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Editorial

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

by
Sanichiro Yoshida
1,*,
Luciano Lamberti
2,* and
Giuseppe Lacidogna
3,*
1
Department of Chemistry and Physics, Southeastern Louisiana University, Hammond, LA 70402, USA
2
Dipartimento di Meccanica, Matematica e Management, Politecnico di Bari, 70125 Bari, Italy
3
Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, 10129 Turin, Italy
*
Authors to whom correspondence should be addressed.
Materials 2025, 18(16), 3767; https://doi.org/10.3390/ma18163767
Submission received: 22 July 2025 / Accepted: 6 August 2025 / Published: 11 August 2025
Recent developments in materials science and technology have increased the structural complexity of materials and the level of sophistication in describing their properties, especially when the material under analysis is required to exhibit high multi-field performances. Such a scenario entails the need to modify the currently available techniques to characterize material properties accurately, while also considering that each investigated field during multi-field characterization may be conducted at different scales. In general, 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 composite materials or biological specimens. In some cases, either a completely new characterization technique is necessary, or a combination of traditional methods may be sufficient. The characterization protocol must be designed so that the desired information can be gathered reliably and accurately. Analytical/numerical methods may also be critical, though their results should always be corroborated by experimental evidence. The efficient extraction of signals buried in noise may improve the effectiveness of a conventional characterization technique. However, analytical manipulation of signals should not create artifacts that may lead to the misinterpretation of experimental data.
Materials characterization is undoubtedly one of the most studied subjects in science, engineering, and technology. Refs. [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20] present just a fraction of the examples of the huge amount of research that has been produced on this topic in the last 15 years. Besides manuals, handbooks, and review articles that describe non-destructive materials characterization methods employing different types of electromagnetic, thermal, and acoustic waves/manipulation, specific surveys on the characterization of composite materials, 2D materials, metamaterials, nanomaterials, biomaterials, biotissues, and living cells are also available in the technical literature.
This Special Issue (released on the 15th anniversary of MDPI’s journal Materials) focuses on the recent advances in the characterization of advanced materials. The Special Issue includes 1 review article and 15 research articles covering various aspects of materials applications, such as the following: (i) data extraction and processing; (ii) design and characterization of new materials with optimized properties; (iii) investigations on mechanical, electromagnetic, and optical properties of new materials; (iv) evaluation of superalloy parts produced by additive manufacturing; (v) materials for biomedical uses; (vi) environmental applications; (vii) energy production; and (viii) analysis and preservation of artistic treasures.
The first issue of paramount importance in materials characterization is determining how to obtain detailed 3D information on a material’s structure. The 3D reconstruction of microscopic structures with a nanometric resolution is a very challenging task. In this regard, Mura et al. [21] reviewed the FIB-SEM tomography technique combining Focused Ion Beam (FIB) [22,23] and Scanning Electron Microscopy (SEM) [24,25]. FIB-SEM tomography bridges the non-destructive X-ray families of tomographic techniques (providing a submicron resolution) with the nano- to atomic-scale resolution achieved by Transmission Electron Microscopy (TEM) tomography [26,27]. This is proven by the extensive survey conducted in Ref. [21] on the most relevant applications of FIB-SEM for fuel cells, batteries, solar cells, nuclear energy, metal alloys, ceramics, fibrous materials, and earth sciences.
Data processing is also fundamental in materials characterization, especially when data from different sources covering a broad spectrum of spatial and temporal scales may interfere by increasing noise. Deconvolution can solve this issue, for example, through application in the analysis of strain maps derived from materials subjected to mechanical testing: deconvolution reduces noise, making it possible to recover actual displacement and strain fields from localized digital image correlation maps or localized spectrum analyses [28]. In Ref. [29], Speranza illustrated the application of deconvolution in analyzing Auger spectra obtained through the X-ray Photoelectron Spectroscopy (XPS) of carbon. The principles of Auger spectra/spectroscopy and the XPS technique are, respectively, detailed in Refs. [30,31] and Refs. [32,33].
The optimization of material properties (also including the optimization of processing parameters to improve material properties) and investigation of properties for newly developed materials represent the backbone of materials characterization. In this regard, Konieczny et al. [34] optimized the mechanical (i.e., Vickers hardness) and electrical properties (i.e., conductivity) of CuNi2Si1 by combining experimental approaches and metaheuristic algorithms. Hardness and conductivity were fitted as quartic polynomial functions with respect to aging temperature and aging duration via the factorial plan of experiments approach. Classical metaheuristic optimization methods such as genetic algorithms (GAs) [35], gray wolf optimization (GWO) [36], particle swarm optimization (PSO) [37], student psychology-based optimization (SPBO) [38], teaching–learning-based optimization (TLBO) [39], and the whale optimization algorithm (WOA) [40] were then used to minimize the difference between the desired properties and the fitted properties. The above-described approach was successfully utilized for both undeformed and cold-rolled CuNi2Si1 specimens, thereby providing realistic values of aging parameters.
