The study examined Ti-6Al-4V weldments produced under conventional direct-current, pulsed direct-current, ultrasonic vibration-assisted direct-current, and ultrasonic vibration-assisted pulsed direct-current conditions. Researchers focused on the fusion zone, where large and uneven grain morphologies can make quantitative metallography vulnerable to differences in how operators identify and measure grain boundaries.

For the automated analysis, the researchers converted fusion-zone micrographs to grayscale and applied filtering, boundary detection, segmentation and morphological operations. The workflow then identified individual grains and calibrated their dimensions spatially at approximately 3.03 micrometers per pixel. Together, these steps allowed grain size to be derived consistently from the images rather than relying entirely on manual boundary selection and measurement.

The OpenCV results were compared with three methods described in ASTM E112: the Jeffries planimetric method, the Heyn linear-intercept method and the Abrams three-circle method. Relative to these reference approaches, the OpenCV workflow had mean biases of −20.18 micrometers against the planimetric method, −65.13 micrometers against the linear-intercept method and −128.12 micrometers against the three-circle method.

Agreement also varied substantially among the comparators. The mean absolute percentage error was 6.47% for the comparison with the Jeffries planimetric method, 17.04% for the Heyn linear-intercept method and 29.60% for the Abrams three-circle method. These values show that the closest numerical agreement was with planimetric analysis, while the three-circle method produced the largest divergence from the automated measurements.

Across the four welding conditions, the automated measurements were associated with progressively smaller mean linear intercepts from conventional direct current to pulsed direct current and then to the two ultrasonic vibration-assisted conditions. The ultrasonic vibration-assisted pulsed condition yielded the smallest reported mean value, 243.9 micrometers, compared with 421.0 micrometers for conventional direct current. Within this experimental set, the measurements therefore showed grain refinement with pulsing and ultrasonic vibration, although the study does not establish that either treatment alone caused the effect.

The findings suggest that computer vision can provide a repeatable route to grain-size characterization in fusion-zone micrographs, particularly where irregular grain shapes complicate manual metallography. The approach also quantifies an important limitation: automated segmentation did not reproduce every ASTM E112 result closely. Its strongest agreement was with the Jeffries method, whereas substantially larger differences appeared in comparisons with the Heyn and Abrams approaches.

Researchers cautioned that the OpenCV workflow should not be treated as an exact replacement for ASTM E112 analysis. The reported evidence supports its use as a consistent supplementary measurement approach for the studied Ti-6Al-4V gas tungsten arc weldments, not as a universally equivalent standard. The comparison was limited to four welding conditions and the fusion-zone images processed under the developed calibration workflow, so the results do not show that the same agreement would apply to every alloy, welding process, imaging system or grain morphology.

Subject of Research: Automated grain-size characterization of Ti-6Al-4V gas tungsten arc weldments using OpenCV and ASTM E112 comparison methods

Article Title: Automated grain-size characterization of GTAW Ti-6Al-4 V weldments using OpenCV and comparison with ASTM-based intercept methods

Article References: Gudisi, M. R., Talari, M. K., Gopal, A. V., & Nagallapati, V. (2026). Automated grain-size characterization of GTAW Ti-6Al-4 V weldments using OpenCV and comparison with ASTM-based intercept methods. Scientific Reports. https://doi.org/10.1038/s41598-026-76591-w

Image Credits: AI Generated

DOI: 10.1038/s41598-026-76591-w

Keywords: OpenCV, grain size, Ti-6Al-4V, GTAW, computer vision, metallography, ultrasonic vibration, pulsed welding, ASTM E112, fusion zone, Automated, grain-size

Samples were collected during field campaigns from 2010 to 2012. In coastal water less than 5 metres deep, investigators used boat-deployed grab samplers or manually operated steel scoops. Grab samples provided at least 1 kilogram of wet sediment, from which the upper 0–5-centimetre layer was taken for analysis. At sites deeper than 5 metres, research vessels used box cores to collect sediment extending to 50 centimetres; the uppermost 5-centimetre mixed layer was subsampled as the surface fraction. Along the rivers, investigators collected 5-centimetre-thick floodplain and point-bar sediments with pre-cleaned stainless-steel scoops at intervals of 3–5 kilometres. The 104 fluvial samples came from the lower reaches and estuarine zones of the Yellow River, Liaohe, Haihe, Luanhe, Daliaohe, Dalinghe, Xiaolinghe, Daqinghe, Yongdingxinhe, Nanpaishuihe, Xiaoqinghe, Mihe, Bailanghe, Yuhe, Weihe and Jiaolaihe rivers. Differential GPS recorded the locations. Although the sediments represent modern depositional conditions, they are not a snapshot of a single moment: the upper marine layer may integrate years to decades depending on sedimentation, sediment mixing and bioturbation.

Each sediment sample was thoroughly homogenized before representative aliquots were taken for pollen and grain-size measurements, ensuring that both records came from the same material. Laboratory preparation removed carbonates with 10% HCl, dissolved silicates with 40% HF and removed humic material with 10% KOH in a heated water bath. Heavy-liquid flotation in zinc bromide, with a density of approximately 2.0 grams per millilitre, concentrated the pollen and spores. One tablet containing 27,637 ± 563 spores of exotic Lycopodium was added before chemical treatment so pollen concentrations could be calculated in grains per gram of dry sediment. Microscopists identified grains at 400× magnification, checking difficult specimens at 600× or under oil immersion at 1,000×. The target was at least 200 terrestrial pollen and spore grains per sample, although some samples fell below that threshold. Taxonomic identifications used the reference collection at the Qingdao Institute of Marine Geology and published pollen atlases. Pollen percentages were calculated from the terrestrial pollen sum, while fern-spore percentages used the total palynomorph sum; the Chenopodiaceae/Amaranthaceae-type designation was retained to match the archived count table and common Quaternary palynological usage.

Count quality varied across the 534-sample archive. Identified pollen grains per sample range from 59 to 1,530, with a mean of 397; the corresponding means are 406 grains in riverine samples and 395 in marine samples. Four riverine and six marine samples contain fewer than 100 identified grains, while 78% of all samples contain 300–600. Counts in that range provide comparatively stable percentages for dominant taxa, but users examining rare pollen must account for count-size effects. Low counts are concentrated mainly in Bohai Bay and at the Luanhe River estuary, where high sedimentation, coarse grain size or oxidation may dilute or degrade palynomorphs. Mean pollen concentration is higher in riverine samples, at 3,531 grains per gram, than in marine samples, at 2,419 grains per gram, a pattern consistent with proximity to terrestrial sources and dilution during marine sediment accumulation. Taxonomic richness ranges from 87 morphotypes in the riverine subset to 95 in the marine subset. The Daqinghe has the highest recorded riverine richness, with 52 morphotypes, and the Weihe the lowest, with 23, but these values are descriptive because they were not standardized by sample number or count size.

Marine assemblages are dominated by arboreal pollen, especially Pinus, Quercus and Betulaceae. Total arboreal pollen ranges from 12% to 98% and generally exceeds 50% in offshore samples. Pinus closely follows that overall pattern, reaching its highest values in the deeper Central Basin and Bohai Strait. Quercus averages 1.7% and reaches 10.6%, with higher values around the Laotieshan Channel and near-shore parts of northern Liaodong Bay and Laizhou Bay. Betulaceae averages 3.3% and reaches about 10.6%, showing a broadly similar distribution. Non-arboreal pollen ranges from 1.5% to 88% and is enriched in northern Liaodong Bay, Bohai Bay, Laizhou Bay and the northern Bohai Strait. Chenopodiaceae/Amaranthaceae-type pollen, the most abundant herbaceous group, has a mean abundance of 14% and a maximum of 59%. Artemisia, Poaceae, Typha and Cyperaceae also have predominantly near-shore distributions. Fern spores, led by Selaginella, Osmunda, Monoletes and Triletes, are relatively abundant in fine-grained Central Basin, Bohai Bay and Liaodong Bay deposits and reach 84% of the total palynomorph sum.

These spatial patterns are consistent with multiple transport and deposition processes rather than a direct representation of vegetation beside each sampling station. Bisaccate Pinus pollen is buoyant, has high production and preservation potential, and can travel through both the atmosphere and marine currents; it is consequently overrepresented in more distal marine sediments. Herbaceous pollen from floodplains, grasslands, salt marshes, wetlands and agricultural coastal plains is more closely associated with river discharge and short-distance coastal transport. Fine-grained deposits in low-energy parts of the basin favor the accumulation of fern spores, whereas coarse deposits and energetic channels can contain more locally sourced pollen. The paired design makes it possible to compare river assemblages with marine assemblages in one harmonized framework. It does not track individual grains or directly demonstrate that a particular assemblage is transformed into another, and some marine surface sediment may also contain reworked or relict material.

