一个 π 堆叠的晶体五烯衍生物中的长寿命相关三元组
Brendan D Folie, Jonah B Haber, Sivan Refaely-Abramson
1Kavli Energy NanoSciences Institute , Berkeley, California 94720, United States.
Journal of the American Chemical Society
|February 3, 2018
概括
单片裂变是一种提高太阳能电池效率的过程,在TIPS-pentacene中进行研究. 研究人员发现三胞胎的结合时间比预期的要长,
科学领域:
- 有机半导体物理
- 光伏设备研究
- 材料科学
背景情况:
- 单子裂变是一种过程,其中一个单子激子产生两个三重激子.
- 这一过程是提高有机光伏设备效率的关键.
- 在TIPS-pentacene薄膜中,中介三重组状态及其动态的直接证据是有限的.
研究的目的:
- 调查TIPS-五中单片裂变的速度.
- 提供相关的三元对中间体的直接证据.
- 了解晶体有机半导体中的三重激子的结合和分离动力学.
主要方法:
- 在TIPS-pentacene的单个晶体域上利用极化解决的短暂吸收显微镜.
- 使用宽带探测器来确定个体激发物种的吸收光谱.
- 分析了三重相互作用对吸收光谱的影响.
主要成果:
- 在TIPS-五中确定单片裂变速率.
- 证明最初创建的三重激子在分离之前保持数百个皮秒.
- 观察到三重相互作用会扰乱吸收光谱,这表明由于 π 堆叠几何学而导致的结合.
结论:
- 三元体对的长结合时间是单元体裂变动态的一个关键因素.
- 由π堆叠影响的三重相互作用会影响电子结构和动力学.
- 了解这些结构动态关系对于设计高效的基于单片裂变的光伏设备至关重要.
更多相关视频
14:12Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells
Published on: December 11, 2021
6.1K
11:26Integrating a Triplet-triplet Annihilation Up-conversion System to Enhance Dye-sensitized Solar Cell Response to Sub-bandgap Light
Published on: September 12, 2014
13.1K
相关概念视频
Correlations
36.6K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
36.6K
Correlation and Causation
43.0K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
43.0K
Polymer Classification: Crystallinity
4.1K
Unlike ionic or small covalent molecules, polymers do not form crystalline solids due to the diffusion limitations of their long-chain structures. However, polymers contain microscopic crystalline domains separated by amorphous domains.
Crystalline domains are the regions where polymer chains are aligned in an orderly manner and held together in proximity by intermolecular forces. For example, chains in the crystalline domains of polyethylene and nylon are bound together by van der Waals...
Crystalline domains are the regions where polymer chains are aligned in an orderly manner and held together in proximity by intermolecular forces. For example, chains in the crystalline domains of polyethylene and nylon are bound together by van der Waals...
4.1K
DNA Base Pairing
33.8K
Erwin Chargaff’s rules on DNA equivalence paved the way for the discovery of base pairing in DNA. Chargaff’s rules state that in a double-stranded DNA molecule,
33.8K
Correlation
15.2K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
15.2K
Sign Test for Matched Pairs
432
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
To conduct the sign test, we first calculate the differences in...
432