Fotouhiardakani et al. [41] employed XPS, Fourier-transform infrared spectroscopy (FTIR) [42,43], and profilometry [44] to analyze the chemistry and growth rate of a coating deposited on a fluoropolymer. Deposition was conducted using dielectric barrier discharge at atmospheric pressure, employing an oxygen-containing organic precursor in a nitrogen environment. The fragmentation process and the growth mechanisms of the coating were optimized with respect to the total flow, precursor concentration, and precursor residence time. Rosic et al. [45] combined differential thermal analyses (DTAs) [46], X-ray diffraction (XRD) [47,48], FTIR, energy-dispersive X-ray spectroscopy (EDX) [49,50], field emission Scanning Electron Microscopy (FESEM) [25,51], and the nitrogen adsorption method [52,53] to analyze the composition and morphology of a molybdenum-based ceramic nanostructured material, such as Co0.9R0.1MoO4; an important novel aspect of Ref. [45] was the use of the glycine nitrate process to synthesize Co0.9R0.1MoO4 nanoparticles.
De Giorgi [54] designed a kirigami-based metamaterial with tailored optical properties that improved common camouflage techniques so as to yield a product that was cheap, light, and easy to manufacture and assemble. A typical kirigami structure geometry is based on rotating squares [55]. Light polarization and birefringence [56] were successfully exploited in [54] to obtain transparency and color-changing properties using two polarizers and common cellophane tape. The electromagnetic properties of advanced materials also were investigated in this Special Issue. In particular, Camacho Hernandez and Link [57] presented an innovative method for estimating the effective permittivity of anisotropic fibrous media and disclosing the orientation and microstructure of fibers. They integrated the method formulated in their previous work with modeling theories of structural anisotropy and wave propagation in anisotropic media [58,59,60]. Interestingly, the resonance frequency of a woven alumina fabric in a microwave resonator, determined experimentally, was consistent with its counterpart evaluated numerically by setting the fabric’s permittivity to the values provided by the proposed approach. In Ref. [61], Dapor studied the differences in the elastic scattering spectra of electrons and positrons in amorphous low-density polyethylene, focusing on the underlying mechanisms that influence spectral features. Elastic Peak Electron Spectroscopy (EPES) [62,63] was used to isolate key factors such as recoil energy, Doppler broadening, and the interplay between elastic and inelastic mean free paths. Monte Carlo simulations [64] were used to systematically compare the elastic scattering interactions of electrons and positrons with polyethylene.
Johnson and Kujawski [65] characterized the notch sensitivity of additively manufactured Inconel 718 parts produced by laser powder bed fusion. Three different root radii were evaluated under tensile conditions for V-notched test specimens and smooth specimens built in vertical and horizontal orientations. Both the total axial strain and localized notch diametral strain were measured. Finite element simulations were in agreement with the actual strain measurements near the notch.
Some important fields that are well documented in this Special Issue are the synthesis and characterization of new materials for biomedical use, environmental applications, and energy production (including fuel cells and renewable energy sources). For example, Wu et al. [66] successfully prepared hydroxyapatite (calcium phosphate, HA) using a precipitation method with eggshell as a raw material. HA is an important material in biomedical applications because it closely resembles human bones. The HA powder synthesized in Ref. [66] was press-formed and sintered at various temperatures (in the range of 800–1400 °C, which has never been conducted before) to investigate the impact of sintering temperature on the mechanical properties, such as hardness, compressive strength, and fracture toughness, of the sintered HA samples (E-HA). The phase content and crystallinity of the sintered E-HA samples were analyzed with XRD [47,48] while the sample microstructure was observed with FESEM [25,51]. The bacterial culture experiments conducted on sintered E-HA indicated that it possessed significant antibacterial efficacy against the Streptococcus mutans, thus highlighting the potential of eggshell-derived HA as an effective material for biomedical applications.
Cho et al. [67] demonstrated the potential application of green-chemically synthesized silver nanoparticles (AgNPs) as selective antibacterial agents. For that purpose, stable AgNPs were biologically synthesized using common walkingstick (Diapheromera femorata) aqueous extract. AgNPs were then UV-treated and tested as antibacterial agents to inhibit the growth of four pathogenic bacteria (Burkholderia cenocepacia K-56, Klebsiella pneumoniae ST258, Pseudomonas aeruginosa PAO1, and Staphylococcus aureus USA300), as well as one common bacterium (Escherichia coli BW25113). Remarkably, UV-treated AgNPs significantly and selectively inhibited the growth of Staphylococcus aureus USA300 and P. aeruginosa PA01. The optimal duration of UV exposure yielding the strongest antibacterial activity was also investigated in Ref. [67].
Regarding environmental applications, Kurbonov et al. [68] studied new materials for water remediation. They prepared mesoporous silica sieves through sol–gel synthesis using diester gemini surfactants as pore templates. Submicron-size mesoporous spherical silica particles were prepared in an alkali-catalyzed reaction using a tetraethyl orthosilicate precursor and bis-quaternary ammonium gemini surfactants with diester spacers of varied lengths as pore-forming agents. The effect of the spacer length on the particle morphology was studied using nitrogen porosimetry [52,53], small-angle X-ray scattering (SAXS) [69,70], ultra-small-angle neutron scattering [71,72], and Scanning and Transmission Electron Microscopy (SEM, TEM) [24,27]. The new materials were tested for the adsorption of Pb(II) in a batch sorption experiment and demonstrated a higher adsorption capacity than that of most silica-based sorbents reported in the recent literature.
