Hydrogen is central to sustainable energy systems, with biological production from waste offering a low-energy, environmentally compatible route. Anaerobic dark fermentation by microbes converts organic substrates into hydrogen, yet yields remain limited by competing metabolic pathways and poor understanding of cellular resource allocation in hydrogen-producing strains. Conventional genome-scale models rely on stoichiometric constraints alone, often failing to capture realistic enzyme limitations or strain-specific biomass composition. Here we show that an enzyme-constrained genome-scale metabolic model (ecGEM) of the hydrogen-producing bacterium Ethanoligenens harbinense YUAN-3, built with experimentally measured biomass composition and predicted k_cat values, quantitatively captures the trade-off between growth and hydrogen production. Enzyme constraints eliminate unrealistic flux predictions of standard models, accurately matching experimental growth rates and yields, and reveal that diversion of carbon and NADH flux into glutamate and glutamine biosynthesis enhances hydrogen production by reducing ethanol formation. In silico single-gene knockouts identify targets such as phosphoglycerate kinase that increase hydrogen flux by up to 30% under low-carbon conditions. These findings elucidate system-level metabolic regulation in fermentative hydrogen production and provide a predictive framework for rational strain engineering. The approach offers a scalable platform for optimizing biohydrogen processes and advancing sustainable hydrogen economies.

The co-pyrolysis technology of agricultural and forestry waste and polluted soil has simultaneously achieved biomass resource utilization and soil remediation. However, the heterogeneity of raw materials, non-linear processes, and long ecological response cycles pose serious challenges to its precise regulation. This article systematically reviews the new paradigm of machine learning driven closed-loop design and its multi-scale improvement mechanism for soil health. Firstly, the key applications of machine learning in co-pyrolysis systems were elucidated: by integrating multimodal sensing data with deep neural networks, random forests, and other algorithms, a proxy model was constructed between raw material attributes, process parameters, product performance, and repair effects. Based on this, an intelligent control loop integrating perception, prediction, optimization, and feedback was formed. Secondly, the hierarchical mechanism of closed-loop system driving soil functional regeneration was revealed layer by layer from four scales: molecular/nano interface (quantum confinement catalysis and covalent bond orientation fixation), micro/millimeter structure (biomimetic topology reconstruction and dynamic pore regulation), in situ field (nutrient cycling activation and pollutant bioavailability reduction), and ecosystem (carbon sink function enhancement and microbial network reconstruction). Furthermore, the core bottlenecks such as path uncertainty caused by raw material fluctuations, quantum tunneling barriers within nanopores, physical limits of multifunctional performance of biochar, thermodynamic contradictions of system energy self-sustaining, unpredictability of long-term ecological response, and adaptation conflicts of current standard systems were summarized. Interdisciplinary integration directions such as quantum chemistry computing, ultrafast laser regulation, biomimetic reactors, synthetic biology biochar symbiotic systems, and blockchain trusted carbon management were also discussed. This review provides a systematic theoretical framework for the deep intersection of machine learning and thermochemical remediation technology, and also points out the technical path for achieving precise, intelligent, and sustainable remediation of polluted soil.

Biomanufacturing of hydrogen by acidogenic fermentation presents a promising avenue for sustainable hydrogen production; however, data on its full-scale application remain limited. Here we evaluate the performance of a 100 m³ continuous-flow stirred-tank reactor (CSTR) utilizing waste molasses and inoculated with aerobic excess sludge for hydrogen production. The reactor operated at 35 ℃ with a constant hydraulic retention time of 5.8 h, while the organic loading rate (OLR) was incrementally increased from 9.3 to 57.3 kg COD m⁻³ d⁻¹. By day 19, stable ethanol-type fermentation was established, yielding an average of 265 m³ of hydrogen per day. Over the subsequent 72 days, the reactor maintained continuous operation, achieving an average hydrogen production rate of 282 m³ d⁻¹ at an average OLR of 28.5 kg COD m⁻³ d⁻¹. Bioaugmentation with Ethanoligenens harbinense YUAN-3 at a 0.5% volume fraction relative to the mixed liquor volatile suspended solids further enhanced hydrogen production to an average of 348 m³ d⁻¹. Despite fluctuations in the OLR between 17.1 and 55.2 kg COD m⁻³ d⁻¹, ethanol-type fermentation persisted throughout the bioaugmentation period. These findings demonstrate the viability of full-scale acidogenic fermentation for efficient hydrogen biomanufacturing from high-strength organic wastewater.

Recently, every industry has been working to achieve carbon neutrality, and the wastewater sector is no exception. However, little research focuses on the carbon accounting of wastewater treatment and the roadmap to carbon neutrality. Here, to systematically perform accounting, we provide a sketch that describes three boundaries of the wastewater system and propose that the carbon neutrality of the wastewater system is far beyond the plant boundary. Moreover, we identify the direct and indirect carbon emissions of wastewater treatment. In addition to direct emissions of CH₄ and N₂O, direct fossil CO₂ emissions from wastewater treatment should be included in accounting to set accurate guidelines. Next, the technologies that assist in achieving carbon-neutral wastewater treatment both within-the-fence of wastewater treatment plants and beyond the plant boundary are summarized. All measurements of energy recovery, resource recovery, and water reuse contribute to reaching this goal. The concepts of energy neutrality and carbon neutrality are identified. Successful wastewater treatment cases in energy self-sufficiency may not achieve carbon neutrality. Meanwhile, resource recovery methods are encouraged, especially to produce carbon-based materials. Ultimately, the trend of preference for the decentralized sewage treatment system is pinpointed, and systematic thinking to set the urban infrastructure layout as a whole is advocated.