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Jiang, Yijie

Publications and source records attributed to Jiang, Yijie.

9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Tailoring Piezoelectricity of 3D Printing PVDF-MoS 2 Nanocomposite via In Situ Induced Shear Stress

3D printing of unique structures with tunable properties offers significant advantages in the fabrication of complex and customized electronic devices. Here, this study introduces a process-microstructure–property-guided manufacturing route to fabricate PVDF-2D MoS 2 piezoelectric nanocomposites with tunable piezoelectric properties without having a postprocess. We control PVDF’s microstructure through direct ink writing (DIW) 3D printing while tuning PVDF-MoS 2 interfacial strain by controlling rheology and 3D printing parameters, such as nozzle size and printing speed. Our approach demonstrates tunable piezoelectricity in PVDF-MoS 2 , achieving a 15-fold increase in the piezoelectric coefficient (d 33 ) at a printing-induced shear stress of 6685 Pa. This enhancement arises from the electrostatic interactions between PVDF and MoS 2 and the filler distribution and alignment caused by the in situ shear stress in 3D printing, as confirmed by XPS and Raman mapping analyses. Our findings advance the understanding of piezoelectric mechanisms in PVDF-based nanocomposites, laying the foundation for 3D printing of piezoelectric sensors in wearable device applications with enhanced performance and customization capabilities.

2D MoS2↗
DOE OSTI2024

Advanced and functional composite materials via additive manufacturing: Trends and perspectives

Additive manufacturing (AM) has many advantages over conventional subtractive manufacturing methods. The cost-effective AM allows for precise fabrication of complex structures with less material waste, making it a popular manufacturing process in many applications. The recent development of AM materials has advanced significantly. Further, by precisely controlling material distribution and microstructural features, AM facilitates the creation of new composites with specific requirements. AM techniques have contributed considerably to integrating unique functionalities for various applications. This review emphasizes multiple categories of materials, including metal alloys, polymer-based composites, and sustainable composites, as well as applications of sensing materials and strategies and emerging artificial intelligence and machine learning.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Distinct Mechanism of Anti-Corrosion and Swelling-Adhesion Modeling of Low-Dimensional Nylon-Fluoropolymer Composite Coatings

Compared to other polymers, composite coatings with fluoropolymers as the matrix have attracted considerable interest due to their mechanical performance, chemical stability, and low surface energy. Herein, we present an anticorrosion mechanism of fluoropolymer composite coatings achieved by dispersing varying amounts of polyamide 12 (PA-12) particles over a poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) matrix. The choice of PVDF-HFP as a coating matrix was driven by its superior processability and mechanical properties over Teflon, while PA-12 afforded swelling capacity, preventing electrolyte permeation, and inhibiting matrix stress crack propagation. The corrosion resistance of the coatings was assessed mainly by potentiodynamic polarization and impedance measurements while being immersed in a NaCl solution. Our electrochemical measurement findings showed that 0.75% w/w PA-12 in the PVDF-HFP matrix significantly enhanced corrosion protection even after 21 days of immersion. Microscopy, dielectric spectroscopy, surface analysis, and mechanical testing (American Society for Testing and Materials standards) corroborated the results. PVDF-HFP coatings containing a minimum percolation threshold of 0.75 wt % PA-12 with 1 mil thickness on mild steel exhibited a remarkable increase in resistance and film adhesion, and a notable corrosion rate decrease. Finite element analysis simulation with a bilinear traction-separation law confirmed the swelling mechanism and adhesion behavior. Finally, this study highlights the potential of nylon particle dispersion in PVDF-HFP matrices as a practical approach to developing advanced anticorrosion coatings with promising practical applications in various industries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Investigation of 3D printed lightweight hybrid composites via theoretical modeling and machine learning

Hybrid composites combine two or more different fillers to achieve multifunctional or advanced material properties, such as lightweight and enhanced mechanical properties. The properties of the composites significantly depend on their microstructures, which can be tailored via advanced 3D printing processes. Understanding the process-structure-property relationships is critical to enable the design and engineering of novel hybrid composites for applications in aerospace, automotive, and protective coatings. Here, for this work, we develop 3D printable and lightweight hybrid composites and leverage the conventional design of experiments, a theoretical hybrid model, and an image-driven machine learning (ML) method to investigate their mechanical behaviors. The hybrid composites are formulated with elastomer matrix, microfillers, and thin-shell particles, enabling a significant degree of design freedom of microstructures with densities and mechanical properties varying up to 70% and 91%, respectively. Our statistical analysis indicates that the 3D printing path direction and the microfibers fraction are dominating process parameters with contribution percentages of 45.3% and 57.7% on the specific stiffness and strength, respectively. A hybrid mechanics model is developed based on a simple Weibull distribution function and classical single-filler models to effectively capture the variations in mechanical properties, however, it overestimates the values due to its statistical constraints and idealization of experimental uncertainty. The image-driven ML model leverages the microscale images directly without losing the structural details, shows more accurate predictions with experimental data, and has 48.6% lower root mean square error than the theoretical model.