Exploratory redundancy analysis found that the retained marine predictors explained 29.6% of the variance in pollen composition, with an adjusted R² of 0.286, pseudo-F of 30.167 and a permutation p-value of 0.005. Water depth was the strongest retained predictor, with an exploratory λ1/λ2 ratio of 0.303, followed by longitude at 0.214 and latitude at 0.099; clay and silt made smaller contributions. Grain-size variables were screened for multicollinearity, with sand and mean grain size removed before the final model. The first two constrained axes accounted for 16.15% and 9.90% of total inertia, respectively, or 88.08% of constrained inertia. The remaining variation indicates that unmeasured factors such as seasonal winds, episodic floods, resuspension and preservation can also influence marine assemblages. Because latitude and longitude are spatial surrogates for environmental and vegetation gradients, they should not be interpreted as direct causal mechanisms.

The pollen-depth relationship was evaluated mainly within the 0–35-metre interval, which captures the principal basin-wide near-shore-to-offshore gradient and excludes the deeper, hydrodynamically distinctive Bohai Strait channel. Within this interval, Pinus percentages and total arboreal pollen increase significantly with water depth. Quercus, total herbaceous pollen, Chenopodiaceae/Amaranthaceae-type pollen and Artemisia decrease with depth. The rise in the arboreal total is therefore driven almost entirely by Pinus rather than by parallel increases in every tree taxon. Shallow sites around 0–15 metres show greater relative representation of river, floodplain, salt-marsh, wetland and agricultural-coastal inputs. Deeper central-basin sites around 15–35 metres contain a larger proportional contribution of buoyant saccate pollen as the fluvial signal is diluted and offshore transport, sorting and sediment focusing become more influential. This relationship is an internally consistent modern analogue, not a direct calibration of absolute palaeo-water depth.

The riverine ordination was significant, with pseudo-F = 6.393 and permutation p = 0.001. Geographic position, water discharge and pollen concentration together explained 20.53% of compositional variance, equivalent to an adjusted 17.3%. The first two constrained axes accounted for 9.32% and 6.09% of total inertia, respectively, representing 75.1% of constrained inertia. Longitude and latitude each had a single-predictor λ1/λ2 ratio of 0.234, ahead of water discharge at 0.129 and pollen concentration at 0.119. Sediment load and watershed area explained variance when fitted individually but were removed from the multivariable model because they were strongly collinear with other regional variables. The ordination shows structured spatial and hydrological associations while leaving most variation unexplained, as expected for pollen delivered through a multi-source system affected by wind, floods, preservation, reworking and other unmeasured processes.

For palaeoenvironmental reuse, the modern gradient is most useful for identifying assemblage tendencies, not assigning an absolute water depth or sea level. Shallow, fluvial-influenced assemblages generally have lower Pinus percentages and lower ratios of arboreal to non-arboreal pollen, Pinus to Artemisia, and bladder-bearing pollen to non-arboreal pollen. They also contain more Artemisia, Chenopodiaceae/Amaranthaceae-type pollen, Poaceae, Cyperaceae, aquatic pollen and freshwater algae. More distal assemblages show the opposite tendencies and may accompany finer offshore muds and open-marine indicators. These signals should be used together rather than individually and cross-checked against pollen concentration, grain size, sedimentary facies, dinoflagellate cysts, foraminifera, geochemical proxies and independent chronological control. Samples with low pollen sums or strong evidence of reworking should be analyzed separately or excluded from transfer-function development. Important limitations include limited investigation of relict deposits, river sampling concentrated in lower reaches and estuaries rather than entire catchments, incomplete representation of ecological gradients by coordinates and hydrology, and the compositional nature of pollen percentages. The dataset’s principal value is its documented modern land-to-sea comparison and explicit uncertainty framework, not a single proxy equation for past environmental change.

Subject of Research: Modern marine and fluvial pollen assemblages and their environmental associations in the Bohai Sea region

Article Title: A paired marine-fluvial modern pollen dataset from the Bohai Sea and its inflowing rivers, China

Article References: Li, J., Yang, S., Ye, S., Meng, X., Chen, X., Mei, X., Gao, M., Hao, Q., Cao, X., Chen, B., Yi, S., & Zhao, G. (2026). A paired marine-fluvial modern pollen dataset from the Bohai Sea and its inflowing rivers, China. Earth System Science Data, 18(10), 7661-7677. https://doi.org/10.5194/essd-18-7661-2026

Image Credits: AI Generated

DOI: 10.5194/essd-18-7661-2026

Keywords: Bohai Sea, modern pollen, fluvial sediments, marine sediments, pollen transport, palaeoenvironment, source-to-sink systems, Pinus pollen, grain size, China, paired, marine-fluvial

The Qaidam Basin is a natural laboratory for studying aridity. Wedged between the Kunlun, Altyn Tagh, Qilian, and Ela mountains on the northeastern Tibetan Plateau, the basin is one of the most enclosed landscapes in Asia. Modern annual precipitation there ranges from a meager 50 to 200 millimeters, while evaporation soars to around 3,000 millimeters, producing a hyper-arid environment where salts such as halite, gypsum, and carbonates accumulate in thick layers. Because the basin sits far from major moisture sources and lies in the rain shadow of the Tibetan Plateau, any signal of increased rainfall is immediately meaningful, and any record of past wetting or drying speaks directly to the reach of the East Asian summer monsoon.

The new study, led by Zeng Luo of Hunan University of Arts and Science with colleagues from Lanzhou University, Hubei Normal University, Linyi University, and other institutions, focuses on the Gansen section, a sedimentary sequence exposed on the northwestern margin of the basin. Previous work had already established a paleomagnetic timeline for the section, anchoring 453 meters of strata to the geomagnetic polarity time scale with ten age control points spanning roughly 3.6 to 1.8 million years ago. Building on that framework, the team collected 149 samples and measured two independent climate-sensitive properties: environmental magnetic parameters and median grain size.

The magnetic approach hinges on a clever ratio. Frequency-dependent magnetic susceptibility, which responds to tiny pedogenic grains of magnetite formed during wet weathering, is divided by hard isothermal remanent magnetization, which reflects hematite produced under drier conditions. The resulting ratio, known as χfd/HIRM, tracks the balance between wet and dry weathering regimes and has proven sensitive to precipitation in arid and semiarid settings. Median grain size provides a complementary signal, because coarser particles in closed, shallow lake environments generally indicate stronger runoff and hydrodynamic energy, both of which scale with rainfall. Together, the two proxies form a paired record of hydroclimatic change.

When the team combined the new measurements with previously published data for the interval from 3.25 to 2.95 million years ago, they obtained an integrated precipitation record covering nearly the entire Late Pliocene. The record divides naturally into three phases. From about 3.6 to 3.25 million years ago, χfd/HIRM values fluctuated widely between 9.2 and 105 times ten to the minus six amperes per meter, averaging 38.3, while median grain size averaged 10.48 micrometers. Three thick halite layers in the stratigraphy corresponded to low points in both proxies, marking pulses of intensified aridity. From 3.25 to 2.95 million years ago, the warm middle Piacenzian interval, both proxies rose and the halite layers vanished, signaling a wetter climate. After 2.95 million years ago, values declined, grain size coarsened and then dropped, and halite layers became more frequent, pointing to renewed drying.

The most striking result emerged from spectral analysis of the densely sampled interval between about 3.6 and 3.3 million years ago. Using the REDFIT procedure, which tests spectral peaks against a red-noise background, the researchers found that the strongest periodicity in the χfd/HIRM record sat near 100,000 years, while the grain-size record peaked closer to 125,000 years. Both signals fall squarely in the eccentricity band, the slow modulation of Earth’s orbital shape that has a far weaker direct effect on insolation than precession or obliquity. A sensitivity test with a coarser interpolation step confirmed that the eccentricity signal was not an artifact of data processing. This pattern contrasts sharply with the 20,000-year precession-dominated cycles that the same team had previously documented for the middle Piacenzian warm period.

