Tongji University

About: Tongji University is a education organization based out in Shanghai, China. It is known for research contribution in the topics: Population & Adsorption. The organization has 76116 authors who have published 81176 publications receiving 1248911 citations. The organization is also known as: Tongji & Tóngjì Dàxué.

Developing High-Performance Lithium Metal Anode in Liquid Electrolytes: Challenges and Progress.

Abstract: Lithium metal anodes are potentially key for next-generation energy-dense batteries because of the extremely high capacity and the ultralow redox potential. However, notorious safety concerns of Li metal in liquid electrolytes have significantly retarded its commercialization: on one hand, lithium metal morphological instabilities (LMI) can cause cell shorting and even explosion; on the other hand, breaking of the grown Li arms induces the so-called "dead Li"; furthermore, the continuous consumption of the liquid electrolyte and cycleable lithium also shortens cell life. The research community has been seeking new strategies to protect Li metal anodes and significant progress has been made in the last decade. Here, an overview of the fundamental understandings of solid electrolyte interphase (SEI) formation, conceptual models, and advanced real-time characterizations of LMI are presented. Instructed by the conceptual models, strategies including increasing the donatable fluorine concentration (DFC) in liquid to enrich LiF component in SEI, increasing salt concentration (ionic strength) and sacrificial electrolyte additives, building artificial SEI to boost self-healing of natural SEI, and 3D electrode frameworks to reduce current density and delay Sand's extinction are summarized. Practical challenges in competing with graphite and silicon anodes are outlined.

Review on landslide susceptibility mapping using support vector machines

Abstract: Landslides are natural phenomena that can cause great loss of life and damage to property. A landslide susceptibility map is a useful tool to help with land management in landslide-prone areas. A support vector machine (SVM) is a machine learning algorithm that uses a small number of samples for prediction and has been widely used in recent years. This paper presents a review of landslide susceptibility mapping using SVM. It presents the basic concept of SVM and its application in landslide susceptibility assessment and mapping. Then it compares the SVM method with four other methods (analytic hierarchy process, logistic regression, artificial neural networks and random forests) used in landslide susceptibility mapping. The application of SVM in landslide susceptibility assessment and mapping is discussed and suggestions for future research are presented. Compared with some of the methods commonly used in landslide susceptibility assessment and mapping, SVM has its strengths and weaknesses owing to its unique theoretical basis. The combination of SVM and other techniques may yield better performance in landslide susceptibility assessment and mapping. A high-quality informative database is essential and classification of landslide types prior to landslide susceptibility assessment is important to help improve model performance.

Checkpoint blockade and nanosonosensitizer-augmented noninvasive sonodynamic therapy combination reduces tumour growth and metastases in mice

Abstract: Combined checkpoint blockade (e.g., PD1/PD-L1) with traditional clinical therapies can be hampered by side effects and low tumour-therapeutic outcome, hindering broad clinical translation. Here we report a combined tumour-therapeutic modality based on integrating nanosonosensitizers-augmented noninvasive sonodynamic therapy (SDT) with checkpoint-blockade immunotherapy. All components of the nanosonosensitizers (HMME/R837@Lip) are clinically approved, wherein liposomes act as carriers to co-encapsulate sonosensitizers (hematoporphyrin monomethyl ether (HMME)) and immune adjuvant (imiquimod (R837)). Using multiple tumour models, we demonstrate that combining nanosonosensitizers-augmented SDT with anti-PD-L1 induces an anti-tumour response, which not only arrests primary tumour progression, but also prevents lung metastasis. Furthermore, the combined treatment strategy offers a long-term immunological memory function, which can protect against tumour rechallenge after elimination of the initial tumours. Therefore, this work represents a proof-of-concept combinatorial tumour therapeutics based on noninvasive tumours-therapeutic modality with immunotherapy.