An example of the application of advanced characterization methods to renewable energy sources documented in this Special Issue is the study by Lejda et al. [73] that used TGA/DTA-QMS (thermogravimetry [74,75] coupled with thermal analysis [46] and quadrupole mass spectroscopy [76,77]) to assess the oxidation susceptibility of a pool of nanocrystalline powders of the semiconductor kesterite Cu2ZnSnS4 for prospective photovoltaic applications. The Cu2ZnSnS4 powders were prepared via a mechanochemically assisted synthesis route from two precursor systems.
Polymer electrolyte membrane fuel cells (PEMFCs) are recognized as the most suitable energy conversion device for next-generation zero-emission electric vehicles. In this context, Yoo et al. [78] analyzed the complex relationships between catalyst degradation and binder performance in high-power PEMFCs with the goal of developing more durable PEMFC components. The study by Ref. [78] allowed for the existing limitations on assessing binder durability to be overcome, and its degradation in situ during the accelerated stress test process was measured. Scanning Electron Microscopy/energy-dispersive spectroscopy (SEM-EDS) [25,79] analysis measured the degradation rates for the catalyst, the support, and the binder. The method assessing the distribution of relaxation times served to measure the increase in oxygen reduction reaction resistance and decrease in proton transport resistance in situ.
The last two papers of this Special Issue focused on the analysis and preservation of artistic treasures. Wu et al. [80] combined energy-dispersive X-ray fluorescence (EDXRF) [81,82], ultra-depth-of-field optical microscopy [83,84], SEM-EDS [25,79], Raman spectroscopy [85,86], and XRD [47,48] to characterize the body and glaze chemical composition, microstructure, and crystalline phases present in high-temperature iron-series glazed wares produced in the Guangyuan kiln during the Song Dynasty (China, 960–1279 A.D.). The study elucidated the compositional characteristics, structural features, and color formation mechanisms of these wares, thereby revealing the compositional and structural variations in ancient Chinese high-temperature iron-series glazed wares and the chemical state of the iron within the glaze matrix.
Colomban et al. [87] analyzed Mīnā’ī decorations. These wares, produced in Persia during the 12th and 13th centuries, are regarded as the first sophisticated painted enamel decorations created by potters, and are considered to be among the most luxurious in the Islamic world. Due to the thinness of these enamel layers, their detailed characterization remains challenging, even with the use of advanced techniques, such as Proton-Induced X-ray Emission (PIXE) [88] analysis and Rutherford Backscattering Spectrometry (RBS) [89]. To solve this issue, Ref. [87] presented the first combined noninvasive analysis ever performed on Mīnā’ī wares by using XRF [81,90] and Raman spectroscopy [85,86]. İznik shards (from the 17th century), which feature similarly styled but thicker enamel decorations, were also analyzed for comparison. Interestingly, the Mīnā’ī paste was found to contain lead and tin, suggesting the use of a lead-rich frit in its composition. This finding was confirmed by SEM–EDS micro-destructive analysis.
The variety of topics covered by this Special Issue demonstrates the broadness of the applications of advanced materials characterization methods, fitting very well into all fields of science, arts, engineering, and technology. The need to combine several experimental protocols to optimize the analysis of materials is fully confirmed by most of the papers included in this Special Issue. Characterization methods for advanced materials (including those used for creating artistic treasures) present high levels of standardization that can be tailored to each specific investigation scale. This ability is very useful in optimizing all stages of product life, from materials characterization to design verification, including the monitoring/improvement of production processes and the evaluation of product quality. Also, it helps to preserve artistic treasures by providing detailed information on composition, state, crafting, and the materials utilized in ancient times.
It should be noted that the studies included in this Special Issue made marginal use of artificial intelligence (AI) techniques in the materials characterization process. AI is expected to become a standard in materials characterization within a few years (see, for example, Refs. [19,91,92,93]). In fact, AI may greatly help analysts to interpret and extract meaningful insights from large and complex datasets generated by experimental techniques such as, for example, SEM, TEM, XRD, Raman spectroscopy, and XPS spectra for surface composition. Furthermore, AI can be used to predict material properties by creating digital twins of experimental campaigns. AI-based materials characterization approaches should certainly be the subject of a future Special Issue of Materials.

