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Special Issue "The 15th Anniversary of Materials—Recent Advances in Advanced Materials Characterization"

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Advanced Materials Characterization".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 688

Special Issue Editors

Department of Chemistry and Physics, Southeastern Louisiana University, SLU 10878, Hammond, LA 70402, USA
Interests: deformation theory; optical techniques for material characterization; acoustical techniques for material characterization; dynamics; field theories
Special Issues, Collections and Topics in MDPI journals
Dipartimento di Meccanica, Matematica e Management, Politecnico di Bari, Viale Orabona 4, 70126 Bari, Italy
Interests: bioengineering and cell mechanics; nanosciences and nanotechnology; optical methods; materials science and characterization; structural optimization
Special Issues, Collections and Topics in MDPI journals
Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca Degli Abruzzi 24, 10129 Turin, Italy
Interests: nondestructive testing (NDT); acoustic emission; electromagnetic emission; critical phenomena in structural mechanics; critical phenomena in geophysics; fracture mechanics; static and dynamic analysis of high-rise buildings
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special issue focuses on the advancement in the characterization of materials. The recent development of materials has made their structures and properties sophisticated. This situation requires us to modify existing techniques to characterize material properties accurately. A well-established method conventionally used for materials at the macroscopic scale may be inapplicable to the same material at the nanoscopic scale. Conventional characterization methods developed for metals may be inappropriate for biological specimens. On some occasions, a completely new characterization technique is necessary. At the same time, a combination of traditional methods may be sufficient. Analytical methods are also critical. Efficient extraction of signals buried in noise may make a conventional characterization technique effective. This special issue welcomes papers that discuss these problems in various science and engineering fields. Interdisciplinary studies are of particular interest.

Submissions should be in the form of original research articles or authoritative review papers. The topics of interest include but are not limited to the following areas:

  • Multiscale and hybrid methods for material characterization;
  • Inverse methods for material characterization;
  • Conventional and Super-resolution microscopy for material characterization;
  • Optical methods (including image correlation) for material characterization;
  • Mechanical characterization (static and dynamic);
  • Acoustic methods (including acoustic microscopy) and characterization;
  • Thermophysical methods and characterization;
  • Electromagnetic methods and characterization;
  • Characterization of biomedical materials;
  • Surface characterization;
  • Machine learning and AI (Artificial Intelligence) for data extraction/analysis and pattern processing.

Prof. Dr. Sanichiro Yoshida
Prof. Dr. Luciano Lamberti
Prof. Dr. Giuseppe Lacidogna
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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. Materials 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 2600 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.


  • biomedical materials
  • material characterization with multiple methods
  • machine learning algorithms for data analysis
  • optical methods
  • acoustic methods
  • electromagnetic methods
  • novel probing techniques

Published Papers (1 paper)

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Advances in Focused Ion Beam Tomography for Three-Dimensional Characterization in Materials Science
Materials 2023, 16(17), 5808; - 24 Aug 2023
Viewed by 454
Over the years, FIB-SEM tomography has become an extremely important technique for the three-dimensional reconstruction of microscopic structures with nanometric resolution. This paper describes in detail the steps required to perform this analysis, from the experimental setup to the data analysis and final [...] Read more.
Over the years, FIB-SEM tomography has become an extremely important technique for the three-dimensional reconstruction of microscopic structures with nanometric resolution. This paper describes in detail the steps required to perform this analysis, from the experimental setup to the data analysis and final reconstruction. To demonstrate the versatility of the technique, a comprehensive list of applications is also summarized, ranging from batteries to shale rocks and even some types of soft materials. Moreover, the continuous technological development, such as the introduction of the latest models of plasma and cryo-FIB, can open the way towards the analysis with this technique of a large class of soft materials, while the introduction of new machine learning and deep learning systems will not only improve the resolution and the quality of the final data, but also expand the degree of automation and efficiency in the dataset handling. These future developments, combined with a technique that is already reliable and widely used in various fields of research, are certain to become a routine tool in electron microscopy and material characterization. Full article
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