Soils and sediments are soft, amorphous materials with complex microstructures and mechanical properties. They are also building blocks for industrial materials such as concrete. These Earth-mediated materials evolve under prolonged environmental pressures such as mechanical stress, chemical gradients, and biological activity. Here, we introduce geomimicry, a new paradigm for designing sustainable materials by learning from the emergent and adaptive dynamics of Earth-mediated matter. Drawing a parallel to biomimicry, we posit that these geomaterials follow evolutionary design rules, adapting their structure and function in response to persistent natural forces through locally evolved interactions and compositions. Our central argument is that by decoding these rules-primarily through understanding the emergence of novel exotic properties from multiscale interactions between heterogenous components-we can engineer a new class of adaptive, sustainable matter. We propose two complementary approaches here. The top-down approach looks to nature to identify building blocks and map them to functional groups defined by their mechanical (rather than chemical) behaviors, and then examine how environmental training tunes interactions among these groups. The bottom-up approach seeks to leverage and test this framework, building earth materials one component at a time under fluctuating environmental stresses that guide assembly of complex and out-of-equilibrium materials. The goal is to create materials with programed functionalities, such as erosion resistance or self-healing capabilities. Geomimicry offers a pathway to truly design Earth-mediated circular materials, with potential applications ranging from climate-resilient soils and smart agriculture to new insights into planetary terraforming, fundamentally shifting the focus from static compositions to dynamic, evolving systems that are mediated via their environment.
Rare earth elements (REEs) are effective tracers of groundwater circulation and water-rock interaction in carbonate aquifers. This study investigates hydrochemical parameters and REE compositions of spring waters from Chongqing (eastern Sichuan Basin) to clarify controls on REE mobilization, fractionation, and redox behavior within a fold-controlled karst aquifer. Spring waters exhibit slightly acidic to slightly alkaline pH values (6.2-8.4) and variable redox conditions (Eh from -338 to 148 mV). Hydrochemical facies evolve from Ca-HCO3 and Ca·Mg-SO4 types to Na-Cl and Na-SO4 types, indicating a transition from shallow recharge systems to deeper, structurally controlled groundwater circulation. Total dissolved REE concentrations (ΣREE) range from 0.025 to 1.732 μg/L and show clear spatial variability, with relatively higher values occurring in structurally complex southeastern zones. Correlation analysis indicates that ΣREE concentrations are weakly related to salinity but are influenced by temperature, redox conditions, and bicarbonate availability, reflecting the importance of groundwater circulation depth and water-rock interaction. NASC-normalized REE patterns consistently display LREE depletion and relative MREE-HREE enrichment, typical of carbonate aquifers. Predominantly, Eu anomalies suggest localized reducing or thermally influenced environments associated with deeper groundwater circulation. REE behavior in Chongqing spring waters is indirectly controlled by tectonic structure through its regulation of groundwater circulation and hydrochemical conditions.
The escalating global demand for rare earth elements (REE) has highlighted the potential of anthropogenic waste streams as secondary resources. This study characterizes the distribution and speciation of REE in Korean municipal solid waste incineration (MSWI) bottom ash (BA) and fly ash (FA). The ashes were subjected to particle size fractionation and magnetic separation to examine REE distribution across different particle sizes and magnetic fractions. In addition, raw BA and FA samples were analyzed using sequential extraction to assess REE speciation and leachability. Elemental composition and mineral phases were determined using inductively coupled plasma optical emission spectrometry (ICP-OES) and X-ray diffraction (XRD). The results show that REEs are enriched in the magnetic fractions of both BA and FA and exhibit slightly higher concentrations in coarser particle fractions. Light REE (LREE) predominate across all particle sizes, including the nonmagnetic fractions. Sequential extraction results show that REEs are predominantly hosted in stable mineral phases, with only a limited proportion present in labile fractions. Overall, these findings reveal the distribution and chemical associations of REE in MSWI ash and provide a basis for future studies on their speciation and potential mobilization.
