Texturation and Functional Behaviors of Polycrystalline Ni-Mn-X Phase Transformation Alloys

Significance of adiabatic heating on phase transformation in titanium-based alloys during severe plastic deformation

Shape memory alloys (SMA) are a class of smart materials with inherent shape memory and superelastic characteristics. Unlike other SMAs, iron-based SMAs (Fe-SMA) offer cost-effectiveness, weldability, and robust mechanical strength for the construction industry. Thus, applying these promising materials to advanced manufacturing processes is of considerable industrial and academic relevance. This study aims to present a pioneer application of a Fe–Mn–Si–Cr–Ni–V-C SMA to arc-based directed energy deposition additive manufacturing, namely wire and arc additive manufacturing (WAAM), examining the microstructure evolution and mechanical/functional response. The WAAM-fabricated Fe-SMAs presented negligible porosity and high deposition efficiency. Microstructure characterization encompassing electron microscopy and high energy synchrotron X-ray diffraction revealed that the as-deposited material is primarily composed by γ FCC phase with modest amounts of VC, ε and σ phases. Tensile and cyclic testing highlighted the Fe-SMA's excellent mechanical and functional response. Tensile testing revealed a yield strength and fracture stress of 472 and 821 MPa, respectively, with a fracture strain of 26%. After uniaxial tensile loading to fracture, the γ → ε phase transformation was clearly evidenced with post-mortem synchrotron X-ray diffraction analysis. The cyclic stability during 100 load/unloading cycles was also evaluated, showcasing the potential applicability of the fabricated material for structural applications.

Design of high entropy alloys (HEA) presents a significant challenge due to the large compositional space and composition-specific variation in their functional behavior. The traditional alloy design would include trial-and-error prototyping and high-throughput experimentation, which again is challenging due to large-scale fabrication and experimentation. To address these challenges, this article presents a computational strategy for HEA design based on the seamless integration of quasi-random sampling, molecular dynamics (MD) simulations and machine learning (ML). A limited number of algorithmically chosen molecular-level simulations are performed to create a Gaussian process-based computational mapping between the varying concentrations of constituent elements of the HEA and effective properties like Young’s modulus and density. The computationally efficient ML models are subsequently exploited for large-scale predictions and multi-objective functionality attainment with non-aligned goals. The study reveals that there exists a strong negative correlation between Al concentration and the desired effective properties of AlCoCrFeNi HEA, whereas the Ni concentration exhibits a strong positive correlation. The deformation mechanism further shows that excessive increase of Al concentration leads to a higher percentage of face-centered cubic to body-centered cubic phase transformation which is found to be relatively lower in the HEA with reduced Al concentration. Such physical insights during the deformation process would be crucial in the alloy design process along with the data-driven predictions. As an integral part of this investigation, the developed ML models are interpreted based on Shapley Additive exPlanations, which are essential to explain and understand the model’s mechanism along with meaningful deployment. The data-driven strategy presented here will lead to devising an efficient explainable ML-based bottom-up approach to alloy design for multi-objective non-aligned functionality attainment.

Ti-alloys represent the principal structural materials in both aerospace development and metallic biomaterials. Key to optimizing their mechanical and functional behaviour is in-depth know-how of their phases and the complex interplay of diffusive vs. displacive phase transformations to permit the tailoring of intricate microstructures across a wide spectrum of configurations. Here, we report on structural changes and phase transformations of Ti–Nb alloys during heating by in situ synchrotron diffraction. These materials exhibit anisotropic thermal expansion yielding some of the largest linear expansion coefficients (+ 163.9×10−6 to −95.1×10−6 °C−1) ever reported. Moreover, we describe two pathways leading to the precipitation of the α-phase mediated by diffusion-based orthorhombic structures, α″lean and α″iso. Via coupling the lattice parameters to composition both phases evolve into α through rejection of Nb. These findings have the potential to promote new microstructural design approaches for Ti–Nb alloys and β-stabilized Ti-alloys in general.

The functional fatigue behavior of Ti50Ni30Cu20 (at. %) shape memory alloy was investigated after subjecting to cold working and heat-treatment. Copper addition modified the phase transformation behavior with the introduction of B19-phase in the binary NiTi alloy. It was observed that aging after annealing and thermal cycling (-60 to 100)°C significantly effect the transformation temperatures. Observations in optical microscope and scanning electron microscope reveal inhomogeneity in the composition in the form of coarse Cu+Ti-rich precipitates. Investigations under transmission electron microscope showed growth of internally twined martensitic plates in solution treated sample. The phase transformation temperatures were determined with differential scanning calorimeter. The transformation temperatures were shifted towards lower side. Dislocations introduced during cold working and fine precipitation after aging, may be responsible for this change in the transformation characteristics of the material.