Why would the same basin respond to different orbital rhythms at different times? The authors argue that the answer lies in the background climate state. During the wetter middle Piacenzian, when the East Asian summer monsoon was strong, summer moisture could penetrate deep into the Asian interior, and rainfall responded directly to the precessional forcing of summer insolation. During the earlier, drier interval around 3.6 to 3.3 million years ago, the monsoon’s reach was more limited, and precipitation appears to have been governed indirectly by large-scale boundary conditions. One plausible mechanism is that eccentricity-paced changes in high-latitude ice volume, potentially including fluctuations of the Antarctic ice sheet, modulated the thermal contrast between Eurasia and the surrounding oceans and altered ocean circulation patterns, which in turn influenced monsoon intensity. Supporting this idea, five high-value intervals in the precipitation proxies broadly match peaks in the benthic oxygen isotope stack, a global record of ice volume, and the two records show similar filtered variations to the isotope stack in the 40,000-year obliquity band as well.

The broader context strengthens the case. Because large Arctic ice sheets did not develop until roughly 2.7 million years ago, Antarctic variability was likely the dominant high-latitude ice signal during the Late Pliocene, making it a plausible mediator of the eccentricity-scale precipitation signal. Similar nonlinear responses to insolation forcing have been documented elsewhere in North China, including 100,000-year eccentricity cycles in magnetic susceptibility records from the Xinding Basin and in rubidium-strontium ratios from the Sanmenxia Basin. Pollen records from the SG-1b core in the western Qaidam Basin likewise revealed a strong link between 100,000-year precipitation cycles and eccentricity forcing during the interval from 3.5 to 3.0 million years ago. The new Gansen record thus fits into a growing picture in which the monsoon’s orbital fingerprint depends on the prevailing climate regime.

The implications reach beyond the Pliocene. The middle Piacenzian warm period, with atmospheric carbon dioxide concentrations of 400 to 450 parts per million, elevated global temperatures, higher sea levels, and small Arctic ice sheets, is widely regarded as one of the best geological analogs for future anthropogenic warming. Climate model simulations suggest that enhanced land-sea thermal contrast during that interval intensified and westward-extended the western Pacific subtropical high, strengthening the East Asian summer monsoon and pushing its precipitation boundary northwestward. If the same mechanism operates under continued modern warming, the Qaidam Basin and other parts of the arid Asian interior could receive increased summer moisture in the decades ahead, a possibility with significant consequences for water resources in a region where every millimeter of rain matters.

At the same time, the study carries a caution. After about 2.95 million years ago, the record shows clear signs of intensifying aridification, with more frequent salt layers and declining proxy values, consistent with other evidence that the western Qaidam Basin became drier no later than around 3 million years ago. The precession-dominated wet pattern of the middle Piacenzian was therefore not a permanent feature of the Late Pliocene but a window tied to specific background conditions. By showing that the dominant periodicity of precipitation can shift from eccentricity to precession scales as climate states change, the research underscores a fundamental point for paleoclimate science and for projections of future hydrology alike: the response of regional rainfall to orbital forcing is not fixed, and understanding when and why it changes may be as important as knowing the forcing itself.

Subject of Research: Orbital-scale precipitation variability in the western Qaidam Basin during the Late Pliocene

Article Title: Orbital-scale precipitation variations in the western Qaidam Basin during the Late Pliocene: evidence from magnetic parameters and median grain-size

Article References: Luo, Z., Wang, H., Yu, X., Zhang, Z., Song, Z., Yang, J., Chen, W., Su, Q., Lu, Y., & Hua, S. (2026). Orbital-scale precipitation variations in the western Qaidam Basin during the Late Pliocene: evidence from magnetic parameters and median grain-size. Climate of the Past, 22(10), 1913-1925. https://doi.org/10.5194/cp-22-1913-2026

Image Credits: AI Generated

DOI: 10.5194/cp-22-1913-2026

Keywords: Qaidam Basin, Late Pliocene, East Asian summer monsoon, orbital forcing, eccentricity, precession, paleoclimate, magnetic susceptibility, grain size, Tibetan Plateau, aridification, mid-Piacenzian warm period

The team approached the problem with discrete element method simulations, a computational technique that models a material as thousands of individual particles interacting through contact forces. Their virtual laboratory consisted of quartz-rich gouge, a close analogue of the crushed quartz that fills many natural faults. Crucially, the simulated grains were angular rather than rounded, and they were allowed to fragment dynamically as stresses built up during shearing. This combination let the researchers watch, grain by grain, how a granular material evolves from an initial assembly of sharp fragments into a mature, localized slip zone.

One of the central results is a robust strain–damage law: damage in the gouge accumulates in a predictable way as grains are progressively consumed by fragmentation. In other words, the mechanical state of the fault material can be tracked by how much of its original grain population has already broken apart. This finding matters because it offers a local fragmentation state variable, a quantity that constitutive laws, the mathematical descriptions engineers and geophysicists use to predict fault friction, can incorporate directly. Instead of treating grain-size evolution as a secondary detail, the new framework makes it a first-order ingredient in models of fault strength.

The simulations revealed that grain size plays a decisive role in shaping where and when deformation localizes. As shearing proceeds, the initially distributed damage begins to concentrate, and the geometry and timing of this localization depend on the size distribution of the grains. Fragmentation also enhances compaction, squeezing the gouge into a denser, more tightly packed fabric. Together, these processes promote the emergence of a dominant Principal Slip Zone, a narrow band within the fault core where most of the relative motion between the fault walls ultimately concentrates. Identifying how such a slip zone self-organizes from grain-scale crushing provides a mechanical bridge between microphysics and the macroscopic behavior of faults.

Perhaps the most striking insight concerns what happens as comminution approaches a saturation state, the point at which grains have been reduced close to a minimum achievable size. Once the gouge can no longer fragment much further, the simulations show that localization can migrate away from the established slip zone into competing bands. This migration offers a mechanical explanation for a puzzle that field geologists and experimentalists have long observed: overprinted shear fabrics, the superimposed layers of aligned, deformed material seen both in laboratory experiments and in rocks retrieved from natural faults. Rather than being oddities, these overprinted fabrics may be the natural record of a fault’s slip zone wandering as its grain population exhausted its capacity for further breakage.

The implications reach to the heart of earthquake physics. Fault weakening, the drop in frictional resistance that allows a fault to accelerate into an earthquake, is thought to depend on processes concentrated in the slip zone, including grain-size reduction, thermal pressurization of pore fluids, and the formation of slip-parallel fabrics. By linking fragmentation, porosity evolution and shear localization within a single framework, the study provides a structural underpinning for these weakening mechanisms. The porosity changes tracked in the simulations are especially relevant, since compaction of gouge controls how fluids pressure up during rapid slip, one of the leading candidates for dynamic fault weakening during large earthquakes.

The work also speaks to a long-standing question in fault zone architecture: why do natural fault cores so often display a hierarchy of structures, from broad damage zones down to ultracomminuted slip surfaces only millimeters thick? The simulation results suggest that this hierarchy is not imposed from outside but emerges spontaneously from the coupling between grain crushing and strain localization. Grain size dictates the geometry of localization, and localization in turn concentrates further crushing into narrow bands, a feedback loop that progressively refines the fault’s internal structure with accumulated slip.

Methodologically, the study demonstrates the power of dynamically fragmenting discrete element models for tectonics research. Earlier granular simulations often treated particles as indestructible, which limited their ability to capture the evolving grain-size distributions that characterize real gouge. By allowing angular grains to break according to the stresses they experience, the researchers could reproduce the full trajectory of gouge evolution, from initial fragmentation-dominated deformation to the saturation regime where localization migrates. The approach turns numerical experiments into a bridge between laboratory rock mechanics experiments, which operate on limited time and grain-size scales, and the vast spatial and temporal scales of natural faults.

For earthquake hazard research, the promise of a fragmentation state variable is considerable. Constitutive laws that couple grain-size evolution to fault strength could improve how scientists model the nucleation conditions for earthquakes, potentially sharpening estimates of when and where faults are primed to slip. The research was supported by the European Research Council through the HYQUAKE project, an effort aimed at unraveling the physics of earthquake nucleation, along with funding from the Italian PRIN Giovani PREVENT program and the French National Research Agency. As seismic networks and laboratory facilities generate ever richer observations of fault materials, frameworks like the one proposed by Casas and colleagues offer a way to translate grain-scale crushing into predictions about the largest earthquakes.

The study, published open access in Nature Communications, ultimately reframes fault gouge not as passive debris but as an active, evolving structure that records and enables the weakening of faults. Every shattered grain, every compacted pore and every migrating shear band contributes to the architecture that governs how faults store and release tectonic stress. In connecting the microscopic fate of individual quartz grains to the macroscopic conditions for earthquake nucleation, the research adds a structural dimension to the physics of fault weakening, one written in the broken fragments of the rocks themselves.

