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Dual-phase ceramic molten carbonate membranes have the ability to permeate carbon dioxide at high temperatures, with or without oxygen. This project studied the novel process of oxygen-driven carbon dioxide permeation, based on the mechanism of carbon dioxide transportation within the molten carbonate phase. In this process, carbonate ions transported across the membrane in the molten phase are driven by the oxygen partial pressure gradient. In our study, the carbon dioxide can permeate through the membrane without its own partial pressure gradient; to achieve this, oxygen potential gradient against the carbon dioxide is required to overcome the carbon dioxide potential (p'co2 ≤ p"co2 and p'o2 > p"o2). The carbon dioxide in p'co2 permeation to p"co2 is defined as the ‘up-hill’ permeation process. This study shows extremely high carbon dioxide permeation with the permeance of the order of 10⁻⁶ mol m⁻² s⁻¹ Pa⁻¹ during up-hill experimentation, and, at this value of permeability, the order of magnitude for carbon dioxide separation is greater than for other membrane technologies in this field. The project also examined the stability of this dual-phase ceramic-carbonate membrane system consisting of La0.6Sr0.4Co0.2Fe0.8 (LSCF) and a eutectic carbonate mixture (Li/Na/K) at about 600ºC. The results showed that this up-hill permeation operating over 200 hours occurred without a dramatic decrease in carbon dioxide permeation. The most important advantage of this up-hill carbon dioxide separation is that there is no energy penalty during the carbon dioxide separation process. For the carbon dioxide removal process, the energy that the system required from the energy input to remove the carbon dioxide against its own chemical potential was the energy penalty for carbon dioxide separation. However, during carbon dioxide up-hill permeation, the oxygen provides a greater potential difference in the opposite direction to overcome that of the carbon dioxide during permeation. However, thermal energy for heating up the membrane is required; no energy is consumed continuously from the environment to maintain the up-hill permeation process at about 600℃.

The computer vision community has been long focusing on classic tasks such as object detection, human attributes classification, action recognition. While the state-of-the-art performance is getting improved every year for a wide range of tasks, it remains a challenge to organize individual pieces into an integral system that parses visual scenes and events jointly. In this dissertation, we explore the problem of joint visual scene parsing in a restricted visual Turing test scenario that encourages explicit concept grounding. The goal is to build a scalable computer vision system that leverages the advancement of individual modules in various tasks and exploits the inherent correlation and constraints between them for a comprehensive understanding of visual scenes. This dissertation contains three main parts.Firstly, we describe a restricted visual Turing test scenario that evaluates computer vision systems across various tasks with a domain ontology and explicitly tests the grounding of concepts with formal queries. We present a benchmark for evaluating long-range recognition and event reasoning in videos captured from a network of cameras. The data and queries distinguish us from visual question answering in images and video captioning in that we emphasize explicit groundings of concepts in a restricted ontology via formal language queries.Secondly, we propose a scalable system which leverages off-the-shelf computer vision modules to parse cross-view videos jointly. The system defines a unified knowledge representation for information sharing and is extendable to new tasks and domains. To fuse information from multiple modules and camera views, we proposed a joint parsing method that integrates view-centric proposals into scene-centric parse graphs that represent a coherent scene-centric understanding of cross-view scenes. Our key observations are that overlapped fields of views embed rich appearance and geometry correlations and that knowledge fragments corresponding to individual vision tasks are governed by consistency constraints available in commonsense knowledge. The proposed method captures such correlations and constraints explicitly and generates semantic scene-centric parse graphs. Quantitative experiments show that scene-centric predictions outperform view-centric proposals.Thirdly, we discuss a principled method to construct parse graph knowledge bases that retains rich structures and grounding details. By casting questions into graph fragments, we present a graph-matching based question-answering system that retrieves answers for questions via graph pattern matching.