Multiscale Characterization, Mechanical Behavior and Computational Simulation of Bulk Materials, Metallic Powders and/or Nanoparticles

Topic Information

Dear Colleagues,

Metallic powders and nanoparticles (NPs) play a pivotal role in advanced manufacturing, energy storage, catalysis and biomedical applications, driven by their unique size-dependent properties and potential for tailoring microstructures. This Topic aims to explore the interdisciplinary landscape of multiscale characterization, mechanical behavior, and computational simulation of these materials, addressing fundamental science and engineering challenges across scales—from atomic/molecular interactions to macroscale performance. Key Focus Areas: Multiscale Characterization: Research will investigate structural, morphological, and chemical properties across scales using experimental techniques (e.g., SEM/TEM, XRD, AFM, spectroscopy) and theoretical frameworks. Studies may include surface/interface phenomena in nanoparticles, powder particle size distribution, grain boundary effects, phase transformations during processing (e.g., sintering and additive manufacturing) and the evolution of hierarchical architectures. Special emphasis is placed on bridging nanoscale features (e.g., defect structures, alloying effects) with meso/macroscale characteristics to establish structure–property relationships. Mechanical Behavior: Research will explore the deformation, failure and functional mechanical responses of metallic powders and NPs, including compaction behavior in powder metallurgy, strength–ductility trade-offs in nanoparticle-reinforced composites, size-dependent plasticity (e.g., Hall–Petch relationships at nanoscales), fatigue and creep. Studies may also address dynamic loading scenarios (e.g., shock, high strain rates) and the role of processing-induced defects (e.g., porosity, agglomeration) on mechanical performance. Cross-scale couplings—such as how nanoscale grain boundaries influence macroscale ductility—are of particular interest. Computational Simulation: Research will advance modeling approaches spanning quantum mechanics, molecular dynamics (MD), discrete dislocation dynamics (DDD), finite element analysis (FEA) and phase-field methods to simulate synthesis, processing and mechanical behavior. The focus includes bridging scales via multiscale modeling frameworks (e.g., MD-to-FEA coupling), predicting powder flow and compaction, simulating sintering kinetics and forecasting mechanical responses under complex loading. Novel algorithms for data-driven modeling (e.g., machine learning-aided material design) and uncertainty quantification in simulations are also welcome. Scope and Applications: Contributions may address pure metallic systems, alloys and nanocomposites (e.g., metal–organic frameworks, core–shell NPs). Applications range from additive manufacturing (e.g., binder jetting, laser powder bed fusion) and thermal management to catalysis and energy storage. Studies integrating experimental characterization with computational tools to validate models or guide material design are highly encouraged, as are investigations into emerging challenges like the environmental stability of nanoparticles and scalable production techniques. Submission Types: Original research articles, reviews, perspectives and methodology papers are welcome, provided they align with the topic’s focus on multiscale analysis, mechanical phenomena and computational innovation. Interdisciplinary works linking materials science, physics, chemistry and engineering are particularly valued.

Dr. Xiangnan Pan
Prof. Dr. Qing Peng
Prof. Dr. Hui Qi
Prof. Dr. Raj Das
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Alloys alloys	-	3.2	2022	19.1 Days	CHF 1000	Submit
Applied Mechanics applmech	1.5	3.5	2020	24.5 Days	CHF 1400	Submit
Crystals crystals	2.4	5.0	2011	12.7 Days	CHF 2100	Submit
Journal of Composites Science jcs	3.7	5.8	2017	15.9 Days	CHF 1800	Submit
Nanomaterials nanomaterials	4.3	9.2	2010	14 Days	CHF 2400	Submit
Powders powders	-	-	2022	29 Days	CHF 1000	Submit

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Published Papers (4 papers)

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Journals

All Alloys Applied Mechanics Crystals J. Compos. Sci. Powders Nanomaterials

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20 pages, 4652 KB  

Open AccessArticle

Effect of YB₄ Reinforcement on the Microstructural Evolution and Mechanical Behaviour of AISI 420 Composites Produced by Vacuum Induction Melting

by M. Sadhasivam, Mainak Saha, L. John Berchmans, S.P. Kumaresh Babu and SankaraRaman Sankaranarayanan

Powders 2026, 5(1), 9; https://doi.org/10.3390/powders5010009 - 3 Mar 2026

Viewed by 538

Abstract

The influence of YB₄ particle addition on the microstructure and the associated thermal and mechanical properties of AISI 420 stainless steel composites fabricated using the vacuum induction melting technique was investigated. Microstructural analysis using scanning electron microscopy (SEM) confirmed the presence of [...] Read more.

