The research team, led by Rakesh K.R. of the National Institute of Technology Karnataka together with Pratyush Mohanty, Srikanth Bontha, Ramesh M.R., and Vamsi Krishna Balla of CSIR-Central Glass and Ceramic Research Institute, focused on two alloy compositions: Mg-1Zn-2Gd-0.4Zr and Mg-1Zn-2Dy-0.4Zr, all concentrations given in weight percent. Gadolinium and dysprosium are rare earth elements prized for their high solubility in magnesium at eutectic temperature, roughly 23.49 and 25.8 weight percent respectively, which makes them ideal for precipitation hardening. Zirconium was included in both alloys as a grain refiner, a role it performs more effectively than any other known refining method in zinc-containing magnesium melts. The alloys were prepared by conventional casting under a protective argon atmosphere containing two percent sulfur hexafluoride, with the melt held and stirred at 750 degrees Celsius before being poured into a cast iron mold.

The centerpiece of the study was the classic T6 heat treatment schedule: solution treatment at 500 degrees Celsius for twelve hours, water quenching, and then artificial aging at 225 degrees Celsius for periods of twelve, eighteen, or twenty-four hours, designated T6-12, T6-18, and T6-24. Hardness measurements using a Brinell tester with a ten millimeter steel ball indenter under a 250 kilogram load revealed that aging for twelve hours produced the most significant gains. The Mg-Zn-Gd alloy climbed from 37 BHN in the as-cast state to 47 BHN after T6-12, while the Mg-Zn-Dy alloy rose from 39 BHN to 46 BHN. Extending the aging time to eighteen or twenty-four hours offered no meaningful further improvement, so the researchers adopted T6-12 as the optimum condition for all subsequent wear and corrosion comparisons.

The microscopic origin of these gains lies in a dramatic restructuring of the secondary phases. In the as-cast condition, both alloys displayed dendritic grains with thick eutectic phases segregated along grain boundaries in a highly non-uniform fashion. After solution treatment, these coarse eutectic networks largely dissolved into the alpha-magnesium matrix, and fine lamellar precipitates of the long period stacking ordered type, known as LPSO phases, nucleated within the grains. Transmission electron microscopy confirmed the lamellar LPSO morphology in both alloys, with energy dispersive spectroscopy showing the phases were strongly enriched in gadolinium at 78.39 weight percent in the Mg-Zn-Gd alloy and dysprosium at 82.99 weight percent in the Mg-Zn-Dy alloy. X-ray diffraction added corroborating evidence: new MgGd3 peaks appeared in the heat-treated Mg-Zn-Gd alloy at approximately 33 and 57 degrees two-theta, while in the heat-treated Mg-Zn-Dy alloy the Mg24Dy5 peaks vanished entirely, indicating dissolution of the eutectic phases.

These microstructural changes translated directly into superior tribological performance. Dry sliding wear tests were conducted on a pin-on-disc apparatus against an EN-24 steel counterface at applied loads of 10 and 20 newtons, temperatures ranging from 200 to 400 degrees Celsius, a sliding velocity of 1.25 meters per second, and a total sliding distance of 1500 meters, following the ASTM G-99 standard. Across every condition, the heat-treated alloys exhibited lower wear rates than their as-cast counterparts, consistent with Archard’s law, which states that wear rate is inversely proportional to hardness. The Mg-Zn-Gd alloy consistently outperformed the Mg-Zn-Dy alloy, and for the gadolinium-bearing composition the wear rate actually fell from 1.6 times ten to the minus three cubic millimeters per millimeter to 1.2 times ten to the minus three as the test temperature rose from 200 to 400 degrees Celsius under a 10 newton load.