Acknowledgments

The Guest Editors of the Special Issue would like to thank all the contributing authors, the reviewers, and the editorial team of Materials. The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Tyagi, A.K.; Roy, M.; Kulshreshtha, S.K.; Banerjee, S. Advanced Techniques for Materials Characterization; Trans Tech Publications: Baech, Switzerland, 2009. [Google Scholar]
  2. Sardela, M. Practical Materials Characterization; Springer: New York, NY, USA, 2014. [Google Scholar]
  3. Krishnan, K.K. Principles of Materials Characterization and Metrology; Oxford University Press: Oxford, UK, 2021. [Google Scholar]
  4. Sultan, K. Practical Guide to Materials Characterization: Techniques and Applications; Wiley: Chichester, UK, 2023. [Google Scholar]
  5. Otsuki, A.; Jose, S.; Mohan, M.; Thomas, S. Non-Destructive Material Characterization Methods; Elsevier: Oxford, UK, 2023. [Google Scholar]
  6. Arnold, W.; Goebbels, K.; Kumar, A. Non-Destructive Materials Characterization and Evaluation; Springer: Berlin, Germany, 2023. [Google Scholar]
  7. Parveen, A.; Ahmad, S.; Sharma, J.; Gambhir, V. Handbook of Sustainable Materials: Modelling, Characterization, and Optimization; CRC Press: Boca Raton, FL, USA, 2023. [Google Scholar]
  8. AhmadvashAghbash, S.; Verpoest, I.; Swolfs, Y.; Mehdikhani, M. Methods and models for fibre–matrix interface characterisation in fibre-reinforced polymers: A review. Int. Mater. Rev. 2023, 68, 1245–1319. [Google Scholar] [CrossRef]
  9. Xiaomin, X.; Zhang, Z.; Mu, X.; Shan, C.; Gao, X.; Zhu, B. Recent progress on interface characterization methods of carbon fiber reinforced polymer composites. Chem. Eng. J. 2024, 499, 156220. [Google Scholar] [CrossRef]
  10. Banks, C.E.; Brownson, D.A.C. 2D Materials Characterization, Production and Applications; CRC Press: Boca Raton, FL, USA, 2018. [Google Scholar]
  11. Mourdikoudis, S.; Pallares, R.M.; Thanh, N.T.K. Characterization techniques for nanoparticles: Comparison and complementarity upon studying nanoparticle properties. Nanoscale 2018, 10, 12871–12934. [Google Scholar] [CrossRef]
  12. Jayawardena, H.S.N.; Liyanage, S.H.; Rathnayake, K.; Patel, U.; Yan, M. Analytical Methods for Characterization of Nanomaterial Surfaces. Anal. Chem. 2021, 93, 1889–1911. [Google Scholar] [CrossRef]
  13. Munaweera, I.; Chamalki Madhusha, M.L. Characterization Techniques for Nanomaterials; CRC Press: Boca Raton, FL, USA, 2023. [Google Scholar]
  14. Mekuye, B.; Abera, B. Nanomaterials: An overview of synthesis, classification, characterization, and applications. Nano Select 2023, 4, 486–501. [Google Scholar] [CrossRef]
  15. Jaffe, M.; Hammond, W.P.; Tolias, P.; Arinzeh, T. Characterization of Biomaterials; Woodhead Publishing: Cambridge, UK, 2013. [Google Scholar]
  16. Mitić, Ž.; Stolić, A.; Stojanović, S.; Najman, S.; Ignjatović, N.; Nikolić, G.; Trajanović, M. Instrumental methods and techniques for structural and physicochemical characterization of biomaterials and bone tissue: A review. Mater. Sci. Eng. C 2017, 79, 930–949. [Google Scholar] [CrossRef] [PubMed]
  17. Läubli, N.F.; Burri, J.T.; Marquard, J.; Vogler, H.; Mosca, G.; Vertti-Quintero, N.; Shamsudhin, N.; deMello, A.; Grossniklaus, U.; Ahmed, D.; et al. 3D mechanical characterization of single cells and small organisms using acoustic manipulation and force microscopy. Nat. Comm. 2021, 12, 2583. [Google Scholar] [CrossRef] [PubMed]
  18. Singh Chandel, A.K.; Parihar, A.; Khan, R. Smart Ways of Biomaterial Designing Synthesis and Characterization: Prospects of Enhanced Application from Labs to Clinics; CRC Press: Boca Raton, FL, USA, 2025. [Google Scholar]
  19. Jin, H.; Zhang, B.; Cao, Q.; Zhang, E.; Bora, A.; Krishnaswamy, S.; Karniadakis, G.E.; Espinosa, H.D. Characterization and Inverse Design of Stochastic Mechanical Metamaterials Using Neural Operators. Adv. Mater. 2025, in press. [Google Scholar] [CrossRef]
  20. Duan, Z. Metamaterial-Based Electromagnetic Radiations and Applications; Springer/Science Press: Beijing, China, 2025. [Google Scholar]