A range of inorganic materials, with structures based on transition metal (TM) centeredpolyhedra linked through polyanionic units, have been reported with a wide variety of applications. Depending upon the TM cation coordination geometry and the surrounding ligand anions, such inorganic materials may exhibit wide structural diversity and polymorphism. One such family of compounds is disodium metal pyrophosphates (Na2MP2O7), which is formed by a condensation reaction of two inorganic phosphate units. The pyrophosphate (P2O7)4- based systems exhibit rich crystal chemistry and play critical role in a wide range of applications such as energy storage, ionic conductivity and near infrared absorption (NIR) depending upon the constituent transition metals. In the present work, we have studied Mn and Co-based pyrophosphates, where we have noticed ‘phase transformation’ depending on the synthesis methods/ conditions. Polymorphs of Na2MnP2O7 and Na2CoP2O7 were characterized by diffraction analyses. Their functional properties have been explored by synergizing electrochemical testing methods along with the bond valence site energy (BVSE) modeling to investigate their ionic conductivity, electrocatalytic and electrochemical performance.

The superelasticity of shape memory alloys (SMA) can be used to provide self-centering and/or energy dissipation characteristics to structures including buildings, bridges, automobiles, and aircrafts. The functional fatigue behavior of SMA is important because it affects the stiffness, strength, strain recovery and energy dissipation of the material. This study investigated the functional fatigue behavior of large diameter Ni–Ti SMA bars under different levels of plastic deformation and different ambient temperatures. Differential scanning calorimetry was used to measure the martensitic transformation temperatures. Cyclic loading with a 1% strain increment was applied to investigate the maximum recovery strain, i.e. the superelastic limit. Low-cycle fatigue loading with different applied peak strains (2%, 3%, 4% and 5%) was performed at different temperatures (−40 °C, −10 °C, 10 °C, 25 °C and 50 °C). The effects of plastic deformation, testing temperature, and number of cycles on the stress-induced martensitic phase transformation, degradation of superelastic properties, and fatigue life were studied. The superelastic properties, such as the changes in the stress–strain curves, elastic modulus, yield stress, damping ratio and recovery strain, were analyzed. It was shown that the functional fatigue resistance (in terms of degradation in the superelastic properties and fatigue life) of Ni–Ti SMA reduced as the applied peak strain increased, particularly when the applied peak strain was higher than the superelastic limit. Additionally, when Ni–Ti SMA was subjected to combined plastic deformation and higher than room temperature, the functional fatigue resistance reduced as the temperature increased.

Shape Memory Alloys present a large variety of behaviour in function of the thermome- chanical loading pathes and the microstructural states of the material. These responses are due to different physical mechanisms of deformation which are associated to the thermoelastic martensitic transformation : - oriented growth of the martensitic plates by the applied stress in Superelasticity ; - mobility of the interfaces between the variants of martensite in the so-called Shape Memory Effect ; - capability of the internal stress field produced in the material by oriented defects left by some previous transformation sequences, to influence the growing of the martensite in the Two-Way Shape Memory Effect. The determination of the constitutive equations for the mechanical behaviour of these alloys must take into account these particular mechanisms of deformation. For each physical mechanism it is ne- cessary, at first, to make a kinematical study of the strain associated to it. After this step, an energy balance between the driving and the resistive forces is established in each case from the analysis of the Gibb's free energy of the transformation or by using the Eshelby formalism of energy momen- tum. Phenomenological flow rules are then determined from the classical concept of normality rule. In this contribution, the micromechanical aspects of the phase transformation mechanisms me presented both from the statical and the kinematical point of view. Special attention is given to the internal stress state associated with variant and grain interactions.

