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Novel Low-Dimensional Material-Based Photodetectors for Sensing and Imaging Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 423

Special Issue Editors


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Guest Editor
School of Physics, Beijing Institute of Technology, Beijing 100081, China
Interests: 2D materials; optoelectronic devices; van der Waals heterostructures; transition metal dichalcogenides; light-matter interation
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
MoE Key Lab of Photoelectronic Imaging Technology and System, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
Interests: optoelectronics; semiconductor; plasmonics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, low-dimensional materials—including quantum dots (QDs), nanowires (NWs), and two-dimensional (2D) materials—have emerged as revolutionary building blocks for next-generation photodetectors. Their exceptional optoelectronic properties, such as tunable bandgaps, high carrier mobility, strong light–matter interactions, and atomic-scale thickness, enable high performance in photodetection, thereby surpassing traditional bulk semiconductors. At the same time, new computational methods are emerging to enable photodetectors to achieve better performance in imaging applications. This Special Issue aims to highlight cutting-edge advances in low-dimensional material-based photodetectors and computational methods for their transformative applications in sensing, imaging, and integrated photonic systems.

The unique advantages of low-dimensional materials lie in their size-dependent quantum effects and versatile integration capabilities. For instance, QDs exhibit size-tunable absorption spectra, enabling broadband or selective photodetection. One-dimensional nanowires offer directional charge transport and enhanced light trapping, which are ideal for high-speed and polarization-sensitive devices. Meanwhile, 2D materials (e.g., graphene, transition metal dichalcogenides, and MXenes) combine ultrahigh surface-to-volume ratios with mechanical flexibility, thereby opening avenues for wearable sensors and ultrathin imaging arrays. Beyond conventional photodetection, these materials are driving innovations in emerging fields such as in-sensor computing, neuromorphic optoelectronics, and photonic logic circuits, where light detection and signal processing converge at the device level.

This Special Issue invites original research articles and reviews covering topics including, but not limited to, the following:

  • Synthesis and characterization of novel low-dimensional optoelectronic
  • High-performance photodetectors with enhanced responsivity, detectivity, speed, and spectral range.
  • Advanced applications: bio-inspired sensors, hyperspectral imaging, LiDAR, night vision, and medical diagnostics.
  • System integration: flexible/wearable optoelectronics, on-chip photonic systems, and integration with silicon photonics.
  • Next-generation concepts: computational imaging, in-sensor computing, optoelectronic synapses, non-volatile photomemory, and light-driven logic devices.

We look forward to your contributions.

Prof. Dr. Li Tao
Dr. Ziyuan Li
Guest Editors

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Keywords

  • low-dimensional materials
  • photodetectors
  • 2D materials
  • nanowires
  • quantum dots
  • in-sensor computing
  • imaging
  • heterostructures

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Published Papers (1 paper)

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Research

20 pages, 3340 KiB  
Article
Infrared Monocular Depth Estimation Based on Radiation Field Gradient Guidance and Semantic Priors in HSV Space
by Rihua Hao, Chao Xu and Chonghao Zhong
Sensors 2025, 25(13), 4022; https://doi.org/10.3390/s25134022 - 27 Jun 2025
Viewed by 287
Abstract
Monocular depth estimation (MDE) has emerged as a powerful technique for extracting scene depth from a single image, particularly in the context of computational imaging. Conventional MDE methods based on RGB images often degrade under varying illuminations. To overcome this, an end-to-end framework [...] Read more.
Monocular depth estimation (MDE) has emerged as a powerful technique for extracting scene depth from a single image, particularly in the context of computational imaging. Conventional MDE methods based on RGB images often degrade under varying illuminations. To overcome this, an end-to-end framework is developed that leverages the illumination-invariant properties of infrared images for accurate depth estimation. Specifically, a multi-task UNet architecture was designed to perform gradient extraction, semantic segmentation, and texture reconstruction from infrared RAW images. To strengthen structural learning, a Radiation Field Gradient Guidance (RGG) module was incorporated, enabling edge-aware attention mechanisms. The gradients, semantics, and textures were mapped to the Saturation (S), Hue (H), and Value (V) channels in the HSV color space, subsequently converted into an RGB format for input into the depth estimation network. Additionally, a sky mask loss was introduced during training to mitigate the influence of ambiguous sky regions. Experimental validation on a custom infrared dataset demonstrated high accuracy, achieving a δ1 of 0.976. These results confirm that integrating radiation field gradient guidance and semantic priors in HSV space significantly enhances depth estimation performance for infrared imagery. Full article
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