Abstract:This paper studies the effect of pyrolysis temperature on the semiconductor-conductor transition of pristine polymer-derived ceramic silicon carbide (PDC SiC). A comprehensive study of microstructural evolution and conduction mechanism of PDC SiC pyrolyzed at the temperature range of 1200°C-1800°C is presented. At relatively lower pyrolysis temperatures (1200°C-1600°C), the carbon phase goes through a microstructural evolution from amorphous carbon to nanocrystalline carbon. The PDC SiC samples behave as a sem… Show more
“…Polymer-derived ceramics (PDCs) exhibit unusual electrical properties compared to conventional polycrystalline ceramics, such as semiconducting nature at very high temperature, [1][2][3] anomalous piezoresistivity, 4 tunable conductivity, 5,6 and good oxidation and creep resistance. [7][8][9][10] The unique microstructure of the amorphous PDCs, consisting of amorphous matrix and free carbon, 11 is responsible for its tailorable electrical properties.…”
This study reports the complex impedance and alternative current conductivity of polymer-derived ceramic SiC (PDC-SiC) annealed at ultrahigh temperatures. The PDC-SiC shows an inductive response when annealed at temperatures of 1700°C-1900°C due to the percolation of turbostratic carbon. The material returns to a capacitive response at an annealing temperature of 2000°C due to the dissolution of carbon into the SiC lattice. The electrical resistance of the carbon phase decreases with the increase in annealing temperature. These results provide new insights into the effects of processing temperature on microstructure evolution and electrical and dielectric property development of the PDC-SiC ceramic system.
“…Polymer-derived ceramics (PDCs) exhibit unusual electrical properties compared to conventional polycrystalline ceramics, such as semiconducting nature at very high temperature, [1][2][3] anomalous piezoresistivity, 4 tunable conductivity, 5,6 and good oxidation and creep resistance. [7][8][9][10] The unique microstructure of the amorphous PDCs, consisting of amorphous matrix and free carbon, 11 is responsible for its tailorable electrical properties.…”
This study reports the complex impedance and alternative current conductivity of polymer-derived ceramic SiC (PDC-SiC) annealed at ultrahigh temperatures. The PDC-SiC shows an inductive response when annealed at temperatures of 1700°C-1900°C due to the percolation of turbostratic carbon. The material returns to a capacitive response at an annealing temperature of 2000°C due to the dissolution of carbon into the SiC lattice. The electrical resistance of the carbon phase decreases with the increase in annealing temperature. These results provide new insights into the effects of processing temperature on microstructure evolution and electrical and dielectric property development of the PDC-SiC ceramic system.
“…In most cases when the macroscopic performance of materials is studied, the focal point is geared toward the structure-performance relationship [151]. AI applications in microscopic property prediction can concentrate on several aspects, including and are not limited to the microstructure, the lattice constant, electron affinity, and molecular atomization energy [150,[152][153][154][155]. Material's microstructure can be characterized through image data such as scanning electron microscope (SEM) as well as transmission electron microscope (TEM).…”
Section: Modeling the Effect Of Manufacturing Conditions Onmentioning
Today's manufacturing systems are becoming increasingly complex, dynamic and connected. The factory operation faces challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in Artificial Intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) Review the State-of-the-Art applications of AI to representative manufacturing problems, (2) Provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) Identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human robotic collaboration, process monitoring, diagnosis and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.
“…Therefore, the shapes of polymers could be tailored according to specific requirements after pyrolysis. At present, the most widely studied PDCs systems are mainly PDCs-SiC [5][6][7][8][9][10][11][12], PDCs-SiOC [13][14][15][16][17][18][19][20][21][22], and PDCs-SiCN [23][24][25][26][27][28][29][30][31][32] systems.…”
Section: Introductionmentioning
confidence: 99%
“…The microstructure of PDCs was greatly influenced by pyrolysis temperature [6,19,22,27]. Taking the polysilazane-derived SiCN as an example, Yu et al [34] systematically summarized the microstructural evolution of PDCs under different pyrolysis temperatures.…”
Section: Introductionmentioning
confidence: 99%
“…Wilhelm's group [18] fabricated PDCs-SiOC with high specific surface area (~400 m 2 •g −1 ) by freeze casting and loading with Ni nanoparticles, and it displayed good catalytic activity; in the CO 2 methanation reaction, the maximum conversion rate was 0.49, and the maximum methane selectivity was 0.74. In addition, by functionalizing polymer-derived Si-based ceramics, it can also significantly improve its mechanical [5,15,43,47,61], thermal [41,43,[62][63][64], and electrical properties [6,14,26,40,43], which could be widely used in the preparation of conductive ceramics, sensors, membranes, coatings, and components for usage under harsh environments.…”
Polymer derived ceramics (PDCs) are promising candidates for usages as the functionalization of inorganic Si-based materials. Compared with traditional ceramics preparation methods, it is easier to prepare and functionalize ceramics with complex shapes by using the PDCs technique, thereby broadening the application fields of inorganic Si-based ceramics. In this article, we summarized the research progress and the trends of PDCs in recent years, especially most recent three years. Fabrication techniques (traditional preparation, 3D printing, template method, freezing casting techniques, etc.), microstructural tailoring mainly via additive doping, and properties (mechanical, thermal, electrical, as well as dielectric and electromagnetic wave absorption properties) of Si-based PDCs were explicated. Meanwhile, challenges and perspectives for PDCs techniques were proposed as well, with the purpose to enlighten multiple functionalized applications of polymer-derived Si-based ceramics.
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