  21. Mura, F.; Cognigni, F.; Ferroni, M.; Morandi, V.; Rossi, M. Advances in Focused Ion Beam Tomography for Three-Dimensional Characterization in Materials Science. Materials 2023, 16, 5808. [Google Scholar] [CrossRef]
  22. Giannuzzi, L.; Prenitzer, B.; Kempshall, B. Ion—Solid Interactions. In Introduction to Focus Ion Beam—Instrumentation, Theory, Techniques & Practice; Giannuzzi, L.A., Stevie, F.A., Eds.; Springer: New York, NY, USA, 2005; pp. 13–52. [Google Scholar]
  23. Cantoni, M.; Holzer, L. Review of FIB tomography. In Nanofabrication Using Focused Ion and Electron Beams—Principles and Applications; Utke, I., Moshkalev, S., Russell, P., Eds.; Oxford University Press: New York, NY, USA, 2012; pp. 410–435. [Google Scholar]
  24. Akhtar, K.; Khan, S.A.; Khan, S.B.; Asiri, A.M. Scanning Electron Microscopy: Principle and Applications in Nanomaterials Characterization. In Handbook of Materials Characterization; Sharma, S.K., Khan, L.U., Kumar, S., Khan, S.B., Eds.; Springer Nature: Cham, Switzerland, 2018; pp. 113–145. [Google Scholar]
  25. Goldstein, J.I.; Newbury, D.E.; Michael, J.R.; Ritchie, N.W.M.; Scott, J.H.J.; Joy, D.C. Scanning Electron Microscopy and X-Ray Microanalysis, 4th ed.; Kluwer Academic/Plenum Publishers: New York, NY, USA, 2018. [Google Scholar]
  26. Javed, Y.; Ali, K.; Akhtar, K.; Jawaria, M.; Hussain, I.; Ahmad, G.; Arif, T. TEM for Atomic-Scale Study: Fundamental, Instrumentation, and Applications in Nanotechnology. In Handbook of Materials Characterization; Sharma, S.K., Khan, L.U., Kumar, S., Khan, S.B., Eds.; Springer Nature Switzerland: Cham, Switzerland, 2018; pp. 147–216. [Google Scholar]
  27. Kirkland, A.I.; Chang, S.L.Y.; Hutchison, J.L. Atomic Resolution Transmission Electron Microscopy. In Springer Handbook of Microscopy; Hawkes, P.W., Spence, J.C.H., Eds.; Springer Nature: Cham, Switzerland, 2019; pp. 3–47. [Google Scholar]
  28. Grediac, M.; Blaysat, B.; Sur, F. A Robust-to-Noise Deconvolution Algorithm to Enhance Displacement and Strain Maps Obtained with Local DIC and LSA. Exp. Mech. 2018, 59, 219–243. [Google Scholar] [CrossRef]
  29. Speranza, G. Application of the Van Cittert Algorithm for Deconvolving Loss Features in X-ray Photoelectron Spectroscopy Spectra. Materials 2024, 17, 763. [Google Scholar] [CrossRef]
  30. Briggs, D.; Grant, J.T. Surface Analysis by Auger and X-Ray Photoelectron Spectroscopies, 1st ed.; IM Publications and Surface Spectra: Trowbridge, UK, 2003. [Google Scholar]
  31. Ilyin, A.M. Auger Electron Spectroscopy. In Microscopy Methods in Nanomaterials Characterization; Thomas, S., Thomas, R., Zachariah, A.K., Mishra, R.K., Eds.; Elsevier: Oxford, UK, 2017; pp. 363–379. [Google Scholar]
  32. Kumar, J. Photoelectron Spectroscopy: Fundamental Principles and Applications. In Handbook of Materials Characterization; Sharma, S.K., Khan, L.U., Kumar, S., Khan, S.B., Eds.; Springer Nature Switzerland: Cham, Switzerland, 2018; pp. 435–495. [Google Scholar]
  33. Baer, D.R.; Artyushkova, K.; Brundle, C.R.; Castle, J.E.; Engelhard, M.H.; Gaskell, K.J.; Grant, J.T.; Haasch, R.T.; Linford, M.R.; Powell, C.J.; et al. Practical Guides for X-ray Photoelectron Spectroscopy: First Steps in Planning, Conducting, and Reporting XPS Measurements. J. Vac. Sci. Technol. A Vac. Surf. Film. 2019, 37, 031401. [Google Scholar] [CrossRef]
  34. Konieczny, J.; Labisz, K.; Ürgün, S.; Yigit, H.; Fidan, S.; Bora, M.Ö.; Atapek, S.H. Metaheuristics Algorithm-Based Optimization for High Conductivity and Hardness CuNi2Si1 Alloy. Materials 2025, 18, 1060. [Google Scholar] [CrossRef] [PubMed]
  35. Goldberg, D.E. Genetic Algorithms in Search, Optimization, and Machine Learning; Addison-Wesley: Reading, MA, USA, 1989. [Google Scholar]
  36. Mirjalili, S.; Mirjalili, S.M.; Lewis, A. Grey Wolf Optimizer. Adv. Eng. Softw. 2014, 69, 46–61. [Google Scholar] [CrossRef]
  37. Clerc, M. Particle Swarm Optimization; ISTE Publishing Company: London, UK, 2006. [Google Scholar]
  38. Das, B.; Mukherjee, V.; Das, D. Student Psychology Based Optimization Algorithm: A New Population based Optimization Algorithm for Solving Optimization Problems. Adv. Eng. Soft. 2020, 146, 102804. [Google Scholar] [CrossRef]
