scholarly journals Machine Learning and High-Throughput Robust Design of P3HT-CNT Composite Thin Films for High Electrical Conductivity

2020 ◽  
Author(s):  
Daniil Bash ◽  
Yongqiang Cai ◽  
Chellappan Vijila ◽  
Swee Liang Wong ◽  
Yang Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Carbon Nanotubes ◽  
Electrical Conductivity ◽  
High Throughput ◽  
Inorganic Materials ◽  
Single Wall Carbon Nanotubes ◽  
Local Optimum ◽  
Charge Delocalization ◽  
Experimental Scheme ◽  
Thin Film Preparation

<p>Combining high-throughput experiments with machine learning allows quick optimization of parameter spaces towards achieving target properties. In this study, we demonstrate that machine learning, combined with multi-labeled datasets, can additionally be used for scientific understanding and hypothesis testing. We introduce an automated flow system with high-throughput drop-casting for thin film preparation, followed by fast characterization of optical and electrical properties, with the capability to complete one cycle of learning of fully labeled ~160 samples in a single day. We combine regio-regular poly-3-hexylthiophene with various carbon nanotubes to achieve electrical conductivities as high as 1200 S/cm. Interestingly, a non-intuitive local optimum emerges when 10% of double-walled carbon nanotubes are added with long single wall carbon nanotubes, where the conductivity is seen to be as high as 700 S/cm, which we subsequently explain with high fidelity optical characterization. Employing dataset resampling strategies and graph-based regressions allows us to account for experimental cost and uncertainty estimation of correlated multi-outputs, and supports the proving of the hypothesis linking charge delocalization to electrical conductivity. We therefore present a robust machine-learning driven high-throughput experimental scheme that can be applied to optimize and understand properties of composites, or hybrid organic-inorganic materials.</p>

scholarly journals Machine Learning and High-Throughput Robust Design of P3HT-CNT Composite Thin Films for High Electrical Conductivity

2020 ◽  
Author(s):  
Daniil Bash ◽  
Yongqiang Cai ◽  
Chellappan Vijila ◽  
Swee Liang Wong ◽  
Yang Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Carbon Nanotubes ◽  
Electrical Conductivity ◽  
High Throughput ◽  
Inorganic Materials ◽  
Single Wall Carbon Nanotubes ◽  
Local Optimum ◽  
Charge Delocalization ◽  
Experimental Scheme ◽  
Thin Film Preparation

<p>Combining high-throughput experiments with machine learning allows quick optimization of parameter spaces towards achieving target properties. In this study, we demonstrate that machine learning, combined with multi-labeled datasets, can additionally be used for scientific understanding and hypothesis testing. We introduce an automated flow system with high-throughput drop-casting for thin film preparation, followed by fast characterization of optical and electrical properties, with the capability to complete one cycle of learning of fully labeled ~160 samples in a single day. We combine regio-regular poly-3-hexylthiophene with various carbon nanotubes to achieve electrical conductivities as high as 1200 S/cm. Interestingly, a non-intuitive local optimum emerges when 10% of double-walled carbon nanotubes are added with long single wall carbon nanotubes, where the conductivity is seen to be as high as 700 S/cm, which we subsequently explain with high fidelity optical characterization. Employing dataset resampling strategies and graph-based regressions allows us to account for experimental cost and uncertainty estimation of correlated multi-outputs, and supports the proving of the hypothesis linking charge delocalization to electrical conductivity. We therefore present a robust machine-learning driven high-throughput experimental scheme that can be applied to optimize and understand properties of composites, or hybrid organic-inorganic materials.</p>

scholarly journals Effect of SWCNT-Tuball Paper on the Lightning Strike Protection of CFRPs and Their Selected Mechanical Properties

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3140
Author(s):  
Kamil Dydek ◽  
Anna Boczkowska ◽  
Rafał Kozera ◽  
Paweł Durałek ◽  
Łukasz Sarniak ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Carbon Nanotubes ◽  
Electrical Conductivity ◽  
Copper Foil ◽  
Impact Resistance ◽  
Mechanical Analysis ◽  
Lightning Strike ◽  
Single Wall Carbon Nanotubes ◽  
Carbon Fibre Reinforced Polymer ◽  
The Impact

The main aim of this work was the investigation of the possibility of replacing the heavy metallic meshes applied onto the composite structure in airplanes for lightning strike protection with a thin film of Tuball single-wall carbon nanotubes in the form of ultra-light, conductive paper. The Tuball paper studied contained 75 wt% or 90 wt% of carbon nanotubes and was applied on the top of carbon fibre reinforced polymer before fabrication of flat panels. First, the electrical conductivity, impact resistance and thermo-mechanical properties of modified laminates were measured and compared with the reference values. Then, flat panels with selected Tuball paper, expanded copper foil and reference panels were fabricated for lightning strike tests. The effectiveness of lightning strike protection was evaluated by using the ultrasonic phased-array technique. It was found that the introduction of Tuball paper on the laminates surface improved both the surface and the volume electrical conductivity by 8800% and 300%, respectively. The impact resistance was tested in two directions, perpendicular and parallel to the carbon fibres, and the values increased by 9.8% and 44%, respectively. The dynamic thermo-mechanical analysis showed higher stiffness and a slight increase in glass transition temperature of the modified laminates. Ultrasonic investigation after lightning strike tests showed that the effectiveness of Tuball paper is comparable to expanded copper foil.