Subject of Research: Grain-scale fragmentation and its role in fault zone architecture and earthquake-related fault weakening

Article Title: From fragmentation to fault zone architecture as a structural framework for fault weakening

Article References: Casas, N., Mollon, G., & Scuderi, M. M. (2026). From fragmentation to fault zone architecture as a structural framework for fault weakening. Nature Communications. https://doi.org/10.1038/s41467-026-78254-w

Image Credits: AI Generated

DOI: 10.1038/s41467-026-78254-w

Keywords: fault gouge, fragmentation, comminution, earthquake nucleation, fault weakening, discrete element simulations, shear localization, Principal Slip Zone, grain size, porosity, fault zone architecture, quartz

Grain filling duration, or GFD, is the window between flowering and physiological maturity, the point at which a dark abscission layer called the black layer seals the grain and shuts down further growth. During this period the kernel accumulates sugars, starch and storage proteins, and the combined rate and duration of that accumulation set the final grain size. Because grain size and grain number together determine yield, GFD has long been considered a promising target for breeders. Studies spanning five decades of maize improvement show that much of the historical gain in kernel weight traces back to extended filling periods, and earlier work by the same Australian team suggested sorghum could benefit in the same way. What remained a mystery was whether the trait had enough natural genetic variation in sorghum to be exploited at all.

To find out, Daniel Otwani of the University of Queensland and colleagues turned to a sorghum diversity panel of 904 genotypes, resequenced with DArTseq technology and representing all the major racial groups described in classical sorghum taxonomy, including Kafir, Caudatum, Durra, Guinea and Bicolor types. The team grew the panel across three field experiments at two Queensland research facilities over two seasons, tagging individual plants at head emergence and recording flowering date, maturity date, plant height, grains per panicle and thousand kernel weight. Rather than measuring filling time in calendar days, the researchers converted it to thermal time, expressed in degree days above a base temperature of 5.7 degrees Celsius, which strips out much of the noise imposed by weather differences between sites.

The phenotypic data alone delivered encouraging news. GFD ranged from roughly 400 to 680 degree days across the panel, with site means between about 506 and 521 degree days, and showed moderate broad-sense heritability estimates of 41 to 61 percent, high enough to justify selection in a breeding program. Strikingly, the Guinea race stood out with consistently longer grain filling durations than every other racial group at every site. Correlation analysis added to the optimism: GFD was positively associated with thousand kernel weight and with maturity date, but showed no significant link to flowering time. That combination is rare and valuable, because it implies breeders could extend the filling window and fatten kernels without delaying flowering or dragging crops past the end of the growing season.

The genomic analysis then revealed just how deep the shared biology runs. Using the FarmCPU genome-wide association method on more than 726,000 single nucleotide polymorphisms, the team identified 117 significant and suggestive marker-trait associations, which collapsed into 86 unique quantitative trait loci distributed across all ten sorghum chromosomes. Two loci, QGFD1.6 on chromosome one and QGFD7.1 on chromosome seven, appeared in all three environments, and both had previously been flagged in independent studies as hotspots for thousand kernel weight, grain length, grain width, grain volume and grain thickness. When the researchers retested every GFD-associated marker against thousand kernel weight, essentially all of them came back significant for kernel weight too, and the SNP effects for the two traits tracked each other so closely that GFD marker effects explained up to 82 percent of the variation in the kernel weight effects. The largest single marker shifted GFD by 51 degree days, equivalent to about three calendar days at ambient temperature, and shifted kernel weight by roughly 10 grams per thousand seeds.

Statistical enrichment tests made the overlap impossible to dismiss as coincidence. Forty-six of the 86 GFD loci sat in linkage disequilibrium with 54 previously reported candidate genes for grain size, starch content or protein content, a highly significant enrichment. Many of these genes are known players in rice: orthologues involved in cell proliferation, cell elongation, and hormone signalling through gibberellin, brassinosteroid, ethylene and cytokinin pathways all clustered within the sorghum GFD regions. One validated sorghum gene, Sobic.001G341700, known as SbGS3, encodes a G-protein subunit that acts as a negative regulator of grain size, mirroring the famous GS3 gene of rice. The team’s haplotype analysis of SbGS3 resolved eight distinct haplotype groups with significantly different effects on grain filling duration, and two of those haplotypes, associated with longer filling, were found only in the Guinea and Asian durra racial groups, a pattern consistent with the large seeds those races are known for.

Perhaps the most conceptually surprising result came from matching the GFD loci against temporal gene expression data spanning pollination to grain maturity. Of the GFD loci that overlapped with genes expressed exclusively at specific developmental stages, 77 percent mapped to genes active in the very earliest phases, one to two days before pollination and the first two days after pollination. In other words, the machinery that determines how long a grain will eventually fill appears to be largely installed before and immediately after fertilisation, not switched on gradually during the filling period itself. This upends the textbook assumption that grain filling duration is purely a post-anthesis phenomenon and suggests that the trait shares developmental roots with grain number determination, since panicle and floral organ sizes are also set early in reproductive development.

For breeders, the practical implications are immediate. Commercial sorghum hybrid programs rely heavily on Kafir-Caudatum crosses constrained by the cytoplasmic male sterility systems used to produce hybrids, which has narrowed the genetic base of elite material. The new results point to the Guinea race, and to a lesser extent Asian durra germplasm, as reservoirs of favourable GFD haplotypes that could be tapped through wider crossing. Because GFD can apparently be extended without disturbing flowering time or maturity windows, and without penalty to kernel weight, introgressing longer-filling haplotypes into elite lines offers a route to heavier grains that sidesteps the classic trade-off between grain size and grain number. The candidate genes identified here could even serve as a molecular selection index alongside grain size and number markers, letting breeders screen seedlings for filling potential long before a panicle ever emerges.

Cautions remain. Kernel weight was recorded in only one environment, so the strength of the GFD-kernel weight link needs confirmation across more sites and populations, and it is not yet clear whether GFD is fundamentally part of grain size biology, acting through pleiotropy, or an independent trait locked to grain size by tightly linked genes. Genetic correlations between sites, ranging from 0.45 to 0.86, hint that factors such as radiation and genotype-specific temperature requirements also modulate the trait. Still, as climate change squeezes yields in an era when cereal productivity must keep rising, the demonstration that a large, heritable and genetically mappable reservoir of grain filling variation sits waiting in sorghum’s own gene pool is a genuinely exciting piece of the global food security puzzle.

Subject of Research: Genetic control of grain filling duration and kernel weight in grain sorghum

Article Title: Common genetic control for grain filling duration and kernel weight in grain sorghum

Article References: Otwani, D., Hunt, C., Cruickshank, A., Tao, Y., Koltunow, A. M. G., Jordan, D., & Mace, E. (2026). Common genetic control for grain filling duration and kernel weight in grain sorghum. Theoretical and Applied Genetics, 139(11), Article 297. https://doi.org/10.1007/s00122-026-05409-0

Image Credits: AI Generated

DOI: 10.1007/s00122-026-05409-0

Keywords: sorghum, grain filling duration, kernel weight, GWAS, quantitative trait loci, plant breeding, genomic selection, grain size, haplotype analysis, crop yield, Guinea race, SbGS3

The firn layer is the transitional zone between fresh snowfall and solid glacial ice, a porous blanket that can extend tens of meters deep. Its pore space acts as a sponge: in healthy, air-rich firn, summer meltwater percolates downward and refreezes or is stored internally, sparing the ice shelf surface from ponding. But when sustained melt and compaction deplete that pore space, the firn becomes ice-saturated, infiltration is blocked, and meltwater pools on the surface. Those ponds deepen fractures in a process called hydrofracturing, which has triggered the catastrophic collapse of ice shelves such as Larsen A and Larsen B on the Antarctic Peninsula. Tracking how much air remains in the firn, and how the snow grains that control its permeability evolve over time, is therefore central to predicting which shelves are most at risk.

The problem has always been that grain size, one of the key microstructural variables, is poorly represented in firn densification models. The Utrecht Firn Densification Model, IMAU-FDM, simulates the vertical evolution of firn layers with high resolution, capturing density, temperature, and firn air content. But its grain-size evolution is parameterized with simplified assumptions that do not explicitly account for refreezing-driven grain growth, a serious limitation in regions where melt-refreeze cycles repeatedly restructure the snowpack. In-situ measurements are sparse and concentrated in dry, non-melt regions, leaving vast stretches of the continent effectively unvalidated. The Delft-led team set out to fill that gap using satellite radar, which penetrates several meters into the firn and responds directly to its microstructure.

