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Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editors: Andrew Dunn and Anthony Durkin
  • Vol. 7, Iss. 6 — May. 25, 2012
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Optimal spectra of white light-emitting diodes using quantum dot nanophosphors

Ping Zhong, Guoxing He, and Minhao Zhang  »View Author Affiliations


Optics Express, Vol. 20, Issue 8, pp. 9122-9134 (2012)
http://dx.doi.org/10.1364/OE.20.009122


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Abstract

The relationship and trade-offs between the performance parameters including color rendering index (CRI), luminous efficacy of radiation (LER) and correlated color temperature (CCT) of white LEDs using quantum dot nanophosphors (QD-WLEDs) are investigated for CRI ≥ 80 and LER ≥ 300 lm/W at 1500 K ≤ CCT ≤ 6500 K. The optimal spectra of QD-WLEDs with CCTs of 2700–6500 K have been obtained with a nonlinear program for maximizing LER under conditions of both CRI and a special CRI of R9 strong red above 90 or 95. Furthermore, high performance QD-WLEDs with LER = 381 lm/W for CRI = R9 = 90 and LER = 371 lm/W for CRI = R9 = 95 at CCT = 3000 K, with LER = 361 lm/W for CRI = R9 = 90 and LER = 352 lm/W for CRI = R9 = 95 at CCT = 4500 K, and with LER = 346 lm/W for CRI = R9 = 90 and LER = 338 lm/W for CRI = R9 = 95 at CCT = 5700 K could be achieved. The LERs of high performance white LEDs using QD nanophosphors increase by 13% to 32% compared with that of white LEDs using traditional phosphors.

© 2012 OSA

1. Introduction

2. Objective function of optimization

Consider a SPD that contains emission spectrum from green-, yellow-, and red-emitting QD, as well as a blue LED die of QD-WLED. The relative spectral power distribution (SPD) of the QD-WLED, SQD-WLED(λ), is given by,
SQDWLED(λ)=qbS(λ,λb,Δλb)+qgS(λ,λg,Δλg)+qyS(λ,λy,Δλy)+qrS(λ,λr,Δλr)
(1)
where S(λ, λb, ∆λb) refers to relative SPD of the blue spectrum transmitted through the QD nanophosphors, S(λ, λg, ∆λg), S (λ, λy, ∆λy),and S(λ, λr,∆λr) refer to the relative emission spectra of green, yellow and red QD nanophosphors, λb, λg, λy, λr refers to peak wavelengths of blue, green, yellow and red color components, ∆λb, ∆λg, ∆λy, ∆λr refers to FWHMs of blue, green, yellow and red color components, qb, qg, qy and qr are proportions of the relative spectra of S(λ, λb, ∆λg), S(λ, λg, ∆λg), S(λ, λy, ∆λy) and S(λ, λr,∆λr), respectively. The emission spectrum of each color source is modeled as a Gaussian function [22

22. S. J. Rosenthal, “Bar-coding biomolecules with fluorescent nanocrystals,” Nat. Biotechnol. 19(7), 621–622 (2001). [CrossRef] [PubMed]

]. The chosen wavelength intervals for each color source are changed between 450 nm and 500 nm for blue, between 500 nm and 550 nm for green, between 550 nm and 600 nm for yellow, and between 600 nm and 650 nm for red. In addition, FWHM of each color component is changed between 30 nm and 54 nm. Subjecting the 3 × 4-dimensional parameter space to four color-mixing constrains results in the location of the feasible vectors on the hypersurface with 9 dimensionality [23

23. I. Moreno and U. Contreras, “Color distribution from multicolor LED arrays,” Opt. Express 15(6), 3607–3618 (2007). [CrossRef] [PubMed]

]. In order to obtain relationships of the maximum obtainable CRI vs. CCT, the maximum obtainable CRI vs. LER, and the highest achievable LER vs. CCT under condition of CRI ≥ 80 and LER ≥ 300 lm/W at CCTs of 1500 K to 6500 K with dC ≤ 0.0054, we introduce three objective functions:

