The transition to sustainable hydrogen and carbon economies is essential for addressing critical global issues such as climate change, resource depletion, and waste management. A vital strategy for low-carbon sustainability in the energy and chemical sectors is the chemical conversion of greenhouse gas into fuels and platform chemicals. Effective waste management, including waste-to-energy conversion and recycling, plays a crucial role in reducing emissions and promoting a circular economy. A key aspect of this transition is the development of innovative technologies that can transform waste into valuable resources while minimizing environmental impacts. Plasma-based recycling presents a promising solution, offering remarkable versatility for applications like waste upcycling and greenhouse gas conversion. These processes play a crucial role in advancing the development of sustainable carbon and hydrogen economies.

An anaerobic digester of sewage sludge or agro-industrial waste produces biogas and ammonia-rich digestate. Three H₂-producing processes exist: dry reforming of methane (from biogas), catalytic decomposition of methane (from biogas after CO₂ capture), and catalytic decomposition of ammonia (from digestate). Dry reforming of methane offers the best syngas yield at 700 °C and for a 50–50 vol % CH₄/CO₂ biogas. Catalytic decomposition of methane achieved a H₂ yield of 95%. Finally, the digestate was stripped and NH₃ was further completely decomposed into H₂ and N₂, for a complete NH₃ conversion at 650 °C. A methanol valorization case study of a wastewater treatment plant of 300000 person equivalents with an anaerobic digester is examined. The methanol production from syngas (H₂/CO) and H₂ product streams is simulated using Aspen Plus^®. This anaerobic digester process will daily generate 4485 m³ CH₄, 2415 m³ CO, and 320 kg NH₃. The methanol production will be 183 kg·h^–1 (1600 t·y^–1). The additional H₂ from ammonia’s catalytic decomposition (631 m³·d^–1) can be valorized with excess biogas in the anaerobic digester-associated combined heat and power unit. Due to a significantly higher ammonia concentration in manure, catalytic decomposition of ammonia will produce more H₂ if manure would be co-digested.

Overuse of fossil fuels led to energy crises and pollution. Thus, alternative energy sources are needed. Hydrogen, with its clean and high-density traits, is seen as a future energy carrier. Producing hydrogen from electricity can store renewable energy for a sustainable hydrogen economy. While much research on water electrolysis hydrogen production systems exists, comprehensive reviews of engineering applications are scarce. This review sums up progress and improvement strategies of common water electrolysis technologies (alkaline water electrolysis, proton exchange membrane water electrolysis, solid oxide water electrolysis, and anion exchange membrane water electrolysis, etc.), including component and material research and development. It also reviews these technologies by development and maturity, especially their engineering applications, discussing features and prospects. Bottlenecks of different technologies are compared and analyzed, and future directions are summarized. The aim is to link academic material research with industrial manufacturing.

Membrane gas separation is an energy-efficient approach to extract helium from natural gas. However, the limited separation performance shown as Robeson’s upper bound has hindered the techno-economic feasibility. This study introduces an advanced copolyimide membrane engineered for He extraction from natural gas. The membranes were facilely achieved by dip-coating the α-alumina substrates in the copolyimide solution followed by in situ thermal rearrangement. In addition to the rigid 5-amino-2-(4-aminobenzene)benzimidazole segments, the active ortho-hydroxyl groups were converted to benzoxazole rings, contributing to extra micropores. The membrane showed an improved mixture selectivity of 120 and He permeance of 23.5 GPU, far surpassing the performance of benchmark membranes for helium separation over CH₄. The membrane also demonstrated long-term stability as evidenced by the continuous operation over 250 h. Additionally, the membrane exhibited resistance to impurities such as CO₂ and C₂H₆, enduring the asymmetric membranes promising for practical helium extraction from natural gas.

For over a decade, regenerative engineering has been defined as the convergence of advanced materials sciences, stem cell sciences, physics, developmental biology, and clinical translation for the regeneration of complex tissues. Recently, the field has made major strides because of new efforts made possible by the utilization of another growing field: artificial intelligence. However, there is currently no term to describe the use of artificial intelligence for regenerative engineering. Therefore, we hereby present a new term, “Regenerative Engineering AI”, which cohesively describes the interweaving of artificial intelligence into the framework of regenerative engineering rather than using it merely as a tool. As the first to define the term, regenerative engineering AI is the interdisciplinary integration of artificial intelligence and machine learning within the fundamental core of regenerative engineering to advance its principles and goals. It represents the subsequent synergetic relationship between the two that allow for multiplex solutions toward human limb regeneration in a manner different from individual fields and artificial intelligence alone. Establishing such a term creates a unique and unified space to consolidate the work of growing fields into one coherent discipline under a common goal and language, fostering interdisciplinary collaboration and promoting focused research and innovation.

