The researchers used repacked columns to represent the upper 30 centimeters of soil, preserving material from the original 0–10, 10–20 and 20–30-centimeter depth intervals rather than mixing them together. Each column was compacted to a dry bulk density of 1.28 grams per cubic centimeter. The experimental design crossed two soil types, three nominal initial-moisture regimes and five freeze-thaw totals of 1, 3, 5, 10 and 15 cycles, with three replicate columns for every combination. Each 24-hour cycle consisted of 12 hours at minus 20 degrees Celsius followed by 12 hours of thawing at room temperature. The outer surface of each column was sealed during cycling to reduce evaporative moisture loss.

The two soils came from the same seasonally frozen region but had markedly different chemical properties. The saline soil had a pH of 9.37, total salinity of 0.27% and an exchangeable sodium percentage of 20.30%. The non-saline counterpart had a pH of 7.67, salinity of 0.04% and an exchangeable sodium percentage of 0.10%. Their carbonate and bicarbonate concentrations and cation exchange capacities also differed. As a result, the study treated comparisons between the soils as differences in the overall saline-soil chemical background, which included salinity, alkalinity and sodicity. The analysis did not isolate total salinity as the only cause of those differences.

After each prescribed freeze-thaw treatment, every column was vacuum-saturated with deionized water before low-field nuclear magnetic resonance measurement. This procedure standardized the amount of liquid pore water across treatments and allowed the researchers to compare the structures that remained after freezing and thawing. It also limits the interpretation: vacuum saturation can dissolve and redistribute soluble salts, so the resulting spectra do not directly represent the in situ water-salt distribution during a freeze or thaw. The measurements are indirect indicators of pore structure under a common saturated condition, not direct images of cracks, pore geometry or connectivity.

In low-field NMR, hydrogen nuclei in pore water produce a signal whose transverse relaxation time, T2, is controlled mainly by interactions with pore surfaces. Under surface-relaxation conditions, shorter T2 values are associated with smaller pores, whereas longer values are associated with larger pores. The researchers divided the T2 distributions into micropore, mesopore and macropore domains corresponding to 0.1–10, 10–100 and 100–1,000 micrometers. Because surface relaxivity was not independently calibrated using mercury intrusion porosimetry, X-ray computed tomography, gas adsorption or image analysis, these categories were interpreted as relative NMR-derived pore-size domains rather than exact measurements of pore diameter.

The averaged T2 spectra from both soil types were dominated by a main peak between approximately 0.1 and 100 milliseconds, showing that the detected water signal was concentrated mainly in micro- and mesopore domains. Some treatments also displayed a weaker shoulder or secondary peak at longer relaxation times, indicating a smaller contribution from larger-pore domains. In saline soil at low moisture, a weak long-T2 component became more evident after the first cycle and then weakened with further cycling. Under medium and high moisture, the saline soil’s long-relaxation-time signal generally declined as cycling progressed. The non-saline soil retained its long-T2 component more consistently, particularly at high moisture, although the main peak positions in the two soils remained broadly similar.

Standardized path analysis identified moisture content as the variable with the largest relative contribution to variation in the NMR-derived pore-size fractions, accounting for 56.0% of the variation within the variables examined. Soil depth contributed 21.3%, the number of freeze-thaw cycles 11.4% and the salinity indicator 11.3%. Moisture was negatively associated with microporosity, with a standardized coefficient of minus 0.655, and positively associated with mesoporosity at 0.694. Its positive association with macroporosity was smaller, at 0.296. The separate models explained 50.0% of microporosity variation, 51.5% of mesoporosity variation and 32.5% of macroporosity variation, indicating moderate explanatory performance for the first two fractions and more limited performance for the largest-pore category.

Cycle number and the salinity indicator had weaker and more selective associations than moisture. The number of cycles was negatively associated with macroporosity, with a standardized coefficient of minus 0.210, while its small positive association with microporosity was not statistically significant. Salinity was not significantly associated with either microporosity or mesoporosity, but it was negatively associated with macroporosity at minus 0.246. At high moisture, macroporosity increased in the non-saline soil but remained relatively low in the saline soil across the cycle sequence. Soil depth was positively associated with microporosity at 0.226 and negatively associated with mesoporosity at minus 0.198 and macroporosity at minus 0.215. The relatively low explanatory value for macroporosity leaves room for unreported influences such as local packing variability, slight compaction differences, aggregate fragmentation and the formation or collapse of isolated large pores.

The pore-water fractal dimension, Dw, provided a complementary description of multi-scale heterogeneity in the T2 distributions. Values ranged from approximately 1.67 to 2.67 across all treatments. Under low moisture, Dw remained between 1.67 and 1.79 in both soils and fluctuated only slightly as the number of cycles increased. Under medium and high moisture, it was generally higher, spanning roughly 2.17–2.67, consistent with the pore-water signal being distributed across a wider range of size domains. At high moisture, Dw in the saline soil stayed within 2.18–2.29 without a clear monotonic trend, whereas the non-saline soil ranged from 2.53 to 2.67 and reached about 2.58 after 15 cycles. After cycling, the positive correlation between moisture and Dw became stronger, salinity showed a negative correlation and cycle number showed only a weak negative relationship. These correlations were descriptive and were not treated as causal paths.

Moisture and salinity followed distinct trajectories through the treatments. In saline soil, mean moisture under the low-moisture condition declined from 0.045 to 0.032, while the medium-moisture treatment rose from 0.250 to 0.295. The high-moisture treatment remained between 0.310 and 0.320. In non-saline soil, low moisture stayed near 0.065, while the medium and high treatments declined to 0.205 and 0.260, respectively. Salinity in the saline soil rose to approximately 0.33–0.36% after one to three low-moisture cycles and then declined slightly, remaining above its initial value. Under wetter conditions, salinity decreased during the early cycles, reached a relatively low level around five cycles and rose again with additional cycles. The non-saline soil remained within 0.015–0.045% throughout. Overall, the measurements associate the initial freeze-thaw event and moisture condition with the clearest pore-structure responses, while the indirect, saturated-state NMR method limits conclusions about field-scale salt movement and actual pore connectivity.

Subject of Research: Associations among moisture, freeze-thaw cycling and NMR-derived soil pore structure

Article Title: Moisture–dependent evolution of NMR–derived pore structure in saline and non–saline soils during freeze–thaw cycles

Article References: Qiu, Y., Gu, Z., Nie, S., & Zou, Y. (2026). Moisture–dependent evolution of NMR–derived pore structure in saline and non–saline soils during freeze–thaw cycles. Environmental Earth Sciences, 85(17), Article 436. https://doi.org/10.1007/s12665-026-13095-w

Image Credits: AI Generated

DOI: 10.1007/s12665-026-13095-w

Keywords: saline soils, freeze-thaw cycles, soil moisture, low-field NMR, pore-size distribution, pore-water fractal dimension, seasonally frozen ground, path analysis, Jilin Province, Moisture, dependent, evolution

The research, led by Wisam Bukaita of Lawrence Technological University, compiled a multi-decadal, species-integrated dataset combining population dynamics, survival metrics, and climate variables for four focal taxa: Blanchard’s cricket frog (Acris blanchardi), the eastern massasauga (Sistrurus catenatus), Kirtland’s snake (Clonophis kirtlandii), and the queen snake (Regina septemvittata). These species were selected because they occupy wetland, prairie, and streamside habitats that depend on stable winter thermal regimes. The dataset drew on long-term monitoring programs from the U.S. Geological Survey, the Michigan Natural Features Inventory, state conservation databases, and peer-reviewed ecological studies spanning the 1980s through 2025.

At the heart of the study is a fully connected neural network designed to predict population persistence, survival probability, and extirpation risk from three winter climate inputs: snow depth, temperature anomalies, and winter precipitation. The architecture comprised three hidden layers with 128, 64, and 32 neurons, using ReLU activation and a dropout rate of 0.2 to reduce overfitting. Training employed mean squared error for continuous outputs and binary cross-entropy for classification, optimized with the Adam algorithm at a learning rate of 0.001 over 500 epochs with early stopping. Model performance was validated using an 80/20 train–test split and 5-fold cross-validation, achieving root mean square error values of 0.08 to 0.15 and R² values between 0.71 and 0.87 for continuous predictions, alongside AUC-ROC scores of 0.84 to 0.93 and F1-scores of 0.79 to 0.88 for extirpation classification.