The seemingly paradoxical improvement of wear resistance at higher temperatures is explained by the behavior of oxide debris. Scanning electron microscopy of the worn surfaces revealed parallel ridges and grooves characteristic of abrasive wear, along with sheet-like delaminated particles and craters at the heavier 20 newton load, signatures of delamination wear in which subsurface cracks propagate parallel to the surface before shearing off thin wear sheets. At elevated temperatures, however, frictional heating oxidized both the sliding surfaces and the wear debris. X-ray diffraction of the worn pins detected zinc oxide on both alloys and magnesium oxide on the dysprosium alloy, confirming that a compact oxide glaze had formed. This oxidized debris fills the valleys of the worn surface, prevents direct metal-to-metal contact with the steel disc, and acts as a solid lubricant, simultaneously reducing the coefficient of friction and the wear rate.

Corrosion testing delivered perhaps the most striking results of the study. The team immersed polished specimens in 3.5 weight percent sodium chloride solution for 72 hours at 30 degrees Celsius, collecting the evolved hydrogen and measuring weight loss to compute corrosion rates. The as-cast Mg-Zn-Gd alloy corroded at 3.86 millimeters per year, but after T6-12 treatment the rate dropped to 1.48 millimeters per year, a reduction of roughly sixty-one percent. The Mg-Zn-Dy alloy improved from 3.92 to 2.62 millimeters per year, a thirty percent reduction. Electron microscopy of the corroded surfaces showed severe galvanic attack and deep pitting penetrating to the subsurface in the as-cast samples, whereas the heat-treated Mg-Zn-Gd surface displayed only mild filiform corrosion with large areas left entirely untouched.

The mechanism behind the corrosion improvement is a textbook illustration of how microstructure governs electrochemistry. In the as-cast alloys, the large, sparsely distributed eutectic phases act as cathodes adjacent to the anodic alpha-magnesium matrix, driving aggressive galvanic corrosion. After solution treatment, these cathodic eutectic phases dissolve, and the resulting fine, homogeneously distributed precipitates including the LPSO phases raise the anode-to-cathode area ratio and act as barriers that impede corrosion propagation. Gadolinium-bearing secondary phases in the treated alloy form a more continuous network along grain boundaries that effectively blocks corrosive attack, while the dysprosium alloy retains some clustered intermetallic precipitates that continue to behave as localized galvanic cathodes, explaining why Mg-Zn-Gd outperformed Mg-Zn-Dy in both as-cast and heat-treated conditions.

The implications reach well beyond the laboratory. Magnesium alloys containing rare earth elements are already irreplaceable in aerospace and defense applications despite their cost, and the automotive industry continues to push magnesium components as a route to lighter, more fuel-efficient vehicles. By demonstrating that a simple, industrially routine T6 treatment, twelve hours of solutionizing at 500 degrees Celsius followed by twelve hours of aging at 225 degrees Celsius, can simultaneously harden these alloys, reduce their wear rates across a broad temperature window, and cut their corrosion rates by up to sixty percent, the researchers have provided a low-cost, scalable route to magnesium alloys that are far more durable in service. The study also underscores the special value of LPSO phases, whose high hardness, thermal stability, and coherent interfaces with the magnesium matrix make them ideal strengthening agents for both tribological and corrosion performance, pointing the way toward the next generation of lightweight magnesium engineering alloys.

Subject of Research: Heat treatment effects on the microstructure, wear, and corrosion behavior of Mg-Zn-Gd and Mg-Zn-Dy magnesium alloys

Article Title: Heat treatment of Mg-Zn-Gd and Mg-Zn-Dy alloys for enhanced wear and corrosion properties

Article References: K.R, R., Mohanty, P., Bontha, S., M.R, R., & Balla, V. K. (2026). Heat treatment of Mg-Zn-Gd and Mg-Zn-Dy alloys for enhanced wear and corrosion properties. Journal of Materials Science: Metallurgy, 1(1), Article 7. https://doi.org/10.1007/s44492-026-00008-y

Image Credits: AI Generated

DOI: 10.1007/s44492-026-00008-y

Keywords: magnesium alloys, Mg-Zn-Gd, Mg-Zn-Dy, heat treatment, T6 temper, LPSO phase, precipitation hardening, wear resistance, corrosion resistance, rare earth elements, microstructure, galvanic corrosion