Despite heat flux's role in regulating energy conversion in collisionless plasmas, its properties and evolution in the magnetosheath downstream of the Earth's bow shock are scarcely explored. We use Magnetospheric Multiscale in situ measurements to quantify and characterize the electron heat flux in the magnetosheath. We find that the heat flux is shaped by the magnetosheath magnetic field as it drapes around the magnetosphere. While it is affected by solar wind upstream conditions and increases with magnetic field strength, it is not substantially changed by local magnetosheath processes. Also, the heat flux is limited by whistler instability thresholds.
Mining and e-waste recycling release trace elements (TEs) that can accumulate in aquatic organisms and impair their vital functions. Predicting whether an element will biomagnify or biodilute in food webs is challenging, as it depends on the element, species, and environment. This is especially true for technology-critical elements (TCEs), including most rare earth elements (REEs), which remain largely unstudied and are currently unregulated in most countries despite growing environmental releases. To address the gaps in understanding pollution fate and ecological implications of these elements, we quantified concentrations of understudied TCEs (Ti, Sr, Co, Tl, REEs (La, Ce)), alongside conventional TEs (Cu, Zn, As, Se, Cd, Pb), in six organism groups spanning multiple trophic levels (Ephemeroptera, Diptera-Chironomidae, Amphipoda, zooplankton, Yellow perch, and Walleye) from six boreal lakes at varying distances from mining and e-waste recycling activities. We then examined biomagnification and biodilution patterns of these elements across food webs using stable isotope δ15N to determine trophic position (TP). TCEs, including Ti, Co, La, and Ce, consistently biodiluted across all lakes, similarly to the better-known TEs (Cu, Zn, As, Cd, Pb). Selenium was the only element to show significant biomagnification, and this pattern was observed exclusively in two remote lakes. Tl also showed a variable pattern, characterized by significant biodilution in some lakes and non-significant trophic relationships in others. The variability for Se and Tl suggests their trophic transfer depends on lake-specific ecological factors rather than exposure alone. Zooplankton consistently showed the highest concentrations within the food web for multiple elements (e.g., Ti, Co, Tl, La, Ce, Cu and Zn), making them a key contaminant entry point. This study provides needed evidence on TCEs trophic dynamics in boreal lakes. These results inform pollution monitoring and ecosystem protection for freshwater environments across boreal regions impacted by mining and e-waste recycling worldwide.
Early post-mainshock catalogs are often incomplete, causing biased parameter estimation and unreliable forecasts. Using the 2021 Qinghai Maduo M7.4 intraplate earthquake, we assess two approaches: data replenishment using the bi-scale empirical probability integral transformation (BEPIT) method and Bayesian spatiotemporal epidemic-type aftershock sequence (ETAS) model. Early catalog incompleteness is the primary cause of ETAS parameter instability. After replenishment, the a value rises from 3.9952 to 4.4836 and the b value from 0.57 to 0.69, indicating more small aftershocks. Bayesian ETAS provides probabilistic forecasts, early overestimation of larger events diminishes as data accumulate, and high-probability zones match observed aftershocks. Statistical tests confirm acceptable performance but show sensitivity to prior settings. As this study is based on a single sequence, generalizability requires validation across diverse tectonic regimes. Nevertheless, BEPIT replenishment and Bayesian updating enhance early post-mainshock parameter estimation and short-term forecasting for intraplate events, supporting rapid response and dynamic risk assessment.
