ABSTRACT As an abundant bio‐based resource, lignin can be converted to functional carbon materials, thereby providing a sustainable pathway toward free‐standing sulfur cathode design. However, rational carbonization engineering remains essential for optimizing sulfur utilization and polysulfide conversion behavior in lithium–sulfur batteries. In this work, lignin valorization and free‐standing electrode design are integrated through electrospinning‐assisted fabrication of nanofibers, followed by high temperature carbonization to produce flexible and reusable sulfur hosts. The interconnected fibrous architecture facilitates homogeneous sulfur distribution, while promoting polysulfide confinement and electrochemical stability under high sulfur loading and lean electrolyte. At a sulfur loading of 3.3 mg cm −2 , the electrode affords a high initial capacity of 1300 mAh g −1 . At an electrolyte content of 5.5 µL E mg S −1 , the coin cells are shown to cycle steadily with capacity retentions of 82.9% after 100 cycles at 0.2 C. Furthermore, the reusability of these electrodes is confirmed via successful recovery, with the recovered electrode demonstrating stable cycling at 1 C, while retaining 79.3% of its initial capacity after 150 cycles. This research thus confirms the potential of lignin‐derived, green, and cost‐effective approach of synthesizing free‐standing electrodes as efficient sulfur host materials with reliable and steady battery cycling performance.
ABSTRACT Lithium is a much in‐demand element that is critical for the transition toward low‐carbon energy systems. Beyond hard‐rock mining, large quantities of lithium are extracted from brines of high‐Andean salars (salares). The functioning of these microbial ecosystems depends on a delicate equilibrium between mineralogy, water, physical conditions, and microbial life. They are polyextreme environments where life thrives under limiting conditions. In the Salar de Atacama, Chile, lithium is generally extracted through sequential brine evaporation, where lithium is enriched from ∼0.2 to ∼6% w/w. This process has been associated with significant environmental and social consequences, leading to growing interest in implementing new processes, such as direct lithium extraction (DLE), promoted as ‘environmentally friendly’ alternatives. However, this involves the use of chemical reagents and the reinjection of treated brines into the salar, which may have ecological implications that have not yet been fully evaluated. Here, we analyse the main DLE techniques and their potential consequences for microorganisms at community and cellular levels. We argue that DLE technologies, though presented as sustainable alternatives, could potentially have environmental impacts and that current lithium extraction practices continue to conflict with several Sustainable Development Goals.
ABSTRACT Flexible strain sensors face an unresolved trade‐off between sensitivity and sensing range. We present a meta‐material‐based sensor platform (meta‐sensor) that overcomes this by engineering local strain input to the sensing element. A strain‐stiffening curved spiral structure and a uniform‐stiffness kirigami‐patterned structure connected in series produce nonlinear strain partitioning: before mechanical transition, global strain concentrates at the sensor interface to amplify sensitivity; after transition, strain redistributes to extend sensing range. Integrating a kirigami crack‐based sensor (maximum applied strain range of 50%) onto this composite substrate extends the operational range to 125% applied strain‐a 2.5× range extension (from 50% to 125%) — while raising the mid‐range response to ≈3.5× that of a wide‐range reference sensor. The platform is validated in respiratory monitoring across rest and vigorous exercise, and knee rehabilitation tracking from early to late recovery. This meta‐material substrate approach offers a generalizable, material‐independent strategy for simultaneously achieving high sensitivity and wide sensing range.
ABSTRACT Carbon dioxide conversion into value‐added products offers a promising strategy to mitigate greenhouse gas emissions while enabling sustainable fuel production. Among the available approaches, photocatalytic CO2 reduction has attracted significant attention, particularly when combined with advanced catalyst design and reaction engineering. In this work, Cu‐doped SrTiO3 is employed as a model photocatalyst to systematically investigate the influence of reaction phase on CO2 reduction performance across three distinct reaction configurations: solid–liquid (SL), solid–gas (SG), and pressurized solid–gas (PSG). The results demonstrate that the reaction phase strongly affects both activity and product selectivity. The SG configuration exhibits the highest performance, reaching accumulated yields of 11.13 µmol g−1 of CH4 and 2.32 µmol g−1 of CO after 4 h of reaction, owing to enhanced reactant accessibility and reduced mass‐transfer limitations. In contrast, the SL configuration shows the lowest gaseous‐product activity due to limited CO2 solubility and accumulation of dissolved intermediates, while favoring the formation of oxygenated liquid products such as methanol and ethanol. The PSG system displays intermediate performance, where increased CO2 availability is partially offset by hindered product desorption. These findings highlight reaction‐phase engineering as a key factor governing photocatalytic CO2 conversion efficiency and selectivity.
ABSTRACT The increasing burden of persistent organic pollutants, per‐ and polyfluoroalkyl substances, heavy metals, and emerging contaminants represents a global threat to human, animal, and ecosystem health. Current management strategies remain fragmented and reactive, limiting the effectiveness of the One Health paradigm. This review introduces an artificial intelligence (AI)‐powered One Health framework designed to transition from surveillance to predictive prevention. Using a literature review across PubMed, Scopus, Google Scholar, and Web of Science (up to October 2025), recent advances in machine learning, deep learning, geospatial AI, and physics‐informed models for chemical risk assessment are synthesized. The proposed framework integrates four core capabilities: (1) geospatial AI for real‐time source tracking and fate modeling, (2) multispecies exposome reconstruction for unified exposure assessment, (3) predictive toxicology engines enabling cross‐species risk extrapolation, and (4) AI‐driven optimization of remediation and policy scenarios. Ethical and governance challenges, including algorithmic bias, transparency, and environmental justice, are critically examined. By integrating the holistic vision of One Health with the predictive capabilities of AI, this framework advances the concept of “Precision Environmental Health”, supporting anticipatory governance and promoting equitable interventions at a global scale.