F1(λb,λg,λy,λr,Δλb,Δλg,Δλy,Δλr,qr)=CRI(underconditionsofLER300lm/WanddC0.0054)
(2)
F2(λb,λg,λy,λr,Δλb,Δλg,Δλy,Δλr,qr)=CRI(underconditionsof1500KCCT6500KanddC0.0054)
(3)
F3(λb,λg,λy,λr,Δλb,Δλg,Δλy,Δλr,qr)=LER(underconditionsofCRI80anddC0.0054)
(4)

In order to optimize spectra of the QD-WLED lamp with different requirements of color rendering at CCTs of 2700 K to 6500 K, we introduce two objective functions:
F4(λb,λg,λy,λr,Δλb,Δλg,Δλy,Δλr,qr)=LER(underconditionsofCRIiatCCT=jwithdC0.0054)
(5)
F5(λb,λg,λy,λr,Δλb,Δλg,Δλy,Δλr,qr)=LER(underconditionsofbothCRIandR9aboveiatCCT=jwithdC0.0054)
(6)
i = (90, 95), j = (2700 K, 3000 K, 3500 K, 4000 K, 4500 K, 5000 K, 5700 K, 6500 K). Hence the optimization problem reduces to finding maxima of the objective function. The parameters in the objective function are dependent on not only the wavelength and FWHM but also the proportion of red color component. The proportions of blue, green and yellow are dependent on the proportion of red color component, CCT and dC. So the spectral optimization is carried out by optimizing the wavelength and FWHM of each color component, and also requires careful tuning of the amplitudes for each color component.

A common approach to such multi-objective problems, where the different objectives might be in trade-off, is to investigate the set of Pareto optimal solutions [24

24. C. A. C. Coello and G. B. Lamont, Applications of Multi-Objective Evolutionary Algorithms (World Scientific, 2004).

]. A Pareto optimal solution is optimal in the sense that improving one objective would degrade the performance for at least one other objective. This means that without any further information, one of these Pareto optimal solutions cannot be regarded as better than any other one. Although there are several global optimization algorithms available in the literature, in this work, genetic algorithms (GA) [25

25. A. Konak, D. Coit, and A. Smith, “Multi-objective optimization using genetic algorithms: a tutorial,” Reliab. Eng. Syst. Saf. 91(9), 992–1007 (2006). [CrossRef]

] were chosen because they are able to scan a vast set of solutions, they do not depend on a starting solution, they are very useful for complex problems, and most importantly, they can be easily modified to estimate the Pareto optimal set. For multi-objective problems, where there might not be one optimal solution, the single-objective GA is modified to evolve towards the Pareto optimal front. Several multi-objective evolutionary algorithms (MOEA) are described in literature [24

24. C. A. C. Coello and G. B. Lamont, Applications of Multi-Objective Evolutionary Algorithms (World Scientific, 2004).

]. The MOEA selected in this research is the non-dominated elitist NSGA-II genetic algorithm, a widely accepted benchmark in the MOEA research community [26

26. H. Eskandari and C. D. Geiger, “A fast Pareto genetic algorithm approach for solving expensive multiobjective optimization problems,” J. Heuristics 14(3), 203–241 (2008). [CrossRef]

]. A complete description of the NSGA-II algorithm is beyond the scope of this article, and the interested reader is kindly referred to the paper by Deb, et al [27

27. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput. 6(2), 182–197 (2002). [CrossRef]

].

