MoS2 nanoparticle formation in a low pressure environment

Formation of MoS2 nanoparticles at pressures between 0.5 and 10 Torr has been studied. Two different chemistries for the particle nucleation are compared: one based on MoCl5 and H2S, and the other based on MoCl5 and S. In both cases particle formation has been studied in a thermal oven and in a radio-frequency discharge. Typically, the reaction rates at low pressures are too low for an efficient thermal particle production. At pressures below 10 Torr no particle production in the oven is achieved in H2S chemistry. In the more reactive chemistry based on sulfur, the optimal conditions for thermal particle growth are found at 10 Torr and low gas flows, using excess of hydrogen. In the radio-frequency discharge, nanoparticles are readily formed in both chemistries at 0.5 Torr and can be detected in situ by laser light scattering. In the H2S chemistry particles smaller than 100 nm diameter have been synthesized, the sulfur chemistry yields somewhat larger grains. Both in thermal and plasma-enhanced particle syntheses, using excess of hydrogen is beneficial for the stability and purity of the particles

Multi-agent deep reinforcement learning with centralized training and decentralized execution for transportation infrastructure management

We present a multi-agent Deep Reinforcement Learning (DRL) framework for managing large transportation infrastructure systems over their life-cycle. Life-cycle management of such engineering systems is a computationally intensive task, requiring appropriate sequential inspection and maintenance decisions able to reduce long-term risks and costs, while dealing with different uncertainties and constraints that lie in high-dimensional spaces. To date, static age- or condition-based maintenance methods and risk-based or periodic inspection plans have mostly addressed this class of optimization problems. However, optimality, scalability, and uncertainty limitations are often manifested under such approaches. The optimization problem in this work is cast in the framework of constrained Partially Observable Markov Decision Processes (POMDPs), which provides a comprehensive mathematical basis for stochastic sequential decision settings with observation uncertainties, risk considerations, and limited resources. To address significantly large state and action spaces, a Deep Decentralized Multi-agent Actor-Critic (DDMAC) DRL method with Centralized Training and Decentralized Execution (CTDE), termed as DDMAC-CTDE is developed. The performance strengths of the DDMAC-CTDE method are demonstrated in a generally representative and realistic example application of an existing transportation network in Virginia, USA. The network includes several bridge and pavement components with nonstationary degradation, agency-imposed constraints, and traffic delay and risk considerations. Compared to traditional management policies for transportation networks, the proposed DDMAC-CTDE method vastly outperforms its counterparts. Overall, the proposed algorithmic framework provides near optimal solutions for transportation infrastructure management under real-world constraints and complexities

Cytokine secretion in breast cancer cells – MILLIPLEX assay data

© 2019 The Author(s) Metastatic breast cancer is the most advanced stage of breast cancer and the leading cause of breast cancer mortality. Although understanding of the cancer progression and metastasis process has improved, the bi-directional communication between the tumor cell and the tumor microenvironment is still not well understood. Breast cancer cells are highly secretory, and their secretory activity is modulated by a variety of inflammatory stimuli present in the tumor microenvironment. Here, we characterized the cytokine expression in human breast cancer cells (MDA-MB-231, MCF-7, T-47D, and BT-474) in vitro using 41 cytokine MILLIPLEX assay. Further, we compared cytokine expression in breast cancer cells to those in non-tumorigenic human breast epithelial MCF-10A cells