five

Giant Impact Factor, Indexing, Ranking 2024

收藏
journalsinsights.com2025-03-22 收录
下载链接:
https://journalsinsights.com/journals/giant
下载链接
链接失效反馈
官方服务:
资源简介:
Giant is an interdisciplinary title focusing on fundamental and applied macromolecular science spanning all chemistry, physics, biology, and materials aspects of the field in the broadest sense. Key areas covered include macromolecular chemistry, supramolecular assembly, multiscale and multifunctional materials, organic-inorganic hybrid materials, biophysics, biomimetics and surface science. Core topics range from developments in synthesis, characterisation and assembly towards creating uniformly sized precision macromolecules with tailored properties, to the design and assembly of nanostructured materials in multiple dimensions, and further to the study of smart or living designer materials with tuneable multiscale properties. The journal seeks to bring an alternative, inclusive perspective on macromolecules and serves as a platform for discussions around emerging concepts in macromolecular science, with emphasis on the ever-expanding scope of new macromolecular architectures and the new understanding of the underlying common features of macromolecular systems. Core topics include but are not limited to: - Macromolecules, biomacromolecules, and molecular clusters - Nanomaterials and hybrid materials - Supramolecular chemistry and hierarchical assembly - Theory, modelling, and simulation in multiple scales - Artificial/bio-inspired intelligent surface, interface and materials - Soft-matter materials for health, energy and other related applications

《巨型》是一本跨学科期刊,专注于宏观分子科学的基础与应用研究,其涵盖范围广泛,包括化学、物理、生物学和材料科学领域的各个方面。该期刊的核心领域包括宏观分子化学、超分子组装、多尺度与多功能材料、有机-无机杂化材料、生物物理学、仿生学和表面科学。其核心议题涵盖从合成、表征和组装技术的发展,旨在制备具有特定性能且尺寸均匀的精确宏观分子,到多维度纳米结构材料的构建设计,以及智能或活体设计材料的多尺度可调性能的研究。该期刊旨在提供一个替代的、包容性的视角,对宏观分子进行探讨,并作为宏观分子科学新兴概念的讨论平台,特别强调不断扩大的新型宏观分子结构及其系统内在共同特性的新理解。核心议题包括但不限于:宏观分子、生物大分子和分子簇;纳米材料和杂化材料;超分子化学与层次组装;多尺度理论、建模与模拟;人工智能/生物启发智能表面、界面和材料;以及用于健康、能源和其他相关应用的功能性软物质材料。
提供机构:
JournalsInsights
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作