Effect of crystallographic anisotropy on phase transformation and tribological properties of Ni-rich NiTi shape memory alloy fabricated by LPBF

Mn–Cu‐based alloys possess excellent mechanical and functional properties (damping capacity and shape memory effect). This work utilizes a typical additive manufacturing technique, selective laser melting (SLM), to fabricate Mn–15 wt%Cu alloys to explore the mechanical and functional behaviors. The results indicate that the γ‐(Mn, Cu) and a small amount of γ′‐(Mn, Cu) phase are the main phase compositions in the as‐SLMed Mn–15 wt%Cu alloy, as well as twins and second‐phase precipitation particles. The microstructure shows a fine cellular γ‐(Mn, Cu) dendrite with primary dendrite spacing of ≈0.9 μm in the columnar grains with a size of ≈8 μm. The martensitic transformation start temperature (MS) and phase transformation hysteresis are ≈132.6 and ≈5.1 °C, respectively. The existing nanoscale chemical segregations of Mn and Cu are attributed to the spinodal decomposition of γ‐(Mn, Cu). A compositional modulation is observed with a wavelength of ≈20 nm and an amplitude fluctuation of Mn (55–85 wt%) and Cu concentrations (15–45 wt%). Finally, the as‐SLMed Mn–15 wt%Cu alloy boasts good tensile properties (359, 268 MPa, and 3.6%), damping (internal friction of 0.032 when the strain amplitude is 9 × 10−4), and shape memory performances (one‐way ηow = 37–48%, and two‐way ηtw = 16–20% under the pre‐deformation strain of 2–3%).

Ni-Co-Mn-Ti-B high performance multiferroic phase transformation material: Simultaneous modulation of mechanical properties and successive caloric effects by B doping

Electrochemical processes in solids—ranging from ion insertion and redox reactions to phase transformations—are inherently inhomogeneous. They often originate at localized nanoscale reaction centers that serve as nucleation sites for new phases, fast-ion conduction pathways, or regions of preferential charge accumulation. Understanding and controlling these centers is critical for optimizing energy storage materials, catalysts, and mixed ionic-electronic conductors. However, conventional electrochemical and spectroscopic methods provide only spatially averaged information, obscuring the role of heterogeneity and making it difficult to identify and study the critical microstructural features that govern functionality. Electrochemical Strain Microscopy (ESM), a technique based on scanning probe microscopy (SPM), offers a route to overcome these limitations. In ESM, a voltage waveform is applied through a conductive probe to locally perturb the ionic distribution and stimulate electrochemical reactions. The resulting strain, generated by compositional, electrostrictive, or redox-induced lattice changes, is detected via the mechanical deflection of the cantilever. This enables local probing of electrochemical activity with sub-100 nm spatial resolution and across a broad frequency range spanning from tens of mHz to hundreds of kHz. Critically, the strain signal encodes the kinetics of ion migration, nucleation and growth of second phases, and electromechanical coupling—offering a powerful window into local electrochemical dynamics. Despite its potential, the widespread adoption of ESM has been limited by several challenges. Chief among them is the irreversibility of many electrochemical transformations, which makes conventional raster-mapping problematic. Repeated excitation of the same area can lead to cumulative changes, damage, or memory effects that obscure intrinsic kinetics. Additionally, the multidimensional nature of the data—spanning space, frequency, voltage, and time—makes interpretation complex and often dependent on user intuition or trial-and-error targeting of regions of interest. To address these limitations, we introduce a framework for autonomous Electrochemical Strain Microscopy, combining computer vision-based navigation with deep kernel learning (DKL)-driven discovery. In the direct workflow, real-time image analytics are used to identify microstructural features of interest—such as grain boundaries, twin planes, or compositional inclusions—directly from topographic or other contrast channels. These features are then prioritized for targeted spectroscopy or waveform-based measurements. This transition from raster mapping to functionality-aware targeting dramatically improves experimental efficiency and enables the preservation of pristine regions for dynamic analysis. The second approach is based on inverse modeling frameworks based on deep kernel learning. In these workflows, ESM data—whether voltage-strain hysteresis loops, bias-frequency sweeps, or relaxation kinetics—are treated as observable outputs of an underlying physical process. By learning the mapping between local microstructure and observed response, the model identifies causal or correlative links between specific material features and functional behavior. This enables the discovery of rare or subtle functional motifs that may otherwise be missed in conventional analysis, and creates opportunities to explore large combinatorial libraries or multi-modal datasets in a coherent and scalable way. We demonstrate these autonomous ESM workflows on model hybrid perovskite systems, where the interplay between mobile ions, electric field, and soft lattice degrees of freedom presents a rich testbed for probing ionic transport, polarization, and degradation phenomena. Using real-time analysis of strain-voltage loops and direct object targeting, we extract spatially resolved maps of reversible and irreversible electrochemical activity, identify microstructural elements responsible for enhanced ion dynamics, and observe the nucleation of new phases with high spatiotemporal precision. The DKL-based analysis further allows us to distinguish functionality associated with grain boundaries from that linked to planar defects or inclusions—showcasing the potential for structure-property relationship discovery. These approaches are broadly applicable to solid-state batteries, mixed ionic-electronic conductors, and a wide variety of functional oxides, sulfides, and nitrides where spatial heterogeneity governs performance and reliability. Importantly, the combination of ESM with multimodal SPM techniques—such as current-voltage spectroscopy, Kelvin probe force microscopy (KPFM), and mechanical mapping—opens the door for a comprehensive, physically grounded, and quantitative framework for studying electrochemical transformations on the nanoscale. Overall, autonomous ESM offers a powerful strategy for exploring electrochemical functionality with high spatial and temporal resolution, addressing key limitations of traditional techniques. By integrating smart targeting, machine learning-driven interpretation, and multi-modal measurement, this platform can transform how we probe and understand reaction kinetics, phase transitions, and ionic transport in complex materials—bringing us closer to a future of self-driving electrochemical experimentation.

