Electronic and Structural Properties of C36_{36} Molecule

The extended SSH model and Bogoliubov-de Gennes(BdeG) formalism are applied to investigate the electronic properties and stable lattice configurations of C$_{36}$. We focus the problem on the molecule's unusual $D_{6h}$ symmetry. The electronic part of the Hamiltonian without Coulomb interaction is solved analytically. We find that the gap between HOMO and LUMO is small due to the long distance hopping between the 2nd and 5th layers. The charge densities of HOMO and LUMO are mainly distributed in the two layers, that causes a large splitting between the spin triplet and singlet excitons. The differences of bond lengths, angles and charge densities among the molecule and polarons are discussed.Comment: 15 pages, 4 figures, 4 Table

Methodology for standard cell compliance and detailed placement for triple patterning lithography

As the feature size of semiconductor process further scales to sub-16nm technology node, triple patterning lithography (TPL) has been regarded one of the most promising lithography candidates. M1 and contact layers, which are usually deployed within standard cells, are most critical and complex parts for modern digital designs. Traditional design flow that ignores TPL in early stages may limit the potential to resolve all the TPL conflicts. In this paper, we propose a coherent framework, including standard cell compliance and detailed placement to enable TPL friendly design. Considering TPL constraints during early design stages, such as standard cell compliance, improves the layout decomposability. With the pre-coloring solutions of standard cells, we present a TPL aware detailed placement, where the layout decomposition and placement can be resolved simultaneously. Our experimental results show that, with negligible impact on critical path delay, our framework can resolve the conflicts much more easily, compared with the traditional physical design flow and followed layout decomposition

Unleashing the potential of bio-based concrete:Investigating its long-term mechanical strength and drying shrinkage in real climatic environments

Natural climatic conditions have significant negative impacts on the long-term mechanical properties and dimensional stability of bio-based concrete considering the high water absorption of bio-aggregates and corresponding biodegradations. In this study, the effects of three hydrophobic treatments (integral mixing, aggregate coating, concrete coating) on the physical properties, mechanical strengths, and drying shrinkage characteristics of bio-based peach kernel shell concrete in real climatic natural environments are investigated. Results show that bio-based peach kernel shell concrete has lower mechanical strength in outdoor climatic conditions than in indoor standard curing conditions. The swelling and shrinkage process causes the visible microcrack and debonding between bio-based materials and mortar interface. The drying shrinkage of bio-based peach kernel shell concrete in outdoor conditions is highly dependent on real climatic environments, including temperature, humidity and rainfall. The hydrophobic surface-coated concrete exhibits excellent resistance to real climatic environments, as well as good mechanical strength and dimensional stability, with 15.7% less drying shrinkage in 18 months, compared to reference concrete. Moreover, cellulose and hemicellulose of heat-treated bio-aggregates do not degrade over time in outdoor conditions due to the enhanced biodegradation resistance. The hydrophobic surface coating treatment is recommended for enhancing the service life of bio-based peach kernel shell concrete in real climatic environments.</p

Edge Detection in UAV Remote Sensing Images Using the Method Integrating Zernike Moments with Clustering Algorithms

Due to the unmanned aerial vehicle remote sensing images (UAVRSI) within rich texture details of ground objects and obvious phenomenon, the same objects with different spectra, it is difficult to effectively acquire the edge information using traditional edge detection operator. To solve this problem, an edge detection method of UAVRSI by combining Zernike moments with clustering algorithms is proposed in this study. To begin with, two typical clustering algorithms, namely, fuzzy c-means (FCM) and K-means algorithms, are used to cluster the original remote sensing images so as to form homogeneous regions in ground objects. Then, Zernike moments are applied to carry out edge detection on the remote sensing images clustered. Finally, visual comparison and sensitivity methods are adopted to evaluate the accuracy of the edge information detected. Afterwards, two groups of experimental data are selected to verify the proposed method. Results show that the proposed method effectively improves the accuracy of edge information extracted from remote sensing images

