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High-speed optoelectronics is central to many important developments in the communication, computing, sensing, imaging, and autonomous vehicle industries. With a sharp rise of attention on energy efficiency, researchers have proposed and demonstrated innovative materials, high-speed devices, and components integrated on a single platform that exhibit ultralow power consumption and ultrawide bandwidth. Recently reported material growth and device fabrication techniques offer the potential for high-density integration of optoelectronics close to the capability and cost of conventional electronics. A tremendous synergy can be attained by integrating multiple materials with superior properties on the same chip using heterogeneous integration, heteroepitaxy, nano-heteroepitaxy, and other co-packaging strategies within the complementary metal oxide semiconductor (CMOS) ecosystem. This issue ofMRS Bulletin offers an overview of the field and covers the latest developments on various ultraefficient materials, high-speed devices, their physical properties, current trends, and future directions in optoelectronics and their integration on a silicon platform.

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Computational fluid dynamics (CFD) and its uncertainty quantification are computationally expensive. We use Gaussian Process (GP) methods to demonstrate that machine learning can build efficient and accurate surrogate models to replace CFD simulations with significantly reduced computational cost without compromising the physical accuracy. We also demonstrate that both epistemic uncertainty (machine learning model uncertainty) and aleatory uncertainty (randomness in the inputs of CFD) can be accommodated when the machine learning model is used to reveal fluid dynamics. The demonstration is performed by applying simulation of Hagen-Poiseuille and Womersley flows that involve spatial and spatial-tempo responses, respectively. Training points are generated by using the analytical solutions with evenly discretized spatial or spatial-temporal variables. Then GP surrogate models are built using supervised machine learning regression. The error of the GP model is quantified by the estimated epistemic uncertainty. The results are compared with those from GPU-accelerated volumetric lattice Boltzmann simulations. The results indicate that surrogate models can produce accurate fluid dynamics (without CFD simulations) with quantified uncertainty when both epistemic and aleatory uncertainties exist. 

To study the sensing mechanism of bat's biosonar system, we propose a fast simulation algorithm to generate natural-looking trees and forest---the primary living habitat of bats. We adopt 3D Lindenmayer system to create the fractal geometry of the trees, and add additional parameters, both globally and locally, to enable random variations of the tree structures. Random forest is then formed by placing simulated trees at random locations of a field according to a spatial point process. By employing a single algorithmic model with different numeric parameters, we can rapidly simulate 3D virtual environments with a wide variety of trees, producing detailed geometry of the foliage such as the leaf locations, sizes, and orientations. Written in C++ and visualized with openGL, our algorithm is fast to implement, easily parallable, and more adaptive to real-time visualization compared with existing alternative approaches. Our simulated environment can be used for general purposes such as studying new sensors or training remote sensing algorithms. 

Airlines have introduced a back-to-front boarding process in response to the COVID-19 pandemic. It is motivated by the desire to reduce passengers’ likelihood of passing close to seated passengers when they take their seats. However, our prior work on the risk of Ebola spread in aeroplanes suggested that the driving force for increased exposure to infection transmission risk is the clustering of passengers while waiting for others to stow their luggage and take their seats. In this work, we examine whether the new boarding processes lead to increased or decreased risk of infection spread. We also study the reasons behind the risk differences associated with different boarding processes. We accomplish this by simulating the new boarding processes using pedestrian dynamics and compare them against alternatives. Our results show that backto-front boarding roughly doubles the infection exposure compared with random boarding. It also increases exposure by around 50% compared to a typical boarding process prior to the outbreak of COVID-19. While keeping middle seats empty yields a substantial reduction in exposure, our results show that the different boarding processes have similar relative strengths in this case as with middle seats occupied. We show that the increased exposure arises from the proximity between passengers moving in the aisle and while seated. Such exposure can be reduced significantly by prohibiting the use of overhead bins to stow luggage. Our results suggest that the new boarding procedures increase the risk of exposure to COVID-19 compared with prior ones and are substantially worse than a random boarding process 

Fabrics and fibrous materials offer a soft, porous, and flexible substrate for microelectromechanical systems (MEMS) packaging in breathable, wearable formats that allow airflow. Device-on-fiber systems require developments in the field of E-Textiles including smart fibers, functional fiber intersections, textile circuit routing, and alignment methods that adapt to irregular materials. In this paper, we demonstrate a MEMS-on-fabric layout workflow that obtains fiber intersection locations from high-resolution fabric images. We implement an image processing algorithm to drive the MEMS layout software, creating an individualized MEMS “gripper” layout designed to grasp fibers on a specific fabric substrate during a wafer-to-fabric parallel transfer step. The efficiency of the algorithm in terms of a number of intersections identified on the complete image is analyzed. The specifications of the MEMS layout design such as the length of the MEMS gripper, spatial distribution, and orientation are derivable from the MATLAB routine implemented on the image. Furthermore, the alignment procedure, tolerance, and hardware setup for the alignment method of the framed sample fabric to the wafer processed using the custom gripper layout are discussed along with the challenges of the release of MEMS devices from the Si substrate to the fabric substrate. 