Plasma-based gas conversion has emerged as a sustainable and promising approach for chemical production, attracting increasing attention in recent years. Significant progress has been achieved in areas such as nitrogen fixation, CO ₂ conversion, methane activation, and others, driven by the contributions of researchers from diverse disciplines. Given that most research in this field is experimental, the methodologies employed play a pivotal role and demand careful consideration. However, due to the interdisciplinary nature of the field and variations in research objectives, available resources, and laboratory standards, experimental set-ups and approaches often differ significantly. Moreover, critical details regarding operational techniques and key methodologies are sometimes overlooked. This paper provides a comprehensive review of the methodologies and experimental approaches used in the study of plasma-based gas conversion for chemical production. It first examines experimental systems, including plasma reactor design, plasma-catalyst integration, and set-up configuration. Subsequently, operational schemes, conditions, and analytical procedures are discussed, with examples showcasing state-of-the-art advancements. Finally, discussion on emerging research trends and potential opportunities are presented, aiming to inspire further advancements and broaden the scope of this growing field.

The growing emphasis on low-carbon lifestyles and the reduction of carbon emissions has spurred interest in renewable energy-driven biomanufacturing. The third-generation biomanufacturing concept leverages microbial cell factories to convert renewable energy sources, including solar and electrical energy, and inorganic materials, into high-value fuels and chemicals. Microbial CO₂ fixation, with its mild reaction conditions and ability to generate diverse products, is a compelling alternative to traditional chemical catalysis, which is generally characterized by high energy demands, pollution, and limited product diversity. Clostridium stands out among microorganisms for its natural ability to fix carbon via the Wood-Ljungdahl pathway, which enables CO₂, CO, and H₂ to be used for growth and product synthesis. Advances in genetic engineering tools for Clostridium have led to the biosynthesis of over 40 natural compounds, expanding its industrial potential. Furthermore, integrating Clostridium into photoelectrochemical systems has demonstrated the feasibility of coupling microbial fermentation with renewable energy inputs. This review comprehensively examines the Wood-Ljungdahl pathway, related metabolic pathways, and key enzymes, along with the latest progress in genetic modification tools. The potential of Clostridium as a biocatalyst for one-carbon gas conversion and its integration with clean energy technologies is highlighted, offering valuable perspectives for future research.

The main challenge in the oxidative coupling of methane to C₂H₆/C₂H₄ (C₂-hydrocarbons) lies in the low selectivity to the desired products due to their high reactivity to form carbon oxides. Herein, we report that the selectivity in chemical looping oxidative coupling of methane over supported Mn-Na₂WO₄-based catalysts can be significantly increased by catalyst promotion with Li₂CO₃ and performing the reaction with co-fed steam. The selectivity reaches 89% (about 60% C₂H₄ selectivity) at a methane conversion of 19%. The best-performing catalyst showed durable within 90 reaction/reoxidation cycles. With the aid of sophisticated catalyst characterization studies combined with temporal analysis of products, the origins of the enhancing effects of the promoter and steam have been elucidated and can be applied for the development of selective catalysts in various alkane oxidation reactions.

Large language model-based agent systems are emerging as transformative technologies in chemical process simulation, enhancing efficiency, accuracy, and decision-making. By automating data analysis across structured and unstructured sources—including process parameters, experimental results, simulation data, and textual specifications—these systems address longstanding challenges such as manual parameter tuning, subjective expert reliance, and the gap between theoretical models and industrial application. This paper reviews the key barriers to broader adoption of large language model-based agent systems, including unstable software interfaces, limited dynamic modeling accuracy, and difficulties in multimodal data integration, which hinder scalable deployment. We then survey recent progress in domain-specific foundation models, model interpretability techniques, and industrial-grade validation platforms. Building on these insights, we propose a technical framework centered on three pillars: multimodal task perception, autonomous planning, and knowledge-driven iterative optimization. This framework supports adaptive reasoning and robust execution in complex simulation environments. Finally, we outline a next-generation intelligent paradigm where natural language-driven agent workflows unify high-level strategic intent with automated task execution. The paper concludes by identifying future research directions to enhance robustness, adaptability, and safety, paving the way for practical integration of large language model based agent systems into industrial-scale chemical process simulation.