Exploitation of Pore Structure for Increased CO2 Selectivity in Type 3 Porous Liquids.

CO2 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. However, the effects of MOF structural features and solvent properties on CO2-MOF interactions within PLs are not well understood. Herein experimental and computational data of CO2 gas adsorption isotherms were used to elucidate both solvent and pore structure influences on ZIF-based PLs. The roles of the pore structure including solvent size exclusion, structural environment, and MOF porosity on PL CO2 uptake were examined. A comparison of the pore structure and pore aperture was performed using ZIF-8, ZIF-L, and amorphous-ZIF-8. Adsorption experiments here have verified our previously proposed solvent size design principle for ZIF-based PLs (1.8× ZIF pore aperture). Furthermore, the CO2 adsorption isotherms of the ZIF-based PLs indicated that judicious selection of the pore environment allows for an increase in CO2 selectivity greater than expected from the individual PL components or their combination. This nonlinear increase in the CO2 selectivity is an emergent behavior resulting from the complex mixture of components specific to the ZIF-L + 2'-hydroxyacetophenone-based PL.

ProcGCN: detecting malicious process in memory based on DGCNN.

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. Therefore, this article proposes the ProcGCN model, a deep learning model based on DGCNN (Deep Graph Convolutional Neural Network), to detect malicious processes in memory images. First, the process dump is extracted from the whole system memory image; then, the Function Call Graph (FCG) of the process is extracted, and feature vectors for the function node in the FCG are generated based on the word bag model; finally, the FCG is input to the ProcGCN model for classification and detection. Using a public dataset for experiments, the ProcGCN model achieved an accuracy of 98.44% and an F1 score of 0.9828. It shows a better result than the existing deep learning methods based on static features, and its detection speed is faster, which demonstrates the effectiveness of the method based on function call features and graph representation learning in memory forensics.

Secure multi-path routing for Internet of Things based on trust evaluation.

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. Leveraging the software-defined networking (SDN) architecture in the data transmission process between edge nodes, TESM incorporates a controller comprising a security verification module, a multi-path routing module, and an anomaly handling module. The security verification module ensures the ongoing security validation of data packets, deriving trust scores for nodes. Subsequently, the multi-path routing module employs multi-objective reinforcement learning to dynamically generate secure multiple paths based on node trust scores. The anomaly handling module is tasked with handling malicious switch nodes and anomalous paths. Our proposed solution is validated through simulation using the Ryu controller and P4 switches in an SDN environment constructed with Mininet. The results affirm that TESM excels in achieving secure data forwarding, malicious node localization, and the secure selection and updating of transmission paths. Notably, TESM introduces a minimal 12.4% additional forwarding delay and a 5.46% throughput loss compared to traditional networks, establishing itself as a lightweight yet robust IoT security defense solution.

Attribute identification based IoT fog data security control and forwarding.

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. To ensure the secure transmission of end-to-end information in the IoT fog architecture, an attribute identification based security control and forwarding method for IoT fog data (AISCF) is proposed. AISCF applies attribute signatures to the IoT fog architecture and uses software defined network (SDN) to control and forward fog layer data flows. Firstly, IoT devices add attribute identifiers to the data they send based on attribute features. The ingress switch then performs fine-grained access control on the data based on these attribute identifiers. Secondly, SDN uses attribute features as flow table matching items to achieve fine-grained control and forwarding of fog layer data flows based on attribute identifiers. Lastly, the egress switch dynamically samples data flows and verifies the attribute signatures of the sampled data packets at the controller end. Experimental validation has demonstrated that AISCF can effectively detect attacks such as data tampering and forged matching items. Moreover, AISCF imposes minimal overhead on network throughput, CPU utilization and packet forwarding latency, and has practicality in IoT fog architecture.

Accelerating Solvent Selection for Type II Porous Liquids.

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 pKa 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. This method is shown to give qualitative agreement with experimental observations for two molecular cages, CC13 and TG-TFB-CHEDA, identifying solvents with higher solubility for these molecules than had previously been known. Ultimately, the algorithm streamlines the downselection of suitable solvents for porous organic cages to enable more rapid discovery of Type II porous liquids.

A prospect of cost-effective handling and transportation of graphene oxides: folding and redispersion of graphene oxide microsheets.

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 2.5 wt%) to maintain their high degree of exfoliation and dispersion. As a result, the shipment of GO in large quantity involves a huge volume of solvent (water) and thus the transportation costs for large sales volume would become extremely high. Through cross-sectional scanning electron microscopy and polarized optical microscopy together with x-ray diffraction and small-angle x-ray scattering studies, we demonstrated that the assembly and structure of GO microsheets can be preserved without restacking, when assembled GO via water-based wet spinning are re-dispersed into solution. A couple of alkyl ammonium bromides, CTAB and TBAB, as well as NaOH, were examined as coagulants and the resulting fibers were redispersed in an aqueous solution. The redispersed solution of fibers that were wet-spun into the commonly used CTAB and TBAB coagulation baths, maintained their physico-chemical properties (similar to the original GO dispersion) however, did not reveal preservation of liquid crystallinity. Meanwhile, the redispersed fibers that were initially spun into NaOH coagulation bath were able to maintain their liquid crystallinity if the lateral size of the GO sheets was large. Based on these findings, a cost-effective solid handling approach is devised which involves (i) processing GO microsheets in solution into folded layers in solid-state, (ii) transporting assembled GO to the customers, and (iii) redispersion of folded GO into a solution for their use. The proposed solid handling of GO followed by redispersion into solution can greatly reduce the transportation costs of graphene oxide materials by reducing the transportation volume by more than 90%.