Harnessing PbS Quantum Dots for High-Precision Near-Infrared Computational Spectroscopy

By Atif SuhailReviewed by Louis CastelAug 12 2025

Computational spectrometers are changing the way spectral analysis is done, using software-based reconstruction to extract detailed spectral information from broadband detectors instead of relying on traditional dispersive optics. This approach dramatically reduces instrument size and cost while maintaining high performance.¹

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Near-infrared (NIR) light (typically 700–1700 nm) is especially valuable in applications ranging from chemical sensing to non-invasive medical diagnostics, which exploit deeper tissue penetration and minimal autofluorescence.¹

Traditional NIR spectrometers, which rely on gratings or prisms, tend to be bulky and costly. A recent study, however, demonstrates a lightweight and scalable alternative by using engineered lead sulfide (PbS) quantum dots (QDs). This approach integrates nanoscale photonic filters with advanced reconstruction algorithms to deliver high-precision NIR computational spectroscopy.^1-2

What Are Computational Spectrometers and Why Do They Matter?

Computational spectrometers are compact optical instruments that determine detailed spectral information without physically dispersing light through gratings or prisms. Instead, they encode the incoming light using a small array of broadband filters or engineered photonic elements, and then reconstruct the full spectrum through software.³

In practice, each filter or detector element has a distinct spectral response, and the set of measured intensities b_i from these n encoders is mapped via a response matrix A to the unknown spectrum x by solving an inverse problem Ax=b. This paradigm decouples spectral resolution from optical path length, enabling ultra-compact, low-cost devices capable of rapid, in-situ analysis across chemistry, biology, environmental monitoring, and beyond.³

Compared with traditional dispersive spectrometers and interferometric systems (e.g., FTIR), computational designs offer three key advantages:

• Size Reduction: Solid-state filter or detector arrays eliminate bulky optical paths and moving parts, shrinking instruments to millimeter-scale footprints suitable for wearable platforms.
• Cost Efficiency: Replacing precision optics with scalable micro-/nano-fabricated filters and leveraging mass-produced CMOS or InGaAs sensors cuts production costs dramatically.
• Mechanical Simplicity: With no scanning interferometers or alignment-sensitive gratings, these spectrometers exhibit enhanced robustness and reliability in field or point-of-care settings.

Early NIR implementations (700–1700 nm) struggled to balance spectral accuracy with working bandwidth. Filter arrays often showed high inter-element correlation and limited complexity, constraining resolution.⁴

Environmental factors, such as angle of incidence, temperature, and humidity, caused drift in spectral responses. In addition, early reconstruction algorithms, typically based on iterative compressive sensing, were noise-robust but too slow for real-time use.⁴

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PbS Quantum Dots: Tunable Tools for Light Detection

PbS quantum dots are colloidal nanocrystals, which are semiconductor particles whose dimensions (typically 3–10 nm in diameter) approach or fall below the exciton Bohr radius. At these scales, quantum confinement dominates: shrinking a dot increases its effective band gap, shifting its first excitonic absorption peak to shorter wavelengths; conversely, larger dots absorb deeper into the NIR.⁵

Surface chemistry offers a second tuning lever: long-chain ligands (e.g., oleic acid) enhance passivation and boost luminescence but impede charge transport, whereas shorter ligands can shift absorption by tens of nanometers and tailor PbS QDs for photoconductors, photodiodes, or phototransistors.⁵

Researchers exploit this tunability by fabricating arrays of PbS QD films, each layer composed of dots of a distinct size or ligand composition. When broadband NIR light impinges on such an array, each pixel’s transmitted or reflected intensity carries a unique spectral fingerprint.⁶

For instance, selecting five dot diameters ranging from 4 nm to 8 nm yields overlapping absorption profiles with peaks spanning 1000–1500 nm. This engineered filter array, deposited atop a simple photodiode array, encodes incident spectra into a small number of intensity readings. A computational reconstruction algorithm can then invert these readings to recover the full high-resolution spectrum without using gratings, prisms, or any moving parts.⁷

Each PbS QD film is sensitive to a specific wavelength range, producing a distinct response when illuminated with broadband light. By measuring the responses from multiple films and knowing each film’s wavelength sensitivity, the original spectrum can be computationally reconstructed.⁶

How the Computational Model Works

A compressive sensing–based reconstruction algorithm lies at the core of the computational spectrometer. When broadband light passes through the PbS QD filter array, each detector records an integrated intensity that represents the projection of the unknown spectrum onto a distinct filter response. Mathematically, these measurements satisfy

S_i=∑_λ I(λ) T_i(λ) η(λ)

where I(λ) is the incident spectrum, T_i(λ) is the ith filter’s calibrated response, and η(λ) captures the detector’s sensitivity. Because the number of filters is far smaller than the number of wavelength bins, the system of equations is underdetermined.¹

By assuming the spectrum is sparse or compressible in an appropriate basis, the algorithm recovers I(λ) via regularized inversion, often minimizing the sum of the squared error plus an L¹-norm penalty that promotes sparsity. Prior to measurement, each filter response T_i(λ) is mapped out using known spectra from a tunable light source with subnanometer steps, building the response matrix essential for accurate inversion.^{1, 8}

