Preparation of Monolayer MoS\u3csub\u3e2\u3c/sub\u3e Quantum Dots using Temporally Shaped Femtosecond Laser Ablation of Bulk MoS\u3csub\u3e2\u3c/sub\u3e Targets in Water

Zero-dimensional MoS2 quantum dots (QDs) possess distinct physical and chemical properties, which have garnered them considerable attention and facilitates their use in a broad range of applications. In this study, we prepared monolayer MoS2 QDs using temporally shaped femtosecond laser ablation of bulk MoS2 targets in water. The morphology, crystal structures, chemical, and optical properties of the MoS2 QDs were characterized by transmission electron microscopy, X-ray diffraction, Raman spectroscopy, X-ray photoelectron spectroscopy, UV–vis absorption spectra, and photoluminescence spectra. The analysis results show that highly pure, uniform, and monolayer MoS2 QDs can be successfully prepared. Moreover, by temporally shaping a conventional single pulse into a two-subpulse train, the production rate of MoS2 nanomaterials (including nanosheets, nanoparticles, and QDs) and the ratio of small size MoS2 QDs can be substantially improved. The underlying mechanism is a combination of multilevel photoexfoliation of monolayer MoS2 and water photoionization–enhanced light absorption. The as-prepared MoS2 QDs exhibit excellent electrocatalytic activity for hydrogen evolution reactions because of the abundant active edge sites, high specific surface area, and excellent electrical conductivity. Thus, this study provides a simple and green alternative strategy for the preparation of monolayer QDs of transition metal dichalcogenides or other layered materials

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Evolution of Luxury Consumption

Mentor: Cynthia Cryder From the Washington University Undergraduate Research Digest: WUURD, Volume 6, Issue 2, Spring 2011. Published by the Office of Undergraduate Research, Joy Zalis Kiefer Director of Undergraduate Research and Assistant Dean in the College of Arts & Sciences; Kristin Sobotka, Editor

Which Gender Makes Smarter Stock Recommendations?

From the Washington University Senior Honors Thesis Abstracts (WUSHTA), Volume 4, Spring 2012. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research / Assistant Dean in the College of Arts & Sciences; E. Holly Tasker, Editor; Kristin Sobotka, Undergraduate Research Coordinator. Mentor: Ohad Kada

Which Gender Makes Smarter Stock Recommendations?

Mentor: Ohal Kadan From the Washington University Undergraduate Research Digest: WUURD, Volume 7, Issue 2, Spring 2012. Published by the Office of Undergraduate Research, Joy Zalis Kiefer Director of Undergraduate Research and Assistant Dean in the College of Arts & Sciences; Kristin Sobotka, Editor

Preparation of Monolayer MoS\u3csub\u3e2\u3c/sub\u3e Quantum Dots using Temporally Shaped Femtosecond Laser Ablation of Bulk MoS\u3csub\u3e2\u3c/sub\u3e Targets in Water

Zero-dimensional MoS2 quantum dots (QDs) possess distinct physical and chemical properties, which have garnered them considerable attention and facilitates their use in a broad range of applications. In this study, we prepared monolayer MoS2 QDs using temporally shaped femtosecond laser ablation of bulk MoS2 targets in water. The morphology, crystal structures, chemical, and optical properties of the MoS2 QDs were characterized by transmission electron microscopy, X-ray diffraction, Raman spectroscopy, X-ray photoelectron spectroscopy, UV–vis absorption spectra, and photoluminescence spectra. The analysis results show that highly pure, uniform, and monolayer MoS2 QDs can be successfully prepared. Moreover, by temporally shaping a conventional single pulse into a two-subpulse train, the production rate of MoS2 nanomaterials (including nanosheets, nanoparticles, and QDs) and the ratio of small size MoS2 QDs can be substantially improved. The underlying mechanism is a combination of multilevel photoexfoliation of monolayer MoS2 and water photoionization–enhanced light absorption. The as-prepared MoS2 QDs exhibit excellent electrocatalytic activity for hydrogen evolution reactions because of the abundant active edge sites, high specific surface area, and excellent electrical conductivity. Thus, this study provides a simple and green alternative strategy for the preparation of monolayer QDs of transition metal dichalcogenides or other layered materials

Analysis of Energy Characteristics and Internal Flow Field of “S” Shaped Airfoil Bidirectional Axial Flow Pump

In order to study the energy characteristics and internal flow field of “S” shaped airfoil bidirectional axial flow pumps, the SST k-ω turbulence model is used to calculate the bidirectional axial flow pump, and the experimental verification is carried out. The results show that the error of numerical calculation of forward and reverse operation is within 5%, and the numerical calculation result is credible. The test results show that the bidirectional axial flow pump has a design flow rate of Q = 368 L/s, head H = 3.767 m, and an efficiency of η = 80.37%. In reverse operation, the flow of the bidirectional axial flow pump under design condition Q = 316 L/s, head H = 3.658 m, efficiency η = 70.37%. The flow of forward operation is about 15% larger than that of reverse operation under design working condition, the design head is about 3.70 m, and the efficiency of design working condition is about 10% higher than that of reverse operation. The numerical calculation results show that under the forward design condition (Q = 368 L/s), the hydraulic loss accounts for 6.22%, and under the reverse design condition (Q = 316 L/s), the hydraulic loss accounts for 11.81%, with a difference of about 6%. The uniformity of impeller inlet flow rate under the forward operation is about 12% higher than that in the reverse operation. In forward and reverse operation, with the increase of flow, the outlet streamline, the outlet total pressure distribution, the uniformity of impeller inlet velocity, and the vortex in the impeller domain are improved, and the forward direction is better than the reverse direction. The research results of this paper can provide a reference for the research and optimal design of the bidirectional axial flow pump

Numerical Analysis and Model Test Verification of Energy and Cavitation Characteristics of Axial Flow Pumps

In order to study the energy and cavitation performance of a high-ratio axial flow pump, the SST k-ω turbulence model and ZGB cavitation model were used to numerically calculate the energy and cavitation performance of a high-ratio axial flow pump, and a model test analysis was carried out. The study concluded that the errors in the numerical calculation of head, efficiency, and critical cavitation margin are within 0.2 m, about 3% and 5%, respectively, and the numerical calculation results are reliable. For the flow conditions of Q = 411 L/s, 380 L/s, 348 L/s, and 234 L/s, the numerically calculated critical cavitation margins are 7.1 m, 5.7 m, 4.6 m, and 9.5 m, respectively, and the experimental critical cavitation margins are 7.5 m, 4.9 m, 4.6 m, and 9.5 m, respectively, with errors of −0.4 m, 0.8 m, 0.0 m, and 0.0 m, in that order; numerical calculations and test results trend the same, with small errors. Under the same inlet pressure, as the flow rate decreases, the vacuole first appears at the head of the blade pressure surface under the large flow rate condition (Q = 411 L/s), and the vacuole appears at the head of the blade suction surface under the small flow rate condition (Q = 234 L/s). As the inlet pressure decreases (pin = 11 × 104–4 × 104 Pa), the vacuole gradually increases under the same flow rate and the cavitation degree increases. The research results of this paper can provide a reference for the study of the energy and cavitation mechanism of the same type of axial flow pump