Collapse of carbon nanotubes due to local high-pressure from van der Waals encapsulation.

Van der Waals (vdW) assembly of low-dimensional materials has proven the capability of creating structures with on-demand properties. It is predicted that the vdW encapsulation can induce a local high-pressure of a few GPa, which will strongly modify the structure and property of trapped materials. Here, we report on the structural collapse of carbon nanotubes (CNTs) induced by the vdW encapsulation. By simply covering CNTs with a hexagonal boron nitride flake, most of the CNTs (≈77%) convert from a tubular structure to a collapsed flat structure. Regardless of their original diameters, all the collapsed CNTs exhibit a uniform height of ≈0.7 nm, which is roughly the thickness of bilayer graphene. Such structural collapse is further confirmed by Raman spectroscopy, which shows a prominent broadening and blue shift in the Raman G-peak. The vdW encapsulation-induced collapse of CNTs is fully captured by molecular dynamics simulations of the local vdW pressure. Further near-field optical characterization reveals a metal-semiconductor transition in accompany with the CNT structural collapse. Our study provides not only a convenient approach to generate local high-pressure for fundamental research, but also a collapsed-CNT semiconductor for nanoelectronic applications.

Tip Growth of Quasi-Metallic Bilayer Graphene Nanoribbons with Armchair Chirality.

Graphene nanoribbons (GNRs), quasi one-dimensional (1D) narrow strips of graphene, have shown promise for high-performance nanoelectronics due to their exceptionally high carrier mobility and structurally tunable bandgaps. However, producing chirality-uniform GNRs on insulating substrates remains a big challenge. Here, we report the successful growth of bilayer GNRs with predominantly armchair chirality and ultranarrow widths (<5 nm) on insulating hexagonal boron nitride (h-BN) substrates using chemical vapor deposition (CVD). The growth of GNRs is catalyzed by transition metal nanoparticles, including Fe, Co, and Ni, through a unique tip-growth mechanism. Notably, GNRs catalyzed by Ni exhibit a high purity (97.3%) of armchair chirality. Electron transport measurements indicate that the ultrathin bilayer armchair GNRs exhibit quasi-metallic behavior. This quasi-metallicity is further supported by density functional theory (DFT) calculations, which reveal a significantly reduced bandgap in bilayer armchair GNRs. The chirality-specific GNRs reported here offer promising advancements for the application of graphene in nanoelectronics.

Tunable growth of one-dimensional graphitic materials: graphene nanoribbons, carbon nanotubes, and nanoribbon/nanotube junctions.

Graphene nanoribbons (GNRs) and carbon nanotubes (CNTs), two representative one-dimensional (1D) graphitic materials, have attracted tremendous research interests due to their promising applications for future high-performance nanoelectronics. Although various methods have been developed for fabrication of GNRs or CNTs, a unified method allowing controllable synthesis of both of them, as well as their heterojunctions, which could largely benefit their nano-electronic applications, is still lacking. Here, we report on a generic growth of 1D carbon using nanoparticles catalyzed chemical vapor deposition (CVD) on atomically flat hexagonal boron nitride (h-BN) substrates. Relative ratio of the yielded GNRs and CNTs is able to be arbitrarily tuned by varying the growth temperature or feeding gas pressures. The tunability of the generic growth is quantitatively explained by a competing nucleation theory: nucleation into either GNRs or CNTs by the catalysts is determined by the free energy of their formation, which is controlled by the growth conditions. Under the guidance of the theory, we further realized growth of GNR/CNT intramolecular junctions through changing H2 partial pressure during a single growth process. Our study provides not only a universal and controllable method for growing 1D carbon nanostructures, but also a deep understanding of their growth mechanism, which would largely benefit future carbon-based electronics and optoelectronics.

Catalytic Growth of Ultralong Graphene Nanoribbons on Insulating Substrates.

Graphene nanoribbons (GNRs) with widths of a few nanometers are promising candidates for future nanoelectronic applications due to their structurally tunable bandgaps, ultrahigh carrier mobilities, and exceptional stability. However, the direct growth of micrometer-long GNRs on insulating substrates, which is essential for the fabrication of nanoelectronic devices, remains an immense challenge. Here, the epitaxial growth of GNRs on an insulating hexagonal boron nitride (h-BN) substrate through nanoparticle-catalyzed chemical vapor deposition is reported. Ultranarrow GNRs with lengths of up to 10 µm are synthesized. Remarkably, the as-grown GNRs are crystallographically aligned with the h-BN substrate, forming 1D moiré superlattices. Scanning tunneling microscopy reveals an average width of 2 nm and a typical bandgap of ≈1 eV for similar GNRs grown on conducting graphite substrates. Fully atomistic computational simulations support the experimental results and reveal a competition between the formation of GNRs and carbon nanotubes during the nucleation stage, and van der Waals sliding of the GNRs on the h-BN substrate throughout the growth stage. This study provides a scalable, single-step method for growing micrometer-long narrow GNRs on insulating substrates, thus opening a route to explore the performance of high-quality GNR devices and the fundamental physics of 1D moiré superlattices.

In-situ twistable bilayer graphene.

The electrical and optical properties of twisted bilayer graphene (tBLG) depend sensitively on the twist angle. To study the angle dependent properties of the tBLG, currently it is required fabrication of a large number of samples with systematically varied twist angles. Here, we demonstrate the construction of in-situ twistable bilayer graphene, in which the twist angle of the two graphene monolayers can be in-situ tuned continuously in a large range with high precision. The controlled tuning of the twist angle is confirmed by a combination of real-space and spectroscopic characterizations, including atomic force microscopy (AFM) identification of crystal lattice orientation, scanning near-field optical microscopy (SNOM) imaging of superlattice domain walls, and resonant Raman spectroscopy of the largely enhanced G-mode. The developed in-situ twistable homostructure devices enable systematic investigation of the twist angle effects in a single device, thus could largely advance the research of twistronics.

Feasibility study of patient-specific dose verification in proton therapy utilizing positron emission tomography (PET) and generative adversarial network (GAN).

PURPOSE: Online dose verification based on proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between dose and the activity distributions, a machine learning-based approach was developed to establish their relationship. METHODS: Simulations were carried out using a pencil beam scanning system and a computed tomography (CT) image-based phantom. A DiscoGAN model was developed to perform dose verification for both central and off-center lines. Besides the activity as input, HU information from CT images and stopping power (SP) prior were incorporated as auxiliary features for the model. The performance was quantitatively studied in terms of mean absolute error (MAE) and mean relative error (MRE), under different signal-to-noise ratios (SNRs). In addition to a dataset comprising monoenergetic beams, two additional datasets were generated to evaluate the model's generalization capability: five reconstructed PET images based on an in-beam PET system and a dataset comprising spread-out Bragg peaks (SOBPs). RESULTS: The feasibility of dose verification was successfully demonstrated for all three datasets. For the monoenergetic case (i.e., raw activity of positron emitters), the MRE is found to be <1% for the central lines and 5% for the off-center lines, respectively. The range uncertainty is found to be less than 1 mm. The prediction based on five PET images, which take into account the detection of 511-keV photons and image reconstruction, yields slightly inferior performance. For the SOBP case, the MRE of the center lines is found to be <3% and the range uncertainty is <1 mm. The inclusion of anatomical information (HU and SP) improves both accuracy and generalization of the DiscoGAN model. CONCLUSION: The combination of proton-induced positron emitters, in-beam PET, and machine learning may become a useful tool allowing for patient-specific online dose verification in proton therapy.