The influence of YB₄ particle addition on the microstructure and the associated thermal and mechanical properties of AISI 420 stainless steel composites fabricated using the vacuum induction melting technique was investigated. Microstructural analysis using scanning electron microscopy (SEM) confirmed the presence of YB₄ particles within the BCC-structured martensitic matrix and also along the grain boundaries across all weight fractions. In addition, YB₄ addition resulted in a pronounced refinement of the martensitic matrix, as evidenced by a progressive reduction in the size of the packets, i.e., a group of martensitic laths/plates sharing the same habit plane variants with the parent austenite grain. The presence of YB₄ particles induced internal stresses and microstrains, leading to peak shifting and broadening of the X-ray diffraction (XRD) peaks corresponding to that of the martensitic matrix phase. The coefficient of thermal expansion (CTE) decreased significantly from 13.4 × 10⁻⁶ K⁻¹ for monolithic AISI 420 to 8.06 × 10⁻⁶ K⁻¹ for the AISI 420/4 wt.% YB₄ composite and was attributed to the excellent dimensional stability of YB₄ particles. The maximum hardness (913.12 HV) and tensile strength (930 MPa) were achieved for the AISI 420/4 wt.% YB₄ composite. Fractographic analysis using SEM indicated a transition from ductile to brittle fracture with increasing YB₄ content, suggesting a reduction in strain-hardening capacity. The contributions of various strengthening mechanisms were quantified using the summation of strengthening and modified Clyne models, revealing that strengthening due to load bearing is dominant across all composites. Insights gained from these results are important to strategize the design of boride-based metal-matrix composites with enhanced strength–ductility synergy for structural applications. Full article

(This article belongs to the Topic Multiscale Characterization, Mechanical Behavior and Computational Simulation of Bulk Materials, Metallic Powders and/or Nanoparticles)

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21 pages, 6725 KB  

Open AccessArticle

Microstructure-Dependent Creep Mechanisms in Heat-Treated CZ1 Zr Alloy at 380 °C

by Haoyu Shi, Jianqiang Wang, Meiqing Chen, Pengliang Liu, Zhixuan Xia, Chenyang Lu, Rui Gao, Weiyang Li, Yujie Zhang, Zhengxiong Su and Jing Hu

Nanomaterials 2025, 15(21), 1624; https://doi.org/10.3390/nano15211624 - 24 Oct 2025

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Abstract

This study investigates the stress-dependent creep behavior of a CZ1 Zr alloy exhibiting two distinct microstructural states induced by different annealing treatments. Creep tests were conducted at 380 °C under applied stresses of 140 MPa and 260 MPa. CZ1-2 (fully recrystallized), characterized by [...] Read more.

This study investigates the stress-dependent creep behavior of a CZ1 Zr alloy exhibiting two distinct microstructural states induced by different annealing treatments. Creep tests were conducted at 380 °C under applied stresses of 140 MPa and 260 MPa. CZ1-2 (fully recrystallized), characterized by coarse grains and low dislocation density, demonstrated superior creep resistance under low stress due to suppressed dislocation activity and diffusion-dominated deformation. Stress exponent analysis revealed n = 5 for CZ1-1 (partially recrystallized) and n = 10 for CZ1-2, confirming a mechanism transition from steady-state dislocation climb to power-law breakdown. TEM characterization provided direct evidence of evolving dislocation networks, stacking faults, and second-phase particle redistribution. These findings underscore the critical role of microstructural conditioning in governing creep pathways and provide a mechanistic basis for tailoring Zr alloys to stress-specific service environments in advanced nuclear applications. Full article

(This article belongs to the Topic Multiscale Characterization, Mechanical Behavior and Computational Simulation of Bulk Materials, Metallic Powders and/or Nanoparticles)

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15 pages, 7305 KB  

Open AccessArticle

Electrochemical Anodization-Induced {001} Facet Exposure in A-TiO₂ for Improved DSSC Efficiency

by Jolly Mathew, Shyju Thankaraj Salammal, Anandhi Sivaramalingam and Paulraj Manidurai

J. Compos. Sci. 2025, 9(9), 462; https://doi.org/10.3390/jcs9090462 - 1 Sep 2025

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Abstract

We developed dye-sensitized solar cells based on anatase–titanium dioxide (A-TiO₂) nanotubes (TiNTs) and nanocubes (TiNcs) with {001} crystal facets generated using simple and facile electrochemical anodization. We also demonstrated a simple way of developing one-dimensional, two-dimensional, and three-dimensional self-assembled TiO₂ [...] Read more.