According to recent field studies, almost half of the New Particle Formation (NPF) events occur aloft, in a residual layer, near the top of the boundary layer. Therefore, measurements of the meteorological parameters, precursor gas concentrations, and aerosol loadings conducted at the ground level are often not representative of the conditions where the NPFs take place. This paper presents new measurements obtained during the Turbulent Flux Measurements of the Residual Layer Nucleation Particles, conducted at the Southern Great Plains research site. Vertical turbulent fluxes of 3–10 nm‐sized particles were measured using a sonic anemometer and two condensation particle counters with nominal cutoff diameters of3 nm and10 nm mounted at the top of the 10‐m telescoping tower. Aerosol number size distribution (5–300 nm) was determined through the ground‐based Scanning Mobility Particle Sizers. The size selected (15–50 nm) particle hygroscopicity was derived with the Humidified Tandem Differential Mobility Analyzer. The ground level observations were supplemented by vertically‐resolved measurements of horizontal and vertical wind speeds and aerosol backscatter. The data analysis suggests that (a) turbulent flux measurements of 3–10 nm particles can distinguish between near‐surface and residual‐layer small particle events; (b) sub‐50 nm particles had a hygroscopicity value of 0.2, suggesting that organic compounds dominate atmospheric nanoparticle chemical composition at the site; and (c) current methodologies are inadequate for estimating the dry deposition velocity of sub‐10 nm particles because it is not feasible to measure particle concentration very near the surface, in the diffusion sublayer.

 

ABSTRACT Animals that are competent reservoirs of zoonotic pathogens commonly suffer little morbidity from the infections. To investigate mechanisms of this tolerance of infection, we used single-dose lipopolysaccharide (LPS) as an experimental model of inflammation and compared the responses of two rodents: Peromyscus leucopus , the white-footed deermouse and reservoir for the agents of Lyme disease and other zoonoses, and the house mouse Mus musculus . Four hours after injection with LPS or saline, blood, spleen, and liver samples were collected and subjected to transcriptome sequencing (RNA-seq), metabolomics, and specific reverse transcriptase quantitative PCR (RT-qPCR). Differential expression analysis was at the gene, pathway, and network levels. LPS-treated deermice showed signs of sickness similar to those of exposed mice and had similar increases in corticosterone levels and expression of interleukin 6 (IL-6), tumor necrosis factor, IL-1β, and C-reactive protein. By network analysis, the M. musculus response to LPS was characterized as cytokine associated, while the P. leucopus response was dominated by neutrophil activity terms. In addition, dichotomies in the expression levels of arginase 1 and nitric oxide synthase 2 and of IL-10 and IL-12 were consistent with type M1 macrophage responses in mice and type M2 responses in deermice. Analysis of metabolites in plasma and RNA in organs revealed species differences in tryptophan metabolism. Two genes in particular signified the different phenotypes of deermice and mice: the Slpi and Ibsp genes. Key RNA-seq findings for P. leucopus were replicated in older animals, in a systemic bacterial infection, and with cultivated fibroblasts. The findings indicate that P. leucopus possesses several adaptive traits to moderate inflammation in its balancing of infection resistance and tolerance. IMPORTANCE Animals that are natural carriers of pathogens that cause human diseases commonly manifest little or no sickness as a consequence of infection. Examples include the deermouse, Peromyscus leucopus , which is a reservoir for Lyme disease and several other disease agents in North America, and some types of bats, which are carriers of viruses with pathogenicity for humans. Mechanisms of this phenomenon of infection tolerance and entailed trade-off costs are poorly understood. Using a single injection of lipopolysaccharide (LPS) endotoxin as a proxy for infection, we found that deermice differed from the mouse ( Mus musculus ) in responses to LPS in several diverse pathways, including innate immunity, oxidative stress, and metabolism. Features distinguishing the deermice cumulatively would moderate downstream ill effects of LPS. Insights gained from the P. leucopus model in the laboratory have implications for studying infection tolerance in other important reservoir species, including bats and other types of wildlife.