LNG Suppression Foam Stabilized By Zirconium Phosphate Nanoplatelets

In this work, a zirconium phosphate based universal foam stabilizer was developed to stabilize and improve performance of firefighting foam and high expansion liquid natural gas (LNG) suppression foam. With the world’s increasing demand for natural gas, a large quantity of natural gas is transported in liquid natural gas form. The safety issues related to LNG are of critical concern in LNG process safety. High expansion LNG suppression foam was developed to mitigate accidental LNG spillage. Particle stabilized Pickering emulsion, which mainly the mixture of oil and water, was studied in detail for application in the chemical and oil industries. The advantage of the particle surfactant compared to the conventional surfactant is well understood. A particle stabilized gas-liquid mixture, Pickering foam, is still an emerging topic in soft matter. Pickering foam is studied in this work. Different foam formulas were mixed with propylamine exfoliated ZrP nanoplatelets. Foam stabilities were tested under different conditions, including high salinity and extreme temperatures. We found reduced drainage rate and extra surface stability induced by platelets were two factors which contributed to the excellent stability of our Pickering foam. LN2 was used to simulate the evaporation process of LNG suppressed by different foam formulas. Experimental results proved LN2 evaporation rate in the ZrP-PA added foam was modestly lower than conventional foam

Surface-Active Nanoplate for Oil Recovery

Janus colloidal surfactants with opposing wettability on two structural parts are receiving attention for their intriguing structural and practical application in various industries. Combining the advantages of molecular surfactants and particle-stabilized Pickering emulsions, Janus colloidal surfactants generate remarkably stable emulsions. This dissertation developed a straightforward and cost-efficient strategy to develop Janus nanoplate surfactants (JNPS) from aluminosilicate nanoclays materials, including Kaoinite and Halloysite, by stepwise surface modifications, including an innovative selective surface modification step. Such colloidal surfactants are found to be able to stabilize Pickering emulsions of different oil/water systems. The microstructural characterization of solidified polystyrene emulsions indicates that the emulsion interface is evenly covered by JNPS. The phase behaviors of water/oil emulsion generated by these novel platelet surfactants were also investigated. Furthermore, this dissertation demonstrated the application of JNPS for enhanced oil recovery with a microfluidic flooding test, showing a dramatic increase of oil recovery ratio. This research provides important insights for the design and synthesis of two-dimensional Janus colloidal surfactants, which could be utilized in biomedical, food and mining industries, especially for circumstances where high salinity and high temperature are involved

MAG-GNN: Reinforcement Learning Boosted Graph Neural Network

While Graph Neural Networks (GNNs) recently became powerful tools in graph learning tasks, considerable efforts have been spent on improving GNNs' structural encoding ability. A particular line of work proposed subgraph GNNs that use subgraph information to improve GNNs' expressivity and achieved great success. However, such effectivity sacrifices the efficiency of GNNs by enumerating all possible subgraphs. In this paper, we analyze the necessity of complete subgraph enumeration and show that a model can achieve a comparable level of expressivity by considering a small subset of the subgraphs. We then formulate the identification of the optimal subset as a combinatorial optimization problem and propose Magnetic Graph Neural Network (MAG-GNN), a reinforcement learning (RL) boosted GNN, to solve the problem. Starting with a candidate subgraph set, MAG-GNN employs an RL agent to iteratively update the subgraphs to locate the most expressive set for prediction. This reduces the exponential complexity of subgraph enumeration to the constant complexity of a subgraph search algorithm while keeping good expressivity. We conduct extensive experiments on many datasets, showing that MAG-GNN achieves competitive performance to state-of-the-art methods and even outperforms many subgraph GNNs. We also demonstrate that MAG-GNN effectively reduces the running time of subgraph GNNs.Comment: Accepted to NeurIPS 202

