分子间力
材料科学
纳米技术
化学物理
电荷(物理)
电子转移
电子传输链
纳米
电化学
密度泛函理论
分子
化学
计算化学
物理化学
物理
电极
量子力学
复合材料
生物化学
有机化学
作者
Leandro Merces,Graziâni Candiotto,Letícia M. M. Ferro,Anerise de Barros,Carlos Vinícius Santos Batista,Ali Nawaz,Antônio Riul,Rodrigo B. Capaz,Carlos César Bof Bufon
出处
期刊:Small
[Wiley]
日期:2021-10-01
卷期号:17 (45)
被引量:20
标识
DOI:10.1002/smll.202103897
摘要
Intermolecular charge transfer reactions are key processes in physical chemistry. The electron-transfer rates depend on a few system's parameters, such as temperature, electromagnetic field, distance between adsorbates and, especially, the molecular reorganization energy. This microscopic greatness is the energetic cost to rearrange each single$-$molecule and its surrounding environment when a charge is transferred. Reorganization energies are measured by electrochemistry and spectroscopy techniques as well as at the single-molecule limit using atomic force microscopy approaches, but not from temperature$-$dependent charge transport measurements nor in a monolithically$-$integrated molecular device. Nowadays self$-$rolling nanomembrane (rNM) devices, with strain$-$engineered mechanical properties, on$-$a$-$chip monolithic integration, and operable in distinct environments, overcome those challenges. Here, we investigate the charge transfer reactions occurring within a ca. 6 nm thick copper$-$phthalocyanine (CuPc) film employed as electrode-spacer in a monolithically integrated nanocapacitor. Employing the rNM technology allows us to measure the molecules' charge$-$transport dependence on temperature for different electric fields. Thereby, the CuPc reorganization energy is determined as (930 $\pm$ 40) meV, whereas density functional theory (DFT) calculations support our findings with the atomistic picture of the CuPc charge transfer reaction. Our approach presents a consistent route towards electron transfer reaction characterization using current$-$voltage spectroscopy and provides insight into the role of the molecular reorganization energy when it comes to electrochemical nanodevices.
科研通智能强力驱动
Strongly Powered by AbleSci AI