Nd掺杂Li4Ti5O12:高效锂离子电池的理想阴极材料

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在《国际电化学科学》(International Journal of Electrochemical Science)的一篇文章中,作者张茜和李喜研究了掺杂Nd的锂钛酸锂(Li4Ti5O12,简称LTO)作为锂离子电池的有效阴极材料。他们通过溶胶-凝胶法制备了这种新型的LTO,并对其结构和电化学性能进行了系统性地探讨。 研究发现,掺杂0.02% Nd的Li4Ti4.98Nd0.02O12表现出卓越的高倍率充放电性能,即使在高达10 C的电流密度下,依然保持了良好的稳定性和循环寿命。这一特性尤为显著,即便没有使用碳黑作为导电剂,Li4Ti4.98Nd0.02O12在5 C的速率下仍展现出出色的速率能力和循环能力。这种优异的高倍率性能可以归因于Nd掺杂带来的影响。Nd掺杂使得晶格常数增大,电子导电性得到提升,这可能是提升其电化学性能的关键因素。 Nd(钕)的掺杂对于LTO的性能优化起到了重要作用,因为它不仅提高了材料的导电性,还可能通过改变晶体结构来增强锂离子的扩散路径,从而降低内阻,使得电池在快速充放电过程中能够保持高效能。这对于寻求高能量密度和快速响应的锂离子电池应用来说具有重大意义。 该研究结果表明,Nd掺杂的Li4Ti5O12是一种有潜力的高性能阴极材料,特别是在需要高倍率充放电的电动汽车、无人机等应用中,它可能提供更长的使用寿命和更快的充电速度。因此,这项工作不仅对电池材料的研发有重要贡献,也为推动锂离子电池技术的发展提供了新的方向。未来的研究可能集中在进一步优化掺杂比例,以达到最佳性能,同时探索其他掺杂元素对LTO性能的影响。

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

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WIDE bandgap devices, such as silicon carbide (SiC) metal–oxide–semiconductor field-effect transis- tors (MOSFETs) present superior performance compared to their silicon counterparts [1]. Their lower ON-state resistance and faster switching capability attract lots of interest in high-power- density applications [2]. Faster switching speed enables lower switching loss and higher switching frequency, which is benefi- cial to high-efficiency and high power density. However, severe electromagnetic interference (EMI) and transient overvoltage issues caused by fast switching speed jeopardize the power quality and reliability of converters [3], [4]. Therefore, there is a tradeoff between efficiency and reliability in the choice of switching speed. An optimized design should ensure theoperation within both safe-operation-area and EMI limits, and switching loss should be as small as possible. A prediction method of switching performance is important and helpful for designer to evaluate and optimize converter design. The most concerned switching characteristics are switching loss, dv/dt, di/dt, and turn-ON/OFF overvoltage generally. These characteristics are crucial for the design of heatsink, filter, and gate driver. Related discussions have been presented in many existing research articles as following.请将这一段进行以下要求,Move analysis 语步(内容成分)分析; Language devices和实现该功能的语言手段(某些关键专有名词提供汉语翻译)

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A problem occurred configuring root project '��ҵ'. > Could not resolve all artifacts for configuration ':classpath'. > Could not resolve org.springframework.boot:spring-boot-gradle-plugin:3.1.0. Required by: project : > org.springframework.boot:org.springframework.boot.gradle.plugin:3.1.0 > No matching variant of org.springframework.boot:spring-boot-gradle-plugin:3.1.0 was found. The consumer was configured to find a runtime of a library compatible with Java 8, packaged as a jar, and its dependencies declared externally but: - Variant 'apiElements' capability org.springframework.boot:spring-boot-gradle-plugin:3.1.0 declares a library, packaged as a jar, and its dependencies declared externally: - Incompatible because this component declares an API of a component compatible with Java 17 and the consumer needed a runtime of a component compatible with Java 8 - Variant 'javadocElements' capability org.springframework.boot:spring-boot-gradle-plugin:3.1.0 declares a runtime of a component, and its dependencies declared externally: - Incompatible because this component declares documentation and the consumer needed a library - Other compatible attributes: - Doesn't say anything about its target Java version (required compatibility with Java 8) - Doesn't say anything about its elements (required them packaged as a jar) - Variant 'mavenOptionalApiElements' capability org.springframework.boot:spring-boot-gradle-plugin-maven-optional:3.1.0 declares a library, packaged as a jar, and its dependencies declared externally: - Incompatible because this component declares an API of a component compatible with Java 17 and the consumer needed a runtime of a component compatible with Java 8 - Variant 'mavenOptionalRuntimeElements' capability org.springframework.boot:spring-boot-gradle-plugin-maven-optional:3.1.0 declares a runtime of a library, packaged as a jar, and its dependencies declared externally: - Incompatible because this component declares a component compatible with Java 17 and the consumer needed a component compatible with Java 8 - Variant 'runtimeElements' capability org.springframework.boot:spring-boot-gradle-plugin:3.1.0 declares a runtime of a library, packaged as a jar, and its dependencies declared externally: - Incompatible because this component declares a component compatible with Java 17 and the consumer needed a component compatible with Java 8 - Variant 'sourcesElements' capability org.springframework.boot:spring-boot-gradle-plugin:3.1.0 declares a runtime of a component, and its dependencies declared externally: - Incompatible because this component declares documentation and the consumer needed a library - Other compatible attributes: - Doesn't say anything about its target Java version (required compatibility with Java 8) - Doesn't say anything about its elements (required them packaged as a jar) * Try: Run with --stacktrace option to get the stack trace. Run with --info or --debug option to get more log output. Run with --scan to get full insights.

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