Electrocatalytic nitric oxide reduction to ammonia couples pollutant valorization with sustainable nitrogen conversion, but high activity and selectivity require concurrent control of NO transport, NO activation, and hydrogenation at the gas-liquid-solid interface. Here, we report a self-supported, noble-metal-free Fe3C/Fe3N@C catalyst, composed of earth-abundant Fe, C, and N, featuring defect-rich Fe3C/Fe3N Janus nanostructures confined within graphitic carbon. The catalyst achieves an NH3 yield rate of 468.3 µmol h-1 cm-2 with a Faradaic efficiency of 94.2% at -0.6 V versus RHE, placing it among the most efficient reported NORR electrocatalysts. Mechanistic studies reveal that the graphitic carbon shell facilitates NO diffusion by alleviating the steric and dynamic constraints imposed by the hydrogen-bonded water network. At the Janus interface, Fe3C sites preferentially adsorb and activate NO, whereas nitrogen-vacancy-rich Fe3N sites promote H2O dissociation to supply reactive *H for subsequent hydrogenation. This spatial coupling of mass-transfer promotion, NO activation, and interfacial *H generation enables efficient and selective NO-to-NH3 electroreduction. These findings establish carbon-confined, earth-abundant carbide/nitride Janus interfaces as a promising design principle for high-performance NORR catalysts.
Ferromanganese (Fe-Mn) concretions are porous accumulations of iron and manganese (hydr)oxides. While recent studies suggest that microbes contribute to metal accumulation in Baltic Sea concretions, the detailed composition of microbial communities and their impact on metal enrichment across different concretion morphotypes remain unexplored. We investigated how microbes influence the accumulation and release of trace metals and rare-earth elements in Fe-Mn concretions from the Gulf of Finland through 15-week microcosm incubation experiments with biotic and abiotic treatments, focusing on three main concretion morphotypes: crust, discoidal, and spheroidal. Elemental analysis showed that microbes enhanced metal incorporation in discoidal and spheroidal morphotypes. Characterisation of microbial composition revealed that all three morphologies host distinct communities. Discoidal and spheroidal morphotypes had a higher relative abundance of Gammaproteobacteria and sulfate-reducing bacteria, and a lower abundance of Entotheonellaeota, compared to crusts. In all morphotypes, the bacterial phylum Pseudomonadota dominated, with several genera of Fe- and Mn-oxidisers and reducers. Fe-Mn concretions also host communities involved in methane oxidation and nitrogen cycling, consistent with decreased methane and increased nitrous oxide, nitrite, and nitrate concentrations in the microcosms. Our findings underscore that distinct microbial communities are associated with different concretion morphotypes, potentially influencing nutrient and metal cycling on the seafloor.
The Atacama Desert, one of the driest and oldest regions on Earth, represents an extreme environment that has driven remarkable adaptive evolution over millions of years. Within this setting, the genus Tillandsia comprises specialists capable of surviving at the dry limit of plant life. These species exhibit crassulacean acid metabolism (CAM), lack functional roots, and possess trichomes adapted for water and nutrient absorption. Of the more c. 750 known Tillandsia species, nine occur in the Chilean-Peruvian Atacama Desert, where they colonize bare sand surfaces (epiarenic growth) under hyperarid conditions without significant rainfall, relying solely on nocturnal fog for moisture. Despite their striking adaptations, the evolutionary mechanisms and timing underlying the emergence of epiarenic growth remain poorly understood. Here, we reconstructed a maximum-likelihood phylogeny based on 278 plastome sequences of Tillandsioideae and other Bromeliaceae subfamilies to study respective sister species relationships. Further, divergence time estimates for the origin of epiarenic Tillandsia were estimated using Bayesian inference in BEAST2. In addition, we analyzed variation in orthologous copies of the nuclear-encoded Agt1 gene to test for interspecific and interploidal gene flow among epiarenic taxa and their closest relatives. This gene has been previously established as a barcoding marker in bromeliads. The results indicate that epiarenic Tillandsia evolved multiple times independently from the Late Pliocene through the Pleistocene, consistent with a long-term hyperarid evolutionary arena promoting extreme adaptations. Moreover, evidence for frequent interspecific hybridization and gene flow suggests that hybridization may have also contributed to the long-term success of epiarenic Tillandsia species and is reflecting also the spatio-temporal dynamics of the Atacama's hyperarid landscapes during the Pleistocene.