What sets this study apart from previous correlative climate–biodiversity analyses is its use of SHapley Additive exPlanations (SHAP), a game-theoretic interpretability framework that decomposes model predictions into individual feature contributions. SHAP attribution revealed that snowpack variables—depth and duration—were the dominant predictors, contributing 42 to 57 percent of total model importance. Temperature anomalies accounted for 28 to 39 percent, while winter precipitation contributed 12 to 21 percent. Snowpack emerged as roughly 1.3 times more influential than temperature and up to 2.1 times more influential than precipitation alone, underscoring that the loss of subnivean insulation—the insulating layer beneath snow—is the single most powerful climatic signal of population collapse in these systems.

Perhaps the most striking finding concerns the interaction between snowpack loss and temperature anomalies. When both stressors occurred simultaneously, extirpation risk increased by 35 to 62 percent beyond what would be expected from additive effects alone, with interaction terms contributing an additional 18 to 26 percent to model output. The model identified clear nonlinear thresholds: extirpation probability rose sharply when snowpack declined below 30 to 40 percent of historical norms and when temperature anomalies exceeded +2 °C. Under these compound conditions, risk accelerated two to three times faster than linear model expectations, suggesting the presence of ecological tipping points where small additional changes in winter climate trigger disproportionately large biological responses.

Species-specific analyses revealed differentiated vulnerability shaped by ecological traits. Kirtland’s snake, a prairie-wetland specialist already constrained by fragmented populations and extremely low detection rates, exhibited the highest sensitivity, with extirpation probability increasing by up to 68 percent under combined stress conditions. Blanchard’s cricket frog showed a 6 to 11 percent increase in mortality probability for every 10 percent reduction in snow depth, consistent with the loss of thermal buffering exposing dormant individuals to lethal soil temperature fluctuations. The eastern massasauga, a wetland-dependent rattlesnake, demonstrated 22 to 38 percent higher mortality risk under elevated winter temperature variability exceeding a standard deviation of 3 °C. The queen snake, a streamside specialist, was most sensitive to the interaction of reduced snowpack and increased winter rainfall, with persistence declining by 30 to 47 percent under combined hydrological stress.

The physiological mechanisms underlying these patterns are well grounded in ectotherm biology. Amphibians and reptiles depend on stable thermal and hydric conditions during dormancy or brumation, and snowpack serves as a critical insulating blanket that stabilizes soil and near-surface temperatures. When snow cover diminishes, overwintering organisms are exposed to repeated freeze–thaw cycles that can induce cellular damage, disrupt osmotic balance, and impair energy regulation. Simultaneously, warmer winter temperatures accelerate metabolic rates in dormant individuals, prematurely depleting lipid reserves—a phenomenon known as metabolic exhaustion. Shifts from snow-dominated to rain-dominated precipitation further alter wetland hydroperiods and stream flow dynamics, destabilizing hibernacula and disrupting breeding habitat availability and prey phenology.

Across all four taxa, the integrated dataset documented long-term population declines ranging from 35 to 65 percent, accompanied by a 40 to 70 percent reduction in occupied habitat patches and a 20 to 35 percent increase in interannual variability after 2000. These trends align with broader evidence that climate-driven extinction risk is often shaped by synergistic interactions among multiple stressors rather than single drivers. The study also notes that winter climate stress can amplify non-climatic pressures such as habitat fragmentation, contaminant exposure, and disease, functioning as a stress multiplier that intensifies the impact of co-occurring environmental pressures beyond what climate-only predictions would suggest.

The deep learning framework outperformed traditional statistical baselines including generalized linear models, generalized additive models, random forests, and XGBoost across all evaluation metrics, with statistically significant improvements confirmed by paired tests. The authors acknowledge limitations, including the challenge of distinguishing statistical attribution from biological causation, potential multicollinearity among climatic variables, and the absence of benchmarking against emerging transformer or graph-based architectures. Nevertheless, the framework represents a pragmatic balance between predictive performance, data availability, and ecological interpretability, moving beyond correlation toward mechanistic understanding of climate-driven extirpation.

The conservation implications are significant. Current climate adaptation frameworks that emphasize growing-season conditions may substantially underestimate species vulnerability by neglecting winter processes. The study recommends prioritizing the protection and restoration of microhabitats that buffer winter thermal variability—including deep soil substrates, wetlands, forested cover, and landscape features that promote snow accumulation and retention—and maintaining at least 30 to 40 percent of historical snowpack buffering capacity. It further advocates integrating winter-specific predictors such as snow water equivalent, snow cover duration, freeze–thaw frequency, and winter temperature variability into species distribution models and monitoring programs. As winter temperatures continue to rise faster than annual averages across temperate regions, the findings make clear that winter climate change is not a secondary component of global warming but a primary, quantifiable driver of ecological instability and species loss.

Subject of Research: Deep learning attribution of winter climate drivers of amphibian and reptile extirpation

Article Title: Winter warming and snowpack loss synergistically drive amphibian and reptile extirpation: a deep learning attribution study of climatic mechanisms

Article References: Bukaita, W. (2026). Winter warming and snowpack loss synergistically drive amphibian and reptile extirpation: a deep learning attribution study of climatic mechanisms. Frontiers in Amphibian and Reptile Science, 4, Article 1841535. https://doi.org/10.3389/famrs.2026.1841535

Image Credits: AI Generated

DOI: 10.3389/famrs.2026.1841535

Keywords: snowpack loss, winter warming, amphibian decline, reptile extirpation, deep learning, SHAP, climate change, biodiversity loss, subnivean insulation, freeze-thaw cycles, Midwestern United States, ecological thresholds

The research team, led by Fuyun Guo, Yan Zhao and Xingmin Meng of Lanzhou University together with colleagues from Gansu geological institutions, combined field surveys, remote sensing analysis and hydrological modeling to reconstruct the disaster from beginning to end. Their investigation documented 280 shallow landslides triggered across the catchment during the storm. The spatial pattern of these failures was primarily controlled by terrain morphology—the shape and steepness of the slopes determined where the ground gave way. But terrain alone could not explain why slopes that had held firm for decades suddenly collapsed en masse under a single rainfall event.

The answer, the researchers found, lay beneath the surface, in the geological composition of the slopes and in the history of freezing and thawing they had endured. Many of the failed slopes consisted of soil and glacial deposits resting on bedrock. Repeated freeze-thaw cycles had progressively weakened the interface between this surficial material and the underlying rock, a process the team describes as freeze-thaw shear displacement. As water infiltrated during the freeze-thaw transitions, it expanded and contracted, gradually shearing and loosening the bond between soil and bedrock. When the intense August rainfall arrived, the preconditioned slopes failed rapidly, with water pressure building along the weakened interface until the entire shallow layer detached.

What happened next transformed a landslide event into something far more dangerous. The landslide materials did not simply come to rest on the hillslopes. Instead, they mobilized into debris flows—fast-moving mixtures of water, mud, rock and vegetation that surged down the drainage channels. In the Maliantan catchment, the peak discharge of the debris flow reached 978.7 cubic meters per second. To appreciate how extraordinary that figure is, the researchers compared it with the modeled clear-water flood discharge for the same catchment under the same rainfall: just 63.3 cubic meters per second. The debris flow was 15.5 times more voluminous and powerful than the flood of clear water alone, a dramatic amplification effect produced entirely by the cascading hazard chain.

The team traced this amplification to a cascading mechanism operating within the channels. As landslide debris poured into the drainage network, it initially formed natural dams that impounded water and sediment. These dams then breached, releasing sudden pulses of water and debris that picked up additional material along the way. Meanwhile, large volumes of woody debris—trees, branches and root material torn from the forested slopes—became lodged at narrow choke points in the channels, causing blockages that concentrated the flow’s energy. Each impoundment and release cycle added momentum and volume, so that by the time the flow reached lower elevations it carried vastly more destructive power than the initial rainfall could have generated on its own.

Perhaps the most counterintuitive finding concerns the role of the dense vegetation itself. The researchers identified what they call a root-moraine coupling effect, a dual mechanism by which forest cover actively contributed to the disaster. Shallow root networks, rather than binding the soil into a stable mat, created preferential infiltration pathways—channels along roots through which rainwater could penetrate rapidly into the subsurface. This accelerated moisture accumulation precisely at the root-moraine interface, the critical boundary where failure ultimately occurred. In effect, the roots acted as plumbing that delivered storm water directly to the weakest layer of the slope, hastening the onset of failure during intense rainfall.

The second half of the coupling effect concerns the debris itself. When the slopes failed, the shallow root networks supplied large volumes of woody material to the moving mass. This woody debris, rather than dissipating the flow’s energy, became a destructive agent in its own right, lodging at channel constrictions and contributing to the damming and blockage processes that amplified the discharge. The study thus demonstrates that dense vegetation, long assumed to be a universal safeguard against slope hazards, can paradoxically serve as both an accelerant of landslide initiation and a fuel source for debris flow amplification in certain geological and climatic settings.

