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Nanomaterials
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New Perspectives in Ultra Precision Manufacturing and Micro-Nano Inspection Technology
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New Perspectives in Ultra Precision Manufacturing and Micro-Nano Inspection Technology

A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Nanophotonics Materials and Devices".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 4168

Special Issue Editors

Prof. Dr. Feng Shi

E-Mail
Guest Editor
College of Intelligent Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: ultra precision polishing; ion beam polishing; magnetorheological finishing; defect detection; AR; precision engineering; ultra smooth; advanced optical fabrication; low damage
Dr. Xing Peng

E-Mail Website
Guest Editor
College of Intelligent Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: defect inspection; optical design; illumination design; machine vision; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ultra-precision manufacturing and micro-nano inspection technology field has witnessed remarkable advancements in recent years, driven by the escalating demand for high-performance optical systems across various applications. Innovations in materials science, fabrication methodologies, and sophisticated measurement techniques have enabled researchers to explore novel ultra-precision manufacturing and inspection approaches that potentially revolutionize this field. This Special Issue on "New Perspectives in Ultra Precision Manufacturing and Micro-Nano Inspection Technology" showcases the latest research and developments, highlighting pioneering concepts, methodologies, and practical applications. Key areas covered in this Special Issue include, but are not limited to, ultra-precision polishing, advanced materials, precision manufacturing techniques, computational methods and algorithms, ion beam polishing, magnetorheological finishing, ultra smooth, low damage, measurement and metrology, optical design, integrated optoelectronic devices, machine vision, and defect detection.

We believe this collection of articles will be an invaluable resource for professionals seeking to advance the frontiers of ultra-precision manufacturing and micro-nano inspection technology. We hope it will inspire further innovation and foster collaboration among scientists, researchers, and engineers, ultimately leading to new perspectives and breakthroughs in this field.

Both theoretical and experimental studies and comprehensive reviews and survey papers are welcome.

Prof. Dr. Feng Shi
Dr. Xing Peng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Nanomaterials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ultra precision polishing
  • advanced materials
  • ion beam polishing
  • magnetorheological finishing
  • ultra smooth
  • low damage
  • machine vision
  • defect detection
  • measurement and metrology
  • optical design
  • integrated optoelectronic devices

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Published Papers (3 papers)