BaZrS3 and the broader class of chalcogenide perovskites are an emerging class of semiconductors that are of particular interest for tandem photovoltaics. Existing synthesis methods often rely on high-temperature processing, posing a significant barrier to future scalable production. Recent research efforts have focused on reducing the processing temperature by adopting novel precursors and synthesis pathways to unlock kinetic enhancements by forming intermediate liquid phases. Significant uncertainty exists as to precisely what liquid phases form, at what conditions they are stable, and how further processing improvements might be made. The uncertainty is due largely to a dearth of experimental thermodynamics reports for melting transitions in the Ba-Zr-S ternary system. In this work, we perform melting point measurements on BaZrS3, obtaining a melting point of 1450 ± 100 °C. We use this value to generate a preliminary model of the Ba-Zr-S liquidus surface near BaZrS3, predicting a liquidus depression of several hundred °C when moving from BaZrS3 composition towards either of the metal trisulfide binaries. Cross-sectional characterization of the samples after resolidification by diffraction, compositional, and spectroscopic methods show signs of decomposition into BaZrS3, Ban+1ZrnS3n+1 Ruddlesden Popper (RP) phases, and ZrO2, with BaZrS3 as the primary phase. This work represents a necessary first step in furthering thermodynamics modeling of melting transitions in the Ba-Zr-S system, with expected relevance for the related A-B-S (A: Ca, Sr, Ba; B: Hf, Ti, Zr) systems for processing of other chalcogenide perovskites.
Laboratory medicine is undergoing a profound transformation driven by advances in artificial intelligence (AI), automation, and data interoperability. By 2050, laboratories may evolve from analytical testing facilities into an interconnected health intelligence capable of translating biological, digital, and environmental data into more personalized, preventive, and sustainable healthcare solutions. Building on previous foresight analysis conducted by the IFCC Emerging Technologies Division (ETD), global healthcare outlook reports, and expert-informed horizon scanning, this paper explores ten megatrends that may shape laboratory medicine by mid-century. These include precision multi-omics, AI-supported diagnostics, distributed healthcare models, patient-owned data ecosystems, digital twins, convergence of imaging and laboratory medicine, population-wide prevention strategies, regenerative therapies, sustainability imperatives, and workforce transformation. Collectively, these developments represent a paradigm shift from data generation to data interpretation, positioning laboratories as trusted nodes within the health intelligence network connecting patients, clinicians, and healthcare systems. The integration of automation with advanced multi-omics technologies, including robotic liquid handling, autonomous LC-MS/MS platforms, spatial proteomics, and real-time metabolomics, will accelerate the transition toward precision and planetary health. Four exploratory scenarios illustrate plausible trajectories for laboratory medicine in 2050. The Hyper-Intelligent Laboratory explores the implications of self-learning systems and advanced analytics capable of supporting earlier disease prediction and intervention. The Sustainable and Regenerative Laboratory illustrates how environmental stewardship and diagnostic excellence may converge within circular healthcare ecosystems. The Patient-in-the-Loop Revolution examines a future in which citizens become active stewards of their health data and participants in healthcare decision-making. The Spacefaring Laboratory extends this reflection beyond Earth, illustrating how extreme environments may accelerate innovation in autonomous diagnostics, sustainability, and human health monitoring. By 2050, laboratory medicine could serve as a central intelligence layer within healthcare systems, transforming biological signals into actionable knowledge. Achieving this vision will require scientific innovation, regulatory adaptability, equitable access, ethical governance, sustainability, and a future-ready workforce.
With its non-uniform distribution of crustal magnetic fields, Mars exhibits complex and highly variable auroral patterns related to both planetary rotation and solar wind conditions. Using in situ electron, ion, and magnetic field data from the Mars Atmospheric and Volatile EvolutioN (MAVEN) mission, we show that auroral processes associated with these small-scale crustal magnetic fields can be understood in terms of a miniature cycle of magnetic flux and plasma circulations that resemble a miniature version of what occurs at the Earth. However, at Earth, this Dungey cycle, named after its discoverer, operates in the presence of a global intrinsic dipole field with a strength approximately 100 times stronger and spatial scales roughly 20 times larger. From a universal perspective, the current finding adds an entry to the zoo of auroral concepts that enriches our understanding of the diversity of (exo)planetary plasma and our understanding of how planets interact with their space environments.