K. S. Swathi and K. Gopalakrishna Naik synthesized titanium dioxide nanoparticles using the sol–gel method, a low-cost wet-chemistry route in which titanium tetra(iv) isopropoxide, a common liquid precursor, is added dropwise to ethanol under constant stirring. Hydrochloric acid was used to set the pH of the solution to 1.4, ensuring that hydrolysis proceeded under acidic conditions. After two hours of stirring at room temperature, the solution was heated to 125 degrees Celsius for an hour, and the resulting gel was pre-heated at 300 degrees Celsius for two hours to drive off reaction by-products and begin crystallizing the material. The white powder was then divided into batches and calcined for two hours in a tubular furnace at temperatures ranging from 300 to 900 degrees Celsius.

The team then subjected each batch to a battery of characterization techniques, including powder X-ray diffraction, Raman spectroscopy, field-emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, transmission electron microscopy, high-resolution TEM, selected area electron diffraction and UV–visible absorption spectroscopy. The X-ray diffraction results told a clear story. Samples calcined at 300 degrees Celsius showed broad, low-intensity diffraction peaks characteristic of the tetragonal anatase phase, a signature of nanoscale crystallites with a large fraction of atoms at surfaces, along with surface defects and amorphous grain boundaries. Samples calcined at 400 and 500 degrees Celsius remained purely anatase, but their diffraction peaks sharpened as crystallinity improved.

The first hint of change came at 600 degrees Celsius, where a very low-intensity peak at around 27.5 degrees in 2-theta, corresponding to the (110) lattice planes of rutile, appeared alongside the dominant anatase reflections. This marked the onset of the anatase-to-rutile transformation. By 700 degrees Celsius the diffraction patterns matched the tetragonal rutile phase, and samples calcined at 800 and 900 degrees Celsius were overwhelmingly rutile. Notably, a faint anatase (101) peak persisted even at 900 degrees Celsius, indicating that the applied temperature and two-hour duration were not sufficient to achieve complete transformation to pure rutile, a phase that in many studies only forms above 1000 degrees Celsius.

Using the Scherrer equation on the most intense diffraction peaks, the researchers estimated that crystallite size grew from about 3.39 nanometers at 300 degrees Celsius to 61.5 nanometers at 900 degrees Celsius. Microstrain and dislocation density, both indicators of crystal defects and distortion, decreased steadily with increasing calcination temperature, confirming that heat treatment suppresses surface defects and amorphous grain boundaries while allowing crystallites to grow. The relative amounts of anatase and rutile were quantified with the Spurr–Myers method, which showed that the rutile weight fraction increased monotonically between 600 and 900 degrees Celsius as enhanced atomic diffusion and grain growth drove the transformation forward.

Raman spectroscopy independently confirmed the phase evolution. Samples calcined up to 600 degrees Celsius displayed only anatase features, dominated by the intense E_g vibrational mode near 144 inverse centimeters, together with the B1g band near 397 inverse centimeters and the combined A1g plus B1g and E_g modes near 513 and 635 inverse centimeters. The 700-degree sample was a hybrid, showing both the anatase E_g band and strong rutile E_g and A1g modes, along with a multi-phonon scattering feature near 230 inverse centimeters. At 800 and 900 degrees Celsius, only rutile bands remained. The team also tracked a gradual red shift of the E_g modes with increasing temperature, attributing it to phonon softening caused by lattice expansion, anharmonic vibrational effects and partial relaxation of residual strain. The phonon confinement model, rooted in the Heisenberg uncertainty principle, explains how smaller particles broaden the phonon momentum distribution and produce asymmetric broadening of Raman bands.

