Phase-Centric MOCVD Enabled Synthetic Approaches for Wafer-Scale 2D Tin Selenides.

Following an initial nucleation stage at the flake level, atomically thin film growth of a van der Waals material is promoted by ultrafast lateral growth and prohibited vertical growth. To produce these highly anisotropic films, synthetic or post-synthetic modifications are required, or even a combination of both, to ensure large-area, pure-phase, and low-temperature deposition. A set of synthetic strategies is hereby presented to selectively produce wafer-scale tin selenides, SnSex (both x = 1 and 2), in the 2D forms. The 2D-SnSe2 films with tuneable thicknesses are directly grown via metal-organic chemical vapor deposition (MOCVD) at 200 °C, and they exhibit outstanding crystallinities and phase homogeneities and consistent film thickness across the entire wafer. This is enabled by excellent control of the volatile metal-organic precursors and decoupled dual-temperature regimes for high-temperature ligand cracking and low-temperature growth. In contrast, SnSe, which intrinsically inhibited from 2D growth, is indirectly prepared by a thermally driven phase transition of an as-grown 2D-SnSe2 film with all the benefits of the MOCVD technique. It is accompanied by the electronic n-type to p-type crossover at the wafer scale. These tailor-made synthetic routes will accelerate the low-thermal-budget production of multiphase 2D materials in a reliable and scalable fashion.

Fabrication of p-type 2D single-crystalline transistor arrays with Fermi-level-tuned van der Waals semimetal electrodes.

High-performance p-type two-dimensional (2D) transistors are fundamental for 2D nanoelectronics. However, the lack of a reliable method for creating high-quality, large-scale p-type 2D semiconductors and a suitable metallization process represents important challenges that need to be addressed for future developments of the field. Here, we report the fabrication of scalable p-type 2D single-crystalline 2H-MoTe2 transistor arrays with Fermi-level-tuned 1T'-phase semimetal contact electrodes. By transforming polycrystalline 1T'-MoTe2 to 2H polymorph via abnormal grain growth, we fabricated 4-inch 2H-MoTe2 wafers with ultra-large single-crystalline domains and spatially-controlled single-crystalline arrays at a low temperature (~500 °C). Furthermore, we demonstrate on-chip transistors by lithographic patterning and layer-by-layer integration of 1T' semimetals and 2H semiconductors. Work function modulation of 1T'-MoTe2 electrodes was achieved by depositing 3D metal (Au) pads, resulting in minimal contact resistance (~0.7 kΩ·µm) and near-zero Schottky barrier height (~14 meV) of the junction interface, and leading to high on-state current (~7.8 µA/µm) and on/off current ratio (~105) in the 2H-MoTe2 transistors.

Simulator acceleration and inverse design of fin field-effect transistors using machine learning.

The simulation and design of electronic devices such as transistors is vital for the semiconductor industry. Conventionally, a device is intuitively designed and simulated using model equations, which is a time-consuming and expensive process. However, recent machine learning approaches provide an unprecedented opportunity to improve these tasks by training the underlying relationships between the device design and the specifications derived from the extensively accumulated simulation data. This study implements various machine learning approaches for the simulation acceleration and inverse-design problems of fin field-effect transistors. In comparison to traditional simulators, the proposed neural network model demonstrated almost equivalent results (R2 = 0.99) and was more than 122,000 times faster in simulation. Moreover, the proposed inverse-design model successfully generated design parameters that satisfied the desired target specifications with high accuracies (R2 = 0.96). Overall, the results demonstrated that the proposed machine learning models aided in achieving efficient solutions for the simulation and design problems pertaining to electronic devices. Thus, the proposed approach can be further extended to more complex devices and other vital processes in the semiconductor industry.

Prediction and Interpretation of Polymer Properties Using the Graph Convolutional Network.

We present machine learning models for the prediction of thermal and mechanical properties of polymers based on the graph convolutional network (GCN). GCN-based models provide reliable prediction performances for the glass transition temperature (T g), melting temperature (T m), density (ρ), and elastic modulus (E) with substantial dependence on the dataset, which is the best for T g (R 2 ∼ 0.9) and worst for E (R 2 ∼ 0.5). It is found that the GCN representations for polymers provide prediction performances of their properties comparable to the popular extended-connectivity circular fingerprint (ECFP) representation. Notably, the GCN combined with the neural network regression (GCN-NN) slightly outperforms the ECFP. It is investigated how the GCN captures important structural features of polymers to learn their properties. Using the dimensionality reduction, we demonstrate that the polymers are organized in the principal subspace of the GCN representation spaces with respect to the backbone rigidity. The organization in the representation space adaptively changes with the training and through the NN layers, which might facilitate a subsequent prediction of target properties based on the relationships between the structure and the property. The GCN models are found to provide an advantage to automatically extract a backbone rigidity, strongly correlated with T g, as well as a potential transferability to predict other properties associated with a backbone rigidity. Our results indicate both the capability and limitations of the GCN in learning to describe polymer systems depending on the property.