The findings carry significant implications for hazard assessment in alpine and high-altitude regions worldwide. Conventional susceptibility models often weight vegetation coverage as a stabilizing factor, and many mapping schemes treat densely forested catchments as low-priority zones. The Yuzhong event shows that such assumptions can be dangerously wrong where shallow soils and glacial deposits overlie bedrock, where freeze-thaw cycling degrades the soil-bedrock interface, and where root systems create fast infiltration pathways. The researchers argue that hazard evaluations for similar environments must account for the preconditioning effects of freeze-thaw cycles and the potential for vegetation to amplify, rather than dampen, cascading geohazards.

The study also underscores the importance of understanding hazard chains as integrated systems rather than isolated events. The 15.5-fold amplification between clear-water flood discharge and debris flow discharge in the Maliantan catchment illustrates how a modest hydrological trigger can be transformed into a catastrophic geomorphic event through cascading processes—landslide initiation, dam formation and breach, woody debris blockage and flow concentration. Early warning systems that monitor rainfall alone may therefore underestimate the true hazard, because the destructive potential of the resulting debris flow depends on the state of the slopes and channels through which it passes, not merely on the intensity of the storm that initiates it.

As climate change intensifies extreme rainfall events and alters freeze-thaw regimes in mountain regions, the mechanisms identified in the Yuzhong event are likely to become increasingly relevant. The researchers suggest that their findings are critical for assessing similar cascading geohazards in alpine regions, where the combination of dense vegetation, glacial deposits and seasonal freezing creates conditions ripe for precisely the kind of disaster chain that struck Maxian Mountain. Recognizing that a green mountainside is not always a safe mountainside may prove to be one of the most important lessons of the 2025 Yuzhong event.

Subject of Research: Mechanisms of rainfall-triggered shallow landslides and debris flow amplification in densely vegetated alpine terrain

Article Title: Causes of mass shallow landslides in densely vegetated areas and their amplifying effect on debris flows: insights from the 2025 Yuzhong event

Article References: Guo, F., Xie, Y., Zhao, Y., Chong, Y., Chen, G., Li, G., Zhang, S., Li, Y., Yue, D., & Meng, X. (2026). Causes of mass shallow landslides in densely vegetated areas and their amplifying effect on debris flows: insights from the 2025 Yuzhong event. Natural Hazards, 122(22), Article 677. https://doi.org/10.1007/s11069-026-08455-5

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08455-5

Keywords: shallow landslides, debris flows, freeze-thaw cycles, vegetation effects, cascading hazards, Maxian Mountain, Yuzhong event, root-moraine coupling, landslide dams, alpine geohazards, hydrological modeling, Gansu Province

The material at the center of the study is called self-protected underwater mortar, or SPUM. Rather than loading the mortar with viscosity-modifying chemicals, the method relies on an underwater protective agent, a water-soluble polymer dissolved in the surrounding water at a concentration of 0.5 percent by mass. The polymer, a linear ionic macromolecule with a weight-average molecular weight of at least 7.5 million, accumulates at the boundary between the fresh paste and the water, forming a stable protective film. This layer suppresses the dispersion of cementitious material into the water without increasing the viscosity of the mortar itself, which means the mix can still flow freely into place. Earlier work has shown that this approach can retain around 90 percent of its compressive strength when cast in seawater compared with dry-land casting, an impressive figure for underwater construction.

The motivation for the new study comes from one of the most demanding environments on Earth: the Qinghai-Tibet Plateau. Hydraulic structures there are often cast underwater and then spend their service lives in water-level fluctuation zones, where seasonal freezing and thawing batter the material year after year. To make matters worse, the plateau is dotted with saline lakes whose surface and groundwater carry high concentrations of sulfate ions. Sulfate attack is a notorious durability problem because sulfate ions react with cement hydration products to form expansive compounds such as ettringite and gypsum, generating internal stresses that crack the material from within. When sulfate attack and freeze-thaw cycles act together, previous research has shown they can accelerate damage far faster than either agent alone.

To probe these effects, the team prepared three versions of the same mortar mix, which used ordinary Portland cement partially replaced with 10 percent fly ash and 30 percent tuff powder, a locally available mineral admixture, along with a polycarboxylate superplasticizer. The first batch, designated SPUM-LE, was cast and cured in a conventional land environment. The second, SPUM-TW, was cast and cured in water containing 0.5 percent of the protective polymer. The third, SPUM-SS, was cast and cured in water containing both the polymer and 5 percent sodium sulfate, and was later exposed to freeze-thaw cycling in the same sulfate solution. The researchers measured slump flow, compressive and flexural strength at 3, 7 and 28 days, and then subjected all specimens to 100 rapid freeze-thaw cycles spanning temperatures from minus 18 to 5 degrees Celsius, tracking mass loss and relative dynamic elastic modulus along the way.

The rheology results showed immediately how much the surrounding medium matters. The land-cast mortar spread the farthest and fastest, while the version cast in the polymer solution lost 11.6 percent of its slump flow and needed 66.8 percent longer to reach a 200-millimeter spread, largely because the viscous solution and added buoyancy resist the flow of fresh mortar. The sulfate-polymer bath was harsher still: slump flow dropped by 33.8 percent and the spread time ballooned by 772.5 percent, a consequence of the denser liquid further suppressing the driving force for spreading. These numbers matter because flowability is the entire selling point of the self-protection concept, and the study shows that the chemistry of the water being poured into can quietly erode that advantage.

Strength development told a more subtle story. At 3 days, the underwater specimens were dramatically weaker than their land-cast counterpart, with the polymer-only batch reaching just 8.73 megapascals against 19.22 megapascals on land. The sulfate batch fared slightly better early on, apparently because sulfate ions initially accelerated hydration. But that early boost did not last. By 28 days, the land mortar reached 34.50 megapascals, the polymer-only batch 27.00 megapascals, and the sulfate batch only 23.40 megapascals, giving underwater-to-land compressive strength ratios of 0.80 and 0.60 respectively. The explanation lies at the interface: the polymer film that protects the mortar from washout also slows the transport of water to cement particles, retarding hydration, while accumulating gypsum and other sulfate products coat unhydrated cement and further impede the reaction. Flexural strength proved even more sensitive, with the sulfate-exposed batch actually declining from 6.29 to 5.55 megapascals between 7 and 28 days.

Freeze-thaw performance split the specimens in an unexpected way. The land-cast mortar held up best, losing only 1.29 percent of its mass and retaining 84.61 percent of its dynamic elastic modulus after 100 cycles. The polymer-only underwater mortar suffered the worst surface damage, shedding 5.30 percent of its mass as scaling and particle detachment, while still retaining 76.09 percent of its modulus. The sulfate-exposed batch showed the opposite pattern: modest mass loss of 3.99 percent, partly masked by white sulfate deposits accumulating on the surface, but the steepest internal degradation, with its dynamic elastic modulus falling to just 67.92 percent. In other words, one material fell apart from the outside in, while the other crumbled from the inside out. Sulfate ions penetrating the specimen during cycling formed expansive products that drove internal microcracking even as deposited crystals compensated for surface mass loss.

Microscopy and porosity measurements explained why. Scanning electron microscopy revealed dense, continuous matrices in the land-cured specimens but looser structures with visible micropores in the underwater batches, and abundant needle-like sulfate products in the sulfate-exposed material at 28 days. Mercury intrusion porosimetry added a twist: after 100 freeze-thaw cycles, the underwater specimens actually showed lower total porosity and fewer large harmful pores than the land-cast material, because continued hydration filled their more open pore systems. Yet they still performed worse. The lesson is that pore structure measured after the fact can be misleading; what governs freeze-thaw durability is the initial pore structure before cycling and the cumulative damage that accumulates within it. A loose starting framework gives hydration products plenty of room to fill, but that same looseness lets ice crystals and expansive reactions inflict damage that no amount of later pore-filling can undo.

To turn these observations into something engineers can use, the team fitted their damage data to a Weibull statistical model, using both mass loss and dynamic elastic modulus as damage indicators. The fits were strong, with coefficients of determination reaching 0.99 for the modulus-based model. Converting laboratory cycles into field years using an acceleration factor of 12 and an estimated 100 natural freeze-thaw cycles per year in the Lhasa region, the model predicted service lives of roughly 23.7 years for the land-cast mortar, 10.9 years for the polymer-only underwater mortar and 13.3 years for the sulfate-exposed batch under the more conservative mass-loss criterion. The authors caution that these figures are engineering estimates, since laboratory acceleration can never perfectly reproduce field conditions, and they note that the coupled nature of casting, curing and freezing makes it difficult to isolate the contribution of each stage.