Electron microscopy added a subtle twist. Field-emission SEM images of samples calcined at 400, 600 and 800 degrees Celsius showed micrometre-sized agglomerates of fine, roughly spherical nanoparticles, with no dramatic morphological change across the temperature range. Energy-dispersive X-ray spectra confirmed the presence of only titanium and oxygen. Transmission electron microscopy, however, revealed that average particle size rose from roughly 8.6 nanometers at 400 degrees Celsius to 25.5 nanometers at 600 degrees Celsius, then unexpectedly fell to about 12 nanometers at 800 degrees Celsius before jumping to around 202 nanometers at 900 degrees Celsius. The authors attribute the dip at 800 degrees Celsius to the breaking and rearranging of titanium–oxygen bonds during the anatase-to-rutile transformation, in which distorted anatase octahedra break apart and reassemble into the denser rutile structure, collapsing loosely bound agglomerates into more compact grains.

High-resolution TEM lattice fringes and selected area electron diffraction patterns corroborated the diffraction results. The d-spacing measured at 400 and 600 degrees Celsius was about 0.35 nanometers, matching the (101) planes of anatase, while the 800-degree sample contained grains with d-spacings of both 0.35 and 0.32 nanometers, corresponding to anatase (101) and rutile (110) planes respectively. At 900 degrees Celsius the d-spacing of about 0.25 nanometers confirmed the rutile (101) plane. Inverse fast Fourier transforms of the 800-degree sample revealed structural distortion from edge dislocations, direct visual evidence of the strain involved in the phase transformation. The SAED ring patterns confirmed that all calcined samples were polycrystalline.

Optical measurements revealed perhaps the most intriguing finding. Using Tauc analysis of UV–visible absorption data, the researchers found that the nanoparticles exhibited apparent direct-transition-like band gap behavior, even though bulk anatase is classically an indirect band gap semiconductor. The direct-transition fits produced more linear absorption edges and better near-band-edge fitting than indirect models, which the authors attribute to quantum confinement, structural disorder and defect-induced localized states that modify the electronic band structure at the nanoscale. The sharp absorption edges, without pronounced tails, suggested low densities of localized defect states and improving crystallinity with temperature. The band gap decreased from about 3.51 electron volts at 300 degrees Celsius to 3.34 electron volts at 700 degrees Celsius as anatase crystallites grew, then settled at 3.33 and 3.25 electron volts for the 800 and 900-degree rutile-dominated samples, consistent with the known band gaps of the two phases.

The study’s significance lies in its integrated view of how a single processing variable, calcination temperature, simultaneously governs phase composition, crystallite size, vibrational response and optical behavior in titanium dioxide. By clarifying why nano-anatase appears to behave like a direct band gap semiconductor and by mapping the gradual, incomplete anatase-to-rutile transformation between 600 and 900 degrees Celsius, the work offers practical guidance for researchers tailoring titanium dioxide nanoparticles for photocatalysis, solar energy conversion and optoelectronic devices, where the balance between anatase and rutile often determines performance.

Subject of Research: Calcination temperature effects on the structural, vibrational and optical properties of sol–gel synthesized TiO2 nanoparticles

Article Title: Calcination effect on the properties of sol–gel synthesized TiO2 nanomaterials

Article References: Swathi, K. S., & Naik, K. G. (2026). Calcination effect on the properties of sol–gel synthesized TiO2 nanomaterials. Discover Industrial Chemistry and Materials, 1(1), Article 3. https://doi.org/10.1007/s44508-026-00003-0

Image Credits: AI Generated

DOI: 10.1007/s44508-026-00003-0

Keywords: titanium dioxide, TiO2 nanoparticles, sol–gel synthesis, calcination, anatase, rutile, phase transformation, X-ray diffraction, Raman spectroscopy, optical band gap, photocatalysis, nanomaterials

The research, carried out by Samuel Onimpa Alfred of the Department of Aerospace Engineering at the University of Michigan, belongs to a rapidly growing field called triboinformatics, where artificial intelligence is used to decode the messy, nonlinear world of friction, lubrication, and wear. Tribology, the science of interacting surfaces in relative motion, is not a niche pursuit. Earlier studies cited in the work estimate that friction and wear-related losses consume a staggering share of global energy, contributing substantially to costs and emissions across industry. Making surfaces last longer, in artificial hip joints or in jet engine bearings, translates directly into energy saved and devices that survive longer inside the human body or inside an aircraft engine.