PDLIM2 Suppression Inhibit Proliferation and Metastasis in Kidney Cancer.

We evaluated the expression of PDLIM2 in human kidney cancer cell lines from primary or metastatic origins and found that PDLIM2 expression was highly elevated in metastatic kidney cancers. We evaluated the effect of PDLIM2 inhibition by RNA interference method. PDLIM2 knockdown showed the decreased proliferation and metastatic character in human metastatic kidney cancer cells. By repeated round of orthotopic injection of RenCa mouse kidney cancer cell line, we obtained metastatic prone mouse kidney cancer cell lines. PDLIM2 expression was highly expressed in these metastatic prone cells comparing parental cells. In addition, we evaluated the in vivo efficacy of PDLIM2 knockout on the tumor formation and metastasis of kidney cancer cells using a PDLIM2 knockout mice. The experimental metastasis model with tail vein injection and orthotopic metastasis model injected into kidney all showed reduced lung metastasis cancer formation in PDLIM2 knockout mice comparing control Balb/c mice. Overall, our findings indicate that PDLIM2 is required for cancer formation and metastasis in metastatic kidney cancer, indicating that PDLIM2 may be a new therapeutic target for metastatic kidney cancer.

Prognostic value of preoperative systemic inflammation markers in localized upper tract urothelial cell carcinoma: a large, multicenter cohort analysis.

BACKGROUND: The aim of this study was to investigate the prognostic value of preoperative systemic inflammation markers in upper tract urothelial carcinoma (UTUC). METHODS: A total of 1137 patients who underwent radical nephroureterectomy with bladder cuff excision at 9 institutions from 2004 to 2015, were retrospectively reviewed. The Glasgow Prognostic Score (GPS), modified GPS (mGPS), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) for each patient were calculated. Univariable and multivariable analysis was performed using the Cox proportional hazards regression model. Cut-off values for NLR and PLR were calculated using a receiver operating characteristic curve. RESULTS: The median follow-up period was 39.1 (interquartile range: 18.3-63.8) months. Univariable analysis revealed that GPS, mGPS, PLR, and NLR (all, P=0.001) were significantly associated with both recurrence-free survival (RFS) and cancer-specific survival (CSS). Multivariable analysis revealed that GPS (P=0.001), PLR (hazards ratio [HR] =1.32; 95% CI: 1.08-1.62, P=0.007 and HR =1.87; 95% CI: 1.21-2.92, P=0.005), NLR (HR =1.38; 95% CI: 1.12-1.69, P=0.003 and HR =1.70; 95% CI: 1.10-2.62, P=0.017) were significantly associated with RFS and CSS. CONCLUSIONS: Our results suggest that preoperative systemic inflammation markers such as GPS, PLR, and NLR are independent prognostic factors in patients with UTUC after surgery.

Prospects for nanowire-doped polycrystalline graphene films for ultratransparent, highly conductive electrodes.

Traditional transparent conducting materials such as ITO are expensive, brittle, and inflexible. Although alternatives like networks of carbon nanotubes, polycrystalline graphene, and metallic nanowires have been proposed, the transparency-conductivity trade-off of these materials makes them inappropriate for broad range of applications. In this paper, we show that the conductivity of polycrystalline graphene is limited by high resistance grain boundaries. We demonstrate that a composite based on polycrystalline graphene and a subpercolating network of metallic nanowires offers a simple and effective route to reduced resistance while maintaining high transmittance. This new approach of "percolation-doping by nanowires" has the potential to beat the transparency-conductivity constraints of existing materials and may be suitable for broad applications in photovoltaics, flexible electronics, and displays.

Mitochondrial microsatellite instability in gastric cancer and gastric epithelial dysplasia as a precancerous lesion.

BACKGROUND: Genetic instability in gastric cancer represents a key molecular step that occurs early in the carcinogenesis process. To clarify the role of genetic instability in the progression from gastric dysplasia to gastric cancer, mitochondrial microsatellite instability (mtMSI) was studied in gastric cancer and gastric dysplasia. METHODS: DNA was isolated from paired normal and tumoral tissues in 24 patients with gastric dysplasia (low grade) and 49 patients with gastric cancer. mtMSI was analyzed using eight microsatellite markers. mtMSI in gastric dysplasia was studied prospectively to elucidate the relation between mtMSI and gastric carcinogenesis. RESULTS: mtMSI was found in 5 (10.2%) of 49 gastric cancer patients. The mtMSI phenotype was not associated with age, gender, and Helicobacter pylori infection. However, all of the mtMSI was found in intestinal-type gastric cancer (20.8%, p=0.02). In gastric dysplasia, mtMSI was detected in 3 (12.5%) of 24 patients with gastric dysplasia. mtMSI-positive gastric dysplasia showed a poor prognosis statistically compared to mtMSI negative through progression to high-grade dysplasia or gastric cancer. CONCLUSIONS: These data suggest that mtMSI may be an early and important event in the progression of gastric carcinogenesis, especially in intestinal-type gastric cancer.