Because all encoding occurs in solid-state QD films and static photodiodes, there are no moving parts, diffraction gratings, or tunable optics. A single snapshot of detector readings suffices to capture the entire spectrum. Remarkably, with just tens of filter channels, these systems routinely achieve 1 nm resolution across several hundred nanometers of NIR bandwidth, a performance on par with benchtop grating spectrometers that require mechanical scanning or high-precision prisms and gratings.^{1, 9}

Applications Across Industries

NIR computational spectrometers have applications across healthcare diagnostics, food and agriculture, environmental sensing, and consumer electronics. In healthcare, compact devices can perform non-invasive, point-of-care assays by analyzing skin reflectance or saliva, enabling rapid biomarker detection at the bedside or in remote clinics.¹⁰

In food and agriculture, these spectrometers monitor water and sugar content in fruits and vegetables throughout the supply chain, ensuring quality and reducing waste. Environmental monitoring benefits from portable sensors that detect pollutants in air or measure nutrient levels in soil on location, supporting real-time decision making in field research and remediation efforts.¹⁰

Thanks to their millimetre-scale form factor and low cost, NIR computational spectrometers can be integrated directly into smartphones, wearable patches, or handheld scanners, bringing high-precision spectral analysis into the hands of non-specialists.¹⁰

Commercial Relevance and Outlook

PbS QDs can be synthesized at scale using colloidal chemistry, while filter arrays can be patterned through inkjet printing or roll-to-roll coating. With CMOS readout circuits processing the signals directly, this approach offers a clear path toward mass production. Several startups are exploring mobile spectrometry modules, and ongoing R&D focuses on extending the spectral range and improving resolution via algorithm-hardware co-optimization.⁶

Looking ahead, the development of multi-element QD mixtures promises filters with tailor-made correlation matrices that dramatically boost encoding efficiency, while on-chip integration of QD filters alongside photonic waveguides and detectors on a unified silicon platform will pave the way for truly monolithic spectrometer chips.¹¹

Meanwhile, advanced reconstruction algorithms, co-designed with the encoding hardware and powered by deep neural networks, are expected to push spectral resolution into the sub-nanometer range, enabling even more precise chemical and biological sensing.¹

Future Challenges and Research Directions

Ensuring batch-to-batch uniformity in PbS quantum-dot size and surface passivation is critical to maintaining consistent spectral responses, while robust, auto-calibrating algorithms driven by machine learning can adapt in real time to environmental fluctuations and signal drift.⁶

Mitigating toxicity concerns by using PbS/CdS  core shell or by developing nanomaterials free of heavy metals is essential to maintaining high performance in the deep near infrared while ensuring safety.⁶

By overcoming these stability, calibration, and toxicity challenges, PbS QD–based computational spectrometers are poised to revolutionize NIR sensing across science, industry, and more.

References and Further Studies

1. Gao, L.; Qu, Y.; Wang, L.; Yu, Z., Computational Spectrometers Enabled by Nanophotonics and Deep Learning. Nanophotonics 2022, 11, 2507-2529.
2. Xue, Q.; Yang, Y.; Ma, W.; Zhang, H.; Zhang, D.; Lan, X.; Gao, L.; Zhang, J.; Tang, J., Advances in Miniaturized Computational Spectrometers. Advanced Science 2024, 11, 2404448.
3. Wen, J.; Shi, W.; Gao, C.; Liu, Y.; Feng, S.; Shao, Y.; Gao, H.; Shao, Y.; Zhang, Y.; Shen, W., A Computational Spectrometer for the Visible, near, and Mid-Infrared Enabled by a Single-Spinning Film Encoder. Communications Engineering 2025, 4, 37.
4. Li, H.; Bian, L.; Gu, K.; Fu, H.; Yang, G.; Zhong, H.; Zhang, J., A near‐Infrared Miniature Quantum Dot Spectrometer. Advanced Optical Materials 2021, 9, 2100376.
5. Moreels, I.; Justo, Y.; De Geyter, B.; Haustraete, K.; Martins, J. C.; Hens, Z., Size-Tunable, Bright, and Stable Pbs Quantum Dots: A Surface Chemistry Study. ACS nano 2011, 5, 2004-2012.
6. Whitworth, G.; Dalmases, M.; Taghipour, N.; Konstantatos, G., Solution-Processed Pbs Quantum Dot Infrared Laser with Room-Temperature Tunable Emission in the Optical Telecommunications Window. Nature Photonics 2021, 15, 738-742.
7. Bothra, U.; Albaladejo-Siguan, M.; Vaynzof, Y.; Kabra, D., Impact of Ligands on the Performance of Pbs Quantum Dot Visible–near-Infrared Photodetectors. Advanced Optical Materials 2023, 11, 2201897.
8. Kamilov, U. S., A Parallel Proximal Algorithm for Anisotropic Total Variation Minimization. IEEE Transactions on Image Processing 2016, 26, 539-548.
9. Zhang, S.; Dong, Y.; Fu, H.; Huang, S.-L.; Zhang, L., A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning. Sensors 2018, 18, 644.
10. Beć, K. B.; Grabska, J.; Huck, C. W., Principles and Applications of Miniaturized near‐Infrared (Nir) Spectrometers. Chemistry–A European Journal 2021, 27, 1514-1532.
11. Guan, Q.; Lim, Z. H.; Sun, H.; Chew, J. X. Y.; Zhou, G., Review of Miniaturized Computational Spectrometers. Sensors 2023, 23, 8768.

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