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18 pages, 3458 KiB  
Article
Shearography-Based Near-Surface Defect Detection in Composite Materials: A Spatiotemporal Object Detection Neural Network Trained Only with Simulated Data
by Guanlin Li, Yao Hu, Hao Wang, Qun Hao and Yu Zhang
Nanomaterials 2025, 15(7), 523; https://doi.org/10.3390/nano15070523 - 30 Mar 2025
Viewed by 274
Abstract
Shearography is a non-destructive defect detection technique that, when combined with neural networks, can efficiently and accurately detect near-surface defects in composite materials. However, the high cost of the dataset significantly limits the application of neural networks in shearography. Current simulation data generation [...] Read more.
Shearography is a non-destructive defect detection technique that, when combined with neural networks, can efficiently and accurately detect near-surface defects in composite materials. However, the high cost of the dataset significantly limits the application of neural networks in shearography. Current simulation data generation techniques fail to eliminate the discrepancies between simulated and experimental data, resulting in suboptimal performance when training neural networks with only simulated data. To address this issue, this paper utilizes phase map sequences measured by shearography as the medium for defect detection and designs a YOWO_SS3D spatiotemporal object detection network. The network simultaneously learns both the spatial distribution features and temporal variation patterns of simulated phase map sequences, achieving high-accuracy detection of defects. The experimental results show that, with only 4000 frames of simulated data for training, our network achieved a detection accuracy of 96.99% on experimental phase maps, which is considerably higher than the 65.37% accuracy achieved by training the YOLOv4 network with the same simulated data. Using our technique, only pre-generated simulation data are required to train the network, enabling YOWO_SS3D to be directly deployed for practical defect detection tasks. This approach eliminates the substantial costs associated with collecting experimental training data and promotes the application of neural network technology in the shearography field. Full article
(This article belongs to the Special Issue New Perspectives in Ultra Precision Manufacturing and Micro-Nano Inspection Technology)
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12 pages, 2586 KiB  
Article
Si-HgTe Quantum Dot Visible-Infrared Photodetector
by Lei Qian, Xue Zhao, Kenan Zhang, Chen Huo, Yongrui Li, Naiquan Yan, Feng Shi, Xing Peng and Menglu Chen
Nanomaterials 2025, 15(4), 262; https://doi.org/10.3390/nano15040262 - 10 Feb 2025
Viewed by 2697
Abstract
Silicon photodetectors are well developed, with the advantage of their low cost and easy fabrication. However, due to the semiconductor band gap limitation, their detection wavelength is limited in the visible and near-infrared ranges. To broaden the detection wavelength, we stacked a mercury [...] Read more.
Silicon photodetectors are well developed, with the advantage of their low cost and easy fabrication. However, due to the semiconductor band gap limitation, their detection wavelength is limited in the visible and near-infrared ranges. To broaden the detection wavelength, we stacked a mercury telluride (HgTe) colloidal quantum dot (CQD) photodiode and a silicon PIN photodiode in series. This detector shows response spectra ranging from visible to short-wave infrared (430 nm to 2800 nm) at room temperature. At zero bias, the total photocurrents are 112.5 μA and 1.24 μA, with a tungsten lamp and a blackbody serving as light sources, respectively. The response speed can reach 1.65 μs, with the calculated detectivities of the visible wavelength D* = 1.01 × 1011 Jones, and that of the short-wave infrared being D* = 2.66 × 1010 Jones at room temperature. At the same time, with a homemade trans-impedance amplifier (TIA) circuit, we demonstrate the device application for figuring out the amplified voltage of the VIS, SWIR, and the VIS-SWIR stacked layers. Full article
(This article belongs to the Special Issue New Perspectives in Ultra Precision Manufacturing and Micro-Nano Inspection Technology)
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11 pages, 5221 KiB  
Article
Green Chemical Shear-Thickening Polishing of Monocrystalline Silicon
by Jiancheng Xie, Feng Shi, Shanshan Wang, Xing Peng and Qun Hao
Nanomaterials 2024, 14(23), 1866; https://doi.org/10.3390/nano14231866 - 21 Nov 2024
Cited by 2 | Viewed by 857
Abstract
A green chemical shear-thickening polishing (GC-STP) method was studied to improve the surface precision and processing efficiency of monocrystalline silicon. A novel green shear-thickening polishing slurry composed of silica nanoparticles, alumina abrasive, sorbitol, plant ash, polyethylene glycol, and deionized water was formulated. The [...] Read more.
A green chemical shear-thickening polishing (GC-STP) method was studied to improve the surface precision and processing efficiency of monocrystalline silicon. A novel green shear-thickening polishing slurry composed of silica nanoparticles, alumina abrasive, sorbitol, plant ash, polyethylene glycol, and deionized water was formulated. The monocrystalline silicon was roughly ground using a diamond polishing slurry and then the GC-STP process. The material removal rate (MRR) during GC-STP was 4.568 μmh−1. The material removal mechanism during the processing of monocrystalline silicon via GC-STP was studied using elemental energy spectroscopy and FTIR spectroscopy. After 4 h of the GC-STP process, the surface roughness (Ra) of the monocrystalline silicon wafer was reduced to 0.278 nm, and an excellent monocrystalline silicon surface quality was obtained. This study shows that GC-STP is a green, efficient, and low-damage polishing method for monocrystalline silicon. Full article
(This article belongs to the Special Issue New Perspectives in Ultra Precision Manufacturing and Micro-Nano Inspection Technology)
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