Publications by authors named "Chaowen Chang"

CO capture requires materials with high adsorption selectivity and an industrial ease of implementation. To address these needs, a new class of porous materials was recently developed that combines the fluidity of solvents with the porosity of solids. Type 3 porous liquids (PLs) composed of solvents and metal-organic frameworks (MOFs) offer a promising alternative to current liquid carbon capture methods due to the inherent tunability of the nanoporous MOFs.

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The combination of memory forensics and deep learning for malware detection has achieved certain progress, but most existing methods convert process dump to images for classification, which is still based on process byte feature classification. After the malware is loaded into memory, the original byte features will change. Compared with byte features, function call features can represent the behaviors of malware more robustly.

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In the realm of the Internet of Things (IoT), ensuring the security of communication links and evaluating the safety of nodes within these links remains a significant challenge. The continuous threat of anomalous links, harboring malicious switch nodes, poses risks to data transmission between edge nodes and between edge nodes and cloud data centers. To address this critical issue, we propose a novel trust evaluation based secure multi-path routing (TESM) approach for IoT.

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As Internet of Things (IoT) applications continue to proliferate, traditional cloud computing is increasingly unable to meet the low-latency demands of these applications. The IoT fog architecture solves this limitation by introducing fog servers in the fog layer that are closer to the IoT devices. However, this architecture lacks authentication mechanisms for information sources, security verification for information transmission, and reasonable allocation of fog nodes.

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Type II porous liquids, comprising intrinsically porous molecules dissolved in a liquid solvent, potentially combine the adsorption properties of porous adsorbents with the handling advantages of liquids. Previously, discovery of appropriate solvents to make porous liquids had been limited to direct experimental tests. We demonstrate an efficient screening approach for this task that uses COSMO-RS calculations, predictions of solvent p values from a machine-learning model, and several other features and apply this approach to select solvents from a library of more than 11,000 compounds.

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Controlling the assembly of 2D materials such as graphene oxides (GO) has a significant impact on their properties and performance. One of the critical issues on the processing and handling of GO is that they need to be in dilution solution (0.5 to 2.

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