Improving the efficiency of solar cells by upconverting sunlight using field enhancement from optimized nano structures

Balling, P.; Christiansen, Jeppe; Christiansen, Rasmus E.; Eriksen, Emil H.; Lakhotiya, Harish; Mirsafaei, Mina; Möller, S.; Nazir, Adnan; Vester-Petersen, Joakim; Jeppesen, Bjarke R.; Jensen, Pia Bomholt; Hansen, J. Lundsgaard; Ram, Sanjay K.; Sigmund, Ole; Madsen, Morten; Madsen, S.; Julsgaard, Brian

doi:10.1016/j.optmat.2018.06.038

Cited by 24 publications

(17 citation statements)

References 113 publications

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“…Particle instances are denoted by the colored regions in the segmentation maps. Images going downwards then right are: Falcaro et al reprinted from ref ( 51 ), Copyright (2016); Jiang et al reprinted from ref ( 52 ), Copyright (2017); Navas and Soni reprinted from ref ( 53 ), Copyright (2016); Meng et al reprinted from ref ( 54 ), Copyright (2017); Li et al reprinted from ref ( 55 ), Copyright (2018); Balling et al reprinted from ref ( 56 ), Copyright (2018); Yang et al reprinted from ref ( 57 ), Copyright (2017); Distaso et al reprinted from ref ( 58 ), Copyright (2017); He et al reprinted from ref ( 59 ), Copyright (2019); Roy et al reprinted from ref ( 60 ), Copyright (2017); Wu et al reprinted from ref ( 61 ), Copyright (2020); Wu et al reprinted from ref ( 62 ), Copyright (2017); Shang et al reprinted from ref ( 63 ), Copyright (2020); Liu et al reprinted from ref ( 64 ), Copyright (2017); Wang et al reprinted from ref ( 65 ), Copyright (2017); and Wang et al reprinted from ref ( 66 ), Copyright (2020). All with permission from Elsevier.…”

Section: System Overviewmentioning

confidence: 99%

Bayesian Particle Instance Segmentation for Electron Microscopy Image Quantification

Yildirim

Cole

2021

J. Chem. Inf. Model.

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Automating the analysis portion of materials characterization by electron microscopy (EM) has the potential to accelerate the process of scientific discovery. To this end, we present a Bayesian deep-learning model for semantic segmentation and localization of particle instances in EM images. These segmentations can subsequently be used to compute quantitative measures such as particle-size distributions, radial- distribution functions, average sizes, and aspect ratios of the particles in an image. Moreover, by making use of the epistemic uncertainty of our model, we obtain uncertainty estimates of its outputs and use these to filter out false-positive predictions and hence produce more accurate quantitative measures. We incorporate our method into the ImageDataExtractor package, as ImageDataExtractor 2.0, which affords a full pipeline to automatically extract particle information for large-scale data-driven materials discovery. Finally, we present and make publicly available the Electron Microscopy Particle Segmentation (EMPS) data set. This is the first human-labeled particle instance segmentation data set, consisting of 465 EM images and their corresponding semantic instance segmentation maps.

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Section: System Overviewmentioning

confidence: 99%

Bayesian Particle Instance Segmentation for Electron Microscopy Image Quantification

Yildirim

Cole

2021

J. Chem. Inf. Model.

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“…Photon upconversion [1,2,3,4], the photoluminescence process in which the emission wavelength is shorter than the excitation wavelength, has exciting applications in many fields such as bio-imaging [5,6], anti-counterfeiting [7,8], and not least in improving the efficiency of solar cells [9,10,11]. Different mechanisms are known to be able to upconvert light, but among the most promising is upconversion from trivalent lanthanide ions embedded in a glass or crystalline host material.…”

Section: Introductionmentioning

confidence: 99%

Strongly enhanced upconversion in trivalent erbium ions by tailored gold nanostructures: Toward high-efficient silicon-based photovoltaics

