A colloidal quantum dot spectrometer

Abstract

Spectroscopy is carried out in almost every field of science, whenever light interacts with matter1. Although sophisticated instruments with impressive performance characteristics are available, much effort continues to be invested in the development of miniaturized, cheap and easy-to-use systems1,2,3,4,5,6,7,8,9,10,11,12,13. Current microspectrometer designs mostly use interference filters2,3,4,5 and interferometric optics3 that limit their photon efficiency, resolution and spectral range2,3. Here we show that many of these limitations can be overcome by replacing interferometric optics with a two-dimensional absorptive filter array composed of colloidal quantum dots14,15,16,17. Instead of measuring different bands of a spectrum individually after introducing temporal or spatial separations with gratings or interference-based narrowband filters, a colloidal quantum dot spectrometer measures a light spectrum based on the wavelength multiplexing principle18: multiple spectral bands are encoded and detected simultaneously with one filter and one detector9,10,11,12, respectively, with the array format allowing the process to be efficiently repeated many times using different filters with different encoding so that sufficient information is obtained to enable computational reconstruction of the target spectrum. We illustrate the performance of such a quantum dot microspectrometer, made from 195 different types of quantum dots with absorption features that cover a spectral range of 300 nanometres, by measuring shifts in spectral peak positions as small as one nanometre. Given this performance, demonstrable avenues for further improvement, the ease with which quantum dots can be processed and integrated, and their numerous finely tuneable bandgaps that cover a broad spectral range, we expect that quantum dot microspectrometers will be useful in applications where minimizing size, weight, cost and complexity of the spectrometer are critical.

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Figure 1: Comparison of spectrometer mechanisms.
Figure 2: Operation of quantum dot spectrometers.
Figure 3: CQD filters and an integrated quantum dot spectrometer.
Figure 4: Quantum dot spectrometer measurements.

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Acknowledgements

The inception, experiments and initial analysis in this work was funded by the ARO through the Institute for Soldier Nanotechnologies (W911NF-07-D-0004). During further analysis and modelling, J.B. was supported by Tsinghua University and the Division of Physics, Mathematics and Astronomy at the California Institute of Technology.

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Authors

Contributions

J.B. designed the experiments with contributions from M.G.B. J.B. performed the experiments. Both authors discussed the results. J.B. wrote the manuscript with contributions from M.G.B.

Corresponding author

Correspondence to Jie Bao.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Transmission spectra for all 195 CQD embedded thin films shown in Fig. 3a.

In each subplot, the horizontal axis measures wavelength (in nanometres) and the vertical axis measures the transmission fraction (×100%).

Extended Data Figure 2 Transmission spectra for selected CQD filters in the CQD filter array.

In each subplot, the horizontal axis measures wavelength (in nanometres) and the vertical axis measures the transmission fraction (×100%).

Extended Data Figure 3 Characterization of the spectral resolution of the quantum dot spectrometer.

The left panels are doublet peaks with peak separations of 2–5 nm measured by a HR2000 spectrometer as a reference. The right panels are the same doublet peaks measured by the quantum dot spectrometer.

Extended Data Figure 4 A simulated spectrum and spectral reconstruction.

a, The green crosses represent the data points of a simulated light spectrum in intervals of 1.6 nm. The separation between the two left-most peaks is 3.2 nm. b, Spectral reconstruction (σ = 0, nF = 147). With σ = 0, the spectral reconstruction process is equivalent to solving a set of linear equations that has a unique solution. The reconstructed spectrum (the red dotted line with red circles representing data points) matches the original (incident) light spectrum perfectly and the two peaks separated by 3.2 nm are resolved. cf, Spectral reconstruction (σ = 0.0001, 0.001, 0.01, 0.1, respectively, nF = 147) using least-squares linear regression. c, The original light spectrum is reproduced accurately and the two peaks separated by 3.2 nm are resolved. d, The original light spectrum is reproduced reasonably well and the peak positions accurately represented. e, The spectral reconstruction does not reproduce the original light spectrum very accurately and some spectral information is lost. f, The spectral reconstruction no longer reproduces the original light spectrum accurately and a lot of spectral information is lost. Nevertheless, major peak information can still be obtained from the simulation.

Extended Data Figure 5 Distribution of simulations.

a, Same as Extended Data Fig. 4d. b, The frequency (of the given value of R occurring in the 100 simulations averaged to produce a) decay with increasing R.

Extended Data Figure 6 Simulated spectra and spectral reconstructions.

a, c, e, Second, third and fourth simulated light spectra. The green crosses represent the simulated data points, in intervals of 1.6 nm. The separation between the two leftmost peaks is about 4.8 nm, 5.6 nm and 13 nm, respectively. b, d, f, Spectral reconstruction (σ = 0.001, 0.01, 0.1, respectively, nF = 147) using least-squares linear regression. b, The original light spectrum is reproduced very accurately and little spectral information is lost. The two left-most peaks are resolved. d, Spectral reconstruction does not reproduce the original light spectrum very accurately and some spectral information is lost. However, the two left-most peaks are resolved. f, Spectral reconstruction no longer reproduces the original light spectrum very accurately and some spectral information is lost. However, the two left-most peaks are resolved.

Extended Data Figure 7 Comparison of spectral reconstructions with nF = 147 and nF = 195.

ac, The separation between the two left-most peaks is 3.2 nm, 4.8 nm and 13 nm, respectively. The spectral reconstruction (σ = 0.001, 0.01, 0.1, respectively) using both filter sets reproduced the main features of the original light spectrum. An improvement in the reconstructed spectrum with 195 filters is observed, compared to that with 147 filters, where all other conditions are the same. However, due to the small relative difference between 147 and 195, the improvement is limited.

Extended Data Figure 8 Effects of algorithms on spectral reconstruction accuracy.

a, Plotted with the red markers is a spectrum measured by the quantum dot spectrometer and reconstructed using least-squares linear regression. The blue line represents the reference spectrum. b, Plotted with the red markers is the spectrum based on the same quantum dot spectrometer measurement data, but reconstructed using a more sophisticated algorithm. The blue line represents the same reference spectrum.

Extended Data Figure 9 Stability analysis.

In both Measurement 1 and Measurement 2 (taken six months apart, without recalibration), the peaks shown are at 400 nm, 450 nm, 500 nm, 501 nm, 503 nm, 505 nm, 550 nm and 600 nm.

Extended Data Table 1 Simulation of the effects of errors on the dynamic range

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Bao, J., Bawendi, M. A colloidal quantum dot spectrometer. Nature 523, 67–70 (2015). https://doi.org/10.1038/nature14576

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