The broader significance of the work is twofold. For engineers planning hydropower and hydraulic projects in cold, chemically aggressive regions, it provides hard numbers on a construction method that could otherwise be adopted on faith, showing that self-protected underwater mortar can genuinely work but that its durability ceiling depends heavily on the water it is born into. For materials science more broadly, it is a vivid demonstration that durability cannot be judged from a single snapshot of microstructure. A material that looks denser after testing may in fact be more damaged than one that looks coarser, because the true story lives in the sequence of events, the initial defects, the migrating ions and the accumulating cracks, rather than in the final photograph.

Subject of Research: Freeze-thaw durability and sulfate resistance of self-protected underwater mortar cast with an underwater protective agent

Article Title: Effects of underwater protective agent and sulfate exposure on the freeze-thaw resistance of self-protected underwater mortar

Article References: Zhang, J., Liu, Z., Shen, D., Lang, T., Li, Y., Sui, H., & An, X. (2026). Effects of underwater protective agent and sulfate exposure on the freeze-thaw resistance of self-protected underwater mortar. Case Studies in Construction Materials, 25, Article e06601. https://doi.org/10.1016/j.cscm.2026.e06601

Image Credits: AI Generated

DOI: 10.1016/j.cscm.2026.e06601

Keywords: self-protected underwater mortar, underwater protective agent, freeze-thaw cycles, sulfate attack, concrete durability, Qinghai-Tibet Plateau, cement hydration, pore structure, Weibull damage model, hydraulic engineering, anti-washout, service life prediction

The research team, led by Wenhao Zhang and Xiaoyan Li of Beijing Normal University, chose the Qinghai Lake Basin as their natural laboratory for good reason. Covering nearly 30,000 square kilometers in the northeastern part of the plateau, the basin is split almost evenly between two very different frozen-ground regimes. Permafrost dominates the upstream, high-altitude areas, accounting for about 41.5 percent of the basin, while seasonally frozen soil covers the remaining 58.5 percent in the midstream, downstream, and lakeside plains. This natural gradient allowed the researchers to compare, within a single watershed, how permanently frozen ground and ground that freezes and thaws each year shape groundwater recharge in distinct ways.

To untangle these processes, the team built a comprehensive ecohydrological monitoring network across the basin. Between May and October 2025, they collected 481 samples in total: 90 of groundwater drawn from wells used by local herders, 306 of soil from three depth intervals — 0–30, 30–60, and 60–90 centimeters — and 85 of precipitation, including both rainfall and snow. Every sample was analyzed for its hydrogen and oxygen stable isotope composition using isotope ratio infrared spectrometry, with analytical precisions of ±0.5 per mil for deuterium and ±0.1 per mil for oxygen-18. Because every source of water — rain, snowmelt, soil moisture at different depths — carries a slightly different isotopic fingerprint, these measurements act like natural tracers, allowing the researchers to work out after the fact which waters ended up in the aquifer and how they got there.

The team divided the study window into three freeze–thaw periods based on sustained air and soil temperature thresholds: a thawing period in May and June, when soils transition from frozen to unfrozen; a thawed period from July to September, when the ground stays entirely unfrozen; and a freezing period in October, when temperatures drop back below zero. They then fed the isotope data into MixSIAR, a Bayesian mixing model that estimates the relative contribution of each potential water source to the groundwater while explicitly accounting for uncertainty. The results were striking. Across the entire freeze–thaw cycle, soil water was the dominant source of shallow groundwater recharge, contributing between 57.0 and 76.3 percent. Rainfall contributed 13.8 to 26.1 percent, and snowmelt — despite its dramatic visual presence on the landscape — contributed only 7.9 to 22.0 percent.

That snowmelt plays such a modest direct role may come as a surprise, and the authors offer two explanations. First, during the thawing period, air temperatures remain low, so snow melts slowly rather than releasing a sudden pulse of water. Second, under the basin’s intense solar radiation and strong winds, a significant share of snow water is lost to runoff or sublimation before it can infiltrate. Snow, in other words, functions largely as temporary storage on the surface rather than as an immediate source of groundwater. Instead, it is the water already held in the soil — moisture that fell as rain or snow in previous seasons and was released as the ground thawed — that does most of the work of refilling the shallow aquifer.

The study also revealed how the physical state of the soil controls the pathways water takes downward. During the thawing period, the active layer thaws only from the top, leaving a near-impermeable frozen layer beneath that blocks vertical infiltration. Meltwater and rain therefore pool in a transient saturated zone near the surface and migrate laterally as interflow, delaying groundwater recharge and often reaching the aquifer only indirectly, through riparian seepage and soil-layer redistribution. Once the thawed period arrives and this barrier disappears, the vertical connectivity of the soil profile increases dramatically, allowing water from the 60–90 centimeter layer and surface-derived water to percolate downward more freely. During the freezing period, bidirectional freezing — from the surface downward and from the base of the active layer upward — traps water above the advancing freezing front, yet some shallow water still finds its way to groundwater through preferential channels before the profile seals completely.

Isotopic evidence pointed to a subtle but important shift in recharge mechanics over the season. The researchers used a metric called line-conditioned excess, or lc-excess, which measures how far a water sample deviates from the local meteoric water line — the reference relationship between hydrogen and oxygen isotopes in precipitation. Water that has undergone evaporation, typical of slow, matrix-driven piston flow through soil pores, carries low lc-excess values, whereas water reaching the aquifer via fast preferential pathways retains an isotopic signature close to that of precipitation. Over the course of the freeze–thaw cycle, groundwater lc-excess values shifted from resembling soil water toward resembling precipitation, indicating that piston flow gradually weakened while preferential flow intensified, producing a recharge regime in which the two mechanisms coexist.

Space mattered as much as time. In the middle and upper reaches of the basin, where permafrost prevails, groundwater is recharged primarily by water from the 0–60 centimeter soil layer traveling along longer, more circuitous hydrological pathways. In the downstream regions dominated by seasonal frozen ground, by contrast, gentle topography and shallow water tables favor rapid infiltration, and groundwater is recharged mainly by water from the 30–90 centimeter layer moving quickly through macropores and fissures. Soil moisture measurements reinforced this picture: water content in the upper reaches remained consistently higher than downstream, and the seasonal patterns of wetting and drying differed markedly between the two zones, reflecting the different thermal regimes of permafrost and seasonally frozen ground.

The authors are candid about the limitations of their approach. Snowmelt samples were collected from the snowpack, so sublimation before melt and evaporation afterward may have altered the isotopic signature used to trace snow contributions. Freezing itself causes isotopic fractionation, as lighter isotopes preferentially remain in unfrozen liquid water while heavier isotopes enrich the ice phase, complicating pathway identification. And because piston flow and preferential flow operate simultaneously, water mixing between soil layers can blur the source signatures that the mixing model relies upon. Future work, they suggest, should combine high-frequency in situ monitoring with multiple tracers, including dye tracing, to pin down recharge pathways with greater precision.

Even with those caveats, the implications are far-reaching. As climate warming deepens the active layer and degrades permafrost across the plateau, the balance between recharge sources is likely to narrow while the differences between recharge pathways widen, reorganizing how water moves through these landscapes. The study demonstrates that assessments focused solely on rainfall risk underestimating the contributions of snowmelt and, above all, soil water to alpine aquifers. For water managers charged with sustaining baseflow in plateau rivers and the communities and ecosystems that depend on them, the message is clear: in frozen ground, the seasons do not merely change the weather — they rewrite the hidden architecture of the water cycle itself.

Subject of Research: Influence of freeze–thaw processes on shallow groundwater recharge sources and pathways in the Qinghai Lake Basin, traced with stable water isotopes

Article Title: Freeze–thaw processes influence shallow groundwater recharge sources and pathways in the Qinghai Lake Basin: insights from water isotopes

Article References: Zhang, W., Li, X., Deng, Y., Hu, G., & Shi, F. (2026). Freeze–thaw processes influence shallow groundwater recharge sources and pathways in the Qinghai Lake Basin: insights from water isotopes. Hydrology and Earth System Sciences, 30(18), 6039-6055. https://doi.org/10.5194/hess-30-6039-2026

Image Credits: AI Generated

DOI: 10.5194/hess-30-6039-2026

Keywords: groundwater recharge, freeze–thaw cycle, permafrost, Qinghai Lake Basin, stable water isotopes, Qinghai–Tibet Plateau, soil water, snowmelt, piston flow, preferential flow, active layer, alpine hydrology

The urgency behind the work is easy to appreciate. Industrial activities such as copper smelting, coal combustion, and metal refining emit arsenic-laden dust that settles onto surrounding soils, and recent global assessments have documented escalating arsenic contamination across Chinese soils in particular. What happens to those freshly deposited particles in the days and weeks after they land has profound consequences: mobile, soluble arsenic species can migrate into groundwater, be taken up by crops, or become bioavailable to humans and wildlife, while arsenic locked into refractory minerals poses far less immediate risk. Yet most risk models treat the post-deposition fate of arsenic as a slow aging process measured in months or years, driven by microbial activity, redox shifts, and mineral weathering. The new study argues that in cold climates, the clock starts ticking much faster, and the mechanism is frozen water.