Christiansen

Vester-Petersen

Roesgaard

et al. 2020

Solar Energy Materials and Solar Cells

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Upconversion of sub-band-gap photons constitutes a promising way for improving the efficiency of silicon-based solar cells beyond the Shockley-Queisser limit. 1500 nm to 980 nm upconversion by trivalent erbium ions is well-suited for this purpose, but the small absorption cross section hinders real-world applications. We employ tailored gold nanostructures to vastly improve the upconversion efficiency in erbium-doped TiO 2 thin films. The nanostructures are found using topology optimization and parameter optimization and fabricated by electron beam lithography. In qualitative agreement with a theoretical model, the samples show substantial electric-field enhancements inside the upconverting films for excitation at 1500 nm for both s-and p-polarization under a wide range of incidence angles and excitation intensities. An unprecedented upconversion enhancement of 913 ± 51 is observed at an excitation intensity of 1.7 W cm −2 . We derive a semi-empirical expression for the photonically enhanced upconversion efficiency, valid for all excitation intensities. This allows us to determine the upconversion properties needed to achieve significant improvements in real-world solar-cell devices through photonic-enhanced upconversion.

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“…Upconversion (UC) materials can absorb two or three energy photons to convert infrared light to visible light [1,2]. In recent years, Ln 3+ -doped UC materials have attracted much attention because of their potential applicability in many attractive research areas, such as solid-state lasers, biological imaging, solar cells, and the quantum dots [3][4][5][6][7][8][9]. It is well known that rare earth metals have a particular electronic structure with different 4f electronic numbers, and these are suitable for the UC process.…”

Section: Introductionmentioning

confidence: 99%

Improving Upconversion Photoluminescence of GdAlO₃:Er³⁺, Yb³⁺ Phosphors via Ga³⁺, Lu³⁺ Doping

Deng

Yan

Jiang

et al. 2019

International Journal of Optics

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GdAlO3:Er3+, Yb3+ phosphors doped with Lu3+, Ga3+ ions were prepared via coprecipitation. The influences of substituting Ga3+ for Al3+ and substituting Lu3+ for Gd3+ on the structure and upconversion photoluminescence (UCPL) of the phosphors were studied. The experimental results show that the crystal textures did not change but the lattice parameters changed slightly via Lu3+, Ga3+ doping. This results in a decrease in the host phonon energy and a marked improvement in the green emission spectrum at 546nm. Moreover, the amounts of surface CO2, CO32-, and OH- species gradually decreased with Lu3+, Ga3+ doping. The combined effects led to an improvement in the UCPL efficiency with Ga3+, Lu3+ doping.

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Improving the efficiency of solar cells by upconverting sunlight using field enhancement from optimized nano structures

Cited by 24 publications

References 113 publications

Bayesian Particle Instance Segmentation for Electron Microscopy Image Quantification

Bayesian Particle Instance Segmentation for Electron Microscopy Image Quantification

Strongly enhanced upconversion in trivalent erbium ions by tailored gold nanostructures: Toward high-efficient silicon-based photovoltaics

Improving Upconversion Photoluminescence of GdAlO₃:Er³⁺, Yb³⁺ Phosphors via Ga³⁺, Lu³⁺ Doping

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Improving the efficiency of solar cells by upconverting sunlight using field enhancement from optimized nano structures

Cited by 24 publications

References 113 publications

Bayesian Particle Instance Segmentation for Electron Microscopy Image Quantification

Bayesian Particle Instance Segmentation for Electron Microscopy Image Quantification

Strongly enhanced upconversion in trivalent erbium ions by tailored gold nanostructures: Toward high-efficient silicon-based photovoltaics

Improving Upconversion Photoluminescence of GdAlO3:Er3+, Yb3+ Phosphors via Ga3+, Lu3+ Doping

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Improving Upconversion Photoluminescence of GdAlO₃:Er³⁺, Yb³⁺ Phosphors via Ga³⁺, Lu³⁺ Doping