.e., entropic and chemical shrinkage and creep elongation). The model demonstrated an additional advantage of offering further insight into the physical and chemical phenomena taking place during the treatment. Validation of PAN-model prediction performance for different processing parameters was achieved as demonstrated by Fourier Transform Infrared Spectroscopy (FTIR) and Differential Scanning Calorimetry (DSC). Τensile testing revealed the effect of processing parameters on fiber quality, while model prediction demonstrated that ladder polymer formation is accelerated at temperatures over 200 °C. Additionally, according the DSC and FTIR measurements predictions from the application of the model during stabilization seem to be more precise at high-temperature stabilization stages. It was shown that mechanical properties could be enhanced preferably by including a treatment step below 200 °C, before the initiation of cyclization reactions. Further confirmation was provided via Raman spectroscopy, which demonstrated that graphitic like planes are formed upon stabilization above 200 °C, and thus multistage stabilization is required to optimize synthesis of carbon fibers. Optical Microscopy proved that isothermal stabilization treatment did not severely alter the cross section geometry of PAN fiber monofilaments.

ACS Style

George Konstantopoulos; Spyros Soulis; Dimitrios Dragatogiannis; Costas Charitidis. Introduction of a Methodology to Enhance the Stabilization Process of PAN Fibers by Modeling and Advanced Characterization. Materials 2020, 13, 2749 .

AMA Style

George Konstantopoulos, Spyros Soulis, Dimitrios Dragatogiannis, Costas Charitidis. Introduction of a Methodology to Enhance the Stabilization Process of PAN Fibers by Modeling and Advanced Characterization. Materials. 2020; 13 (12):2749.

Chicago/Turabian Style

George Konstantopoulos; Spyros Soulis; Dimitrios Dragatogiannis; Costas Charitidis. 2020. "Introduction of a Methodology to Enhance the Stabilization Process of PAN Fibers by Modeling and Advanced Characterization." Materials 13, no. 12: 2749.

Journal article
Published: 26 May 2020 in Fibers
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The aim of this work is to review a possible correlation of composition, thermal processing, and recent alternative stabilization technologies to the mechanical properties. The chemical microstructure of polyacrylonitrile (PAN) is discussed in detail to understand the influence in thermomechanical properties during stabilization by observing transformation from thermoplastic to ladder polymer. In addition, relevant literature data are used to understand the comonomer composition effect on mechanical properties. Technologies of direct fiber heating by irradiation have been recently involved and hold promise to enhance performance, reduce processing time and energy consumption. Carbon fiber manufacturing can provide benefits by using higher comonomer ratios, similar to textile grade or melt-spun PAN, in order to cut costs derived from an acrylonitrile precursor, without suffering in regard to mechanical properties. Energy intensive processes of stabilization and carbonization remain a challenging field of research in order to reduce both environmental impact and cost of the wide commercialization of carbon fibers (CFs) to enable their broad application.

ACS Style

Spyridon Soulis; George Konstantopoulos; Elias P. Koumoulos; Costas A. Charitidis. Impact of Alternative Stabilization Strategies for the Production of PAN-Based Carbon Fibers with High Performance. Fibers 2020, 8, 1 .

AMA Style

Spyridon Soulis, George Konstantopoulos, Elias P. Koumoulos, Costas A. Charitidis. Impact of Alternative Stabilization Strategies for the Production of PAN-Based Carbon Fibers with High Performance. Fibers. 2020; 8 (6):1.

Chicago/Turabian Style

Spyridon Soulis; George Konstantopoulos; Elias P. Koumoulos; Costas A. Charitidis. 2020. "Impact of Alternative Stabilization Strategies for the Production of PAN-Based Carbon Fibers with High Performance." Fibers 8, no. 6: 1.