To probe that mechanism, the researchers assembled an unusually broad evidence base: fifty diverse soil samples collected from across China, spanning the range of textures, pH values, and mineralogies where industrial arsenic deposition is a realistic concern. They spiked these soils with freshly deposited arsenic particles representative of industrial emissions and subjected them to controlled wetting followed by repeated freeze–thaw cycles, mimicking the sequence that occurs when precipitation coincides with or precedes subzero temperatures, a pattern common in many industrialized regions. The headline result was striking. Across the fifty soils, the relative leachability ratio of arsenic, measured against otherwise identical soils that never froze, dropped by an average of 71 percent. In practical terms, arsenic that had recently settled from the air became dramatically less likely to be washed out by percolating water once the soil had cycled through freezing and thawing.

Just as important as the magnitude of the effect was its timing. The suppression of bioavailability potential was concentrated overwhelmingly in the first three freeze–thaw cycles, after which the transformation largely plateaued. This rapid onset matters because it means the critical window of vulnerability, when freshly deposited arsenic is at its most mobile, may be closed off within days of the first hard frost. For environmental risk assessors, that suggests seasonal timing should be built into models of industrial contamination: arsenic deposited in autumn or winter in cold regions may follow a fundamentally different trajectory than arsenic deposited in spring, even if the total mass of contamination is identical.

The team did not stop at bulk leaching measurements. Using quasi-in situ techniques and single-particle analysis, including synchrotron-based spectroscopy at the Shanghai Synchrotron Radiation Facility with partial standard support from the Stanford Synchrotron Radiation Lightsource, they tracked what actually happened to arsenic at the level of individual particles and nanoscale phases. The answer was a story of mineralogical locking. During freeze–thaw cycling, arsenic shifted into comparatively stable forms, most notably ferric arsenate and arsenic associated with haematite, an iron oxide renowned for its strong binding of arsenate. Both of these phases are well known from the broader geochemical literature as sinks that sequester arsenic over long timescales. The freeze–thaw process, in effect, accelerated an immobilization pathway that might otherwise take far longer under ambient conditions.

The mechanistic explanation centers on the peculiar chemistry of the ice–water interface. When soil water freezes, solutes are progressively excluded from the growing ice lattice and concentrated into thin films of unfrozen liquid coating ice crystals and soil particles. Within these microenvironments, concentrations of dissolved species can spike, pH can shift, and reaction rates can be transformed. The researchers found that these concentration-driven fluctuations in the boundary chemistry steered the speciation changes, promoting the precipitation and association of arsenic with iron-bearing phases. This is consistent with a growing body of work on so-called freeze concentration effects, which have been shown to accelerate oxidation reactions and enhance the dissolution of iron oxides in frozen solutions. In the soil context, the ice boundary acts as a nanoscale chemical reactor, cycling solutes through extreme conditions each time the ground freezes and releases them again on thaw.

Chemistry, however, was only half of the story. The physical side of freeze–thaw cycling also proved consequential. The expansion of water as it freezes exerts pressure on soil aggregates and particles, and repeated cycles are known to fragment and rework the soil matrix. The study showed that this physical fragmentation redistributed arsenic mass across particle-size fractions, changing which size classes carried the contaminant and how it was hosted within the soil fabric. Because particle size governs surface area, transport behavior, and biological accessibility, this redistribution feeds directly into estimates of bioavailability potential. The interplay between the chemical transformation at ice boundaries and the physical sorting of particles by frost action thus operates as a coupled system, with each process reinforcing the overall shift toward less reactive arsenic.

The implications extend well beyond China’s industrial parks. Many of the world’s smelters, power plants, and heavy industry clusters sit in regions where subzero temperatures regularly follow precipitation, meaning that freeze–thaw processing of freshly deposited metals and metalloids is likely a global phenomenon that has simply gone unmeasured. The findings also intersect with climate change in a nuanced way. As the Northern Hemisphere’s frozen soil season shortens and permafrost warms, the frequency and distribution of freeze–thaw events are shifting. In some regions, fewer freeze–thaw cycles could mean less natural immobilization of industrial arsenic and a longer window of mobility; in others, more erratic winter thawing could produce additional cycles that lock contaminants in place. The study’s authors explicitly frame their work as a step toward understanding how climate dynamics modulate the initial geochemical fate of industrially emitted particles, and they provide climate-change analysis code available from the corresponding author on reasonable request.

There are also practical consequences for soil management and remediation. Stabilization strategies for arsenic-contaminated soils often rely on promoting exactly the kinds of mineral associations, arsenate bound to iron oxides and ferric arsenate phases, that the freeze–thaw process generates naturally. Understanding that winter conditions can accomplish part of this stabilization on their own could inform when and how remediation is scheduled, how site risk is assessed seasonally, and how the bioavailability of arsenic in agricultural soils near industry is estimated for food safety purposes. The results may also generalize beyond arsenic: the same ice–water interface chemistry and frost-driven fragmentation should influence the speciation and reactivity of other freshly deposited heavy metal particles, a possibility the researchers highlight as a frontier for future work.

What makes the study compelling is its demonstration that a process as ordinary as the ground freezing overnight can reorganize the chemistry of a toxic contaminant within a handful of cycles, at a scale visible only through synchrotron light and single-particle analysis. It is a reminder that some of the most consequential geochemistry on Earth happens in the thin, fleeting films of water where ice meets soil, far from laboratories and long before conventional monitoring programs think to look. For the millions of people living downwind of industrial emissions in cold climates, the winter freeze may be quietly working in their favor, converting airborne arsenic into forms the soil can hold. Capturing that effect in risk models, and tracking how a warming world alters it, is now an urgent task for environmental scientists.

Subject of Research: Transformation of freshly deposited arsenic speciation and bioavailability in soils during freeze–thaw cycles

Article Title: Ice–water interfaces drive speciation transformation and suppress bioavailability potential of freshly deposited arsenic during soil freeze–thaw cycles

Article References: Miao, C., Zeng, Y., Wu, N., Zhou, Q., Chen, C., Yao, C., Jiang, X., Sheng, Y., Yang, K., Lin, D., Zhu, L., & Xu, J. (2026). Ice–water interfaces drive speciation transformation and suppress bioavailability potential of freshly deposited arsenic during soil freeze–thaw cycles. Nature Water. https://doi.org/10.1038/s44221-026-00730-w

Image Credits: AI Generated

DOI: 10.1038/s44221-026-00730-w

Keywords: arsenic, freeze–thaw cycles, ice–water interface, soil contamination, speciation, bioavailability, heavy metals, industrial emissions, geochemistry, iron oxides, ferric arsenate, climate change

The research team, led by Hui Huang and Cong Zhang, prepared three mixtures under an identical binder–aggregate–water framework: a plain mortar control, a PP-fiber-reinforced composite, and a PVA-fiber-reinforced composite. Both fiber types were 6 millimeters long and added at a volume fraction of 1.0 percent, so that fiber dosage would not confound the comparison. The fibers themselves, however, are chemically and mechanically very different. The PP fiber, with a diameter of 20 micrometers, has a tensile strength of 800 megapascals and an elastic modulus of 5 gigapascals, and its surface is hydrophobic. The finer PVA fiber, at 14 micrometers, is roughly twice as strong at 1,800 megapascals and nearly three times stiffer at 14 gigapascals, and its hydrophilic chemistry bonds more readily with cement hydration products. Those differences turned out to matter enormously.

The specimens, 70.7-millimeter cubes, were cured for 28 days and then subjected to a rapid water-freezing and water-thawing regime in which temperatures swung between −18 °C and 5 °C every four hours, with the samples fully immersed. Measurements were taken after 0, 25, 50, 75, 100, 125, and 150 cycles. Before exposure, the plain mortar was actually the strongest material, at 65.03 megapascals of compressive strength, compared with 61.92 megapascals for the PVA composite and 54.61 megapascals for the PP composite. But strength at the start is not the same as strength that lasts.