3. Results

3.1 Relations between CRI vs. CCT, CRI vs. LER, and LER vs. CCT

The relationships of CRI vs. CCT, CRI vs. LER, and LER vs. CCT of the QD-WLED under conditions of CRI ≥ 80 and LER ≥ 300 lm/W at CCTs of 1500 K to 6500 K with dC ≤ 0.0054 have been obtained by nonlinear program for maximizing F1, F2 and F3, respectively. The results are shown in Fig. 1(a)
Fig. 1 Relationships of (a) CRI vs. CCT, (b) CRI vs. LER, and (c) LER vs. CCT with dC ≤ 0.0054.
, 1(b) and 1(c), respectively. In Fig. 1(a), the maximum obtainable CRI value quickly increases to 99.6 from 1500 to 5000 K. After 5000 K, the CRI starts to decrease very slowly to 99.2. The simulation results show that the maximum obtainable CRIs are higher than 99 at CCTs of 2700 to 6500 K. In Fig. 1(b), the maximum obtainable CRI value decreases when increasing LER where the fundamental trade-off between CRI and LER appears. The high performance QD-WLEDs with CRI ≥ 90 and LER ≥ 400 lm/W as well as with CRI ≥ 95 and LER ≥ 380 lm/W could be achieved using QD nanophosphors. In Fig. 1(c), the highest achievable LER value increases at CCTs of 1500 to 2500 K. The LER value is 430 lm/W at 2500 K. After 2500 K, the LER value starts to decrease when increasing CCT where the fundamental trade-off between LER and CCT appears.

3.2 Optimal spectra of QD-WLEDs with excellent color rendering and high efficient

The optimal peak WL, FWHM and relative radiant flux (Φe%) of each color component as well as their performance of QD-WLEDs with CRIs above 90 or 95 at CCTs of 2700 K to 6500 K (dC ≤ 0.0054) have been obtained by nonlinear program for maximizing F4. The simulation results are shown in Table 1

Table 1. Optimal peak WL, FWHM and Φe% of each color component, their performance of QD-WLEDs with CRI = 90 at CCTs of 2700 K to 6500 K (dC ≤ 0.0054).

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and Table 2

Table 2. Optimal peak WL, FWHM and Φe% of each color component, their performance of QD-WLEDs with CRI = 95 at CCTs of 2700 K to 6500 K (dC ≤ 0.0054).

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, respectively. The simulation results show that the choice of optimal FWHM for each color component should be as narrow as possible (the smallest value in our simulations is 30 nm). Table 1 indicates that WLEDs with LER = 401 lm/W at CCT = 3000 K, LER = 372 lm/W at CCT = 4500 K, and LER = 355 lm/W at CCT = 5700 K for CRI = 90 could be achieved using QD nanophosphors. Table 2 indicates that WLEDs with LER = 382 lm/W at CCT = 3000 K, LER = 358 lm/W at CCT = 4500 K, and LER = 342 lm/W at CCT = 5700 K for CRI = 95 could be achieved using QD nanophosphors. Unfortunately, all special CRI of R9s of QD-WLED with the highest is below 56. The special CRI of R9 is very important to visual color rendering so that both CRI and R9 are considered in optimization of high color rendering QD-WLED. Excellent color rendering and high efficacy QD-WLEDs with both CRI and R9 above 90 or 95 at CCTs of 2700 K to 6500 K (dC ≤ 0.0054) have been obtained by nonlinear program for maximizing F5. The optimal peak WL, FWHMand relative radiant flux (Φe%) of each color component, and their performance of QD-WLEDs are given in Table 3

Table 3. Optimal peak WL, FWHM and Φe% of each color component, their performance of QD-WLEDs with CRI = 90 and R9 = 90 at CCTs of 2700 K to 6500 K (dC ≤ 0.0054).

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and Table 4

Table 4. Optimal peak WL, FWHM and Φe% of each color component, their performance of QD-WLEDs with CRI = 95 and R9 = 95 at CCTs of 2700 K to 6500 K (dC ≤ 0.0054).

