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Direct conversion of methane into ethylene through the oxidative coupling of methane (OCM) is a technically important reaction. However, conventional co-fed fixed-bed OCM reactors still face serious challenges in conversion and selectivity. In this paper, we apply a finite element model to simulate OCM reaction in a plug-flow CO₂/O₂transport membrane (CTM) reactor with a directly captured CO₂and O₂mixture as a soft oxidizer. The CTM is made of three phases: molten carbonate, 20% Sm-doped CeO₂, and LiNiO₂. The membrane parameters are first validated by CO₂/O₂flux data obtained from CTM experiments. The OCM reaction is then simulated along the length of tubular plug-flow reactors filled with a La₂O₃-CaO-modified CeO₂catalyst bed, while a mixture of CO₂/O₂is gradually added through the wall of the tubular membrane. A 12-step OCM kinetic mechanism is considered in the model for the catalyst bed and validated by data obtained from a co-fed fixed-bed reactor. The modeled results indicate a much-improved OCM performance by membrane reactor in terms of C₂-yield and CH₄conversion rate over the state-of-the-art, co-fed, fixed-bed reactor. The model further reveals that improved performance is fundamentally rooted in the gradual methane conversion with CO₂/O₂offered by the plug-flow membrane reactor.

Many robotic tasks rely on physical interactions with the task environment. Sensing when and where links make physical contacts can be crucial in several applications, including but not limited to grasping, locomotion, collaborative robotics and navigation. While sensorizing robot end effectors with intrinsic tactile devices is a logical approach, current and accessible options are often expensive or require invasive modifications. This paper presents a prototype method of both sensing and localizing contacts along a rigid link that can be readily incorporated into existing machines. The mechanism is lightweight and low-cost, and functions by actively providing an oscillatory mechanical actuation signal to a rigid link, whose mechanical response is measured with an inertial device and is used to localize touch at one of five designated contact points. Classification is performed with supervised methods using transient behavior and spectral features. Evaluation is conducted with five-fold cross validation, and preliminary results indicate promising performance in localizing the point of contact on the rigid link with accuracy of over 97%.

This paper presents an approach to enhanced endoscopic tool segmentation combining separate pathways utilizing input images in two different coordinate representations. The proposed method examines U-Net convolutional neural networks with input endoscopic images represented via (1) the original rectangular coordinate format alongside (2) a morphological polar coordinate transformation. To maximize information and the breadth of the endoscope frustrum, imaging sensors are oftentimes larger than the image circle. This results in unused border regions. Ideally, the region of interest is proximal to the image center. The above two observations formed the basis for the morphological polar transformation pathway as an augmentation to typical rectangular input image representations. Results indicate that neither of the two investigated coordinate representations consistently yielded better segmentation performance as compared to the other. Improved segmentation can be achieved with a hybrid approach that carefully selects which of the two pathways to be used for individual input images. Towards that end, two binary classifiers were trained to identify, given an input endoscopic image, which of the two coordinate representation segmentation pathways (rectangular or polar), would result in better segmentation performance. Results are promising and suggest marked improvements using a hybrid pathway selection approach compared to either alone. Themore »experiment used to evaluate the proposed hybrid method utilized a dataset consisting of 8360 endoscopic images from real surgery and evaluated segmentation performance with Dice coefficient and Intersection over Union. The results suggest that on-the-fly polar transformation for tool segmentation is useful when paired with the proposed hybrid tool-segmentation approach.« less

A pair-density-wave (PDW) is a superconducting state with an oscillating order parameter. A microscopic mechanism that can give rise to it has been long sought but has not yet been established by any controlled calculation. Here we report a density-matrix renormalization-group (DMRG) study of an effectivet-J-Vmodel, which is equivalent to the Holstein-Hubbard model in a strong-coupling limit, on long two-, four-, and six-leg triangular cylinders. While a state with long-range PDW order is precluded in one dimension, we find strong quasi-long-range PDW order with a divergent PDW susceptibility as well as the spontaneous breaking of time-reversal and inversion symmetries. Despite the strong interactions, the underlying Fermi surfaces and electron pockets around theKand$${K}^{\prime}$$$K^{\prime }$points in the Brillouin zone can be identified. We conclude that the state is valley-polarized and that the PDW arises from intra-pocket pairing with an incommensurate center of mass momentum. In the two-leg case, the exponential decay of spin correlations and the measured central chargec ≈ 1 are consistent with an unusual realization of a Luther-Emery liquid.

Exposure to ideas in domains outside a scientist's own may benefit her in reformulating existing research problems in novel ways and discovering new application domains for existing solution ideas. While improved performance in scholarly search engines can help scientists efficiently identify relevant advances in domains they may already be familiar with, it may fall short of helping them explore diverse ideas \textit{outside} such domains. In this paper we explore the design of systems aimed at augmenting the end-user ability in cross-domain exploration with flexible query specification. To this end, we develop an exploratory search system in which end-users can select a portion of text core to their interest from a paper abstract and retrieve papers that have a high similarity to the user-selected core aspect but differ in terms of domains. Furthermore, end-users can `zoom in' to specific domain clusters to retrieve more papers from them and understand nuanced differences within the clusters. Our case studies with scientists uncover opportunities and design implications for systems aimed at facilitating cross-domain exploration and inspiration.