Crack-Net: Prediction of Crack Propagation in Composites

Computational solid mechanics has become an indispensable approach in engineering, and numerical investigation of fracture in composites is essential as composites are widely used in structural applications. Crack evolution in composites is the bridge to elucidate the relationship between the microstructure and fracture performance, but crack-based finite element methods are computationally expensive and time-consuming, limiting their application in computation-intensive scenarios. Here we propose a deep learning framework called Crack-Net, which incorporates the relationship between crack evolution and stress response to predict the fracture process in composites. Trained on a high-precision fracture development dataset generated using the phase field method, Crack-Net demonstrates a remarkable capability to accurately forecast the long-term evolution of crack growth patterns and the stress-strain curve for a given composite design. The Crack-Net captures the essential principle of crack growth, which enables it to handle more complex microstructures such as binary co-continuous structures. Moreover, transfer learning is adopted to further improve the generalization ability of Crack-Net for composite materials with reinforcements of different strengths. The proposed Crack-Net holds great promise for practical applications in engineering and materials science, in which accurate and efficient fracture prediction is crucial for optimizing material performance and microstructural design

Study of peripheral dose from low-dose CT to adaptive radiotherapy of postoperative prostate cancer

ObjectivesThe increasing use of computed tomography (CT) for adaptive radiotherapy (ART) has raised concerns about the peripheral radiation dose. This study investigates the feasibility of low-dose CT (LDCT) for postoperative prostate cancer ART to reduce the peripheral radiation dose, and evaluates the peripheral radiation dose of different imaging techniques and propose an image enhancement method based on deep learning for LDCT.Materials and methodsA linear accelerator integrated with a 16-slice fan-beam CT from UIH (United Imaging Healthcare, China) was utilized for prostate cancer ART. To reduce the tube current of CT for ART, LDCT was acquired. Peripheral doses of normal-dose CT (NDCT), LDCT, and mega-voltage computed tomography (MV-CT) were measured using a cylindrical Virtual Water™ phantom and an ion chamber. A deep learning model of LDCT for abdominal and pelvic-based cycle-consistent generative adversarial network was employed to enhance the image quality of LDCT. Six postoperative prostate cancer patients were selected to evaluate the feasibility of low-dose CT network restoration images (RCT) by the deep learning model for ART. The three aspects among NDCT, LDCT, and RCT were compared: the Hounsfield Unit (HU) of the tissue, the Dice Similarity Coefficient (DSC) criterion of target and organ, and dose calculation differences.ResultsIn terms of peripheral dose, the LDCT had a surface measurement point dose of approximately 1.85 mGy at the scanning field, while the doses of NDCT and MV-CT were higher at 22.85 mGy and 29.97 mGy, respectively. However, the image quality of LDCT was worse than NDCT. When compared to LDCT, the tissue HU value of RCT showed a significant improvement and was closer to that of NDCT. The DSC results for target CTV between RCT and NDCT were also impressive, reaching up to 94% for bladder and femoral heads, 98% for rectum, and 94% for the target organ. Additionally, the dose calculation differences for the ART plan based on LDCT and NDCT were all within 1%. Overall, these findings suggest that RCT can provide an effective alternative to NDCT and MV-CT with similar or better outcomes in HU values of tissue and organ damage. More testing is required before clinical application

Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman

Message passing neural networks (MPNNs) have emerged as the most popular framework of graph neural networks (GNNs) in recent years. However, their expressive power is limited by the 1-dimensional Weisfeiler-Lehman (1-WL) test. Some works are inspired by $k$-WL/FWL (Folklore WL) and design the corresponding neural versions. Despite the high expressive power, there are serious limitations in this line of research. In particular, (1) $k$-WL/FWL requires at least $O(n^k)$ space complexity, which is impractical for large graphs even when $k=3$; (2) The design space of $k$-WL/FWL is rigid, with the only adjustable hyper-parameter being $k$. To tackle the first limitation, we propose an extension, $(k,t)$-FWL. We theoretically prove that even if we fix the space complexity to $O(n^k)$ (for any $k\geq 2$) in $(k,t)$-FWL, we can construct an expressiveness hierarchy up to solving the graph isomorphism problem. To tackle the second problem, we propose $k$-FWL+, which considers any equivariant set as neighbors instead of all nodes, thereby greatly expanding the design space of $k$-FWL. Combining these two modifications results in a flexible and powerful framework $(k,t)$-FWL+. We demonstrate $(k,t)$-FWL+ can implement most existing models with matching expressiveness. We then introduce an instance of $(k,t)$-FWL+ called Neighborhood$^2$-FWL (N$^2$-FWL), which is practically and theoretically sound. We prove that N$^2$-FWL is no less powerful than 3-WL, and can encode many substructures while only requiring $O(n^2)$ space. Finally, we design its neural version named N$^2$-GNN and evaluate its performance on various tasks. N$^2$-GNN achieves record-breaking results on ZINC-Subset (0.059), outperforming previous SOTA results by 10.6%. Moreover, N$^2$-GNN achieves new SOTA results on the BREC dataset (71.8%) among all existing high-expressive GNN methods.Comment: Accepted to NeurIPS 202

