Laura E. Dalton

Over the past 80 years, biotechnology has advanced agriculture, health care, and economic development by harnessing biological processes from the organism inward, i.e., from the organ system to the molecular scale. Today’s global challenges, including biodiversity loss, climate change, and pollution, demand a complementary technological expansion inspired by processes operating from the organism outward, i.e., at the levels of populations, communities, ecosystems, and the biosphere. Here, we present the components of this technological expansion through ecosystem technology, or ecotech. We propose a framework for ecotech to integrate elements of ecology, engineering, and earth science and to function as a practical and conceptual convergence accelerator. Ecotech will advance critical environmental solutions by uniting independently evolving technologies, generating diverse fields of inquiry (e.g., ecomimicry, ecosystem materials science, ecosystem sensing and signaling), and inspiring innovation. To harness this innovation to improve nature restoration, carbon storage, water quality, ecosystem energy, and infrastructure resistance to disasters, ecotech is guided by cross-cutting actions to ensure scalability, equity, and accountability. When integrated into strategies across nongovernmental organizations, business, and governments, ecotech offers a pathway to advance climate adaptation, biodiversity recovery, and economic diversification and growth. By uniting ecology, engineering, and earth sciences at scale, ecotech transforms technology into a tool to confront humanity’s most urgent existential needs and secure a livable future.

Non-destructive methods are essential for linking the microstructural geometry of porous materials to their mechanical behavior, as destructive testing is often infeasible due to limited material availability or irreproducible conditions. Micro-computed tomography (micro-CT) provides high resolution three dimensional reconstructions of porous microstructures, enabling direct quantification of geometric descriptors. Recent advances in morphometric theory have demonstrated that four independent morphometric measures (porosity, surface area, mean curvature, and Euler characteristic) are required to capture the relationship between microstructure and strength, thereby forming the basis of generalized strength laws. To facilitate practical application of this framework, a Fiji plugin was developed to extract the four morphometric measures (porosity, surface area, mean curvature, Euler characteristic) from micro-CT datasets automatically. The plugin integrates within the Fiji platform to provide reproducible, accessible, and user friendly analysis. The application of the tool demonstrates that the extracted descriptors can be readily incorporated into constitutive models and machine learning workflows, enabling the forward prediction of stress-strain behavior as well as the inverse design of microstructures. This approach supports non-destructive evaluation, accelerates materials selection, and advances the integration of imaging with predictive modeling in porous media research.

Advancements in deep learning (DL) and machine learning (ML) have improved the ability to model complex, nonlinear relationships, such as those encountered in complex material inverse problems. However, the effectiveness of these methods often depends on large datasets, which are not always available. In this study, the incorporation of domain-specific knowledge of the mechanical behaviour of material microstructures is investigated to evaluate the effect on the predictive performance of the models in data-scarce scenarios. To overcome data limitations, a two-step framework, learning latent hardening (LLH), is proposed. In the first step of LLH, a deep neural network (DNN) is employed to reconstruct full stress–strain curves from randomly selected portions of the stress–strain curves to capture the latent mechanical response of a material based on key microstructural features. In the second step of LLH, the results of the reconstructed stress–strain curves are leveraged to predict key microstructural features of porous materials. The performance of six DL and/or ML models trained with and without domain knowledge are compared: convolutional neural networks (CNNs), DNN, extreme gradient boosting (XGBoost), K-nearest neighbours (KNN), long short-term memory (LSTM) and random forest (RF). The results from the models with domain-specific information consistently achieved higher R 2 values compared to models without prior knowledge. When the models did not include domain knowledge, meaningful patterns in the model result, such as the link between stress–strain behaviour and underlying microstructural changes not being recognized, while those enhanced with domain knowledge insights showed better feature selection, in which they identified key stress–strain characteristics that are most relevant for predicting microstructure. These findings reveal the critical role domain-specific knowledge can provide in guiding DL models, further highlighting the need to combine domain expertise with data-driven approaches to achieve reliable and accurate outcomes in materials science and related fields. This article is part of the theme issue ‘Frontiers of applied inverse problems in science and engineering’.

Super-resolution remains a promising technique to enhance the quality of low-resolution images. This study introduces CATformer (Contrastive Adversarial Transformer), a novel neural network integrating diffusion-inspired feature refinement with adversarial and contrastive learning. CATformer employs a dual-branch architecture combining a primary diffusion-inspired transformer, which progressively refines latent representations, with an auxiliary transformer branch designed to enhance robustness to noise through learned latent contrasts. These complementary representations are fused and decoded using deep Residual-in-Residual Dense Blocks for enhanced reconstruction quality. Extensive experiments on benchmark datasets demonstrate that CATformer outperforms recent transformer-based and diffusion-inspired methods both in efficiency and visual image quality. This work bridges the performance gap among transformer-, diffusion-, and GAN-based methods, laying a foundation for practical applications of diffusion-inspired transformers in super-resolution.