After 150 freeze–thaw cycles, the ranking had reversed in the way that matters for durability. The fiber-reinforced composites retained 82.29 percent (PP) and 84.50 percent (PVA) of their initial compressive strength, while the unreinforced mortar retained only 66.45 percent. Mass loss told a parallel story: the mortar reached 5 percent mass loss by 125 cycles through continuous surface scaling, whereas both fiber mixtures degraded more slowly in a three-stage pattern—slow loss in the first 50 cycles, acceleration between 50 and 100 cycles as cracks connected, and a slowdown thereafter. Even after 150 cycles, the fibers still reduced surface material loss by more than 59 percent relative to plain mortar, with PVA consistently outperforming PP.

Perhaps the most striking finding was that stiffness, not strength, is the more sensitive fingerprint of freeze–thaw damage. The apparent elastic modulus, derived from the slope of the compressive stress–strain curve between 10 and 30 percent of peak stress, fell by 32.9 percent for PP-FRCC and 29.2 percent for PVA-FRCC over 150 cycles—nearly double the corresponding strength losses. Because the modulus reflects the load-transfer continuity of the internal microstructure, it registers the accumulation of pores, microcracks, and debonded interfaces long before catastrophic strength loss appears. For engineers assessing aging infrastructure, this suggests that stiffness-based or non-destructive measurements could serve as an early-warning system for frost damage.

Scanning electron microscopy revealed why the two fibers behaved differently. Using calibrated image analysis with two semi-quantitative descriptors—a visible defect area ratio and an interfacial deterioration ratio—the researchers quantified what they saw in the micrographs. The visible defect area ratio climbed from 1.8 to 8.2 percent in mortar, from lower starting values to 6.8 percent in PP-FRCC and 5.6 percent in PVA-FRCC. More telling was the interface: the fraction of visibly deteriorated fiber–matrix boundary rose from 3.0 to 21.2 percent for PP-FRCC but only from 2.7 to 17.4 percent for PVA-FRCC. The hydrophobic PP surface, chemically stable and inexpensive, gradually separated from the matrix under repeated hydraulic and crystallization pressures, while the hydrophilic PVA fiber maintained more continuous contact with surrounding hydration products.

Those microscopic differences echoed upward into failure behavior. Under splitting tensile loading, PVA-FRCC specimens displayed primary or deflected splitting cracks in 17 of 21 observations (81.0 percent), whereas PP-FRCC showed localized or branched damage features in 15 of 21 (71.4 percent). The tensile-to-compressive strength ratio, a marker of balanced degradation, stayed remarkably stable for PVA-FRCC—fluctuating within a coefficient of variation of just 0.83 percent—while mortar’s ratio swung the most. In compression, fiber-bridged cracking kept the reinforced cubes intact even after 150 cycles, while the plain mortar failed brittly with severe spalling and a conical crushing zone at its core.

To connect these observations mechanically, the team built a three-dimensional mesoscale model in ABAQUS, representing the composite as a cementitious matrix, randomly distributed equivalent fibers, and an equivalent pore phase whose volumetric expansion stands in for freeze–thaw damage. Calibrated against the measured compressive response at each cycle stage, the model reproduced the monotonic strength decline well at early and intermediate stages, with discrepancies under about 4 percent through 75 cycles. By 150 cycles the gap widened to roughly 17 percent for both composites—a limitation the authors attribute to the simplified pore-expansion representation and the absence of explicit fiber–matrix bond-slip and pull-out. Still, the simulated damage fields were revealing: equivalent strain, stiffness degradation, and compressive damage, initially scattered around isolated weak regions, progressively coalesced into connected damage paths as freeze–thaw pre-damage accumulated, mirroring the experimentally observed shift from distributed fine cracking to localized through-cracks.

Two-way analysis of variance confirmed that material type, cycle number, and their interaction significantly shaped mass loss, compressive strength retention, and stiffness retention, with strikingly large effect sizes—partial eta squared above 0.9 for the cycle effect on mass loss. For splitting tensile strength retention, the interaction was not significant, indicating that tensile degradation followed overall material and cycle effects rather than a material-specific trajectory. Together, the statistics reinforce the central message: fiber reinforcement does not merely slow damage, it changes the pathway by which deterioration proceeds.

The practical implications reach cold-region roads, bridges, repair overlays, and any concrete that may sit water-saturated through winter. Adding just 1 percent by volume of either fiber substantially curbed surface scaling and strength loss, with PVA offering slightly better residual strength, stiffness retention, and interfacial stability under this particular mixture design, while PP delivered comparable protection at lower cost. The authors are careful to note that the comparison is bounded by their specific mix proportions, cube geometry, and exposure regime, and that dosage optimization, salt frost exposure, flexural testing, and quantitative porosity measurements remain for future work. Even so, the study delivers a rare, unified account of frost damage—from the swelling pore to the debonding fiber to the cracking cube—and a compelling case that the humble fiber–matrix interface may be the most important square micrometers in frozen concrete.

Subject of Research: Freeze–thaw damage evolution and durability of polypropylene- and polyvinyl alcohol-fiber-reinforced cementitious composites

Article Title: Freeze–thaw damage evolution of PP- and PVA-fiber-reinforced cementitious composites: Mechanical degradation, interfacial deterioration and mesoscale modeling

Article References: Huang, H., Zhang, C., Liu, X., Xu, X., Jia, B., Gu, S., & Wang, W. (2026). Freeze–thaw damage evolution of PP- and PVA-fiber-reinforced cementitious composites: Mechanical degradation, interfacial deterioration and mesoscale modeling. Case Studies in Construction Materials, 25, Article e06594. https://doi.org/10.1016/j.cscm.2026.e06594

Image Credits: AI Generated

DOI: 10.1016/j.cscm.2026.e06594

Keywords: freeze–thaw cycles, fiber-reinforced cementitious composites, polypropylene fibers, PVA fibers, concrete durability, interfacial deterioration, mesoscale modeling, elastic modulus degradation, scanning electron microscopy, compressive strength retention, cold-region infrastructure, damage localization

The research team, led by Hangli Yang and Haoxiang Luan with colleagues including Fei Geng, Yupeng Xu, Hongke Zheng, and Jianguang Xie, built a three-dimensional discrete element model, or DEM, of macroporous recycled concrete and constrained it with an unusually rich set of laboratory measurements. Unlike continuum models that treat concrete as a smeared material, DEM represents the structure as thousands of individual particles connected by bonds that can stretch, shear, and snap. That makes it possible to watch the internal load-bearing network, known as force chains, reorganize as frost damage accumulates. Because no existing simulation platform can directly solve heat transfer, ice–water phase change, and moisture migration together, the team used an experimentally calibrated equivalent-damage approach, updating contact stiffness and bond strengths cycle by cycle rather than applying a single blanket reduction.

The experimental foundation came from the group’s earlier tests on three concrete mixes, labeled M1–20%, M2–25%, and M5–30%, where the numbers denote paste type and designed porosity. The recycled coarse aggregate, crushed from demolished concrete with particles between 9.5 and 37.5 millimeters, consisted of a natural stone core wrapped in a layer of old cement paste covering 23.1 percent of its surface. The researchers measured an impressive array of properties at different numbers of freeze–thaw cycles: compressive, splitting tensile, and shear strengths of the new paste; nanoindentation elastic moduli of the paste, the old paste, and their interfaces with natural aggregate; the rate at which old paste detached from recycled aggregate; and the mass change, strength, and elastic modulus of full specimens.

One of the most striking findings emerged immediately. The specimens did not simply decay monotonically. Instead, their mass and elastic modulus both rose during the first 40 cycles before declining. The explanation lies in a chemical race: water entering the connected pores promoted continued hydration of residual unhydrated cement, and the additional hydration products partially filled small pores, stiffening the paste and its interfaces faster than early frost microdamage could soften them. Only after about 40 cycles did accumulated microcracking, interfacial debonding, and surface spalling win out, sending stiffness into a steep decline. Freeze–thaw deterioration, the study shows, is a staged process of competing mechanisms rather than a steady slide.

To capture the geometric side of damage, the team converted measured mass loss into a count of lost surface paste particles. Because spalling occurred mainly in new-paste bridge regions rather than in aggregate cores, the net solid mass loss could be mapped onto removal of surface paste elements from the model. The algorithm allowed only particles on the current external surface, identified by a bond coordination number below six, to be deleted, so erosion progressed gradually inward without creating nonphysical internal voids. By 160 cycles, the models had shed 817 particles for the densest mix and up to 1,775 for the most porous one, each removal representing a real loss of load-bearing contact.