We developed dye-sensitized solar cells based on anatase–titanium dioxide (A-TiO₂) nanotubes (TiNTs) and nanocubes (TiNcs) with {001} crystal facets generated using simple and facile electrochemical anodization. We also demonstrated a simple way of developing one-dimensional, two-dimensional, and three-dimensional self-assembled TiO₂ nanostructures via electrochemical anodization, using them as an electron-transporting layer in DSSCs. TiNTs maintain tubular arrays for a limited time before becoming nanocrystals with {001} facets. Using FESEM and TEM, we observed that the TiO₂ nanobundles were transformed into nanocubes with {001} facets and lower fluorine concentrations. Optimizing the reaction approach resulted in better-ordered, crystalline anatase TiNTs/Ncs being formed on the Ti metal foil. The anatase phase of as-grown TiO₂ was confirmed by XRD, with (101) being the predominant intensity and preferred orientation. The nanostructured TiO₂ had lattice values of a = 3.77–3.82 and c = 9.42–9.58. The structure and morphology of these as-grown materials were studied to understand the growth process. The photoconversion efficiency and impedance spectra were explored to analyze the performance of the designed DSSCs, employing N719 dye as a sensitizer and the I⁻/I³⁻ redox pair as electrolytes, sandwiched with a Pt counter-electrode. As a result, we found that self-assembled TiNTs/Ncs presented a more effective photoanode in DSSCs than standard TiO₂ (P25). TiNcs (0.5 and 0.25 NH₄F) and P25 achieved the highest power conversion efficiencies of 3.47, 3.41, and 3.25%, respectively. TiNcs photoanodes have lower charge recombination capability and longer electron lifetimes, leading to higher voltage, photocurrent, and photovoltaic performance. These findings show that electrochemical anodization is an effective method for preparing TiNTs/Ncs and developing low-cost, highly efficient DSSCs by fine-tuning photoanode structures and components. Full article

(This article belongs to the Topic Multiscale Characterization, Mechanical Behavior and Computational Simulation of Bulk Materials, Metallic Powders and/or Nanoparticles)

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35 pages, 33285 KB  

Open AccessArticle

Chaotic Vibration Prediction of a Laminated Composite Cantilever Beam Subject to Random Parametric Error

by Lin Sun, Xudong Li and Xiaopei Liu

J. Compos. Sci. 2025, 9(8), 442; https://doi.org/10.3390/jcs9080442 - 17 Aug 2025

Cited by 1 | Viewed by 940

Abstract

Random parametric errors (RPEs) are introduced into the model establishment of a laminated composite cantilever beam (LCCB) to demonstrate the accuracy and robustness of a recurrent neural network (RNN) in predicting the chaotic vibration of a LCCB, and a comparative analysis of training [...] Read more.

Random parametric errors (RPEs) are introduced into the model establishment of a laminated composite cantilever beam (LCCB) to demonstrate the accuracy and robustness of a recurrent neural network (RNN) in predicting the chaotic vibration of a LCCB, and a comparative analysis of training performance and generalization capability is conducted with a convolutional neural network (CNN). In the process of dynamic modeling, the nonlinear dynamic system of a LCCB is established by considering RPEs. The displacement and velocity time series obtained from numerical simulation are used to train and test the RNN model. The RNN model converts the original data into a multi-step supervised learning format and normalizes it using the MinMaxScaler method. The prediction performance is comprehensively evaluated through three performance indicators: coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The results show that, under the condition of introducing RPEs, the RNN model still exhibits high prediction accuracy, with the maximum R² reaching 0.999984548634328, the maximum MAE being 0.075, and the maximum RMSE being 0.121. Furthermore, performing predictions at the free end of the LCCB verifies the applicability and robustness of the RNN model with respect to spatial position variations. These results fully demonstrate the accuracy and robustness of the RNN model in predicting the chaotic vibration of a LCCB. Full article

(This article belongs to the Topic Multiscale Characterization, Mechanical Behavior and Computational Simulation of Bulk Materials, Metallic Powders and/or Nanoparticles)

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