3D printing↗
DOE OSTI2023

Boosting Piezoelectricity by 3D Printing PVDF‐MoS 2 Composite as a Conformal and High‐Sensitivity Piezoelectric Sensor

Abstract Additively manufactured flexible and high‐performance piezoelectric devices are highly desirable for sensing and energy harvesting of 3D conformal structures. Herein, the study reports a significantly enhanced piezoelectricity in polyvinylidene fluoride (PVDF) achieved through the in situ dipole alignment of PVDF within PVDF‐2D molybdenum disulfide (2D MoS 2 ) composite by 3D printing. The shear stress‐induced dipole poling of PVDF and 2D MoS 2 alignment are harnessed during 3D printing to boost piezoelectricity without requiring a post‐poling process. The results show a remarkable, more than the eight‐fold increment in the piezoelectric coefficient ( d 33 ) for 3D printed PVDF‐8wt.% MoS 2 composite over cast neat PVDF. The underlying mechanism of piezoelectric property enhancement is attributed to the increased volume fraction of β phase in PVDF, filler fraction, heterogeneous strain distribution around PVDF‐MoS 2 interfaces, and strain transfer to the nanofillers as confirmed by microstructural analysis and finite element simulation. These results provide a promising route to design and fabricate high‐performance 3D piezoelectric devices via 3D printing for next‐generation sensors and mechanical–electronic conformal devices.

2D MoS2↗
DOE OSTI2022

Recent Advances in 3D Printed Sensors: Materials, Design, and Manufacturing

Sensors are of great importance in different aspects of research and industry. Future sensors will require high-efficient and low-cost manufacturing, as well as high-performance functionality in areas, such as mechanical sensing, biomedical, and optical applications. Recent advances in 3D printing open a new paradigm for sensors fabrication as a precision, customizable, and seamless process. Here, in this article, the state-of-the-art 3D printing methods in sensors manufacturing is reviewed and the performance of the 3D printed sensing materials and devices is summarized. Special attention is paid to emerging multimaterial printing and 4D printing technologies, which will benefit the fabrication of a new generation of structures with multifunctionalities. The content on 3D printed sensors covers piezoelectric sensors, medical, and optical sensing devices. The performance of 3D printed sensors in comparison with the sensors made by traditional manufacturing is also covered. Finally, section 4 provides the viewpoints on the future development of 3D printed sensors.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Integrating helicoid channels for passive control of fiber alignment in direct-write 3D printing

3D printing of fiber-reinforced composites has been receiving increasing attention as an efficient additive approach enabling lightweight, functional, and high-performance components required for industrial applications. The properties of fiber composites significantly depend on internal microstructures, including fiber orientation, distribution, and degree of alignment. Although multiple strategies have been introduced for controlling the fiber composites microstructures, these strategies are mostly active approaches relying on additional control of mechatronics parts, external forces, or intentionally introduced unstable flows inside narrow nozzles. These methods suffer from higher risk of nozzle clogging and requiring extra parts in 3D printing. Here, we introduce helicoid channels into extrusion system as a passive approach to control the fiber alignment without any additional moving parts in direct-write 3D printing. The helicoid channels automatically guide the composite inks and align fibers before flowing into narrow nozzle space, avoiding clogging and improving printability. The analyses indicate that both helicoid surface to volume ratio and helix angle affect the pre-alignment of fibers, leading to tunable mechanical properties of printed fiber-reinforced composites with increased stiffness and strength up to 77.6% and 47.8%, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models

Abstract Interfacial mechanical properties are important in composite materials and their applications, including vehicle structures, soft robotics, and aerospace. Determination of traction–separation (T–S) relations at interfaces in composites can lead to evaluations of structural reliability, mechanical robustness, and failures criteria. Accurate measurements on T–S relations remain challenging, since the interface interaction generally happens at microscale. With the emergence of machine learning (ML), data-driven model becomes an efficient method to predict the interfacial behaviors of composite materials and establish their mechanical models. Here, we combine ML, finite element analysis (FEA), and empirical experiments to develop data-driven models that characterize interfacial mechanical properties precisely. Specifically, eXtreme Gradient Boosting (XGBoost) multi-output regressions and classifier models are harnessed to investigate T–S relations and identify the imperfection locations at interface, respectively. The ML models are trained by macroscale force–displacement curves, which can be obtained from FEA and standard mechanical tests. The results show accurate predictions of T–S relations ( R 2 = 0.988) and identification of imperfection locations with 81% accuracy. Our models are experimentally validated by 3D printed double cantilever beam specimens from different materials. Furthermore, we provide a code package containing trained ML models, allowing other researchers to establish T–S relations for different material interfaces.

97 MATHEMATICS AND COMPUTING↗