What makes Nitinol such a fascinating test case is its shape-memory and superelastic character. Deform it and it snaps back; heat it past a critical temperature and it returns to a previously memorized configuration. But these same properties make its tribological behavior notoriously complex. In this study, Alfred examined four distinct Nitinol conditions: an equiatomic titanium-nickel alloy, and three versions of a nickel-rich composition known as 60NiTi that had been aged, annealed, or solution-treated. Each heat treatment changes the alloy’s microstructure, hardness, and elasticity, and therefore changes how it wears when scraped against a counterface in dry, reciprocating sliding. Classical wear laws, which tend to assume wear scales simply with load and sliding distance while inversely scaling with hardness, capture these shifts poorly.

To tame that complexity, Alfred assembled a dataset of 336 individual measurements drawn from previously published, peer-reviewed reciprocating dry-sliding experiments, covering weight loss, cumulative wear over time, and steady-state coefficient of friction for all four material conditions. The modeling strategy then subjected a battery of algorithms to a demanding examination. Instead of splitting the data randomly, the study used grouped cross-validation, meaning that entire series of wear tests were withheld from the model during training. This is the scientific equivalent of asking a student to answer questions about chapters of a book they were never allowed to read, and it guards against the overly optimistic predictions that plague many machine-learning studies in materials science.

Even under this harsh test, gradient boosting, an ensemble method that builds a predictive model from many sequentially corrected decision trees, emerged as the clear winner. It predicted weight loss with an R-squared of 0.92 and the coefficient of friction with an R-squared of 0.91, beating support-vector regression, random forests, and simple linear baselines. When the cross-validation was made less restrictive and random splits were allowed, those scores climbed to 0.98 for wear and 0.94 for friction. The result confirms that gradient boosting does not just memorize data; it learns genuinely transferable relationships between operating conditions and tribological outcomes, even for material-test combinations it has never encountered.

Crucially, the study did not stop at prediction. Using SHapley Additive exPlanations, or SHAP, a technique borrowed from cooperative game theory that assigns each input variable its fair share of credit for a model’s output, Alfred opened the black box. The analysis revealed that applied load is the dominant driver of wear, while oscillation frequency dominates friction behavior, with higher frequencies associated with lower friction coefficients, a trend consistent with frictional heating at the sliding interface. This kind of transparency matters enormously for engineers, because a model that merely outputs numbers without explanations offers no guidance on which design levers to pull.

Perhaps the most striking achievement is a deep learning result: a gated recurrent unit network, a type of neural network designed for sequential data, reproduced the full time-resolved wear trajectories of completely unseen tests with an R-squared of 0.91. In other words, given the early portion of a wear test, the network could accurately trace how material loss would accumulate over the entire remaining test, test after test, across all four heat treatments. That capability opens the door to digital wear forecasting, where a short initial experiment or monitoring window could stand in for long and expensive laboratory campaigns.

The study then confronted the granddaddy of wear science, the Archard equation, formulated in 1953, which states that wear volume is proportional to load and sliding distance and inversely proportional to hardness. When that classical law was fitted to the Nitinol dataset, it managed an R-squared of only 0.27, and performed even worse, going negative, when hardness was imposed rather than fitted. Alfred instead let the data speak, recovering a generalized Archard-type law in which the exponents on load, sliding distance, frequency, and hardness are free parameters. The resulting equation, weight loss equals 0.0425 times load to the power 0.78, sliding distance to the power 0.54, frequency to the power minus 0.14, and hardness to the power minus 0.26, described all four heat treatments with an R-squared of 0.79, using only measured hardness rather than material identity labels.