ASCAT, flying aboard the EUMETSAT Metop satellites, transmits C-band microwave pulses at 5.255 GHz and measures the energy scattered back from the snowpack. At this wavelength, the radar return is strongly modulated by the size and arrangement of ice grains in the upper roughly twenty meters of firn. The researchers used vertically polarized backscatter data processed by Brigham Young University, normalized to a forty-degree incidence angle, and focused on winter means from June through August for each year between 2007 and 2021. Restricting the analysis to winter minimizes the confounding influence of transient liquid water and short-lived seasonal effects, isolating a more stable signal tied to firn microstructure. Grid cells affected by major calving events were excluded to keep the record consistent.

The heart of the method is a coupling between two models. IMAU-FDM, forced by three-hourly output from the RACMO2.3p2 regional climate model, prescribes the physical state of the firn: layer thickness, density, temperature, and liquid water content. That stratigraphy is then fed into the Snow Microwave Radiative Transfer model, SMRT, which simulates how the C-band signal scatters through the layered snowpack. Within this framework, grain size is treated as the single unknown. The team optimized one annual, column-wide correlation length for each twenty-seven-kilometer grid cell, minimizing the mismatch between simulated and observed backscatter using a bounded L-BFGS-B algorithm, and then converted the result into an effective grain-size parameter. The computation, run across roughly two thousand grid cells on the DelftBlue supercomputer, yields a fifteen-year record of ASCAT-conditioned firn microstructure spanning the continent’s ice shelves.

The retrieved parameter is deliberately described as effective rather than absolute. Because a single annual scalar is fitted to a single annual observation, the inversion cannot resolve vertical or sub-seasonal variations in microstructure, and the result should be read as an observationally constrained measure within the adopted modeling framework rather than a direct measurement of individual grains. Even so, the record reveals striking spatial patterns. In dry, high-firn-air-content regions such as the Ross and Filchner-Ronne ice shelves, the optimized values closely match the IMAU-FDM estimates, both indicating small grains below roughly half a millimeter. In melt-affected East Antarctica and the Antarctic Peninsula, the satellite-constrained values are substantially larger than the model predicts, pointing to coarsening that the model’s refreezing scheme, which caps refreeze-driven growth at 0.25 millimeters, fails to capture.

The most dramatic discrepancies appear where firn is most depleted. Near the grounding line of the Amery Ice Shelf, where the firn column becomes ice-saturated within about two meters of the surface, the model’s grain-size estimates exceed the satellite-conditioned values by up to eight millimeters, likely because the model lets grains grow without an upper bound across the shelf’s very old firn-ice transition age of one to 1.5 thousand years. On Larsen-C, where the transition is far younger, the pattern reverses and the model underestimates grain size. A limited plausibility check against in-situ grain-size measurements near Dumont d’Urville in Adélie Land found that the retrieved value of 0.56 millimeters sat closer to the field-observed means of 0.55 and 0.43 millimeters than the unadjusted model estimate of one millimeter, though the authors caution that differences in season and scale make this a consistency check rather than a strict validation.

Sensitivity experiments illuminated where the radar signal is most informative. Simulated backscatter rises steeply as grain size grows from near zero to about one millimeter, then plateaus, and the observed ASCAT values at most sites fall on that steep limb, where even small errors in modeled grain radius translate into multi-decibel biases. A two-dimensional sensitivity analysis showed that at intermediate densities, roughly 200 to 700 kilograms per cubic meter, backscatter is dominated by grain size, while at the extremes of very high or very low firn air content the signal is governed mainly by density and saturation state. A variance-partitioning analysis confirmed the regime dependence: on dry shelves like Ross and Filchner-Ronne, interannual backscatter variability projects primarily onto grain size, whereas in depleted regions such as Amery, George-VI, and Larsen-C, it is firn air content that dominates the signal.

The team also tested the robustness of the retrieval on a high-firn-air-content benchmark on the Ross Ice Shelf, perturbing the prescribed density profile, vertical layer aggregation, idealized ice lenses, and the backscatter target itself. The retrieval proved comparatively insensitive to moderate porosity changes and vertical discretization, shifting by less than one percent, while idealized dense layers and interannual profile variability produced changes of a few percent. The largest response came from surface-related backscatter offsets of half a decibel, which moved the retrieved parameter by roughly five percent, a reminder that unmodeled near-surface processes such as wind-formed sastrugi, roughness, and snowdrift remain an important source of uncertainty even in the most favorable firn regimes.

Perhaps the most forward-looking result is a proof of concept for reading firn air content directly from backscatter. Because grain-size variability scatters the relationship between radar return and firn air content into a broad band, the team statistically standardized grain size using a fitted power-law relationship, collapsing the data onto a cleaner inverted U-shaped curve: backscatter is low in deeply porous firn, peaks at intermediate air content, and drops sharply as firn becomes ice-saturated. Converting that curve into an operational retrieval will require calibration, independent priors to resolve the ambiguity between dry and depleted low-backscatter states, and better treatment of surface scattering. But as warming intensifies surface melt across Antarctica, the framework gives glaciologists something they have lacked: a scalable, physically grounded way to monitor the hidden grain structure of the firn, and with it, an early-warning signal for the shelves that buffer the ice sheet from the ocean.

Subject of Research: Satellite radar inversion of effective firn grain size and firn air content across Antarctic ice shelves

Article Title: Inferring effective firn grain size across Antarctic ice shelves from ASCAT observations

Article References: Shukla, S., Wouters, B., Veldhuijsen, S., de Roda Husman, S., Li, W., & Lhermitte, S. (2026). Inferring effective firn grain size across Antarctic ice shelves from ASCAT observations. Earth Observation, 1(1), 15-41. https://doi.org/10.5194/eo-1-15-2026

Image Credits: AI Generated

DOI: 10.5194/eo-1-15-2026

Keywords: Antarctic ice shelves, firn, grain size, firn air content, ASCAT, radar backscatter, remote sensing, firn densification model, radiative transfer, hydrofracturing, meltwater retention, ice shelf stability

The study, published in the Journal of Materials Science by Mahdieh Sabbaghian and Mohammad Zaman Kabir, focuses on a copper-aluminum-manganese alloy with a composition of 15.8 percent aluminum and 11 percent manganese by atomic fraction. Copper-based shape memory alloys have attracted attention for decades precisely because their raw materials are cheap and abundant compared with nickel and titanium. Yet they have historically suffered from a critical weakness: in polycrystalline form, meaning materials made of many small crystals rather than a single crystal, they tend to crack along grain boundaries during repeated elastic cycling. The intergranular brittleness has been the Achilles heel that kept them confined to laboratory curiosities and niche applications.

The Iranian team approached this challenge through the lens of thermomechanical processing, the art and science of combining heat treatment with mechanical deformation to sculpt a material’s internal structure. They compared several processing routes applied to the same base alloy: hot rolling followed by quenching, conventional annealing, and a more exotic procedure called cyclic heat treatment, in which the material is repeatedly heated and cooled through a critical temperature range. Each route leaves a distinct fingerprint on the microstructure, and the researchers set out to map exactly how those fingerprints translate into functional performance.

Characterization was carried out with an impressive battery of techniques. Differential scanning calorimetry measured the temperatures at which the alloy transforms between its high-temperature austenite phase and its low-temperature martensite phase, the reversible crystallographic shift that underpins all shape memory behavior. X-ray diffraction identified the phases present, while optical and scanning electron microscopy revealed the grain structures, and microhardness testing probed local mechanical resistance. Superelasticity itself was evaluated through cyclic tensile testing, in which specimens are stretched and unloaded repeatedly while stress-strain curves are recorded, and the shape memory effect was assessed through bending recovery experiments in which pre-strained samples are heated and their recovery angle measured.

The physics here deserves a moment of explanation. Superelasticity is not ordinary elasticity. In a superelastic alloy, applying stress at a temperature above the austenite finish temperature triggers a stress-induced transformation from austenite to martensite. The material yields at a nearly constant stress plateau as the transformation proceeds, absorbing enormous strain, sometimes several percent, which would permanently deform an ordinary metal. When the load is removed, the martensite becomes unstable and reverts to austenite, and the material snaps back almost completely. The catch is that this reversible transformation must sweep through entire grains without generating damaging stress concentrations at grain boundaries. Large grains, and ideally single crystals, make this far easier, which is why grain size control is the central battleground for polycrystalline shape memory alloys.

