As a promising approach for carbon dioxide capture, chemical looping combustion has been extensively investigated for more than two decades. However, the chemical looping strategy can be and has been extended well beyond carbon capture. In fact, significant impacts on emission reduction, energy conservation, and value-creation can be anticipated from chemical looping beyond combustion (CLBC). This article aims to demonstrate the versatility and transformational benefits of CLBC. Specifically, we focus on the use of oxygen carriers or redox catalysts for chemical production – a $4 trillion industry that consumes 40.9 quadrillion BTU of energy. Compared to state-of-the-art chemical production technologies, we illustrate that chemical looping offers significant opportunities for process intensification and exergy loss minimization. In many cases, an order of magnitude reduction in energy consumption and CO2 emission can be realized without the needs for carbon dioxide capture. In addition to providing various CLBC examples, this article elaborates on generalized design principles for CLBC, potential benefits and pitfalls, as well as redox catalyst selection, design, optimization, and redox reaction mechanism.

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Chemical looping beyond combustion – a perspective

Catalytic selectivity in the epoxidation of ethylene to form ethylene oxide on alumina-supported silver catalysts is dependent on the geometric structure of catalytically active Ag particles and reaction conditions. Shape and size controlled synthesis of Ag nanoparticles is used to show that silver nanocubes exhibit higher selectivity than nanowires and nanospheres. For a given shape, larger particles offer improved selectivity. The enhanced selectivity toward ethylene oxide is attributed to the nature of the exposed Ag surface facets; Ag nanocubes and nanowires are dominated by (100) surface facet and Ag nanospheres are dominated by (111). Furthermore, the concentration of undercoordinated surface sites is related to diminished selectivity to ethylene oxide. We demonstrate that a simple model can account for the impact of chemical and physical factors on the reaction selectivity. These observations have also been used to design a selective catalyst for the ethylene epoxidation reaction.

Shape- and size-specific chemistry of Ag nanostructures in catalytic ethylene epoxidation

Cancer is a serious health issue in the world due to a large body of cancer-related human deaths, and there is no current treatment available to efficiently treat the disease as the tumor is often diagnosed at a serious stage. Moreover, Cancer cells are often resistant to chemotherapy, radiotherapy, and molecular-targeted therapy. Upon further knowledge of mechanisms of tumorigenesis, aggressiveness, metastasis, and resistance to treatments, it is necessary to detect the disease at an earlier stage and for a better response to therapy. The hippo pathway possesses the unique capacity to lead to tumorigenesis. Mutations and altered expression of its core components (MST1/2, LATS1/2, YAP and TAZ) promote the migration, invasion, malignancy of cancer cells. The biological significance and deregulation of it have received a large body of interests in the past few years. Further understanding of hippo pathway will be responsible for cancer treatment. In this review, we try to discover the function of hippo pathway in different diversity of cancers, and discuss how Hippo pathway contributes to other cellular signaling pathways. Also, we try to describe how microRNAs, circRNAs, and ZNFs regulate hippo pathway in the process of cancer. It is necessary to find new therapy strategies for cancer.

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Analysis of the role of the Hippo pathway in cancer

Recent developments of metallic nanoparticle-graphene nanocatalysts

Ethylene oxidation by Ag catalysts has been extensively investigated over the past few decades, but many key fundamental issues about this important catalytic system are still unresolved. This over...

Overview of Selective Oxidation of Ethylene to Ethylene Oxide by Ag Catalysts

Glioblastoma multiforme (GBM) is the most prevalent form of malignant brain tumor. Amlexanox, a novel compound, has been shown to have anti-cancer potential. In this study, the anti-tumoral effects and the underlying mechanisms of amlexanox were investigated. Amlexanox significantly suppressed proliferation and invasion and induced apoptosis in glioblastoma cells. Furthermore, we found that amlexanox altered the protein expression of the Hippo pathway by downregulating IKBKE. Our data indicates that IKBKE directly targets LATS1/2 and induces degradation of LATS1/2, thereby inhibiting the activity of the Hippo pathway. In vivo results further confirmed the tumor inhibitory effect of amlexanox via the downregulation of IKBKE, and amlexanox induced no apparent toxicity. Collectively, our studies suggest that amlexanox is a promising therapeutic agent for the treatment of GBM.