Light-induced material organization

An analytical study of the solid-solid phase transformation from the cubic zinc blende or sphalerite (S) metastable modification of CdS (\ensuremath{\beta}-CdS) to the wurtzite (W) hexagonal stable phase (\ensuremath{\alpha}-CdS) is presented. Polycrystalline CdS layers in the cubic phase were prepared on glass substrates by means of chemical bath deposition. Films were heated in ${\mathrm{A}\mathrm{r}+\mathrm{S}}_{2}$ in the temperature (T) range 100--550 \ifmmode^\circ\else\textdegree\fi{}C. X-ray diffraction data of annealed samples allowed us to observe a process of gradual change from \ensuremath{\beta}- to \ensuremath{\alpha}-CdS. Optical absorption spectra let us obtain the band-gap energy ${(E}_{g})$ of samples (both as grown and annealed). The unit-cell volume $(V)$ vs T line shape was explained as an interface movement of the $S\ensuremath{-}W$ interdomain wall driven by T. The ${E}_{g}$ vs T plot exhibits a minimum as a sharp peak at 300 \ifmmode^\circ\else\textdegree\fi{}C, which is the T value assigned by us to the critical point ${(T}_{c})$ of the experimental structural transformation. The ${E}_{g}$ vs $V$ curve indicates an ${E}_{g}{=AV}^{2}+BV+C$ functional behavior. This ${E}_{g}$ vs $V$ dependence was explained through a relation between $(\ensuremath{\partial}{E}_{g}/\ensuremath{\partial}{T)}_{P}$ and ${C}_{P},$ where ${C}_{P}$ is the specific heat at constant pressure. The ${C}_{P}$ against T function was also calculated in the interval 300--800 K.

Cochlear implants (CI) are complex medical implants used as a common therapeutic measure for deaf people who suffer from damage to the inner ear. The success of CI insertion, a manual surgery procedure, is highly dependent on the surgeon's experience. Additionally, more precise positioning of the electrode close to the membrane structures could increase the effectiveness of frequency selectivity and stimulus conduction. To overcome these limitations, the degree of deformation of the electrode during its insertion has to be controllable. This ability can be achieved by integrating micro-actuator elements of a nickel titanium (NiTi) shape memory alloy (SMA) inside the electrode. These elements are manufactured using selective laser micromelting (SLμM). Initially, different concepts of activation mechanisms for SMA actuators for CI electrodes are discussed. Following the rules of additive manufacturing on a microscale, the corresponding actuator design and manufacturing strategies are presented. Suitable SLμM process parameters to achieve high spatial resolution are identified. Due to the high process temperatures, material chemical properties, respectively, its functional behavior, may be affected using SLμM. Therefore, analyses of SLμM NiTi parts manufactured using carrier gas hot extraction as well as differential scanning calorimetry (DSC) are carried out. Force measurements verify the available recovery forces of the produced micro-actuators activated thermally by one way effect. A suitable additive manufacturing strategy that allows the repeatable production of micro-actuators at a resolution of less than 100 μm could be evaluated. Different anatomical geometries could be transferred from clinical data model to the manufacturing process. The processed NiTi parts meet the requirements of the ASTM F2063 concerning oxygen inclusion, which is an important condition to preserve shape memory functionality. DSC analyses reflect stable functional properties of the processed NiTi alloy independent of the adjusted laser parameters. Phase transformation of actuators could be actively proved using electrical current and passively using an external heat source.