This is where cyclic heat treatment enters the story, and it is the most striking part of the new work. The technique, pioneered by researchers including Takashi Omori and Ryosuke Kainuma at Tohoku University, exploits a phenomenon called abnormal grain growth. Under normal circumstances, heating a metal causes its grains to grow uniformly and modestly. But under specific cyclic conditions, a select few grains develop a growth advantage, consuming their neighbors until they dominate the entire microstructure. The result can be grains centimeters long, effectively transforming a polycrystal into an oligocrystal or even a single crystal without any of the expensive directional solidification or crystal-pulling methods traditionally required. The mechanism is thought to involve the repeated dissolution and reprecipitation of phases during each thermal cycle, which selectively favors grains with particular orientations or defect structures.

Applying cyclic heat treatment to their Cu-Al-Mn alloy, the researchers achieved dramatic results. Differential scanning calorimetry of the treated specimens revealed austenite finish temperatures below zero degrees Celsius, a crucial finding because it means the alloy is fully austenitic, and therefore superelastic, at room temperature. Cyclic tensile testing then showed that the cyclically heat treated material exhibited pronounced superelasticity, with a recoverable strain of 6.24 percent, more than five times that of the untreated control specimen. For a polycrystalline copper-based alloy, that is an extraordinary figure, and it demonstrates that the abnormal grain growth strategy transfers effectively from the iron-manganese-aluminum-nickel systems where it was first demonstrated to the copper-based family.

Hot rolling, meanwhile, delivered a different but complementary set of benefits. The rolled specimens showed ultimate tensile strength and cumulative energy density to fracture increased by approximately 74 percent and 39 percent respectively compared with the control. Rolling refines the microstructure, breaks up casting defects, and can introduce beneficial texture, aligning the crystallographic grains in ways that accommodate the transformation strain more gracefully. The most exciting result, however, came from combining the two processes. A route that paired hot rolling with cyclic heat treatment promoted pronounced abnormal grain growth while simultaneously enhancing strength, ductility, and superelasticity, yielding an ultimate tensile strength of up to 572 megapascals, a total strain at fracture of up to 15.4 percent, and a recoverable superelastic strain of up to 6.86 percent. Achieving all three properties together is rare; usually improving one comes at the expense of another.

The implications extend well beyond the laboratory. Shape memory alloys are increasingly specified in civil infrastructure, where their superelasticity allows them to absorb seismic energy and re-center structures after earthquakes, and where their shape memory effect lets them apply permanent prestressing forces to concrete elements as they are heated. Reviews of the field have highlighted applications ranging from bridge shear reinforcement to column retrofitting and vibration damping systems. In these large-scale structural uses, the cost of nickel-titanium is often prohibitive, since tonnage quantities would be required. A copper-based alternative with competitive functional properties, produced from inexpensive raw materials through conventional rolling and furnace treatments, could change the economics of resilient infrastructure design entirely.

There are, of course, questions that future work must address. Cyclic heat treatment schedules can be time-consuming, and scaling the abnormal grain growth process from laboratory coupons to commercial bar and plate stock will demand careful process control. Fatigue behavior over thousands of superelastic cycles, corrosion resistance in realistic environments, and compatibility with welding and joining methods all remain to be fully characterized for the processed alloy. Still, the demonstration that a simple combination of rolling and thermal cycling can coax a cheap copper alloy into delivering nearly seven percent recoverable strain alongside 572 megapascals of strength marks a genuine advance. It suggests that the era of superelastic metals may not belong exclusively to nickel and titanium after all, and that with clever processing, some of the oldest and most familiar metals in human history still have remarkable new tricks to teach us.

Subject of Research: Thermomechanical processing and cyclic heat treatment of Cu–Al–Mn shape memory alloys to enhance superelasticity

Article Title: Adapting superelasticity in Cu–Al–Mn shape memory alloys via thermomechanical processing and cyclic heat treatment

Article References: Sabbaghian, M., & Kabir, M. Z. (2026). Adapting superelasticity in Cu–Al–Mn shape memory alloys via thermomechanical processing and cyclic heat treatment. Journal of Materials Science, 61(43), 34049-34087. https://doi.org/10.1007/s10853-026-13738-0

Image Credits: AI Generated

DOI: 10.1007/s10853-026-13738-0

Keywords: shape memory alloys, superelasticity, Cu–Al–Mn, cyclic heat treatment, abnormal grain growth, thermomechanical processing, hot rolling, martensitic transformation, grain size, structural materials, seismic resilience, Journal of Materials Science

The team worked with two full-depth cores recovered from the accumulation zones of Tibetan glaciers. The first, 169.45 meters long, was drilled in November 2020 from the summit of the Weigeledangxiong Glacier on Mount Animaqing in the eastern Kunlun Mountains, at 5750 meters above sea level. The second, 77.6 meters long, came in November 2022 from the southern slope of Mount Bugyai Kangri in the eastern Tanggula Mountains, at 6180 meters. Both regions are dominated by the Asian monsoon and both glaciers have been retreating in recent decades, but they differ in a crucial way: the Animaqing site is warmer, with borehole temperatures ranging from about -0.22 to -6.84 degrees Celsius, while the Bugyai Kangri core is colder, spanning roughly -1.54 to -8.31 degrees Celsius. That temperature contrast turned out to be the key to interpreting everything the crystals had to say.

Measuring grain size continuously along an ice core is a formidable technical challenge, and the authors solved it with a Microstructure Mapping system adapted from protocols developed for polar cores. Vertical ice slices, cut with a band saw and shaved flat with a Leica microtome, were sublimated at -20 degrees Celsius for about fifteen hours until grain boundaries stood out in sharp relief. Each slice was then photographed under vertical coaxial illumination, in which air bubbles and grain boundaries appear dark against the translucent ice, producing panoramic images at a resolution of roughly eight micrometers per pixel. A U-Net neural network performed the binary segmentation of grain boundaries, and the ImageJ software package with its MorphoLibJ plugin extracted the area, position and shape of every grain. In total the researchers analyzed 1890 slices from Animaqing, covering 91 percent of the core, and 560 slices from Bugyai Kangri, covering 71 percent, a sampling density that captures centimeter-scale variations invisible to traditional discrete sampling.

The resulting profiles divide naturally into four depth zones that mirror, in modified form, the classic three-stage model established for polar ice cores. In the firn layer, extending to about 35 meters in both cores, snowflakes of roughly one square millimeter are slowly compressed as irregular pores close off; average grain areas remain small, below about two square millimeters at the surface, and grow gently with depth. Both cores reached the critical density of 0.830 grams per cubic centimeter for pore closure at approximately 35 meters, sealing the atmospheric archive in bubbles. Below that lies a ten-meter coarsening zone in which the firn becomes true ice and average grain areas rise rapidly, reaching 18.00 square millimeters in the Animaqing core and 8.06 square millimeters at Bugyai Kangri, as larger grains grow by consuming their smaller neighbors.

This early growth is governed by Normal Grain Growth, a textbook process in which grain boundaries migrate to reduce the total surface energy of the ice, and it is strongly temperature-dependent: warmer ice allows boundaries to migrate faster, so crystals grow larger. The data show this vividly. The Animaqing core, several degrees warmer than Bugyai Kangri, maintains substantially larger grains at every comparable depth even where it bears greater overburden stress. In the recrystallization zone that follows, the trend reverses. From about 45 meters down to 140 meters at Animaqing and 73 meters at Bugyai Kangri, average grain area stabilizes and declines below five square millimeters, small grains become more abundant, and micrographs fill with subgrain boundaries, the telltale signature of Rotation Recrystallization. Under accumulating stress, subgrain boundaries rotate and gradually mature into true grain boundaries, splitting large crystals into smaller ones and setting up a dynamic balance between stress-driven refinement and temperature-driven growth.

The deepest zone belongs to Strain-Induced Boundary Migration, a rapid recrystallization mechanism that flourishes when ice is warmer than about -10 degrees Celsius, a condition satisfied at the base of both cores. Here average grain areas surge, the population of large crystals grows roughly ten times faster than in the coarsening layer, and micrographs show interlocked grains with highly curved boundaries. At Animaqing the effect is spectacular: below 150 meters, where temperatures climb above -6.27 degrees Celsius, crystals balloon to more than 400 square millimeters, and near the very base they exceed 3000 square millimeters, their boundaries spilling beyond the edges of the prepared slices. There, the researchers infer, repeated freeze-thaw cycles in excessively warm ice generated a five-meter-thick bubble-free layer of refrozen ice with gigantic, nearly transparent crystals.