The Synthesis of Amphiphilic Luminescent Graphene Quantum Dot and Its Application in Miniemulsion Polymerization

Although emulsion applications of microscale graphene sheets have attracted much attention recently, nanoscale graphene platelets, namely, graphene quantum dots (GQDs), have been rarely explored in interface science. In this work, we study the interfacial behaviors and emulsion phase diagrams of hydrophobic-functionalized graphene quantum dots (C18-GQDs). Distinctive from pristine graphene quantum dots (p-GQDs), C18-GQDs show several interesting surface-active properties including high emulsification efficiency in stabilizing dodecane-in-water emulsions. We then utilize the C18-GQDs as surfactants in miniemulsion polymerization of styrene, achieving uniform and relatively small polystyrene nanospheres. The high emulsification efficiency, low production cost, uniform morphology, intriguing photoluminescence, and extraordinary stability render C18-GQDs an attractive alternative in surfactant applications

Nano-encapsulated PCM via Pickering Emulsification

We designed a two-step Pickering emulsification procedure to create nano-encapsulated phase changing materials (NEPCMs) using a method whose simplicity and low energy consumption suggest promise for scale-up and mass production. Surface-modified amphiphilic zirconium phosphate (ZrP) platelets were fabricated as the Pickering emulsifiers, nonadecane was chosen as the core phase change material (PCM), and polystyrene, the shell material. The resultant capsules were submicron in size with remarkable uniformity in size distribution, which has rarely been reported. Differential scanning calorimetry (DSC) characterization showed that the capsulation efficiency of NEPCMs, and they were found to be thermal stable, as characterized by the DSC data for the sample after 200 thermal cycles. NEPCMs exhibit superior mechanical stability and mobility when compared with the well-developed micro-encapsulated phase change materials (MEPCMs). NEPCMs find useful applications in thermal management, including micro-channel coolants; solar energy storage media; building temperature regulators; and thermal transfer fabrics

LNG Suppression Foam Stabilized By Zirconium Phosphate Nanoplatelets

In this work, a zirconium phosphate based universal foam stabilizer was developed to stabilize and improve performance of firefighting foam and high expansion liquid natural gas (LNG) suppression foam. With the world’s increasing demand for natural gas, a large quantity of natural gas is transported in liquid natural gas form. The safety issues related to LNG are of critical concern in LNG process safety. High expansion LNG suppression foam was developed to mitigate accidental LNG spillage. Particle stabilized Pickering emulsion, which mainly the mixture of oil and water, was studied in detail for application in the chemical and oil industries. The advantage of the particle surfactant compared to the conventional surfactant is well understood. A particle stabilized gas-liquid mixture, Pickering foam, is still an emerging topic in soft matter. Pickering foam is studied in this work. Different foam formulas were mixed with propylamine exfoliated ZrP nanoplatelets. Foam stabilities were tested under different conditions, including high salinity and extreme temperatures. We found reduced drainage rate and extra surface stability induced by platelets were two factors which contributed to the excellent stability of our Pickering foam. LN2 was used to simulate the evaporation process of LNG suppressed by different foam formulas. Experimental results proved LN2 evaporation rate in the ZrP-PA added foam was modestly lower than conventional foam