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. The simulation results show that the optimal FWHM of each color component is still 30 nm. The optimal relative spectral powerdistributions (SPDs) of QD-WLEDs with CRI = 90 and R9 = 90, as well as CRI = 95 and R9 = 95 at CCTs of 2700 K to 6500 K (dC ≤ 0.0054) are shown in Fig. 2
Fig. 2 Optimal relative SPDs of QD-WLEDs with CRI = 90 and R9 = 90 at CCTs of 2700 K to 6500 K (dC≤ 0.0054).
and Fig. 3
Fig. 3 Optimal relative SPDs of QD-WLEDs with CRI = 95 and R9 = 95 at CCTs of 2700 K to 6500 K (dC≤ 0.0054).
, respectively. The results show that high performance QD-WLEDs with CRI = 90, R9 = 90 and LER = 381 lm/W as well as CRI = 95, R9 = 95 and LER = 371 lm/W at a CCT of 3000 K, with CRI = 90, R9 = 90 and LER = 361 lm/W as well as CRI = 95, R9 = 95 and LER = 352 lm/W at a CCT of 4500 K, and with CRI = 90, R9 = 90 and LER = 346 lm/W as well as CRI = 95, R9 = 95 and LER = 338 lm/W at a CCT of 5700 K could be achieved using QD nanophosphors. The results in tables of 3 and 4 show that the CQS score is consistent with the CRI for QD-WLED with CRI = R9. Furthermore, their special CRIs of R13 and R15 corresponding to the colors of the skin on the face of European and Chinese women are also very high. Both R13 and R15 are especially important for interior lighting.

As compared with the traditional phosphor-coated white LEDs (p-W LEDs), the p-W LEDs consisting of a blue chip, green, yellow and red phosphors with at CCTs of 2700 K to 6500 K are simulated. Eleven green (507 nm~546 nm), nine yellow (554 nm~606 nm) silicate phosphors and six red (631 nm~667 nm) nitride phosphors are used in optimization [28

28. G. X. He and H. F. Yan, “Optimal spectra of the phosphor-coated white LEDs with excellent color rendering property and high luminous efficacy of radiation,” Opt. Express 19(3), 2519–2529 (2011). [CrossRef] [PubMed]

]. The highest achievable LER values of QD-WLEDs with both CRI and R9 above 90 or 95 at CCTs of 2700 K to 6500 K (dC ≤ 0.0054) are shown in Table 5

Table 5. Highest achievable LER values of p-W LEDs with CRI = 90 and R9 = 90 as well as CRI = 95 and R9 = 95 at CCTs of 2700 K to 6500 K (dC ≤ 0.0054).

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. The results show that LERs of WLEDs with QD nanophosphors increase by 13% to 32% compared with that of p-W LEDs with traditional phosphors.

3.3 Effects of the deviations from peak emission wavelengths and FWHMs of the spectra

In order to achieve QD-WLEDs with high optical performance, we simulate the effect of the deviations from peak emission wavelengths and FWHMs of the spectra. Average and standard deviation (σ) values of peak wavelengths and FWHMs of the spectra satisfying the conditions of LER ≥ 370 lm/W at CCT = 2700 K, LER ≥ 370 lm/W at CCT = 3000 K, LER ≥ 365 lm/W at CCT = 3500 K, LER ≥ 360 lm/W at 4000 K, LER ≥ 350 lm/W at CCT = 4500 K, LER ≥ 345 lm/W at CCT = 5000 K, LER ≥ 335 lm/W at CCT = 5700 K and LER ≥ 325 lm/W at CCT = 6500 K for both CRI ≥ 90 and R9 ≥ 90 are shown in Table 6

Table 6. Average and standard deviation (σ) values of the wavelength and FWHM of each color component satisfying the conditions of LER ≥ 370 lm/W at CCT = 2700 K, LER ≥ 370 lm/W at CCT = 3000 K, LER ≥ 365 lm/W at CCT = 3500 K, LER ≥ 360 lm/W at CCT = 4000 K, LER ≥ 350 lm/W at CCT = 4500 K, LER ≥ 345 lm/W at CCT = 5000 K, LER ≥ 335 lm/W at CCT = 5700 K, and LER ≥ 325 lm/W at CCT = 6500 K for both CRI ≥ 90 and R9 ≥ 90.

table-icon
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. Those satisfying the conditions of LER ≥ 360 lm/W at CCT = 2700 K, LER ≥ 360 lm/W at CCT = 3000 K, LER ≥ 360 lm/W at CCT = 3500 K, LER ≥ 350 lm/W at CCT = 4000 K, LER ≥ 345 lm/W at CCT = 4500 K, LER ≥ 340 lm/W at CCT = 5000 K, LER ≥ 330 lm/W at CCT = 5700 K and LER ≥ 320 lm/W at CCT = 6500 K for both CRI ≥ 95 and R9 ≥ 95 are shown in Table 7