Between these hundred-meter trends lie two centimeter-scale troublemakers, and both are ultimately climatic in origin. The first is melt-refreezing. Because surface temperatures at Animaqing approach zero, summer meltwater percolates into the firn and refreezes, producing centimeter-scale ice layers in which liquid-mediated recrystallization drives crystal sizes upward in abrupt jumps. The colder Bugyai Kangri site rarely experiences this, following a classic cold-type ice formation without meltwater involvement. The second troublemaker is impurity. Both cores contain millimeter to centimeter bands rich in dust and soluble impurities, and within these bands crowds of small grains, typically under two square millimeters, pile into walls that drag average grain area sharply down. In the Animaqing core a dense zone of impurity layers between 120 and 140 meters contains up to 90 centimeters of impurity-bearing ice per meter of core, and impurity thickness correlates strongly and negatively with grain size, with a correlation coefficient of -0.715. The mechanism is thought to be pinning: impurity particles obstruct grain boundary migration, holding grains small in much the same way documented in polar cores from Greenland and Antarctica and in the Monte Perdido Glacier of the Pyrenees.

The decisive test of the study is whether grain size survives this barrage of confounding influences and still records temperature. To find out, the authors compared average grain area with the oxygen isotope ratio, expressed as δ18O, the workhorse paleoclimate proxy in Tibetan ice core research. In the interval between 45 and 51 meters, where vertical pressures are low enough that Rotation Recrystallization is negligible and Normal Grain Growth dominates, the Bugyai Kangri core shows a statistically significant correlation between grain area and δ18O, with r equal to -0.286, corresponding to ice dated between 1975 and 1985. The correlation at Animaqing is obscured by heavy refreezing, but in the dense impurity zone the Animaqing core instead delivers a significant positive correlation of 0.205, tying small crystals to cold, dusty periods on an inferred centennial scale. In both settings, the physical texture of the ice is keeping step with the chemical thermometer.

Comparisons with other mountain glaciers reinforce the temperature story. When the authors placed their cores alongside four others from the eastern Tibetan Plateau, the Tianshan Mountains and the European Alps, the ranking of grain sizes at 35 meters depth, where vertical pressures are broadly comparable, broadly tracked the ranking of ice temperatures. The Guliya core tops the grain size list and the Tsanfleuron core tops the temperature list, with Animaqing, Bugyai Kangri and the KCC core following in the expected order. The consistency, the authors argue, marks temperature as the dominant control on grain growth in mountain glaciers, outweighing even the greater stresses borne by deeper, warmer ice.

The implications reach well beyond crystallography. Ice cores from the polar sheets have long used grain size as a supplementary climate archive, with abrupt changes in crystal dimensions marking cold-warm transitions, but mountain glaciers were widely considered too warm, too fast-flowing and too contaminated to preserve such signals. This study demonstrates that a mountain glacier ice core can, in specific layers where stress-driven grain refinement is suppressed, hold onto temperature information despite melt layers, impurities and rapid flow. That matters for the Third Pole, where observational records are short and paleoclimate archives are scarce, and it suggests that physical properties of ice, from grain size to bubble number density, could eventually complement isotope chemistry in reconstructing how High Mountain Asia responded to past warming. As glaciers across the Tibetan Plateau retreat under modern climate change, their crystals may prove to be among the last witnesses of the climates that built them, recording in millimeter-scale geometry the temperatures of decades, centuries and perhaps millennia gone by.

Subject of Research: Temperature-dependent grain size variation and climate signal preservation in Tibetan Plateau ice cores

Article Title: Temperature dependence of grain size in Tibetan ice core

Article References: He, Z., & Xu, B. (2026). Temperature dependence of grain size in Tibetan ice core. The Cryosphere, 20(10), 5745-5759. https://doi.org/10.5194/tc-20-5745-2026

Image Credits: AI Generated

DOI: 10.5194/tc-20-5745-2026

Keywords: ice cores, Tibetan Plateau, grain size, recrystallization, paleoclimate, glaciology, oxygen isotopes, Third Pole, The Cryosphere, ice microstructure, mountain glaciers, climate change

The team’s strategy rested on a classic tool of quantitative genetics: the recombinant inbred line population. Starting from a single cross between Svevo, an elite Italian durum wheat cultivar prized for its quality, and Zavitan, a wild emmer wheat accession originally collected in Israel, the researchers generated 135 recombinant inbred lines, or RILs. Each of these lines carries a unique mosaic of chromosome segments inherited from the two parents, produced through repeated self-pollination over multiple generations until the lines became genetically fixed. This means that every line is a stable, reproducible combination of Svevo and Zavitan DNA, allowing researchers to ask, for any given trait, which parental chromosome segments are associated with better performance.

To capture the traits that matter most for yield, the team measured four grain characteristics across four different environments: grain length, grain width, the ratio of length to width, and thousand-grain weight, a standard agronomic measure of how heavy an average sample of one thousand grains is. Evaluating the population in multiple environments is critical because grain traits are notoriously sensitive to growing conditions, and a genetic effect that appears in one field may vanish in another. Only loci that show consistent effects across environments, so-called stable quantitative trait loci or QTL, are truly useful for breeding, since breeders need markers that will deliver results regardless of where or when a variety is grown.

Genotyping the 135 lines was accomplished with the wheat 90K single nucleotide polymorphism array, a platform that interrogates tens of thousands of genetic markers spread across the wheat genome. Wheat is a challenging genome to work with: durum wheat carries two subgenomes, designated A and B, each contributing seven chromosome pairs, and the genome is enormous by any standard. The SNP array allowed the researchers to construct a linkage map and then scan the genome systematically for regions where the inheritance pattern of DNA markers correlated with the measured grain traits. Statistical mapping, using established QTL detection methods with empirical significance thresholds, then pinpointed the chromosomal intervals most likely to harbor genes influencing each trait.

The scan yielded eleven stable QTL, each detected consistently across the four test environments. These intervals ranged in physical size from 0.7 to 15.6 megabases on the durum wheat reference genome, a span that in some cases is small enough to narrow down candidate genes with reasonable confidence. Three of the stable loci, named QGLsv.swust-5AL, QGLsv.swust-5BL, and QGLsv.swust-7AL, influenced grain length; two, QGWsv.swust-4AL and QGWsv.swust-5AL, influenced grain width; and two, QTGWsv.swust-2BL and QTGWsv.swust-5BL, influenced thousand-grain weight. For all seven of these loci, the favorable allele, meaning the version of the gene region associated with larger or heavier grains, came from the cultivated parent Svevo, reflecting the cumulative effect of decades of selection by durum breeders.

The most intriguing finding, however, concerned the remaining four loci. All four QTL for grain length-width ratio, designated QLWRzv.swust-2BS, QLWRzv.swust-4AL, QLWRzv.swust-4BL, and QLWRzv.swust-6AS, carried their positive alleles from Zavitan, the wild parent. Grain shape, as captured by the length-to-width ratio, matters for both milling performance and market quality in durum wheat, and the fact that the wild ancestor consistently contributed the favorable alleles at these loci demonstrates that domestication did not exhaust the wild gene pool’s value. Wild emmer wheat, which grows naturally in the Fertile Crescent and has survived thousands of years of environmental fluctuation without human intervention, retains genetic variation that modern cultivars have lost, and this study shows that some of that variation is directly relevant to yield-related traits.

Quantitatively, the eleven stable QTL each explained between 9.22 and 19.22 percent of the phenotypic variation in their respective traits. These are substantial effects for complex agronomic traits, which are typically controlled by many genes of small effect. The fact that individual loci account for nearly a fifth of the variation in a trait like grain weight suggests that marker-assisted selection targeting these regions could produce measurable improvements in a breeding program. Marker-assisted selection, in which breeders use DNA markers rather than visible traits to track the inheritance of favorable genes, is particularly powerful for traits like grain weight that are difficult or expensive to measure directly on large numbers of early-generation plants.

To move from statistical associations to biological understanding, the researchers projected their QTL intervals onto three available reference genomes: Chinese Spring, the standard reference for bread wheat, and the Svevo and Zavitan genome assemblies for durum wheat. This cross-genome comparison identified 67 high-confidence candidate genes within the QTL intervals on the Chinese Spring reference, 88 on the Svevo reference, and 70 on the Zavitan reference. Functional annotation of these candidate genes revealed that they are enriched for roles in processes that make intuitive sense for grain development: seed development itself, transcriptional regulation, hormone signaling pathways, ubiquitin-mediated protein degradation, and carbohydrate metabolism. Each of these processes has well-documented connections to how a developing grain accumulates starch and protein and how large it ultimately grows.

The candidate gene list connects to a growing body of wheat functional genomics. Recent studies have identified genes such as TaSWEET11 and TaSWEET13h, sucrose transporters that are critical for grain filling, transcription factors like TaDOF6 that regulate sugar and gibberellin transport into the endosperm, and cytochrome P450 genes of the CYP78A family that influence seed size through hormone-mediated pathways. Several of the candidate genes identified in the new study fall into these same functional categories, suggesting that the mapped QTL may harbor orthologs or paralogs of genes whose effects have already been validated in bread wheat and rice. This convergence strengthens confidence that the mapped loci are not statistical artifacts but genuine biological regulators of grain development.

