Application and Performance Analysis of Nanoparticles and Nanomaterials

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Particle Processes".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 1183

Special Issue Editors


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Guest Editor
Department of Physics, University of Pavia, 27100 Pavia, Italy
Interests: magnetic nanoparticles; NMR; MRI; radiomics; machine learning; magnetic fluid hyperthermia
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Physics and Astronomy, Univeristà di Catania, 95123 Catania, Italy
Interests: medical physics; applied physics; dosimetry
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Nanotechnology Research Center, Faculty of Energy Engineering, Aswan University, Aswan, Egypt
Interests: hybrid materials; polymer nanoscience; nanostructured materials; nanofluids; nanomaterial synthesis; carbon nanotubes

Special Issue Information

Dear Colleagues,

We kindly invite you to submit your contribution to this Special Issue, entitled “Application and Performance Analysis of Nanoparticles and Nanomaterials”.

The trends shaping materials science have applications across various industries: carbon nanomaterials, semiconductor nanodevices, nanotech power applications, green nanotechnology, nanocomposites, nanosensors, nanofilms, nanoencapsulation, energy nanomaterials, and computational nanotechnology. These trends are driven by atomic-scale innovations, material science breakthroughs, and microscopy advancements. Nanotechnology allows tailoring material structures at extremely small scales, leading to specific properties. Nanotechnology can make materials stronger, lighter, more durable, more reactive, more sieve-like, and better insulation or electrical conductors for nanoparticles.

This Special Issue explores the innovative applications and comprehensive performance evaluations of nanoparticles and nanomaterials across various sectors. Its scope is wide-ranging, spanning from health and medicine, where they serve in drug delivery and diagnostics, to electronics, where they enhance device properties and efficiency. Additionally, this Special Issue scrutinizes their roles in environmental applications like pollution control and water purification, as well as in food and agricultural engineering, civil engineering, and energy sectors such as battery technology and solar cells.

Furthermore, this Special Issue places a strong emphasis on the analytical methods used to evaluate the performance and safety of nanoparticles and nanomaterials. It discusses advancements in characterization techniques that help understand particle behavior at the nanolevel. Contributions also explore the economic and environmental implications of nanoparticle and nanomaterial production and usage, including life-cycle assessments and sustainability evaluations. Regulatory challenges and compliance with global standards are also significant themes, reflecting the growing attention to governance in nanotechnology deployment. Research articles and review articles on experimental techniques, mathematical methods, simulation, or computation are welcome.

Dr. Francesca Brero
Dr. Salvatore Gallo
Prof. Dr. Ahmed Thabet Mohamed
Guest Editors

Manuscript Submission Information

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Keywords

  • nanoparticles
  • nanotechnology applications
  • nanocomposites
  • performance evaluation
  • electronic enhancements
  • nanostructured materials
  • nanofluids
  • nanomaterial synthesis
  • characterization techniques
  • safety assessments
  • global nanotechnology standards

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Published Papers (1 paper)

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Research

14 pages, 1368 KiB  
Article
Automatic Active Contour Algorithm for Detecting Early Brain Tumors in Comparison with AI Detection
by Mohammed Almijalli, Faten A. Almusayib, Ghala F. Albugami, Ziyad Aloqalaa, Omar Altwijri and Ali S. Saad
Processes 2025, 13(3), 867; https://doi.org/10.3390/pr13030867 - 15 Mar 2025
Viewed by 486
Abstract
The automatic detection of objects in medical photographs is an essential component of the diagnostic procedure. The issue of early-stage brain tumor detection has progressed significantly with the use of deep learning algorithms (DLA), particularly convolutional neural networks (CNN). The issue lies in [...] Read more.
The automatic detection of objects in medical photographs is an essential component of the diagnostic procedure. The issue of early-stage brain tumor detection has progressed significantly with the use of deep learning algorithms (DLA), particularly convolutional neural networks (CNN). The issue lies in the fact that these algorithms necessitate a training phase involving a large database over several hundred images, which can be time-consuming and require complex computational infrastructure. This study aimed to comprehensively evaluate a proposed method, which relies on an active contour algorithm, for identifying and distinguishing brain tumors in magnetic resonance images. We tested the proposed algorithm using 50 brain images, specifically focusing on glioma tumors, while 2000 images were used for DLA from the BRATS Challenges 2021. The proposed segmentation method is made up of an active contour algorithm, an anisotropic diffusion filter for pre-processing, active contour segmentation (Chan-Vese), and morphologic operations for segmentation refinement. We evaluated its performance using various metrics, such as accuracy, precision, sensitivity, specificity, Jaccard index, Dice index, and Hausdorff distance. The proposed method provided an average of the first six performance metrics of 0.96, which is higher than most classical image segmentation methods and was comparable to the deep learning methods, which have an average performance score of 0.98. These results indicate its ability to detect brain tumors accurately and rapidly. The results section provided both numerical and visual insights into the similarity between segmented and ground truth tumor areas. The findings of this study highlighted the potential of computer-based methods in improving brain tumor identification using magnetic resonance imaging. Future work must validate the efficacy of these segmentation approaches across different brain tumor categories and improve computing efficiency to integrate the technology into clinical processes. Full article
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