Table 7. Average and standard deviation (σ) values of the wavelength and FWHM of each color component satisfying the conditions of LER ≥ 360 lm/W at CCT = 2700 K, LER ≥ 360 lm/W at CCT = 3000 K, LER ≥ 360 lm/W at CCT = 3500 K, LER ≥ 350 lm/W at CCT = 4000 K, LER ≥ 345 lm/W at CCT = 4500 K, LER ≥ 340 lm/W at CCT = 5000 K, LER ≥ 330 lm/W at CCT = 5700 K, and LER ≥ 320 lm/W at CCT = 6500 K for both CRI ≥ 95 and R9 ≥ 95.

table-icon
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. The results show that the peak wavelength for each color component has a strong restriction, and that the choice of FWHM for each color component should be as narrow as possible (the smallest value in our simulations is 30 nm) for high performance QD-WLEDs. The relative radiant flux Φe% of each color component, and their performance of QD-WLEDs with (λ¯, Δλ¯), (λ¯ + σλ, Δλ¯ + σ∆λ) and (λ¯- σλ, Δλ¯- σ∆λ) of each color component at CCTs of 2700 K to 6500 K under conditions of CRI ≥ 90 and R9 ≥ 90 are shown in Table 8

Table 8. Φe% of each color component, their performance of QD-WLEDs with λ¯andΔλ¯ of each color component at CCTs of 2700 K to 6500 K under conditions of CRI 90 and R9 90.

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, Table 9

Table 9. Φe% of each color component, their performance of QD-WLEDs with λ¯+ σλ andΔλ¯+ σ∆λ of each color component at CCTs of 2700 K to 6500 K under conditions of CRI 90 and R9 90.

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and Table 10

Table 10. Φe% of each color component, their performance of QD-WLEDs with λ¯- σλ andΔλ¯- σ∆λ of each color component at CCTs of 2700 K to 6500 K under conditions of CRI 90 and R9 90.

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, respectively. Those under conditions of CRI ≥ 95 and R9 ≥ 95 are shown in Table 11

Table 11. Φe% of each color component, their performance of QD-WLEDs with λ¯andΔλ¯of each color component at CCTs of 2700 K to 6500 K under conditions of CRI ≥ 95 and R9 ≥ 95.

table-icon
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, Table 12

Table 12. Φe% of each color component, their performance of QD-WLEDs with λ¯+ σλ andΔλ¯+ σ∆λ of each color component at CCTs of 2700 K to 6500 K under conditions of CRI 95 and R9 95.

table-icon
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and Table 13

Table 13. Φe% of each color component, their performance of QD-WLEDs with λ¯- σλ andΔλ¯- σ∆λ of each color component at CCTs of 2700 K to 6500 K under conditions of CRI ≥ 95 and R9 ≥ 95.

table-icon
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, respectively.

4. Conclusion

The relationship and trade-offs between the performance parameters including CRI, LER and CCT of WLEDs using QD nanophosphors are presented for CRI ≥ 80 and LER ≥ 300 lm/W at 1500 K ≤ CCT ≤ 6500 K with dC ≤ 0.0054. The optimal peak WL, FWHM and relative radiant flux (Φe%) of each color component of QD-WLEDs with both CRI and R9 above 90 or 95 at CCTs of 2700 K to 6500 K (dC ≤ 0.0054) are obtained. Furthermore, high performance QD-WLEDs with CRI = 90, R9 = 90 and LER = 381 lm/W as well as CRI = 95, R9 = 95 and LER = 371 lm/W at CCT = 3000 K, with CRI = 90, R9 = 90 and LER = 361 lm/W as well as CRI = 95, R9 = 95 and LER = 352 lm/W at CCT = 4500 K, and with CRI = 90, R9 = 90 and LER = 346 lm/W as well as CRI = 95, R9 = 95 and LER = 338 lm/W at CCT = 5700 K could be achieved using QD nanophosphors. The LERs of WLEDs with QD nanophosphors increase by 13% to 32% compared with that of p-W LEDs with traditional phosphors. Additionally, average and standard deviation values of peak wavelength and FWHM for each color component of QD-WLED are investigated in order to achieve QD-WLEDs with high optical performance,

Acknowledgments

This work was supported by Shanghai Science and Technology Committee (No.09DZ1141100) and the National “ITER” Project of Ministry of Science and Technology of P. R. China (No.2009GB107006 and No.2010GB107003).