Validation against compression tests was convincing. Mean errors in peak stress across all groups and cycle counts ranged from 5.2 to 6.0 percent, while peak-strain errors stayed between 12.0 and 15.5 percent. The simulated elastic moduli, though noisier with errors of 10.8 to 18.1 percent, reproduced the characteristic rise-and-fall trend, and a new porosity-controlled prediction equation for post-freeze–thaw elastic modulus achieved a mean calculation error of just 7.6 percent. After 160 cycles, the peak-stress losses reached 38.3 percent for M2–25%, 35.8 percent for M5–30%, and 32.6 percent for M1–20%, matching the laboratory ordering and confirming that freeze–thaw resistance is governed jointly by porosity and the quality of the paste and its interfaces.

The deepest insight came from tracking force chains, the quasi-linear chains of highly stressed contacts that carry most of the load through granular skeletons. In undamaged and early-cycle specimens, load flowed through a few dominant strong chains concentrated in the specimen center and along inclined diagonal paths, producing the classic X-shaped shear failure. The coefficient of variation of force-chain intensity peaked at 1.847 at 40 cycles, when stress concentration was greatest. As cycling continued, however, the degradation of paste-related interfaces and the loss of surface particles progressively severed these dominant chains, forcing load to redistribute from a few high-capacity paths to many dispersed low-capacity ones. The coefficient of variation fell to 1.609 by 160 cycles.

That decline might superficially look like homogenization, as if the material were becoming more uniform and therefore tougher. The researchers show it is anything but. They call the phenomenon damage-induced pseudo-homogenization: the apparent evening-out of the force-chain distribution arises purely because the strongest load paths have failed, at the very moment peak stress and elastic modulus continue to fall. Simultaneously, the proportion of tensile bond failures grew, rising by 4.3 to 6.3 percent across the groups after 160 cycles, and the macroscopic failure mode shifted from X-shaped shear to longitudinal splitting as cracks found weakened interfaces and coalesced parallel to the loading direction. The toughness index dropped by up to 33.1 percent in the most porous mix, meaning the extra deformation capacity reflected damage, not ductility.

The bond-failure statistics also pinpointed the material’s Achilles’ heel. Across all freeze–thaw states, the failure rate ordering never changed: the interfaces between new and old paste, and between new paste and natural aggregate, failed most often, followed by paste-to-paste bonds, with old-paste-related bonds failing least. After 160 cycles, the fastest-growing failure rates belonged precisely to those new-paste interfaces, identifying them as the weak zones through which frost damage first develops and then transfers into the load-bearing skeleton. Notably, the tensile-failure proportion began climbing after 120 cycles for the densest mix but after only 40 cycles for the most porous one, showing how higher porosity concentrates stress and accelerates the onset of terminal damage.

Beyond its scientific contribution, the work carries practical weight for cold-region infrastructure. Permeable pavements, municipal and airport drainage bases, and sponge-city surfaces built with recycled aggregate can now be evaluated with a model that distinguishes which phases and interfaces are degrading, rather than relying on bulk indicators like mass loss or dynamic modulus that blur the picture. The authors suggest their framework can guide material selection, pore-structure design, durability assessment, and maintenance planning. The study’s validation remains an internal consistency check within a single material system, so independent confirmation on other mixes and laboratories will be the next step. But the central message stands: when ice attacks porous recycled concrete, the most dangerous change is not the damage you can weigh, but the silent rewiring of the force chains that hold the pavement together.

Subject of Research: Mesoscopic discrete element modeling of freeze–thaw deterioration mechanisms in macroporous recycled concrete

Article Title: Mesoscopic DEM study of freeze–thaw deterioration in macroporous recycled concrete: Interfacial damage, particle spalling, and force-chain reconstruction

Article References: Yang, H., Luan, H., Geng, F., Xu, Y., Zheng, H., & Xie, J. (2026). Mesoscopic DEM study of freeze–thaw deterioration in macroporous recycled concrete: Interfacial damage, particle spalling, and force-chain reconstruction. Case Studies in Construction Materials, 25, Article e06561. https://doi.org/10.1016/j.cscm.2026.e06561

Image Credits: AI Generated

DOI: 10.1016/j.cscm.2026.e06561

Keywords: macroporous recycled concrete, freeze–thaw cycles, discrete element method, force chains, interfacial transition zone, recycled aggregate, surface spalling, pseudo-homogenization, permeable pavement, sponge city, elastic modulus, bond failure

The research team, led by Huasu Fang of the Heilongjiang Province Hydraulic Research Institute together with colleagues from the Institute of Engineering Mechanics of the China Earthquake Administration and Harbin University of Science and Technology, focused on canal slopes in a cold region of northeastern China. Their goal was deceptively simple to state but difficult to achieve: estimate temperatures at depths of 100 to 250 centimeters at the same moments when shallower measurements were taken, using only information available at or before those moments. Deep temperature monitoring of this kind is expensive, and sensors buried in frozen, wet soil are notoriously prone to failure, leaving dangerous gaps in the record precisely where frost damage begins.

To build their dataset, the researchers assembled 204 irregularly spaced multi-depth observations collected between 2018 and 2022, pairing each with a rich set of predictors drawn from meteorological conditions, hydrological variables, surface and shallow-ground temperatures, soil moisture, and seasonal indicators such as the phase of the annual cycle. This multi-source framing matters because the temperature at two meters below a canal slope is not simply a delayed copy of the air temperature above it; it is shaped by snow cover, solar radiation, water movement in the soil, and the slow conduction of heat through layered earth materials. By feeding all of these influences into the models, the team gave the algorithms a physically meaningful picture of the system rather than a single temperature trace.

Seven machine-learning models were put through their paces, spanning classical approaches such as support vector regression, random forests, and XGBoost alongside deep learning architectures including long short-term memory networks, better known as LSTM, and the Transformer, the attention-based architecture that has revolutionized fields from language translation to weather forecasting. Model selection was carried out with two-fold forward-chaining validation, a scheme that respects the ordered nature of time-series data by training on earlier periods and testing on later ones. The models were then refitted on data from 2018 to 2021 and evaluated on an independent test set from 2022, a design that guards against the optimism that arises when a model is judged on data it has already seen.

The verdict was that the two deep learning approaches stood out. Transformer and LSTM delivered the most competitive overall performance across the depth range, with the Transformer achieving depth-averaged error metrics of 0.968 degrees Celsius in mean absolute error and 1.188 degrees Celsius in root mean square error, a normalized root mean square error of 30.89 percent, and a coefficient of determination of 0.886. In practical terms, the model could reconstruct temperatures several feet underground with an accuracy close to one degree, a level of precision that could make the difference between detecting an incipient frost problem and missing it entirely. Yet the team was careful about overclaiming: when pairwise comparisons of root mean square error were subjected to paired moving-block bootstrap tests and corrected for multiple comparisons using the Holm procedure, none of the Transformer’s advantages over rival models remained statistically significant. The edge was real in magnitude but not decisive in a statistical sense, a nuance that reflects the genuine difficulty of the prediction task.

Before turning to prediction, the researchers first characterized how temperature behaves as it travels downward through the slope. The records showed the two classic signatures of heat conduction in soil: progressive attenuation, meaning the seasonal swing in temperature shrinks with depth, and phase delay, meaning the annual warm and cold peaks arrive later the deeper one looks. Summer heat that scorchers the surface in July may not reach its maximum influence at 250 centimeters until well into autumn, and winter cold penetrates in similarly lagged fashion. These evolutionary patterns are not academic curiosities; they dictate how deeply frost penetrates, how long frozen layers persist, and how much heave and settlement a slope endures over a season.

The team also tested how well the models could identify whether the ground was frozen or thawed, a binary distinction that matters enormously for frost heave prediction. Frozen-state identification proved reliable at depths of 100 and 120 centimeters, where freeze-thaw transitions are frequent and strongly expressed, but weakened at 150 and 180 centimeters. At 200 and 250 centimeters, the test set contained no frozen observations at all, meaning the deepest soil at the site never crossed the freezing threshold during the evaluation year. This depth-dependent reliability is a candid reminder that data-driven models inherit the structure of their training data: where the physics is active and the labels abundant, they excel; where transitions are rare or absent, their skill remains unverified.

One of the study’s most valuable contributions is its use of SHAP, or SHapley Additive exPlanations, a technique borrowed from cooperative game theory that assigns each input variable a quantified share of the model’s output. Rather than treating the neural networks as black boxes, the researchers could ask which predictors actually drove the deep temperature estimates. The answer aligned neatly with the physics. Temperatures measured at 50 and 80 centimeters, the shallow layers closest to the atmosphere, dominated the model’s contributions, acting as a thermal memory from which deeper conditions could be inferred. Meanwhile, the importance of seasonal-phase variables grew steadily with depth, mirroring the increasing phase lag of the seasonal signal. The explainability analysis thus served as an independent check that the models had learned genuine thermal dynamics rather than spurious correlations.

