Tunable light filtering by a Bragg mirror/heavily doped semiconducting nanocrystal composite

Indium Tin Oxide Nanoparticle: TiO2: Air Layers for One-Dimensional Multilayer Photonic Structures

In this work we study the optical properties of one-dimensional photonic crystals in which layers of silica nanoparticles are alternated with layers of indium tin oxide nanoparticle (ITO)/titania nanoparticle mixture, using the transfer matrix method. The dielectric function of the mixed ITO/TiO2 nanoparticle layer is carefully accounted for with a generalized Rayleigh equation for the ternary mixture ITO:TiO2:air. We studied the light transmission of the multilayer photonic crystal as a function of the ITO/TiO2 ratio. We observe that, by increasing the ITO content and its carrier density in the three-phase mixture, the intensity of the plasmon resonance in the near infrared (NIR) increases and the intensity of the photonic band gap (visible) decreases. Thus, our study is of major importance for the realization of electrochromic smart windows, in which separate and independent NIR and visible light control is required.

Solution processable and optically switchable 1D photonic structures

We report the first demonstration of a solution processable, optically switchable 1D photonic crystal which incorporates phototunable doped metal oxide nanocrystals. The resulting device structure shows a dual optical response with the photonic bandgap covering the visible spectral range and the plasmon resonance of the doped metal oxide the near infrared. By means of a facile photodoping process, we tuned the plasmonic response and switched effectively the optical properties of the photonic crystal, translating the effect from the near infrared to the visible. The ultrafast bandgap pumping induces a signal change in the region of the photonic stopband, with recovery times of several picoseconds, providing a step toward the ultrafast optical switching. Optical modeling uncovers the importance of a complete modeling of the variations of the dielectric function of the photodoped material, including the high frequency region of the Drude response which is responsible for the strong switching in the visible after photodoping. Our device configuration offers unprecedented tunability due to flexibility in device design, covering a wavelength range from the visible to the near infrared. Our findings indicate a new protocol to modify the optical response of photonic devices by optical triggers only.

Framework for automatic information extraction from research papers on nanocrystal devices

To support nanocrystal device development, we have been working on a computational framework to utilize information in research papers on nanocrystal devices. We developed an annotated corpus called " NaDev" (Nanocrystal Device Development) for this purpose. We also proposed an automatic information extraction system called "NaDevEx" (Nanocrystal Device Automatic Information Extraction Framework). NaDevEx aims at extracting information from research papers on nanocrystal devices using the NaDev corpus and machine-learning techniques. However, the characteristics of NaDevEx were not examined in detail. In this paper, we conduct system evaluation experiments for NaDevEx using the NaDev corpus. We discuss three main issues: system performance, compared with human annotators; the effect of paper type (synthesis or characterization) on system performance; and the effects of domain knowledge features (e.g., a chemical named entity recognition system and list of names of physical quantities) on system performance. We found that overall system performance was 89% in precision and 69% in recall. If we consider identification of terms that intersect with correct terms for the same information category as the correct identification, i.e., loose agreement (in many cases, we can find that appropriate head nouns such as temperature or pressure loosely match between two terms), the overall performance is 95% in precision and 74% in recall. The system performance is almost comparable with results of human annotators for information categories with rich domain knowledge information (source material). However, for other information categories, given the relatively large number of terms that exist only in one paper, recall of individual information categories is not high (39-73%); however, precision is better (75-97%). The average performance for synthesis papers is better than that for characterization papers because of the lack of training examples for characterization papers. Based on these results, we discuss future research plans for improving the performance of the system.