  39. Rao, R.V.; Savsani, V.J.; Vakharia, D.P. Teaching-Learning-Based Optimization: A Novel Method for Constrained Mechanical Design Optimization Problems. CAD Comput. Aided Des. 2011, 43, 303–315. [Google Scholar] [CrossRef]
  40. Mirjalili, S.; Lewis, A. The Whale Optimization Algorithm. Adv. Eng. Soft. 2016, 95, 51–67. [Google Scholar] [CrossRef]
  41. Fotouhiardakani, F.; Destrieux, A.; Profili, J.; Laurent, M.; Ravichandran, S.; Dorairaju, G.; Laroche, G. Investigating the Behavior of Thin-Film Formation over Time as a Function of Precursor Concentration and Gas Residence Time in Nitrogen Dielectric Barrier Discharge. Materials 2024, 17, 875. [Google Scholar] [CrossRef]
  42. Griffiths, P.; de Hasseth, J.A. Fourier Transform Infrared Spectrometry, 2nd ed.; John Wiley & Sons: Chichester, UK, 2007. [Google Scholar]
  43. Khan, S.A.; Khan, S.B.; Khan, L.U.; Farooq, A.; Akhtar, K.; Asiri, A.M. Fourier Transform Infrared Spectroscopy: Fundamentals and Application in Functional Groups and Nanomaterials Characterization. In Handbook of Materials Characterization; Sharma, S.K., Khan, L.U., Kumar, S., Khan, S.B., Eds.; Springer Nature Switzerland: Cham, Switzerland, 2018; pp. 317–344. [Google Scholar]
  44. Valli, J. A Review of Adhesion Test Methods for Thin Hard Coatings. J. Vac. Sci. Technol. A Vac. Surf. Film 1986, 4, 3007–3014. [Google Scholar] [CrossRef]
  45. Rosic, M.; Miloševic, M.; Cebela, M.; Dodevski, V.; Lojpur, V.; Cakar, U.; Stopic, S. Spectroscopic and Morphological Examination of Co0.9R0.1MoO4 (R = Ho, Yb, Gd) Obtained by Glycine Nitrate Procedure. Materials 2025, 18, 397. [Google Scholar] [CrossRef]
  46. Ozawa, T. Thermal analysis—Review and prospect. Thermochim. Acta 2000, 355, 35–42. [Google Scholar] [CrossRef]
  47. Cullity, B.D.; Stock, S.R. Elements of X-Ray Diffraction, 3rd ed.; Pearson Education, Inc.: Upper Saddle River, NJ, USA, 2001. [Google Scholar]
  48. Adams, F.C. X-Ray Absorption and Diffraction | Overview. In Encyclopedia of Analytical Science, 3rd ed.; Worsfold, P., Poole, C., Townshend, T., Miró, M., Eds.; Elsevier: Amsterdam, The Netherlands, 2019; pp. 391–403. [Google Scholar]
  49. Abd Mutalib, M.; Rahman, M.A.; Othman, M.H.D.; Ismail, A.F.; Jaafar, J. Scanning electron microscopy (SEM) and energy-dispersive X-ray (EDX) spectroscopy. In Membrane Characterization; Hilal, N., Ismail, A.F., Matsuura, T., Oatley-Radcliffe, D., Eds.; Elsevier: Oxford, UK, 2017; pp. 161–179. [Google Scholar]
  50. Neikov, O.D.; Yefimov, N.A. Powder Characterization and Testing. In Handbook of Non-Ferrous Metal Powders, 2nd ed.; Neikov, O.D., Naboychenko, S.S., Yefimov, N.A., Eds.; Elsevier: Amsterdam, The Netherlands, 2019; pp. 3–62. [Google Scholar]
  51. Freeland, B.; Ul Ahad, I.; Foley, G.; Brabazon, D. Advanced characterization techniques for nanostructures. In Micro and Nanomanufacturing; Jackson, M.J., Ahmed, W., Eds.; Springer Nature Switzerland: Cham, Switzerland, 2025; Volume 2, pp. 53–89. [Google Scholar]
  52. Barret, E.P.; Joyner, L.G.; Halenda, P.P. The Determination of Pore Volume and Area Distributions in Porous Substances. I. Computations from Nitrogen Isotherms. J. Am. Chem. Soc. 1951, 73, 373–380. [Google Scholar] [CrossRef]
  53. Lippens, B.C.; Linsen, B.G.; de Boer, J.H. Studies on Pore Systems in Catalysts I. The Adsorption of Nitrogen; Apparatus and Calculation. J. Catal. 1964, 3, 32–37. [Google Scholar] [CrossRef]
  54. De Giorgi, M. Design of an Optical Device Based on Kirigami Approach. Materials 2024, 17, 1211. [Google Scholar] [CrossRef] [PubMed]
  55. Grima, J.N.; Evans, K.E. Auxetic behavior from rotating squares. J. Mater. Sci. Lett. 2000, 19, 1563–1565. [Google Scholar] [CrossRef]
  56. Sciammarella, C.A.; Sciammarella, F.M. Experimental Mechanics of Solids; Wiley: Chichester, UK, 2012. [Google Scholar]
  57. Camacho Hernandez, J.N.; Link, G. Innovative Approaches on the Estimation of the Effective Permittivity of Fibrous Media. Materials 2025, 18, 14. [Google Scholar] [CrossRef]
  58. Bal, K.; Kothari, V.K. Study of dielectric behaviour of woven fabric based on two phase models. J. Electrostat. 2009, 67, 751–758. [Google Scholar] [CrossRef]