References and links

1.

E. F. Schubert and J. K. Kim, “Solid-state light sources getting smart,” Science 308(5726), 1274–1278 (2005). [CrossRef] [PubMed]

2.

E. F. Schubert, Light Emitting Diodes (Cambridge University Press, 2006).

3.

International Commission on Illumination, Method of Measuring and Specifying Colour Rendering Properties of Light Sources (Commission Internationale de l'Éclairage, 1995).

4.

S. V. Gaponenko, Introduction to Nanophotonics (Cambridge University Press, 2010).

5.

V. L. Colvin, M. C. Schlamp, and A. P. Alivisatos, “Light-emitting diodes made from cadmium selenide nanocrystals and a semiconducting polymer,” Nature 370(6488), 354–357 (1994). [CrossRef]

6.

C. B. Murray, C. R. Kagan, and M. G. Bawendi, “Synthesis and characterization of monodisperse nanocrystals and close-packed nanocrystal assemblies,” Annu. Rev. Mater. Sci. 30(1), 545–610 (2000). [CrossRef]

7.

R. Osovsky, D. Cheskis, V. Kloper, A. Sashchiuk, M. Kroner, and E. Lifshitz, “Continuous-wave pumping of multiexciton bands in the photoluminescence spectrum of a single CdTe-CdSe core-shell colloidal quantum dot,” Phys. Rev. Lett. 102(19), 197401 (2009). [CrossRef] [PubMed]

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T. Erdem, S. Nizamoglu, X. W. Sun, and H. V. Demir, “A photometric investigation of ultra-efficient LEDs with high color rendering index and high luminous efficacy employing nanocrystal quantum dot luminophores,” Opt. Express 18(1), 340–347 (2010). [CrossRef] [PubMed]

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T. Erdem, S. Nizamoglu, and H. V. Demir, “Computational study of power conversion and luminous efficiency performance for semiconductor quantum dot nanophosphors on light-emitting diodes,” Opt. Express 20(3), 3275–3295 (2012). [CrossRef] [PubMed]

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S. Nizamoglu, T. Erdem, X. W. Sun, and H. V. Demir, “Warm-white light-emitting diodes integrated with colloidal quantum dots for high luminous efficacy and color rendering,” Opt. Lett. 35(20), 3372–3374 (2010). [CrossRef] [PubMed]

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G. X. He, “Warm-white light-emitting diodes integrated with colloidal quantum dots for high luminous efficacy and color rendering: comment,” Opt. Lett. 36(15), 2851, discussion 2852 (2011). [CrossRef] [PubMed]

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ENERGY STAR for SSL Luminaries ver. 1.1, 2008.

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American National Standard, Specifications for the Chromaticity of Solid State Lighting Products (ANSI C78.377), NEMA, 2008.

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N. Narendran and L. Deng, “Color rendering properties of LED light sources,” Proc. SPIE 4776, 61–67 (2002). [CrossRef]

15.

N. Sándor and J. Schanda, “Visual colour rendering based on colour difference evaluations,” Lighting Res. Tech. 38(3), 225–239 (2006). [CrossRef]

16.

K. Hashimoto and Y. Nayatani, “Visual clarity and feeling of contrast,” Color Res. Appl. 19(3), 171–185 (1994). [CrossRef]

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J. Worthey, “Color rendering: asking the questions,” Color Res. Appl. 28(6), 403–412 (2003). [CrossRef]

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W. Davis and Y. Ohno, “Color quality scale,” Opt. Eng. 49(3), 033602 (2010). [CrossRef]

19.