The practical implications extend beyond basic science. The stable QTL and their linked SNP markers constitute a direct resource for marker-assisted breeding in durum wheat, allowing breeders to introgress favorable alleles from wild emmer into elite backgrounds while simultaneously tracking the cultivated alleles that boost grain size and weight. Because the QTL were validated across four environments, breeders can have reasonable confidence that these markers will perform consistently across the diverse conditions in which durum wheat is grown, from the rain-fed fields of the Mediterranean to irrigated plains elsewhere. More broadly, the study reinforces a central lesson of modern crop improvement: the wild relatives of our staple crops are not relics of the past but living repositories of genetic variation, and interspecific crosses that tap into them can enrich the genetic basis of yield in ways that within-crop selection alone cannot achieve. As the global population grows and climate change pressures wheat production systems, tools like these that bridge the gap between wild genetic resources and elite cultivars will become increasingly indispensable.

Subject of Research: Mapping stable quantitative trait loci for grain yield-related traits in a durum wheat Svevo × Zavitan recombinant inbred line population

Article Title: Identification of stable QTL for yield-related traits in a durum wheat Svevo × Zavitan RIL population

Article References: Zhou, X., Zhou, B., Zhang, G., Zou, G., Yang, X., Xia, C., Li, X., Zheng, S., & Ren, Y. (2026). Identification of stable QTL for yield-related traits in a durum wheat Svevo × Zavitan RIL population. Theoretical and Applied Genetics, 139(10), Article 275. https://doi.org/10.1007/s00122-026-05389-1

Image Credits: AI Generated

DOI: 10.1007/s00122-026-05389-1

Keywords: durum wheat, wild emmer, QTL mapping, grain weight, grain size, recombinant inbred lines, marker-assisted breeding, SNP array, candidate genes, crop yield, genetic improvement, Triticum turgidum

At the heart of the work is a fundamental challenge in additive manufacturing: the properties of a 3D-printed metal part depend intimately on its grain structure, the spatial arrangement and orientation of crystalline regions separated by grain boundaries. Fine grains typically enhance strength and toughness through grain-boundary strengthening, while larger or anisotropic grains can improve ductility and creep resistance. Controlling these attributes during printing requires understanding how processing conditions such as laser scanning velocity, melt pool geometry, and heat-affected zone dimensions translate into grain evolution, a relationship complicated by vast multidimensional parameter spaces and non-linear thermal dynamics.

The research team, led by Dipayan Sanpui and Subramanian K.R.S. Sankaranarayanan, built their framework on a repository of 1,524 successfully retrieved three-dimensional polycrystalline microstructures generated with the open-source SPPARKS kinetic Monte Carlo code. These simulations modeled hundreds of laser passes over a substrate using a modified Potts Monte Carlo approach, capturing curvature-driven grain growth, melting, remelting, and nucleation in the heat-affected zone. Rather than relying on abstract statistical descriptors such as two-point statistics or chord length distributions used in earlier studies, the team extracted physically interpretable features directly from two-dimensional slices of the simulated structures.

Four descriptors formed the quantitative vocabulary of the study: average grain size, surface-to-volume ratio, sphericity, and roundness. Grain size serves as a first-order measure linked directly to mechanical performance through the Hall-Petch relationship, while surface-to-volume ratio distinguishes microstructures with similar grain dimensions but different boundary complexity. Sphericity captures overall grain compactness and elongation, whereas roundness reflects the sharpness of grain edges and corners, quantities that differ when two grains share similar elongation but diverge in boundary smoothness due to solidification dynamics. The team computed these measures directly from grain identifiers in the SPPARKS output files, streamlining integration into automated materials design pipelines.

With the features extracted, the researchers framed the problem as supervised regression, using seven processing conditions, including scanning velocity, scanning pattern, molten zone width, depth, and tail length, and heat-affected zone width and tail length, as inputs. They trained and compared multiple algorithms: Random Forest, Gradient Boosting Machine, Extreme Gradient Boosting (XGBoost), a feed-forward neural network, and a one-dimensional convolutional neural network, with hyperparameters optimized through six-fold grid search cross-validation and Keras-Tuner’s Hyperband search for the neural architectures.

Tree-based ensemble methods consistently outperformed the neural networks. XGBoost emerged as the best overall performer, achieving a coefficient of determination of 0.977 for grain size prediction, 0.946 for surface-to-volume ratio, 0.954 for sphericity, and 0.957 for roundness on test data, with a mean absolute error of just 6.62 pixels for grain size. The authors attribute grain size’s high predictability to its status as a first-order reflection of the temperature distribution across the simulation domain, while shape descriptors such as sphericity and roundness depend on secondary morphological effects, including grain boundary curvature and irregular grain interactions, that are harder to infer from processing conditions alone.

A particularly striking finding emerged from the parity plots: grain size predictions split into two distinct clusters corresponding to different microstructural regimes. The first cluster, with average grain sizes between 0 and 450 pixels, represented anisotropic structures of alternating equiaxed and elongated grains formed at higher scanning velocities and broader heat-affected zones. The second cluster, spanning 600 to 1200 pixels, arose from low-velocity, narrow-heat-affected-zone conditions that promote localized remelting and coarser, irregular grains. When trained and evaluated within the well-behaved first regime, the model reached an R² of 0.982 with an error of only 2.305 pixels; in the morphologically heterogeneous second regime, accuracy dropped to R² of 0.561, revealing where the surrogate’s reliability limits lie.

To open the black box, the team applied Shapley Additive exPlanations (SHAP), a game-theory-based framework that quantifies each input feature’s marginal contribution to predictions. The analysis showed that scanning velocity and heat-affected zone characteristics dominate grain size outcomes globally, consistent with established solidification theory in which thermal gradients, cooling rates, and grain-boundary mobility govern grain evolution. At high velocities, scanning speed and HAZ tail length controlled the outcome, with rapid cooling producing fine, anisotropic grains. At minimum velocity, scanning direction and HAZ tail length became the primary drivers: cross-hatch patterns induced multidirectional heat flux and rapid cooling that favored clusters of fine equiaxed grains, while unidirectional scanning promoted elongated, directionally aligned grains.

Beyond interpretation, the framework demonstrated practical utility for process optimization. By filtering candidate parameter combinations according to the relative error between predicted and desired grain sizes, the researchers identified viable alternative processing conditions under fixed maximum or minimum laser velocities. Under high velocity, neighboring points in parameter space with variations in melt-zone geometry yielded nearly identical grain structures, suggesting flexibility in achieving consistent microstructures. Under low velocity, HAZ tail length and scanning direction offered the most meaningful degrees of freedom. The computational payoff is substantial: simulating a single 100 × 100 × 100 site microstructure took roughly 25 minutes across four compute nodes, while the trained models deliver predictions in minutes once trained.

The authors envision their approach as a foundation for digital twins and closed-loop process control, in which sensor-derived process variables feed continuously into trained models for online quality monitoring and adaptive parameter optimization. They acknowledge limitations, notably that featurization currently operates on two-dimensional slices rather than full three-dimensional volumes, and they point toward extending the framework to experimental microscopy images, inverse design, and active-learning-based calibration as next steps. By coupling high-fidelity physics simulation with interpretable machine learning, the study illustrates how explainable AI can transform additive manufacturing from a process of empirical trial and error into a predictive, quantitatively grounded engineering discipline.

Subject of Research: Interpretable machine learning prediction of process–structure relationships in metal additive manufacturing using kinetic Monte Carlo microstructure simulations

Article Title: Data-driven discovery of process–structure relationships in additive manufacturing via featurization from kinetic Monte Carlo simulations and interpretable machine learning

Article References: Sanpui, D., Chan, H., Koneru, A., Banik, S., Manna, S., & Sankaranarayanan, S. K. (2026). Data-driven discovery of process–structure relationships in additive manufacturing via featurization from kinetic Monte Carlo simulations and interpretable machine learning. Machine Learning with Applications, 25, Article 100985. https://doi.org/10.1016/j.mlwa.2026.100985

Image Credits: AI Generated

DOI: 10.1016/j.mlwa.2026.100985

Keywords: additive manufacturing, kinetic Monte Carlo, machine learning, microstructure, XGBoost, SHAP, grain size, process-structure linkages, featurization, surrogate modeling, explainable AI, metal 3D printing