The exponents themselves tell a physical story. The sub-linear load exponent of 0.78 suggests that superelastic Nitinol distributes contact stress in a way that softens the wear increase as loads climb, while the negative frequency exponent quantifies the frictional-heating effect seen in the SHAP analysis. Most intriguingly, the fitted wear coefficient for each material correlated almost perfectly, at r equals 0.97, with the ratio of elastic modulus to hardness, a dimensionless quantity long championed in surface engineering as an indicator of elastic, wear-tolerant contact. When the modulus-to-hardness ratio served as the sole material descriptor in the generalized law, the fit reached an R-squared of 0.83, essentially matching models that knew which alloy they were dealing with.

For a metal that must survive inside arteries, bearings, and aerospace mechanisms without the benefit of lubrication, these findings provide something genuinely new: accurate, transparent, and physically consistent models that connect processing, properties, and performance. A designer can now estimate how a given heat treatment, hardness, and duty cycle will translate into wear and friction, before a single prototype is machined. More broadly, the work is a template for how triboinformatics should be done, with strict grouped validation, interpretable explanations, and laws recovered from data that honor the physics of contact rather than discarding it. As Nitinol finds its way into ever more demanding applications, the machines that predict its wear are, fittingly, learning from the metal that never forgets.

Beneath the headline results lies a dataset with an unusually clean provenance. The 336 measurements were not generated afresh for the modeling study but were compiled from two previously published, peer-reviewed experimental campaigns on superelastic TiNi and 60NiTi, with every table—loads, frequencies, durations, sliding distances, specimen masses before and after testing, cumulative weight loss, and steady-state friction coefficients—reproduced in the new paper’s appendix. That decision to expose the full experimental record alongside the models is itself a small contribution to a field where data scarcity and fragmentation remain the chief obstacles to progress.

The geometry underlying those tables also rewards a closer look. Each test used a 5.03 millimeter reciprocating stroke, so every cycle covered just over a centimeter of sliding, and the sliding distances reported for all four material conditions satisfy an exact arithmetic relation linking distance to frequency and test duration. All fifty-six wear-time series were strictly monotonic, with no missing entries, meaning the recurrent network tasked with reconstructing wear trajectories never had to impute gaps—an often unappreciated advantage when deep learning meets sparse laboratory data.

The strong correlation between the fitted wear coefficient and the elastic-modulus-to-hardness ratio also has a pedigree worth noting. Surface engineers have argued for decades that this ratio, rather than hardness alone, governs how well a material tolerates elastic contact and resists abrasion, particularly for coatings and for alloys whose elastic resilience absorbs deformation that would otherwise be permanent. The Nitinol results give that long-standing heuristic a quantitative, data-driven endorsement for shape-memory metals specifically.

The study situates itself in a broader movement. Recent systematic reviews of machine learning in tribology have catalogued a wave of applications, from aluminum-matrix composites to modified zinc alloys to diamond-like carbon coatings, where algorithms predict friction and wear from operating parameters. What distinguishes the present work within that wave is its insistence on withholding whole test series during validation and on recovering an interpretable wear law from the same data used to train opaque models. The author notes that the code underlying the analysis is available on reasonable request, and the supplementary data file, roughly 800 kilobytes in spreadsheet form, invites others to replicate or extend the models for their own shape-memory alloy systems.

Subject of Research: Machine learning prediction of wear and friction behavior in heat-treated Nitinol shape-memory alloys.

Article Title: Triboinformatic modeling of nitinol alloys under different heat-treatment regimes

Article References: Alfred, S. O. (2026). Triboinformatic modeling of nitinol alloys under different heat-treatment regimes. Journal of Materials Science. https://doi.org/10.1007/s10853-026-13713-9

Image Credits: AI Generated

DOI: 10.1007/s10853-026-13713-9

Keywords: Nitinol, tribology, machine learning, wear prediction, shape-memory alloys, gradient boosting, SHAP, Archard equation, deep learning, heat treatment, friction, triboinformatics