Y. Ohno and W. Davis, “Rationale of color quality scale,” (2010): http://www.digikey.com/us/en/techzone/lighting/resources/articles/rationale-of-color-quality-scale.html.

20.

S. Nizamoglu, T. Erdem, X. W. Sun, and H. V. Demir, “Warm-white light-emitting diodes integrated with colloidal quantum dots for high luminous efficacy and color rendering: reply to comment,” Opt. Lett. 36(15), 2852 (2011). [CrossRef]

21.

J. M. Phillips, M. F. Coltrin, M. H. Crawford, A. J. Fischer, M. R. Krames, R. Mueller-Mach, G. O. Mueller, Y. Ohno, L. E. S. Rohwer, J. A. Simmons, and J. Y. Tsao, “Research challenges to ultra-efficient inorganic solid state lighting,” Laser Photon. Rev. 1(4), 307–333 (2007). [CrossRef]

22.

S. J. Rosenthal, “Bar-coding biomolecules with fluorescent nanocrystals,” Nat. Biotechnol. 19(7), 621–622 (2001). [CrossRef] [PubMed]

23.

I. Moreno and U. Contreras, “Color distribution from multicolor LED arrays,” Opt. Express 15(6), 3607–3618 (2007). [CrossRef] [PubMed]

24.

C. A. C. Coello and G. B. Lamont, Applications of Multi-Objective Evolutionary Algorithms (World Scientific, 2004).

25.

A. Konak, D. Coit, and A. Smith, “Multi-objective optimization using genetic algorithms: a tutorial,” Reliab. Eng. Syst. Saf. 91(9), 992–1007 (2006). [CrossRef]

26.

H. Eskandari and C. D. Geiger, “A fast Pareto genetic algorithm approach for solving expensive multiobjective optimization problems,” J. Heuristics 14(3), 203–241 (2008). [CrossRef]

27.

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput. 6(2), 182–197 (2002). [CrossRef]

28.

G. X. He and H. F. Yan, “Optimal spectra of the phosphor-coated white LEDs with excellent color rendering property and high luminous efficacy of radiation,” Opt. Express 19(3), 2519–2529 (2011). [CrossRef] [PubMed]

OCIS Codes
(230.3670) Optical devices : Light-emitting diodes
(230.5590) Optical devices : Quantum-well, -wire and -dot devices
(250.0250) Optoelectronics : Optoelectronics
(330.1715) Vision, color, and visual optics : Color, rendering and metamerism

ToC Category:
Optical Devices

History
Original Manuscript: January 31, 2012
Revised Manuscript: March 14, 2012
Manuscript Accepted: March 27, 2012
Published: April 4, 2012

Virtual Issues
Vol. 7, Iss. 6 Virtual Journal for Biomedical Optics

Citation
Ping Zhong, Guoxing He, and Minhao Zhang, "Optimal spectra of white light-emitting diodes using quantum dot nanophosphors," Opt. Express 20, 9122-9134 (2012)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=oe-20-8-9122


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References

  1. E. F. Schubert and J. K. Kim, “Solid-state light sources getting smart,” Science308(5726), 1274–1278 (2005). [CrossRef] [PubMed]
  2. E. F. Schubert, Light Emitting Diodes (Cambridge University Press, 2006).
  3. International Commission on Illumination, Method of Measuring and Specifying Colour Rendering Properties of Light Sources (Commission Internationale de l'Éclairage, 1995).
  4. S. V. Gaponenko, Introduction to Nanophotonics (Cambridge University Press, 2010).
  5. V. L. Colvin, M. C. Schlamp, and A. P. Alivisatos, “Light-emitting diodes made from cadmium selenide nanocrystals and a semiconducting polymer,” Nature370(6488), 354–357 (1994). [CrossRef]
  6. C. B. Murray, C. R. Kagan, and M. G. Bawendi, “Synthesis and characterization of monodisperse nanocrystals and close-packed nanocrystal assemblies,” Annu. Rev. Mater. Sci.30(1), 545–610 (2000). [CrossRef]
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