In recent times metal hydrides have reinvigorated the search for room temperature superconductivity owing to their intriguing physical properties. Among these, rare earth hydrides are unique due to their unequivocal properties related with hydrogen storage, potential for ambient superconductivity and switchable mirror technology. Towards this, we have synthesized higher erbium (Er) hydrides using pure Er metal and paraffin oil as an alternate hydrogen source by employing laser heated diamond anvil cell. Thus, synthesized hydride has been investigated using synchrotron-based angle dispersive x-ray diffraction (ADXRD) technique and Raman spectroscopy. At synthesized conditions (P = 36 GPa and T= 1800 K) the erbium hydride is found to be in face centred cubic (fcc) structure with stoichiometry as ErH3. Its compression behaviour has been studied up to 40 GPa during compression and decompression both. On decompression, it transforms to hexagonal close pack (hcp) structure of ErH3at ~ 5.2 GPa. The cubic phase is found to be partially recoverable on complete release of pressure. Equation of state for cubic phase has been established. The bulk modulus is determined to be 74±4 GPa for fcc phase. Furthermore, to study the possible chemical reaction of paraffin with Er and the effect of quasi hydrostaticity on its phase transition sequence, high pressure ADXRD measurements were carried out at room temperature. The detailed structural analysis establishes the phase transition sequence as hcp → hR24 (Sm-type structure) → dhcp beyond 9.6 GPa and across 23.8 GPa respectively.
Islands sustain an outstanding proportion of Earth's biodiversity. However, modern molecular studies on oceanic island floras have typically focused on endemics, which leaves many research questions involving other types of taxa unaddressed. In this study, we analyse the patterns of genetic variation in two co-occurring native nonendemics (NNE) that are common components of lowland habitats across the Canarian archipelago: Launaea arborescens (Asteraceae), a wind-dispersed species, and Lycium intricatum (Solanaceae), an endozoochorus species. We predicted that, given their presumably recent colonization of the archipelago, both lineages should show a genetic pattern compatible with a stepping-stone model, i.e. from the easternmost islands (closest to mainland Africa) to the western islands. We carried out exhaustive sampling in all the islands of distribution and in neighbouring mainland areas (SW Morocco). Using three plastid DNA regions, we calculated levels of haplotype diversity, conducted analyses of spatial distribution of molecular variance, and tested various models of colonization and gene flow with complementary coalescent-based approaches. For comparison, we additionally performed a literature review to assess general patterns of haplotype diversity on plant species with similarly widespread distributions across the archipelago. A stepping-stone model of island colonization was only partly supported by our results, since back-colonization of mainland areas was a scenario equally supported by coalescent analyses and estimates of historical gene flow. Both species showed that the easternmost islands represented de facto a genetic continuum of the neighbouring mainland area. However, Lycium generally displayed much higher rates of gene flow than Launaea, which may be due to secondary dispersal mediated by predatory birds. Lastly, our literature survey revealed that both NNE harbour unprecedented high levels of genetic diversity on the easternmost Canary islands. This study illustrates that the phylogeographical analysis of NNE provides novel insights into island biogeography, since these taxa can deviate from common patterns observed in endemic lineages.
