An official website of the United States government
Spread tow carbon fiber composites are receiving increased attention for diverse applications in space and sports gear due to their thin form, which is suitable for deployable structures, and high tensile strength. Their compressive strength, however, is much lower than their tensile strength due to low interlaminar strength. Herein we report a facile technique to enhance their performance through interlaminar insertion of aligned carbon nanotube (CNT) sheets. The inserted CNT sheets also provide electrical conductivity in the composites even at a low CNT loading below the electrical percolation threshold established for CNT-filled composites. Mechanical and electrical characterization was conducted on the CNT sheet-inserted composites and the baseline composites. Results show that the CNT sheets increase the compressive strength by 14.7% compared with the baseline. Such an increase is attributed to the increased adhesion provided by the inserted CNT sheets at the interface between neighboring plies, which also increases the interlaminar shear strength by 33.0% and the interfacial mode-II fracture toughness by 34.6% compared with the baseline composites without inserting CNT sheets. The well-aligned CNT sheet structure maintained between the neighboring plies contributed to a 64.7% increase in electrical conductivity compared with the baseline composites. The findings indicate that the insertion of well-aligned ultrathin CNT sheets in the interlaminar region of a spread tow carbon fiber composite provides significant enhancement in mechanical and electrical performance, paving the path toward applications where both mechanical and electrical performances are crucial, such as for structural health monitoring, lightning protection, and de-icing in aircraft and wind blades.
more »
« less
Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning
Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress–strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships. By leveraging the database and physics- and chemistry-informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than efficient MD simulations. Hierarchical Graph Neural Networks with Spatial Information (HS-GNNs) are introduced, which integrate chemistry knowledge. HS-GNNs as well as extreme gradient boosted trees (XGBoost) achieve forecasts of mechanical properties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The reliability equals experimental accuracy and is up to 20 times higher than other ML methods. Predictions maintain 8 to 18% accuracy for large CNT bundles, CNT junctions, and carbon fiber cross-sections outside the training distribution. The physics- and chemistry-informed HS-GNN works remarkably well for data outside the training range while XGBoost works well with limited training data inside the training range. The carbon nanostructure database is designed for integration with multimodal experimental and simulation data, scalable beyond 100 nm size, and extendable to chemically similar compounds and broader property ranges. The ML approaches have potential for applications in structural materials, nanoelectronics, and carbon-based catalysts.
more »
« less
- PAR ID:
- 10574960
- Publisher / Repository:
- Proceedings of the National Academy of Sciences
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 122
- Issue:
- 10
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The substitution of traditional copper power transmission cables with lightweight copper–carbon nanotube (Cu–CNT) composite fibers is critical for reducing the weight, fuel consumption, and CO2 emissions of automobiles and aircrafts. Such a replacement will also allow for lowering the transmission power loss in copper cables resulting in a decrease in coal and gas consumption, and ultimately diminishing the carbon footprint. In this work, we created a lightweight Cu–CNT composite fiber through a multistep scalable process, including spinning, densification, functionalization, and double-layer copper deposition. The characterization and testing of the fabricated fiber included surface morphology, electrical conductivity, mechanical strength, crystallinity, and ampacity (current density). The electrical conductivity of the resultant composite fiber was measured to be 0.5 × 106 S/m with an ampacity of 0.18 × 105 A/cm2. The copper-coated CNT fibers were 16 times lighter and 2.7 times stronger than copper wire, as they revealed a gravimetric density of 0.4 g/cm3 and a mechanical strength of 0.68 GPa, suggesting a great potential in future applications as lightweight power transmission cables.more » « less
-
Limestone calcined clay cement (LC3) is a sustainable alternative to ordinary Portland cement, capable of reducing the binder’s carbon footprint by 40% while satisfying all key performance metrics. The inherent compositional heterogeneity in select components of LC3, combined with their convoluted chemical interactions, poses challenges to conventional analytical models when predicting mechanical properties. Although some studies have employed machine learning (ML) to predict the mechanical properties of LC3, many have overlooked the pivotal role of feature selection. Proper feature selection not only refines and simplifies the structure of ML models but also enhances these models’ prediction performance and interpretability. This research harnesses the power of the random forest (RF) model to predict the compressive strength of LC3. Three feature reduction methods—Pearson correlation, SHapley Additive exPlanations, and variable importance—are employed to analyze the influence of LC3 components and mixture design on compressive strength. Practical guidelines for utilizing these methods on cementitious materials are elucidated. Through the rigorous screening of insignificant variables from the database, the RF model conserves computational resources while also producing high-fidelity predictions. Additionally, a feature enhancement method is utilized, consolidating numerous input variables into a singular feature while feeding the RF model with richer information, resulting in a substantial improvement in prediction accuracy. Overall, this study provides a novel pathway to apply ML to LC3, emphasizing the need to tailor ML models to cement chemistry rather than employing them generically.more » « less
-
The parameter space of CNT forest synthesis is vast and multidimensional, making experimental and/or numerical exploration of the synthesis prohibitive. We propose a more practical approach to explore the synthesis-process relationships of CNT forests using machine learning (ML) algorithms to infer the underlying complex physical processes. Currently, no such ML model linking CNT forest morphology to synthesis parameters has been demonstrated. In the current work, we use a physics-based numerical model to generate CNT forest morphology images with known synthesis parameters to train such a ML algorithm. The CNT forest synthesis variables of CNT diameter and CNT number densities are varied to generate a total of 12 distinct CNT forest classes. Images of the resultant CNT forests at different time steps during the growth and self-assembly process are then used as the training dataset. Based on the CNT forest structural morphology, multiple single and combined histogram-based texture descriptors are used as features to build a random forest (RF) classifier to predict class labels based on correlation of CNT forest physical attributes with the growth parameters. The machine learning model achieved an accuracy of up to 83.5% on predicting the synthesis conditions of CNT number density and diameter. These results are the first step towards rapidly characterizing CNT forest attributes using machine learning. Identifying the relevant process-structure interactions for the CNT forests using physics-based simulations and machine learning could rapidly advance the design, development, and adoption of CNT forest applications with varied morphologies and propertiesmore » « less
-
Cellulose nanocrystal (CNCs) assisted carbon nanotubes (CNTs) and graphene nanoplatelets (GnP) were used to modify the interfacial region of carbon fiber (CF) and polymer matrix to strengthen the properties of carbon fiber-reinforced polymer (CFRP). Before transferring CNC-CNTs and CNC-GnPs on the CF surface by an immersion coating method, the nanomaterials were dispersed in DI water homogeneously by using probe sonication technique without additives. The results showed that the addition of CNC-CNT and CNC-GnP adjusted the interfacial chemistry of CFRP with the formation of polar groups. Furthermore, according to the single fiber fragmentation test (SFFT), the interfacial shear strength (IFSS) of CNC-GnP 6:1 and CNC-CNT 10:1 added CFRP increased to 55 MPa and 64 MPa due to modified interfacial chemistry by the incorporation of the nanomaterials. This processing technique also resulted in improvement in interlaminar shear strength (ILSS) in CFRPs from 35 MPa (neat composite) to 45 (CNC-GnP 6:1) MPa and 52 MPa (CNC-CNT 10:1).more » « less