  59. Numan, A.B.; Sharawi, M.S. Extraction of Material Parameters for Metamaterials Using a Full-Wave Simulator [Education Column]. IEEE Antennas Propag. Mag. 2013, 55, 202–211. [Google Scholar] [CrossRef]
  60. Smit, T.H.; Schneider, E.; Odgaard, A. Star length distribution: A volume-based concept for the characterization of structural anisotropy. J. Microsc. 1998, 191 Pt 3, 249–257. [Google Scholar] [CrossRef]
  61. Dapor, M. Comparison of Electron Compton Scattering with Positron Compton Scattering in Polyethylene. Materials 2025, 18, 1609. [Google Scholar] [CrossRef]
  62. Gergely, G. Elastic backscattering of electrons: Determination of physical parameters of electron transport processes by elastic peak electron spectroscopy. Progr. Surf. Sci. 2002, 71, 31–88. [Google Scholar] [CrossRef]
  63. Morawski, I.; Nowicki, M. Directional Auger and elastic peak electron spectroscopies: Versatile methods to reveal near-surface crystal structure. Surf. Sci. Rep. 2019, 74, 178–212. [Google Scholar] [CrossRef]
  64. Brandimarte, P. Handbook in Monte Carlo Simulations; Elsevier: New York, NY, USA, 2014. [Google Scholar]
  65. Johnson, J.; Kujawski, D. Impact of Notches on Additively Manufactured Inconel 718 Tensile Performance. Materials 2023, 16, 6740. [Google Scholar] [CrossRef] [PubMed]
  66. Wu, S.-C.; Hsu, H.-C.; Liu, M.-Y.; Ho, W.-F. Phase Transformation and Mechanical Optimization of Eggshell-Derived Hydroxyapatite across a Wide Sintering Temperature Range. Materials 2024, 17, 4062. [Google Scholar] [CrossRef] [PubMed]
  67. Cho, J.L.; Allain, L.G.; Yoshida, S. Study on the Influence of UV Light on Selective Antibacterial Activity of Silver Nanoparticle Synthesized Utilizing Protein/Polypeptide-Rich Aqueous Extract from The Common Walkingstick, Diapheromera femorata. Materials 2024, 17, 713. [Google Scholar] [CrossRef] [PubMed]
  68. Kurbonov, S.; Pisárcik, M.; Lukác, M.; Czigány, Z.; Kovács, Z.; Tolnai, I.; Kriechbaum, M.; Ryukhtin, V.; Petrenko, V.; Avdeev, M.V.; et al. Ordered Mesoporous Silica Prepared with Biodegradable Gemini Surfactants as Templates for Environmental Applications. Materials 2025, 18, 773. [Google Scholar] [CrossRef]
  69. Londoño, O.M.; Tancredi, P.; Rivas, P.; Muraca, D.; Socolovsky, L.M.; Knobel, M. Small-Angle X-Ray Scattering to Analyze the Morphological Properties of Nanoparticulated Systems. In Handbook of Materials Characterization; Sharma, S.K., Khan, L.U., Kumar, S., Khan, S.B., Eds.; Springer Nature: Cham, Switzerland, 2018; pp. 37–75. [Google Scholar]
  70. Li, T.; Senesi, A.J.; Lee, B. Small Angle X-ray Scattering for Nanoparticle Research. Chem. Rev. 2016, 116, 11128–11180. [Google Scholar] [CrossRef]
  71. Strunz, P.; Saroun, J.; Mikula, P.; Lukas, P.; Eichhorn, F. Double-Bent-Crystal Small-Angle Neutron Scattering Setting and its Applications. J. Appl. Cryst. 1997, 30, 844–848. [Google Scholar] [CrossRef]
  72. Harada, T.; Matsuoka, H. Ultra-small-angle X-ray and neutron scattering study of colloidal dispersions. Curr. Opin. Colloid Interface Sci. 2004, 8, 501–506. [Google Scholar] [CrossRef]
  73. Lejda, K.; Partyka, J.; Janik, J.F. Thermogravimetric/Thermal–Mass Spectroscopy Insight into Oxidation Propensity of Various Mechanochemically Made Kesterite Cu2ZnSnS4 Nanopowders. Materials 2024, 17, 1232. [Google Scholar] [CrossRef]
  74. Coats, A.W.; Redfern, J.P. Thermogravimetric Analysis. Analyst 1963, 88, 906–924. [Google Scholar] [CrossRef]
  75. Tanzi, M.C.; Farè, S.; Candiani, G. Foundations of Biomaterials Engineering; Academic Press: London, UK, 2019. [Google Scholar]
  76. de Hoffmann, E.; Stroobant, V. Mass Spectrometry, 3rd ed; Wiley: Chichester, UK, 2013. [Google Scholar]
  77. Vadakedath, S.; Kandi, V.; Godishala, V.; Kumar Pinnelli, V.B.; Alkafaas, S.S.; EIkafas, S.S. The Principle, Types, and Applications of Mass Spectrometry: A Comprehensive Review. Biomed. Biotechnol. 2022, 7, 6–22. [Google Scholar] [CrossRef]
  78. Yoo, D.; Park, S.; Oh, S.; Kim, M.P.; Park, K. In Situ Analysis of Binder Degradation during Catalyst-Accelerated Stress Test of Polymer Electrolyte Membrane Fuel Cells. Materials 2024, 17, 4425. [Google Scholar] [CrossRef] [PubMed]