In recent decades, the monitoring of volcanoes has been revolutionized by the launch of Earth-observing satellites and advances in thermal infrared remote sensing. These developments have revealed a wide range of thermal responses of volcanic surfaces to subsurface processes, even demonstrating that eruptions are often preceded by measurable thermal anomalies. This recognition highlights the need for robust tools to systematically detect and track such anomalies, making full use of existing satellite datasets and maximizing the value of current instruments in orbit. To address this challenge, we present the Subtle Surface Thermal Anomalies Recognizer (SSTAR), a versatile and user-friendly application designed to analyze diffuse thermal anomalies, i.e., subtle thermal unrest (~ 1 K) across large areas (several km2). SSTAR leverages data from NASA's Terra and Aqua satellites, which host the Moderate Resolution Imaging Spectroradiometers (MODIS), and builds upon a robust statistical framework. By processing pixel-level data, SSTAR tracks the temporal evolution of diffuse thermal anomalies at specific target sites and maps their spatiotemporal distribution across extended areas. Key features include filtering tools that distinguish between long-term (years) and short-term (weeks) anomalies, as well as uncertainty quantification using bootstrapping. The application is standalone, features an interactive interface for streamlined analysis, and is accessible to newcomers to satellite-based thermal remote sensing. At the same time, specialized users can customize the underlying scripts for other specific research needs. As a demonstration, we apply SSTAR to Shishaldin volcano (Alaska), revealing the emergence of significant thermal anomalies around the summit crater and flanks prior to eruptions. We envision SSTAR as a valuable resource for studying subtle thermal unrest at active volcanoes and hydrothermal systems, where the detection of faint and spatially coherent anomalies may help identify subsurface fluid pathways. Its flexible design enables integration with additional satellite datasets, positioning SSTAR as a forward-looking tool for advancing space-based volcanic thermal monitoring. Building on this capability, daily updated diffuse thermal anomalies are provided for target volcanoes through an open web platform hosted at Geosciences Barcelona-CSIC (https://sstar.geo3bcn.csic.es/), to support surveillance agencies and expert committees in alert-level assessments. The online version contains supplementary material available at 10.1186/s40623-026-02497-6.
To address the poor moldability, high cost, and insufficient structural stability of conventional CO₂ adsorbents, a low-cost amine-grafted spherical silica/cellulose composite aerogel (AGSSCA) was fabricated in this work. Using defatted cotton-derived cellulose and diatomaceous earth-derived sodium silicate as raw materials, a homogeneous solution was obtained through alkali dissolution and blending, and silica/cellulose spherical gels were fabricated in situ via a droplet gelation approach; a series of AGSSCA materials were prepared through graft modification using the amino silane coupling agent (APTES); the effects of component ratios and grafting conditions on the material structure and CO₂ adsorption performance were systematically investigated. The results show that at 25 °C, the material exhibits an optimal CO₂ adsorption capacity of 2.14 mmol/g, which decreased by only 5% after five cycles. The CO₂ adsorption capacity increased with increasing silica content in the composite framework. Structural characterization suggested that APTES formed Si-O-Si and Si-O-cellulose covalent linkages within the silica/cellulose framework, thereby generating a porous three-dimensional interpenetrating network structure, with a maximum BET surface area of 300.87 m2/g.
Human pressures on Earth's life‒support systems have exceeded multiple planetary boundaries, highlighting the need for sectors, including veterinary healthcare, to reduce environmental impacts. A prospective convenience sample of 10 horses undergoing arthroscopy at a UK equine hospital between January and March 2025 was studied. Data were collected on anaesthesia, volatile capture, pharmaceuticals, single-use consumables, waste, building energy and travel by horses and staff. Carbon emissions (kgCO2e) were calculated using published conversion factors. A hybrid methodology combined bottom‒up process-based calculations with environmentally extended input-output (EEIO) modelling where primary data were unavailable. Total emissions from the 10 procedures were 2044 kgCO2e. Transport was the largest contributor (42%), followed by single-use consumables (21%), pharmaceuticals (11%), waste (3%) and building energy (<1%). Owner transport (median distance 251 km) accounted for 86% of travel emissions. The Vet-Can captured 490 g of isoflurane over 1199 minutes, with a median in vivo transfer efficiency of 39%. Findings were constrained by emission factor availability, system-boundary inconsistencies, reliance on EEIO modelling and a small single-site sample, limiting generalisability. Volatile anaesthetic impact should be interpreted cautiously, as CO2e-based quantification remains contentious. Transport and single-use consumables represent priority targets for sustainability interventions.