/pdf/amlexanox-a-selective-inhibitor-of-ikbke-generates-anti-419muxvdr7.pdf

Amlexanox, a selective inhibitor of IKBKE, generates anti-tumoral effects by disrupting the Hippo pathway in human glioblastoma cell lines

Recently, large-scale pre-training methods like CLIP have made great progress in multi-modal research such as text-video retrieval. In CLIP, transformers are vital for modeling complex multi-modal relations. However, in the vision transformer of CLIP, the essential visual tokenization process, which produces discrete visual token sequences, generates many homogeneous tokens due to the redundancy nature of consecutive and similar frames in videos. This significantly increases computation costs and hinders the deployment of video retrieval models in web applications. In this paper, to reduce the number of redundant video tokens, we design a multi-segment token clustering algorithm to find the most representative tokens and drop the non-essential ones. As the frame redundancy occurs mostly in consecutive frames, we divide videos into multiple segments and conduct segment-level clustering. Center tokens from each segment are later concatenated into a new sequence, while their original spatial-temporal relations are well maintained. We instantiate two clustering algorithms to efficiently find deterministic medoids and iteratively partition groups in high dimensional space. Through this token clustering and center selection procedure, we successfully reduce computation costs by removing redundant visual tokens. This method further enhances segment-level semantic alignment between video and text representations, enforcing the spatio-temporal interactions of tokens from within-segment frames. Our method, coined as CenterCLIP, surpasses existing state-of-the-art by a large margin on typical text-video benchmarks, while reducing the training memory cost by 35% and accelerating the inference speed by 14% at the best case. The code is available at https://github.com/mzhaoshuai/CenterCLIP https://github.com/mzhaoshuai/CenterCLIP.

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CenterCLIP: Token Clustering for Efficient Text-Video Retrieval

Ethylene epoxidation reaction to selectively produce the ethylene oxide (EO) is a crucial heterogeneous catalytic chemical process in industry. However, the intrinsic catalytic selectivity depending on the facet of commonly used Ag catalysts is still far from understanding. In this work, we used the periodical density functional theory (DFT) calculations to gain an atomistic insight into the facet-dependent reaction selectivity of the ethylene epoxidation on Ag catalysts. We investigated the adsorption of atomic or molecular oxygen and oxametallacycle (OMC) intermediate, and further study the dissociation of molecular oxygen and the reaction mechanism of ethylene epoxidation on (111), (110), (100), and (211) surfaces of Ag catalysts. Our results indicate that the adsorption of the O atom is not facet-dependent under low oxygen coverage. Moreover, although the adsorption of molecular oxygen is facet-dependent, the dissociation of molecular oxygen would not have the essential influence on selectively producing EO in the ethylene epoxidation reaction. In addition, the catalytic selectivity of ethylene epoxidation reaction is facet-dependent, following the order of (110) > (100) > (111) > (211), which is consistent with the order of the adsorption strength of the OMC intermediate. Our results show that the stability of the OMC intermediate has an essential effect on the selectivity to EO in the ethylene epoxidation reaction. We have faith in that our results can provide useful information which would guide the intellectual design of the catalysts for ethylene epoxidation by controlling their morphologies.

Mechanistic insight into the facet-dependent selectivity of ethylene epoxidation on Ag nanocatalysts

The pristine transition metals (TMs) offer limited choices for high-performance heterogeneous catalysts because they are usually restricted in less optimal regions on the reactivity volcano map. Us...

Revisit the Role of Metal Dopants in Enhancing the Selectivity of Ag-Catalyzed Ethylene Epoxidation: Optimizing Oxophilicity of Reaction Site via Cocatalytic Mechanism

As an important area in computer vision, object tracking has formed two separate communities that respectively study Single Object Tracking (SOT) and Multiple Object Tracking (MOT). However, current methods in one tracking scenario are not easily adapted to the other due to the divergent training datasets and tracking objects of both tasks. Although UniTrack [45] demonstrates that a shared appearance model with multiple heads can be used to tackle individual tracking tasks, it fails to exploit the large-scale tracking datasets for training and performs poorly on the single object tracking. In this work, we present the Unified Transformer Tracker (UTT) to address tracking problems in different scenarios with one paradigm. A track transformer is developed in our UTT to track the target in both SOT and MOT where the correlation between the target feature and the tracking frame feature is exploited to localize the target. We demonstrate that both SOT and MOT tasks can be solved within this framework, and the model can be simultaneously end-to-end trained by alternatively optimizing the SOT and MOT objectives on the datasets of individual tasks. Extensive experiments are conducted on several benchmarks with a unified model trained on both SOT and MOT datasets.

Lin Zhu

Papers

Amlexanox, a selective inhibitor of IKBKE, generates anti-tumoral effects by disrupting the Hippo pathway in human glioblastoma cell lines

CenterCLIP: Token Clustering for Efficient Text-Video Retrieval

Mechanistic insight into the facet-dependent selectivity of ethylene epoxidation on Ag nanocatalysts

Revisit the Role of Metal Dopants in Enhancing the Selectivity of Ag-Catalyzed Ethylene Epoxidation: Optimizing Oxophilicity of Reaction Site via Cocatalytic Mechanism

Unified Transformer Tracker for Object Tracking