  79. Newbury, D.E.; Ritchie, N.W.M. Is Scanning Electron Microscopy/Energy Dispersive X-ray Spectrometry (SEM/EDS) Quantitative? Scanning 2013, 35, 141–168. [Google Scholar] [CrossRef]
  80. Wu, L.; Nie, Y.; Li, J.; Wu, J.; Shi, W.; Wu, Y.; Jiang, Y. Chemical Compositions and Chromatic Mechanism of High-Temperature Iron-Series Glazed Wares from the Guangyuan Kiln in Sichuan Province, Southwest China During the Song Dynasty. Materials 2024, 17, 6221. [Google Scholar] [CrossRef]
  81. Kramar, U. X-Ray Fluorescence Spectrometers. In Encyclopedia of Spectroscopy and Spectrometry, 2nd ed.; Lindon, J.C., Ed.; Academic Press: Oxford, UK, 2016; pp. 2989–2999. [Google Scholar]
  82. Silveira, P.; Falcade, T. Applications of energy dispersive X-ray fluorescence technique in metallic cultural heritage studies. J. Cult. Herit. 2022, 57, 243–255. [Google Scholar] [CrossRef]
  83. Dodt, H.U.; Saghafi, S.; Becker, K.; Jahrling, N. Ultramicroscopy: Development and outlook. Neurophotonics 2015, 2, 041407. [Google Scholar] [CrossRef]
  84. Aflalo, K.; Gao, P.; Trivedi, V.; Sanjeev, A.; Zalevsky, Z. Optical super-resolution imaging: A review and perspective. Opt. Lasers Eng. 2024, 183, 108536. [Google Scholar] [CrossRef]
  85. Colthup, N.B.; Daly, L.H.; Wiberley, S.E. Introduction to Infrared and Raman Spectroscopy, 3rd ed; Academic Press: New York, NY, USA, 1990. [Google Scholar]
  86. Larkin, P.J. Infrared and Raman Spectroscopy—Principles and Spectral Interpretation, 2nd ed.; Elsevier: San Diego, CA, USA, 2018. [Google Scholar]
  87. Colomban, P.; Simsek Franci, G.; Ngo, A.-T.; Gallet, X. Non-Invasive Raman and XRF Study of Mīnā’ī Decoration, the First Sophisticated Painted Enamels. Materials 2025, 18, 575. [Google Scholar] [CrossRef]
  88. Johansson, S.A.E.; Campbell, J.L.; Malmqvist, K.G. Particle-Induced X-Ray Emission Spectrometry. Wiley: Chichester, UK, 1995. [Google Scholar]
  89. Zhang, Y.; Debelle, A.; Boulle, A.; Kluth, P.; Tuomisto, F. Advanced techniques for characterization of ion beam modified materials. Curr. Opin. Solid State Mater. Sci. 2015, 19, 19–28. [Google Scholar] [CrossRef]
  90. Pessanha, S.; Queralt, I.; Carvalho, M.L.; Sampaio, J.M. Determination of gold leaf thickness using X-ray fluorescence spectrometry: Accuracy comparison using analytical methodology and Monte Carlo simulations. Appl. Radiat. Isot. 2019, 152, 6–10. [Google Scholar] [CrossRef]
  91. Lau, M.L.; Burleigh, A.; Terry, J.; Long, M. Materials characterization: Can artificial intelligence be used to address reproducibility challenges? J. Vac. Sci. Technol. A 2023, 41, 060801. [Google Scholar] [CrossRef]
  92. Argyriou, D.N.; Bordallo, H.N.; Srinivasan, G.; Sundararaghavan, V. (Eds.) Machine Learning for Materials Characterisation. In Scientific Reports; 2023–2025; Available online: https://www.nature.com/collections/ghabgbifhh (accessed on 21 July 2025).
  93. Chávez-Angel, E.; Eriksen, M.B.; Castro-Alvarez, A.; Garcia, J.H.; Botifoll, M.; Avalos-Ovando, O.; Arbiol, J.; Mugarza, A. Applied Artificial Intelligence in Materials Science and Material Design. Adv. Intell. Syst. 2025, in press. [Google Scholar] [CrossRef]
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Yoshida, S.; Lamberti, L.; Lacidogna, G. The 15th Anniversary of Materials—Recent Advances in Advanced Materials Characterization. Materials 2025, 18, 3767. https://doi.org/10.3390/ma18163767

AMA Style

Yoshida S, Lamberti L, Lacidogna G. The 15th Anniversary of Materials—Recent Advances in Advanced Materials Characterization. Materials. 2025; 18(16):3767. https://doi.org/10.3390/ma18163767

Chicago/Turabian Style

Yoshida, Sanichiro, Luciano Lamberti, and Giuseppe Lacidogna. 2025. "The 15th Anniversary of Materials—Recent Advances in Advanced Materials Characterization" Materials 18, no. 16: 3767. https://doi.org/10.3390/ma18163767

APA Style

Yoshida, S., Lamberti, L., & Lacidogna, G. (2025). The 15th Anniversary of Materials—Recent Advances in Advanced Materials Characterization. Materials, 18(16), 3767. https://doi.org/10.3390/ma18163767

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