The implications extend well beyond a single canal. Canals in seasonally frozen regions are lifelines for agriculture and water delivery across vast stretches of North America, northern Europe, Central Asia, and China, and climate warming is altering freeze-thaw regimes in ways that make historical design assumptions increasingly unreliable. Related efforts have applied artificial intelligence to permafrost and active-layer temperatures on the Alaskan North Slope and to soil temperature forecasting in subtropical grazing lands, suggesting a broader movement toward data-driven cryosphere monitoring. The framework presented here adds an important engineering dimension to that movement, showing that a site-specific model can serve as a complement to direct deep-ground monitoring, particularly when deep sensors are sparse, degraded, or temporarily offline.

The authors are careful to position the approach as a supplement rather than a replacement for physical instrumentation. Machine-learning reconstructions are only as good as the shallow observations and auxiliary data they are trained on, and the site-specific nature of the models means they cannot simply be transplanted to new locations without retraining. Still, the combination of rigorous time-series validation, bootstrap-based significance testing, phase-specific error analysis, and SHAP interpretability sets a high methodological standard for a field where black-box predictions are too often accepted at face value. For engineers charged with keeping frozen slopes stable, the study offers something genuinely useful: a way to estimate what is happening at depths they cannot easily measure, grounded in data they already have, with an honest accounting of where the estimates can be trusted and where uncertainty still reigns. As sensors fail and climates shift, that ability to intelligently fill the gaps in the thermal record may prove one of the quiet but essential technologies of cold-region water infrastructure.

Subject of Research: Machine-learning-based prediction of deep ground temperature fields in cold-region canal slopes

Article Title: Evolutionary patterns and intelligent prediction of temperature fields in cold-region canal slopes

Article References: Fang, H., Wang, M., Hai, M., Zhou, B., Liu, Y., & Chen, L. (2026). Evolutionary patterns and intelligent prediction of temperature fields in cold-region canal slopes. Earth Science Informatics, 19(11), Article 193. https://doi.org/10.1007/s12145-026-02245-0

Image Credits: AI Generated

DOI: 10.1007/s12145-026-02245-0

Keywords: cold-region canals, soil temperature prediction, machine learning, Transformer model, LSTM, freeze-thaw cycles, frost heave, SHAP interpretability, soil freezing, geotechnical engineering, time-series forecasting, canal slope stability

The development of jelly ice emerged from a simple but profound observation in grocery store seafood sections, where melting ice pools raised concerns about pathogen spread and food contamination. This practical problem inspired researchers Jiahan Zou and Gang Sun to explore gelatin as the base for a reusable cooling medium that could retain water even through multiple freeze-thaw cycles. Gelatin’s unique molecular composition — long proteins that form a three-dimensional hydrogel network — enables it to trap water molecules tightly within tiny pores. This structural characteristic resists water leakage even when the gel transitions from frozen to liquid states, a critical improvement over typical ice or frozen tofu, which releases water upon thawing.

Gelatin hydrogels, used here as a starting material, possess critical attributes for a practical reusable ice substitute. From a materials science perspective, these gels are physically crosslinked polymeric networks capable of phase transitions without structural collapse. They jellify due to the physical entanglement of protein strands, producing a stable mesh that hosts water uniformly. This uniform encapsulation allows jelly ice to maintain cooling properties based on the latent heat absorbed or released during water’s phase changes, although with an efficiency slightly below that of pure ice. Despite this, the material excels in reusability, microbial resistance, and avoiding contamination, offering up to 80% of the heat absorption capacity of conventional ice.

One of the most significant advantages of jelly ice is its sustainability. Unlike conventional ice packs, which are typically encased in plastic and often discarded after use, jelly ice is fully compostable, breaking down into environmentally benign components. Moreover, it eliminates microplastic pollution risks, a growing concern linked to synthetic cooling gels. The research team reported that composted jelly ice even improves plant growth, suggesting an added circular economy benefit for agricultural waste integration. This eco-conscious design signals an important advancement in the drive to replace synthetic materials with bio-based, biodegradable alternatives that do not contribute to long-term pollution.

Beyond basic applications, jelly ice shows promise in specialized contexts where traditional ice is suboptimal or unavailable. Medical and biotechnological industries, for example, require reliable cool storage solutions that maintain sterile conditions and prevent moisture contamination. Jelly ice’s ability to withstand repeated freeze-thaw cycles and resist microbial proliferation makes it ideal for shipping sensitive pharmaceuticals and biological samples. Additionally, its moldability and customizable shapes allow tailoring for diverse containers and packaging sizes, markedly improving logistic flexibility compared to rigid ice blocks or bulky gel packs.

The underlying fabrication method for jelly ice emphasizes efficiency and scalability. Employing a streamlined one-step synthesis, the production process involves hydrating gelatin polymers with water and subjecting the mixture to controlled cooling, which prompts gel formation. This approach not only simplifies manufacturing but also allows easy adjustment of the material’s physical properties, such as stiffness and freezing point, by altering gelatin concentration or adding safe additives. Such tunability sets the stage for widespread adoption across industries with differing cooling requirements.

The conceptual origin of jelly ice traces back to the behavior of frozen tofu, a material known for entrapping water in a porous matrix. However, unlike tofu, which releases water upon thawing due to its structural breakdown, gelatin’s hydrogel maintains its structural fidelity and prevents leakage. This biological inspiration highlights the power of leveraging natural materials and mechanisms to meet engineering challenges — a principle increasingly embraced in sustainable materials science. The researchers’ success with gelatin hydrogels paves the way for exploring other biopolymers with similar properties for future innovations.

While jelly ice represents a notable breakthrough, market integration still lies ahead. Researchers acknowledge that comprehensive market analyses, product design refinements, and large-scale production validations will be necessary before jelly ice becomes a commercially available consumer product. The technology is licensed, suggesting active interest from industry stakeholders eager to capitalize on its sustainability advantages. If successfully commercialized, jelly ice could disrupt standard practices in food retail, cold chain logistics, and even everyday consumer use, offering a mess-free and eco-friendly cooling experience.

Importantly, the researchers are extending their work into developing protein-based coatings and scaffolds derived from plant proteins, such as soy, which hold potential for cultivating meat and other cellular agriculture applications. These efforts aim to broaden the suite of bio-derived polymeric materials contributing to a circular bioeconomy by valorizing agricultural byproducts and reducing reliance on petrochemical plastics. Advances in ultrasonic and chemical processing techniques have improved the processability of such plant proteins, opening exciting avenues in food safety and sustainability-focused materials science.

Zou, a lead investigator, emphasizes the power and versatility of natural biopolymers in crafting next-generation materials. Her research shows that mimicking nature’s design principles can yield functional materials with desirable mechanical, thermal, and environmental properties suited for food security and safety. This approach aligns well with the global imperative to innovate sustainable alternatives that balance performance with ecological responsibility, reflecting a growing recognition that future material solutions must be inherently regenerative.

Presented at the prestigious American Chemical Society’s Fall 2025 meeting, this research attracted attention for its interdisciplinary impact — bridging chemistry, materials science, food technology, and environmental sustainability. The jelly ice innovation exemplifies the transformative potential inherent in bio-based functional materials to address major societal challenges such as food waste, plastic pollution, and pathogen contamination. As the scientific community advances these innovations, the commercial ecosystem will likely witness an expansion of green materials replacing conventional synthetic solutions.

Ultimately, jelly ice is more than just a clever substitute for melting ice; it represents a step toward circular, regenerative materials that support sustainable food systems and cold chain logistics worldwide. Future developments may see the technology integrated into diverse applications—from everyday grocery displays and home use to critical medical shipments and cellular agriculture. This versatile biomaterial, derived from a simple yet powerful natural polymer, exemplifies the promise of biopolymer science in harmonizing technological progress with environmental stewardship.

Subject of Research: Sustainable bio-derived polymeric materials improving food security, food safety, and circular bioeconomy

Article Title: Sustainable bio-derived polymeric materials improving food security, food safety, and circular bioeconomy

News Publication Date: August 18, 2025

Web References:
https://acs.digitellinc.com/live/35/page/1204
https://acs.digitellinc.com/live/35/session/560625
https://youtu.be/qXofDLPriwg

Image Credits: UC Davis

Keywords

Chemistry, Materials, Food safety