The use of inorganic nutrient fertilizers in crop agriculture is often inefficient due to rapid leaching of nutrients with percolating water in soil. To address this, a liposomal carrier was developed to slow transport and reduce leaching of inorganic nutrient loads within agricultural soils. Liposomes, spherical lipid bilayers, have been widely used in medicine but remain under-characterized for agricultural applications. Their biocompatibility, high loading capacity, and stability under certain compositions and environmental conditions suggest they could effectively deliver agrochemicals to crops. We present soil column experiments to evaluate the ability of a liposomal carrier to reduce the transport of an ionic tracer load, sodium bromide, under varying soil and water saturation conditions. Results from saturated column experiments demonstrate that encapsulation in liposomes slowed tracer transport and reduced leachate concentrations in sand and silty clay loam soils. Reduction in tracer leachate was also observed for unsaturated column experiments in silty clay loam soil. Subsequent experiments suggested a combination of processes (i.e., attachment, aggregation, physical exclusion, and biogenic immobilization) were responsible for the observed behavior of liposome encapsulated tracer in the soil column experiments. These findings support the use of liposomes as an effective carrier of inorganic nutrients to reduce leaching.

Porous media, ranging from bones to concrete and from batteries to architected lattices, pose difficult challenges in fully harnessing for engineering applications due to their complex and variable structures. Accurate and rapid assessment of their mechanical behavior is both challenging and essential, and traditional methods such as destructive testing and finite element analysis can be costly, computationally demanding, and time consuming. Machine learning (ML) offers a promising alternative for predicting mechanical behavior by leveraging data-driven correlations. However, with such structural complexity and diverse morphology among porous media, the question becomes how to effectively characterize these materials to provide robust feature spaces for ML that are descriptive, succinct, and easily interpreted. Here, we developed an automated methodology to determine porous material strength. This method uses scalar morphological descriptors, known as Minkowski functionals, to describe the porous space. From there, we conduct uniaxial compression experiments for generating material stress-strain curves, and then train an ML model to predict the curves using said morphological descriptors. This framework seeks to expedite the analysis and prediction of stress-strain behavior in porous materials and lay the groundwork for future models that can predict mechanical behaviors beyond compression.

The primary objective of carbon capture, utilization, and storage (CCUS) applications in various natural and engineered porous materials is to achieve a stable and planar CO2 displacement front, thereby suppressing viscous fingering. A stable front can ensure uniform and exhaustive CO2 mineralization throughout a reactive medium (i.e., mineral carbonation). Drawing inspiration from experimental observations of CO2 flooding into cores of portland cement-based materials, we examine the stability of such systems. Under these conditions, the injected CO2 continuously dissolves into the resident water phase, which becomes chemically disequilibrated with the solid minerals and leads to mineral carbonation on the wetted surfaces. Focusing on the early injection time allows us to reduce the complex multiphysical problem to a simple two-phase flow scenario of immiscible displacement with a CO2 interfacial flux sink. The formulated equations are investigated using numerical simulations and linear stability analysis, which results in a closed-form criterion, and provide fundamental insights into system stability. Overall, the results show that several effects combine to stabilize the system, including the sink effect, which acts to eliminate instability; the reduction in flow velocity along the flow path, which limits flow focusing; and the relative increase in stabilizing capillary forces. Therefore, if the system is stable at early stages, it will likely remain stable later on. Finally, this research demonstrates the use of theory to simplify complex problems and shows that even when flow is inherently coupled, the state of systems can often be determined from fluid stability alone.

Subsurface non-isothermal fluid injection is a ubiquitous scenario in energy and water resource applications, which can lead to geochemical disequilibrium and thermally driven solubility changes and reactions. Depending on the nature of the solubility of a mineral, the thermal change can lead to either mineral dissolution or precipitation (due to undersaturation or supersaturation conditions). Here, by considering this thermo-hydro-chemical (THC) scenario and by calculating the temperature-dependent solubility using a non-isothermal solution (the so-called Lauwerier solution), thermally driven reactive transport solutions are derived for a confined aquifer. The coupled solutions, hereafter termed the “reactive Lauwerier problem”, are developed for axisymmetric and Cartesian symmetries and additionally provide the porosity evolution in the aquifer. The solutions are then used to study two common cases: (I) hot CO₂-rich water injection into a carbonate aquifer and (II) hot silica-rich water injection into a sandstone aquifer, leading to mineral dissolution and precipitation, respectively. We discuss the timescales of such fluid–rock interactions and the changes in hydraulic system properties. The solutions and findings contribute to the understanding and management of subsurface energy and water resources, such as aquifer thermal energy storage, aquifer storage and recovery and reinjection of used geothermal water. The solutions are also useful for developing and benchmarking complex coupled numerical codes.