Preprint
Published: 26 May 2020
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A methodology is proposed for designing the stabilization process of polyacrylonitrile (PAN) fibers. In its core, this methodology is based on a model that describes the characteristic fiber length change during the treatment, through the de-convolution of the three main contributors (i.e. entropic shrinkage, creep, and chemical shrinkage). The model has the additional advantage of offering further insight into the physical and chemical phenomena taking place during the treatment. Validation of PAN-model prediction performance for different processing parameters was achieved as demonstrated by FTIR and DSC. Τensile testing revealed the effect of processing parameters on fiber quality, while model prediction demonstrated that ladder polymer formation is accelerated at temperatures over 200oC. Additionally, according the DSC and FTIR measurements predictions from the application of the model during stabilization seem to be more precise at high-temperature stabilization stages. It was shown that mechanical properties could be enhanced preferably by including a treatment step below 200oC, before the initiation of cyclization reactions. Further confirmation was provided via Raman spectroscopy, which demonstrated that graphitic like planes are formed upon stabilization above 200oC, and thus multistage stabilization is required to optimize synthesis of carbon fibers. Optical Microscopy proved that isothermal stabilization treatment did not severy alter the cross section geometry of PAN fiber monofilaments.

ACS Style

George Konstantopoulos; Spyros Soulis; Dimitrios Dragatogiannis; Costas Charitidis. Introduction of a Methodology to Enhance the Stabilization Process of PAN Fibers by Modeling and Advanced Characterization. 2020, 1 .

AMA Style

George Konstantopoulos, Spyros Soulis, Dimitrios Dragatogiannis, Costas Charitidis. Introduction of a Methodology to Enhance the Stabilization Process of PAN Fibers by Modeling and Advanced Characterization. . 2020; ():1.

Chicago/Turabian Style

George Konstantopoulos; Spyros Soulis; Dimitrios Dragatogiannis; Costas Charitidis. 2020. "Introduction of a Methodology to Enhance the Stabilization Process of PAN Fibers by Modeling and Advanced Characterization." , no. : 1.

Journal article
Published: 30 March 2020 in Nanomaterials
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Nanoindentation was utilized as a non-destructive technique to identify Portland Cement hydration phases. Artificial Intelligence (AI) and semi-supervised Machine Learning (ML) were used for knowledge gain on the effect of carbon nanotubes to nanomechanics in novel cement formulations. Data labelling is performed with unsupervised ML with k-means clustering. Supervised ML classification is used in order to predict the hydration products composition and 97.6% accuracy was achieved. Analysis included multiple nanoindentation raw data variables, and required less time to execute than conventional single component probability density analysis (PDA). Also, PDA was less informative than ML regarding information exchange and re-usability of input in design predictions. In principle, ML is the appropriate science for predictive modeling, such as cement phase identification and facilitates the acquisition of precise results. This study introduces unbiased structure-property relations with ML to monitor cement durability based on cement phases nanomechanics compared to PDA, which offers a solution based on local optima of a multidimensional space solution. Evaluation of nanomaterials inclusion in composite reinforcement using semi-supervised ML was proved feasible. This methodology is expected to contribute to design informatics due to the high prediction metrics, which holds promise for the transfer learning potential of these models for studying other novel cement formulations.

ACS Style

Georgios Konstantopoulos; Elias P. Koumoulos; Costas A. Charitidis. Testing Novel Portland Cement Formulations with Carbon Nanotubes and Intrinsic Properties Revelation: Nanoindentation Analysis with Machine Learning on Microstructure Identification. Nanomaterials 2020, 10, 645 .

AMA Style

Georgios Konstantopoulos, Elias P. Koumoulos, Costas A. Charitidis. Testing Novel Portland Cement Formulations with Carbon Nanotubes and Intrinsic Properties Revelation: Nanoindentation Analysis with Machine Learning on Microstructure Identification. Nanomaterials. 2020; 10 (4):645.

Chicago/Turabian Style

Georgios Konstantopoulos; Elias P. Koumoulos; Costas A. Charitidis. 2020. "Testing Novel Portland Cement Formulations with Carbon Nanotubes and Intrinsic Properties Revelation: Nanoindentation Analysis with Machine Learning on Microstructure Identification." Nanomaterials 10, no. 4: 645.

