CATL 2019:动力电池技术前瞻,驱动电动车未来

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"CATL(宁德时代)在2019年的《电池:电动汽车关键技术视角》报告中,探讨了电动车动力系统的关键技术——电池包。作为全球领先的电池制造商,CATL对未来电池技术发展有着深远影响。电池包在电动汽车中的重要性主要体现在以下几个方面: 1. 能源效率与排放减少:电池是电动汽车的核心组成部分,它通过高效的化学储能技术(如锂离子电池)显著提升了电动汽车的能效,有助于降低碳排放,实现可持续出行。 2. 主导能量存储:电池包在电动车中的占据主导地位,负责存储并转换电能,是决定车辆行驶范围和性能的关键因素。 3. 空间与重量:由于电池包的体积庞大,它占据了车辆相当大的空间,并对车辆的整体设计和重量分配产生重大影响。 4. 成本影响:电池包作为车辆的主要成本之一,其价格和性能直接影响电动车的售价和竞争力。 5. 技术进步:电池包技术包括化学成分、封装设计、控制算法以及应用层面的创新,这些都是推动电动车普及和优化的关键要素。 报告还详细介绍了CATL的细胞生产和模块生产路线图,强调了电池安全性、性能、寿命、成本和环境影响的五重考量。其中,安全性的核心在于高能量密度电池的滥用性能测试,例如耐穿刺、短路、过充和过放等极限情况下的表现,这些测试符合欧洲汽车工业协会(EUCAR)的严格标准。 CATL秉承着一套黄金法则哲学,致力于在确保安全的前提下,持续提升电池的性能、寿命和环境友好性。电池技术的发展不仅关乎电动车产业的进步,也是实现绿色交通转型的关键驱动力。随着CATL这样的领军企业不断推动技术创新,我们期待看到更加高效、可靠且环保的动力电池包解决方案在未来几年内的广泛应用。"

润色下面英文:The controlled drug delivery systems, due to their precise control of drug release in spatiotemporal level triggered by specific stimulating factors and advantages such as higher utilization ratio of drug, less side-effects to normal tissues and so forth, provide a new strategy for the precise treatment of many serious diseases, especially tumors. The materials that constitute the controlled drug delivery systems are called “smart materials” and they can respond to the stimuli of some internal (pH, redox, enzymes, etc.) or external (temperature, electrical/magnetic, ultrasonic and optical, etc.) environments. Before and after the response to the specific stimulus, the composition or conformational of smart materials will be changed, damaging the original balance of the delivery systems and releasing the drug from the delivery systems. Amongst them, the photo-controlled drug delivery systems, which display drug release controlled by light, demonstrated extensive potential applications, and received wide attention from researchers. In recent years, photo-controlled drug delivery systems based on different photo-responsive groups have been designed and developed for precise photo-controlled release of drugs. Herein, in this review, we introduced four photo-responsive groups including photocleavage groups, photoisomerization groups, photo-induced rearrangement groups and photocrosslinking groups, and their different photo-responsive mechanisms. Firstly, the photocleavage groups represented by O-nitrobenzyl are able to absorb the energy of the photons, inducing the cleavage of some specific covalent bonds. Secondly, azobenzenes, as a kind of photoisomerization groups, are able to convert reversibly between the apolar trans form and the polar cis form upon different light irradiation. Thirdly, 2-diazo-1,2-naphthoquinone as the representative of the photo-induced rearrangement groups will absorb specific photon energy, carrying out Wolff rearrangement reaction. Finally, coumarin is a promising category photocrosslinking groups that can undergo [2+2] cycloaddition reactions under light irradiation. The research progress of photo-controlled drug delivery systems based on different photo-responsive mechanisms were mainly reviewed. Additionally, the existing problems and the future research perspectives of photo-controlled drug delivery systems were proposed.

2023-02-06 上传

Compared with homogeneous network-based methods, het- erogeneous network-based treatment is closer to reality, due to the different kinds of entities with various kinds of relations [22– 24]. In recent years, knowledge graph (KG) has been utilized for data integration and federation [11, 17]. It allows the knowledge graph embedding (KGE) model to excel in the link prediction tasks [18, 19]. For example, Dai et al. provided a method using Wasser- stein adversarial autoencoder-based KGE, which can solve the problem of vanishing gradient on the discrete representation and exploit autoencoder to generate high-quality negative samples [20]. The SumGNN model proposed by Yu et al. succeeds in inte- grating external information of KG by combining high-quality fea- tures and multi-channel knowledge of the sub-graph [21]. Lin et al. proposed KGNN to predict DDI only based on triple facts of KG [66]. Although these methods have used KG information, only focusing on the triple facts or simple data fusion can limit performance and inductive capability [69]. Su et al. successively proposed two DDIs prediction methods [55, 56]. The first one is an end-to-end model called KG2ECapsule based on the biomedical knowledge graph (BKG), which can generate high-quality negative samples and make predictions through feature recursively propagating. Another one learns both drug attributes and triple facts based on attention to extract global representation and obtains good performance. However, these methods also have limited ability or ignore the merging of information from multiple perspectives. Apart from the above, the single perspective has many limitations, such as the need to ensure the integrity of related descriptions, just as network-based methods cannot process new nodes [65]. So, the methods only based on network are not inductive, causing limited generalization [69]. However, it can be alleviated by fully using the intrinsic property of the drug seen as local information, such as chemical structure (CS) [40]. And a handful of existing frameworks can effectively integrate multi-information without losing induction [69]. Thus, there is a necessity for us to propose an effective model to fully learn and fuse the local and global infor- mation for improving performance of DDI identification through multiple information complementing.是什么意思

2023-06-11 上传