The leaf photosynthesis-transpiration-stomatal conductance model, which consistently describes leaf photosynthesis, transpiration, and stomatal conductance, has been widely used as a standard for quantifying these processes in terrestrial plants. However, since its proposal more than 30 years ago, the model has faced a fundamental mathematical problem: Does a solution always exist? And even if a solution is obtained, can it be guaranteed to be the correct one among potentially multiple mathematical solutions-that is, the one actually realized in nature? Here, we resolve this problem by mathematically proving that the model always yields a unique solution satisfying biologically and physically meaningful criteria. This result establishes a rigorous mathematical theorem on the existence and uniqueness of solutions in the model, thereby ruling out concerns about the non-existence of solutions and ensuring that past and future estimates satisfying the criteria are correct. These findings provide a robust theoretical foundation for the model and have far-reaching implications for a broad range of fields, spanning plant and ecosystem research to climate and Earth system studies.
Coal fly ash (FA), a major byproduct of coal-fired power plants, remains underutilized despite its high content of silica (SiO2) and alumina (Al2O3). This study investigates alkali activation as a route for selective Si and Al recovery, combining experimental leaching, thermodynamic modeling, and response surface methodology (RSM). Parametric analysis revealed that NaOH concentration, temperature, and reaction time critically influenced dissolution behavior. Optimal conditions5 M NaOH, 140 °C, and 1 hachieved up to 50.4% Si dissolution while minimizing Al solubilization (<3%). Mechanistic evaluation identified a three-stage process: (i) initial extraction, (ii) concurrent dissolution-precipitation governed by zeolite and phillipsite formation, and (iii) diffusion-limited Si release under surface passivation. XRD, SEM-EDS, and equilibrium modeling confirmed sodalite-type precipitation as the principal limitation to Al recovery. RSM-derived regression models (R 2 > 0.94) accurately predicted Si and Al dissolution across variable conditions, enabling process optimization with reduced experimental burden. Beyond resource recovery, selective removal of Si and Al facilitates enrichment of rare-earth elements in the residue, enhancing downstream extraction potential. Recovered silica and alumina can be valorized in advanced alloys, ceramics, adsorbents, and photovoltaic applications, positioning alkali activation as a viable pathway for circular utilization of FA within low-carbon materials innovation.
This study evaluated standard digital photographs, combined with automated image segmentation and machine learning, to rapidly and non-destructively predict compost physicochemical properties. A total of 230 compost samples were collected from retail stores and commercial composting facilities across Georgia, South Carolina, and Tennessee, USA. Images were acquired using an iPhone 13 Pro Max under controlled lighting conditions, and compost regions were automatically isolated using a U-Net convolutional neural network. Image-derived colour, texture, and morphological features were then used to predict pH, electrical conductivity (EC), volatile solids (VS), ash content, and bulk density (BD). Ten regression algorithms were compared; K-nearest neighbours (KNN) regression provided optimal predictive performance. Internal validation of predictive model performance was strong: EC (R2 = 0.97, RMSE = 490 µS cm-1), pH (R2 = 0.95, RMSE = 0.21), VS (R2 = 0.92, RMSE = 5.5%), ash content (R2 = 0.94, RMSE = 23.0%), and BD (R2 = 0.87, RMSE = 26.2 kg m-3). External independent blind validation performance via KNN models remained encouraging: EC (R2 = 0.69, RMSE = 134 µS cm-1), pH (R2 = 0.75, RMSE = 0.27), VS (R2 = 0.81, RMSE = 11.5%), ash content (R2 = 0.70, RMSE = 14.4%), and BD (R2 = 0.63, RMSE = 96.2 kg m-3). Smartphone-based imaging with U-Net segmentation and KNN regression, can provide a rapid and low-cost screening approach for selected, visually linked compost-quality indicators. These findings encourage broader validation across diverse feedstocks, composting systems, and conditions in support of field-scale deployment.