Journal article
Published: 21 December 2019 in Fibers
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Carbon fiber reinforced polymers (CFRPs) are continuously gaining attention in aerospace and space applications, and especially their multi-scale reinforcement with nanoadditives. Carbon nanotubes (CNTs), graphene, carbon nanofibers (CNFs), and their functionalized forms are often incorporated into interactive systems to engage specific changes in the environment of application to a smart response. Structural integrity of these nanoscale reinforced composites is assessed with advanced characterization techniques, with the most prominent being nanoindentation testing. Nanoindentation is a well-established technique, which enables quantitative mapping of nanomechanical properties with the μm surficial and nm indentation resolution scale and high precision characterization. This feature enables the characterization of the interface in a statistical and quantitative manner and the correlation of (nano-) reinforcement to interface properties of CFRPs. Identification of reinforcement is performed with k-Nearest Neighbors and Support Vector Machine classification algorithms. Expertise is necessary to describe the physical problem and create representative training/testing datasets. Development of open source Machine Learning algorithms can have an influential impact on uniformity of nanometry data creation and management. The statistical character of nanoindentation is a key factor to supply information on heterogeneity of multiscale reinforced composites. Both the identification of (nano-) reinforcement and quality assessment of composites are provided by involving artificial intelligence.

ACS Style

Elias Koumoulos; George Konstantopoulos; Costas Charitidis. Applying Machine Learning to Nanoindentation Data of (Nano-) Enhanced Composites. Fibers 2019, 8, 3 .

AMA Style

Elias Koumoulos, George Konstantopoulos, Costas Charitidis. Applying Machine Learning to Nanoindentation Data of (Nano-) Enhanced Composites. Fibers. 2019; 8 (1):3.

Chicago/Turabian Style

Elias Koumoulos; George Konstantopoulos; Costas Charitidis. 2019. "Applying Machine Learning to Nanoindentation Data of (Nano-) Enhanced Composites." Fibers 8, no. 1: 3.

Journal article
Published: 08 December 2019 in Applied Sciences
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In this study, the carbon fiber manufacturing process is investigated, using high-density polyethylene (HDPE) and esterified lignin either with lactic acid (LA) or with poly(lactic acid) (PLA) as precursors. More specifically, lignin was modified using either LA or PLA in order to increase its chemical affinity with HDPE. The modified compounds were continuously melt spun to fibrous materials by blending with HDPE in order to fabricate a carbon fiber precursor. The obtained products were characterized with respect to their morphology, as well as their structure and chemical composition. Moreover, an assessment of both physical and structural transformations after modification of lignin with LA and PLA was performed in order to evaluate the spinning ability of the composite fibers, as well as the thermal processing to carbon fibers. This bottom–up approach seems to be able to provide a viable route considering large scale production in order to transform lignin in value-added product. Tensile tests revealed that the chemical lignin modification allowed an enhancement in its spinning ability due to its compatibility improvement with the commercial low-cost and thermoplastic HDPE polymer. Finally, stabilization and carbonization thermal processing was performed in order to obtain carbon fibers.

ACS Style

Panagiotis Goulis; Ioannis A. Kartsonakis; George Konstantopoulos; Costas A. Charitidis. Synthesis and Processing of Melt Spun Materials from Esterified Lignin with Lactic Acid. Applied Sciences 2019, 9, 5361 .

AMA Style

Panagiotis Goulis, Ioannis A. Kartsonakis, George Konstantopoulos, Costas A. Charitidis. Synthesis and Processing of Melt Spun Materials from Esterified Lignin with Lactic Acid. Applied Sciences. 2019; 9 (24):5361.

Chicago/Turabian Style

Panagiotis Goulis; Ioannis A. Kartsonakis; George Konstantopoulos; Costas A. Charitidis. 2019. "Synthesis and Processing of Melt Spun Materials from Esterified Lignin with Lactic Acid." Applied Sciences 9, no. 24: 5361.