25 research outputs found

    A Promising Beginning for Perovskite Nanocrystals: A Nano Letters

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    Measuring <i>n</i> and <i>k</i> at the Microscale in Single Crystals of CH<sub>3</sub>NH<sub>3</sub>PbBr<sub>3</sub> Perovskite

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    Lead-based, inorganic–organic hybrid perovskites have shown much promise in photovoltaics, and the ability to tune their band gap makes them attractive for tandem solar cells, photodetectors, light-emitting diodes, and lasers. A crucial first step toward understanding a material’s behavior in such optoelectronic devices is determining its complex refractive index, <i>n + ik</i>; however, optically smooth films of hybrid perovskites are challenging to produce, and the optical properties of films of these materials have been shown to depend on the size of their crystallites. To address these challenges, this work reports quantitative reflectance and transmittance measurements performed on individual microcrystals of CH<sub>3</sub>NH<sub>3</sub>PbBr<sub>3</sub>, with thicknesses ranging from 155 to 1907 nm. The single crystals are formed by spin-coating a film of precursor solution and then stamping it with polydimethylsiloxane (PDMS) during crystallization. By measuring crystals of varying thickness, <i>n</i> and <i>k</i> values at each wavelength (405–1100 nm) have been determined, which agree with recently reported values extracted by ellipsometry on millimeter-sized single crystals. This approach can be applied to determine the optical constants of any material that presents challenges in producing smooth films over large areas, such as mixed-halide hybrid and inorganic perovskites, and to micro- or nanoplatelets

    Machine learning in nanoscience: big data at small scales

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    Recent advances in machine learning (ML) offer new tools to extract new insights from large data sets and to acquire small data sets more effectively. Researchers in nanoscience are experimenting with these tools to tackle challenges in many fields. In addition to ML’s advancement of nanoscience, nanoscience provides the foundation for neuromorphic computing hardware to expand the implementation of ML algorithms. In this mini-review, which is not able to be comprehensive, we highlight some recent efforts to connect the ML and nanoscience communities focusing on three types of interaction: (1) using ML to analyze and extract new information from large nanoscience data sets, (2) applying ML to accelerate materials discovery, including the use of active learning to guide experimental design, and (3) the nanoscience of memristive devices to realize hardware tailored for ML. We conclude with a discussion of challenges and opportunities for future interactions between nanoscience and ML researchers
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