The environmental impacts of hydrogen production can vary widely depending on the production energy source and process. This implies that the collection and management of sustainability data for hydrogen production globally is desired to ensure accountable development of the sector. Life cycle assessment (LCA) is an internationally recognized tool for environmental impact assessment. Integrating LCA in the holistic evaluation of the hydrogen value chain is desirable to ensure the cleanness and sustainability of the various available hydrogen production pathways. The objective of this review is to evaluate the methodology used in assessing the life cycle impact of hydrogen production including proposed documentation such as the guarantee of origin (GO) and certification schemes, and review case studies from Australia. An analysis of the sustainability strategies and schemes designed by the Australian government, aimed at mitigating climate change and promoting the hydrogen economy, was conducted. The case studies that were discussed identified the preferred available scaled routes of clean hydrogen production to be water electrolysis, which is based on technologies using renewable energy. Other dominant technologies which incorporate carbon capture and storage (CCS) were envisaged to continue playing a role in the transition to a low carbon economy. Additionally, it is critical to assess the greenhouse gas (GHG) emissions using appropriate system boundaries, in order to classify clean hydrogen production pathways. Harmonizing regulatory stringency with appropriate tracking of renewable electricity can promote clean hydrogen production through certification and GO schemes. This approach is deemed critical for the sustainable development of the hydrogen economy at the international level.

Ammonia, with its high hydrogen storage density of 17.7 wt.% (mass fraction), cleanliness, efficiency, and renewability, presents itself as a promising zero-carbon fuel. However, the traditional Haber–Bosch (H–B) process for ammonia synthesis necessitates high temperature and pressure, resulting in over 420 million tons of carbon dioxide emissions annually, and relies on fossil fuel consumption. In contrast, dielectric barrier discharge (DBD) plasma-assisted ammonia synthesis operates at low temperatures and atmospheric pressures, utilizing nitrogen and hydrogen radicals excited by energetic electrons, offering a potential alternative to the H-B process. This method can be effectively coupled with renewable energy sources (such as solar and wind) for environmentally friendly, distributed, and efficient ammonia production. This review delves into a comprehensive analysis of the low-temperature DBD plasma-assisted ammonia synthesis technology at atmospheric pressure, covering the reaction pathway, mechanism, and catalyst system involved in plasma nitrogen fixation. Drawing from current research, it evaluates the economic feasibility of the DBD plasmaassisted ammonia synthesis technology, analyzes existing dilemmas and challenges, and provides insights and recommendations for the future of nonthermal plasma ammonia processes.

A research team from the Swire Institute of Marine Science (SWIMS) and the School of Biological Sciences of The University of Hong Kong (HKU) has helped address this question by developing an innovative mathematical model that allows conservationists to easily assess feeding strategies of different giant clam species. This knowledge can help conservationists to predict the future vulnerability of different giant clam species and identify those in need of timely protection. 

A research team led by Dr. Sung Mook Choi of the Energy & Environment Materials Research Division at the Korea Institute of Materials Science (KIMS), in collaboration with a team headed by Professor Seung-Hwa Lee at Changwon National University, has successfully designed and developed a proprietary non-precious metal oxygen evolution reaction (OER) catalyst featuring a layered structure optimized for anion exchange membrane water electrolysis (AEMWE) environments.

Researchers at Fondazione Policlinico Universitario Agostino Gemelli IRCCS have developed a promising machine learning algorithm capable of predicting survival and cause of death for patients with bladder cancer undergoing radical cystectomy.

POSTECH Professor Inseok Hwang’s team unveils ArithMotion, enabling socially-aligned avatar motions with simple arithmetic inputs.