Polypyrrole coating of inorganic and organic materials by chemical oxidative polymerisation

2012 ◽  
Vol 66 (5) ◽  
Author(s):  
Mária Omastová ◽  
Matej Mičušík
Keyword(s):  
Carbon Nanotubes ◽  
Electrical Conductivity ◽  
Conducting Polymers ◽  
Metal Oxides ◽  
Clay Minerals ◽  
Radiation Shielding ◽  
Inorganic Materials ◽  
High Electrical Conductivity ◽  
New Materials ◽  
Nanoparticles Of Metal Oxides

AbstractPolypyrrole is one of the most frequently studied conducting polymers, having high electrical conductivity and stability, suitable for multi-functionalised applications. Coatings of chemically synthesised polypyrrole applied onto various organic and inorganic materials, such as polymer particles and films, nanoparticles of metal oxides, clay minerals, and carbon nanotubes are reviewed in this paper. Its primary subject is the formation of new materials and their application in which chemical oxidative polymerisation of pyrrole was used. These combined materials are used in antistatic applications, such as anti-corrosion coating, radiation-shielding, but also as new categories of sensors, batteries, and components for organic electronics are created by coating substrates with conducting polymer layers or imprinting technologies.

Modification of Electrical and Mechanical Properties of Single Wall Carbon Nanotubes by Reaction with SOCl2

2003 ◽  
Vol 772 ◽  
Author(s):  
Urszula Dettlaff-Weglikowska ◽  
Viera Skakalova ◽  
Ralf Graupner ◽  
Lothar Ley ◽  
Siegmar Roth
Keyword(s):  
Mechanical Properties ◽  
Carbon Nanotubes ◽  
Electrical Conductivity ◽  
Functional Groups ◽  
Carboxyl Groups ◽  
Single Wall Carbon Nanotubes ◽  
Chemical Methods ◽  
Chemical Functionalization ◽  
Single Wall Carbon ◽  
Bucky Paper

AbstractAttaching chemical functional groups to single wall carbon nanotubes (SWNTs) has been achieved by chemical methods. Oxidized purified nanotubes have been treated by thionyl chloride in order to convert carboxyl groups into acylchloride groups. We observe by XPS and EDX that not only chlorine atoms but sulphur containing functional groups are covalently bound to the nanotubes. This chemical functionalization also causes significant changes in the electrical and mechanical properties of the nanotubes. The electrical conductivity measured on mats (bucky paper) increases from 500 S/cm in pristine tubes to 2500 S/cm in modified tubes. Similarly, the Young's modulus of bucky paper increases by about 100 %.

Characterization of Thermo-Electric Interface Material with Carbon Nanotubes

2007 ◽  
Vol 1056 ◽  
Author(s):  
Piyush R Thakre ◽  
Yordanos Bisrat ◽  
Dimitris C Lagoudas
Keyword(s):  
Thermal Conductivity ◽  
Carbon Nanotubes ◽  
Electrical Conductivity ◽  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Epoxy Matrix ◽  
Loss Modulus ◽  
Single Wall Carbon Nanotubes ◽  
Electrical Percolation

ABSTRACTAn approach has been presented in the current work to fabricate and characterize nanocomposite systems for optimizing electrical and thermal properties without sacrificing mechanical properties. An epoxy matrix based nanocomposite system has been processed with different volume fractions of carbon nanotubes. The purpose was to tailor macroscale properties to meet competing performance requirements in microelectronics industy. The nanofiller consisted of comparatively low cost XD grade carbon nanotubes (XD-CNTs) that are optimized for electrical properties. This system was compared with another system consisting of single wall carbon nanotubes (SW-CNTs) as nano-reinforcements in epoxy matrix. The electrical percolation threshold (about seven orders of magnitude increase in electrical conductivity) measured by dielectric spectroscopy was found to be at lower loading weight fraction of SWCNTs (0.015 weight %) as compared to XD-CNTs (0.0225 weight %). However, the electrical conductivity after percolation was higher for XD-CNTs reinforced epoxy with respect to SW-CNTs filled nanocomposites. The governing mechanisms for this phenomenon were investigated using transmission optical microscope. The enhancement in thermal conductivity, measured using differential scanning calorimetry, was found to be moderate at lower weight loadings corresponding to electrical percolation. However, a 90% improvement in thermal conductivity was observed for 0.3 weight percent of XD-CNTs. Dynamic mechanical analysis was performed to measure the storage and loss modulus along with the glass transition temperature. No significant change in modulus values and glass transition temperature was measured for nanocomposites varied filler contents with respect to neat matrix.

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