Experimental Evidence of Ferroelectric Negative Capacitance in Nanoscale Heterostructures

We report a proof-of-concept demonstration of negative capacitance effect in a nanoscale ferroelectric-dielectric heterostructure. In a bilayer of ferroelectric, Pb(Zr0.2Ti0.8)O3 and dielectric, SrTiO3, the composite capacitance was observed to be larger than the constituent SrTiO3 capacitance, indicating an effective negative capacitance of the constituent Pb(Zr0.2Ti0.8)O3 layer. Temperature is shown to be an effective tuning parameter for the ferroelectric negative capacitance and the degree of capacitance enhancement in the heterostructure. Landau's mean field theory based calculations show qualitative agreement with observed effects. This work underpins the possibility that by replacing gate oxides by ferroelectrics in MOSFETs, the sub threshold slope can be lowered below the classical limit (60 mV/decade)

Large area growth and electrical properties of p-type WSe2 atomic layers.

Transition metal dichacogenides represent a unique class of two-dimensional layered materials that can be exfoliated into single or few atomic layers. Tungsten diselenide (WSe(2)) is one typical example with p-type semiconductor characteristics. Bulk WSe(2) has an indirect band gap (∼ 1.2 eV), which transits into a direct band gap (∼ 1.65 eV) in monolayers. Monolayer WSe(2), therefore, is of considerable interest as a new electronic material for functional electronics and optoelectronics. However, the controllable synthesis of large-area WSe(2) atomic layers remains a challenge. The studies on WSe(2) are largely limited by relatively small lateral size of exfoliated flakes and poor yield, which has significantly restricted the large-scale applications of the WSe(2) atomic layers. Here, we report a systematic study of chemical vapor deposition approach for large area growth of atomically thin WSe(2) film with the lateral dimensions up to ∼ 1 cm(2). Microphotoluminescence mapping indicates distinct layer dependent efficiency. The monolayer area exhibits much stronger light emission than bilayer or multilayers, consistent with the expected transition to direct band gap in the monolayer limit. The transmission electron microscopy studies demonstrate excellent crystalline quality of the atomically thin WSe(2). Electrical transport studies further show that the p-type WSe(2) field-effect transistors exhibit excellent electronic characteristics with effective hole carrier mobility up to 100 cm(2) V(-1) s(-1) for monolayer and up to 350 cm(2) V(-1) s(-1) for few-layer materials at room temperature, comparable or well above that of previously reported mobility values for the synthetic WSe(2) and comparable to the best exfoliated materials

SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging

Automated classification of liver lesions in multi-phase CT and MR scans is of clinical significance but challenging. This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework, specifically designed for liver lesion classification in 3D multi-phase CT and MR imaging with varying phase counts. The proposed SDR-Former utilizes a streamlined Siamese Neural Network (SNN) to process multi-phase imaging inputs, possessing robust feature representations while maintaining computational efficiency. The weight-sharing feature of the SNN is further enriched by a hybrid Dual-Resolution Transformer (DR-Former), comprising a 3D Convolutional Neural Network (CNN) and a tailored 3D Transformer for processing high- and low-resolution images, respectively. This hybrid sub-architecture excels in capturing detailed local features and understanding global contextual information, thereby, boosting the SNN's feature extraction capabilities. Additionally, a novel Adaptive Phase Selection Module (APSM) is introduced, promoting phase-specific intercommunication and dynamically adjusting each phase's influence on the diagnostic outcome. The proposed SDR-Former framework has been validated through comprehensive experiments on two clinical datasets: a three-phase CT dataset and an eight-phase MR dataset. The experimental results affirm the efficacy of the proposed framework. To support the scientific community, we are releasing our extensive multi-phase MR dataset for liver lesion analysis to the public. This pioneering dataset, being the first publicly available multi-phase MR dataset in this field, also underpins the MICCAI LLD-MMRI Challenge. The dataset is accessible at:https://bit.ly/3IyYlgN.Comment: 13 pages, 7 figure