An official website of the United States government
Two-dimensional (2D) materials assembled into van der Waals (vdW) heterostructures contain unlimited combinations of mechanical, optical, and electrical properties that can be harnessed for potential device applications. Critically, these structures require control over interfacial adhesion for enabling their construction and have enough integrity to survive industrial fabrication processes upon their integration. Here, we promptly determine the adhesion quality of various exfoliated 2D materials on conventional SiO 2 /Si substrates using ultrasonic delamination threshold testing. This test allows us to quickly infer relative substrate adhesion based on the percent area of 2D flakes that survive a fixed time in an ultrasonic bath, allowing for control over process parameters that yield high or poor adhesion. We leverage this control of adhesion to optimize the vdW heterostructure assembly process, where we show that samples with high or low substrate adhesion relative to each other can be used selectively to construct high-throughput vdW stacks. Instead of tuning the adhesion of polymer stamps to 2D materials with constant 2D-substrate adhesion, we tune the 2D-substrate adhesion with constant stamp adhesion to 2D materials. The polymer stamps may be reused without any polymer melting steps, thus avoiding high temperatures (<120 °C) and allowing for high-throughput production. We show that this procedure can be used to create high-quality 2D twisted bilayer graphene on SiO 2 /Si, characterized with atomic force microscopy and Raman spectroscopic mapping, as well as low-angle twisted bilayer WSe 2 on h-BN/SiO 2 /Si, where we show direct real-space visualization of moiré reconstruction with tilt-angle dependent scanning electron microscopy.
more »
« less
Physically informed machine-learning algorithms for the identification of two-dimensional atomic crystals
Abstract After graphene was first exfoliated in 2004, research worldwide has focused on discovering and exploiting its distinctive electronic, mechanical, and structural properties. Application of the efficacious methodology used to fabricate graphene, mechanical exfoliation followed by optical microscopy inspection, to other analogous bulk materials has resulted in many more two-dimensional (2D) atomic crystals. Despite their fascinating physical properties, manual identification of 2D atomic crystals has the clear drawback of low-throughput and hence is impractical for any scale-up applications of 2D samples. To combat this, recent integration of high-performance machine-learning techniques, usually deep learning algorithms because of their impressive object recognition abilities, with optical microscopy have been used to accelerate and automate this traditional flake identification process. However, deep learning methods require immense datasets and rely on uninterpretable and complicated algorithms for predictions. Conversely, tree-based machine-learning algorithms represent highly transparent and accessible models. We investigate these tree-based algorithms, with features that mimic color contrast, for automating the manual inspection process of exfoliated 2D materials (e.g., MoSe2). We examine their performance in comparison to ResNet, a famous Convolutional Neural Network (CNN), in terms of accuracy and the physical nature of their decision-making process. We find that the decision trees, gradient boosted decision trees, and random forests utilize physical aspects of the images to successfully identify 2D atomic crystals without suffering from extreme overfitting and high training dataset demands. We also employ a post-hoc study that identifies the sub-regions CNNs rely on for classification and find that they regularly utilize physically insignificant image attributes when correctly identifying thin materials.
more »
« less
- PAR ID:
- 10406993
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Advancements in low‐dimensional functional device technology heavily rely on the discovery of suitable materials which have interesting physical properties as well as can be exfoliated down to the 2D limit. Exfoliable high‐mobility magnets are one such class of materials that, not due to lack of effort, has been limited to only a handful of options. So far, most of the attention has been focused on the van der Waals (vdW) systems. However, even within the non‐vdW, layered materials, it is possible to find all these desirable features. Using chemical reasoning, it is found that NdSb2is an ideal example. Even with a relatively small interlayer distance, this material can be exfoliated down to few layers. NdSb2has an antiferromagnetic ground state with a quasi 2D spin arrangement. The bulk crystals show a very large, non‐saturating magnetoresistance along with highly anisotropic electronic transport properties. It is confirmed that this anisotropy originates from the 2D Fermi pockets which also imply a rather quasi 2D confinement of the charge carrier density. Both electron and hole‐type carriers show very high mobilities. The possible non‐collinear spin arrangement also results in an anomalous Hall effect.more » « less
-
Mechanical bonds arise between molecules that contain interlocked subunits, such as one macrocycle threaded through another. Within polymers, these linkages will confer distinctive mechanical properties and other emergent behaviors, but polymerizations that form mechanical bonds efficiently and use simple monomeric building blocks are rare. In this work, we introduce a solid-state polymerization in which one monomer infiltrates crystals of another to form a macrocycle and mechanical bond at each repeat unit of a two-dimensional (2D) polymer. This mechanically interlocked 2D polymer is formed as a layered solid that is readily exfoliated in common organic solvents, enabling spectroscopic characterization and atomic-resolution imaging using advanced electron microscopy techniques. The 2D mechanically interlocked polymer is easily prepared on multigram scales, which, along with its solution processibility, enables the facile fabrication of composite fibers with Ultem that exhibit enhanced stiffness and strength.more » « less
-
Here, we present a novel mechano-spectroscopic atomic force microscopy (AFM-MS) technique that overcomes the limitations of current spectroscopic methods by combining the high-resolution imaging capabilities of AFM with machine learning (ML) classification. AFM-MS employs AFM operating in sub-resonance tapping imaging mode, which enables the collection of multiple physical and mechanical property maps of a sample with sub-nanometer lateral resolution in a highly repeatable manner. By comparing these properties to a database of known materials, the technique identifies the location of constituent materials at each image pixel with the assistance of ML algorithms. We demonstrate AFM-MS on various material mixtures, achieving an unprecedented lateral spectroscopic resolution of 1.6 nm. This powerful approach opens new avenues for nanoscale material study, including the material identification and correlation of nanostructure with macroscopic material properties. The ability to map material composition with such high resolution will significantly advance the understanding and design of complex, nanostructured materials.more » « less
-
Abstract Since the first discovery of graphene, 2D materials are drawing tremendous attention due to their atomic thickness and superior properties. Fabrication of high‐quality micro‐/nanopatterns of 2D materials is essential for their applications in both nanoelectronics and nanophotonics. In this work, an all‐optical lithographic technique, optothermoplasmonic nanolithography (OTNL), is developed to achieve high‐throughput, versatile, and maskless patterning of different atomic layers. Low‐power (≈5 mW µm−2) and high‐resolution patterning of both graphene and MoS2monolayers is demonstrated through exploiting thermal oxidation and sublimation at the highly localized thermoplasmonic hotspots. Density functional theory simulations reveal that Au nanoparticles reduce the formation energy (≈0.6 eV) of C monovacancies through bonding between undercoordinated C and Au, leading to a significant Au‐catalyzed graphene oxidation and a reduction of the required laser operation power. Programmable patterning of 2D materials into complex and large‐scale nanostructures is further demonstrated. With its low‐power, high‐resolution, and versatile patterning capability, OTNL offers the possibility to scale up the fabrication of nanostructured 2D materials for many applications in photonic and electronic devices.more » « less
