Editorial: Sustainable Hydrogen for Energy, Fuel and Commodity Applications
Bibliographic record
Abstract
Hydrogen has an essential role as an energy vector in a sustainable low-carbon future, as shown in Figure 1.It has a high gravimetric density and is an effective energy storage medium for the energy and transportation sectors.Efficient and clean power and heat generation from hydrogen fuel cells and turbines provide new routes to decarbonize the energy and building sectors.Hydrogen is also an important chemical feedstock for various industries such as ammonia and steel to decrease their carbon footprints.Recently, several countries and regions released their hydrogen strategies and roadmaps, such as Canada (Natural Resources Canada, 2020), European Union (European Commission, 2020), and Australia (Council of Australian Governments Energy Council, 2019).It is urgent for the scientific community to provide valuable insights for the transition to the sustainable hydrogen production and utilization by inventing new low-carbon hydrogen production and distribution technologies, quantifying the benefits of hydrogen, and optimizing the hydrogen utilization in various sectors.This research topic addresses different scientific, technical and economic aspects of using hydrogen for energy, fuel, and commodity applications.The scope of the articles published ranges from hydrogen and hydrogen carrier production to hydrogen utilization in transportation and power sectors.This research topic showcases diverse techniques and capabilities available in the scientific communities to solve hydrogen related problems: literature review and expert opinions, experimental investigation, system-scale modeling, and sector-scale analysis.Authors from China, the United Kingdom, the United States, France, Thailand and Germany contributed to the publications in this research topic.The benefits of hydrogen depend on how it is produced.Among the 69 Mt hydrogen produced globally (excluding the by-product hydrogen), nearly 99% is from fossil fuels (i.e., 76% from natural gas and 23% from coal) (International Energy Agency, 2019), resulting in significant carbon emissions.It is urgent to develop more sustainable hydrogen production technologies to decrease the associated carbon intensity, as the global demand for hydrogen rises.In this research topic, Zhang et al. studied the system optimization of a biomass-based hydrogen and electricity co-production system.Feedstocks such as wood chips, daily manure, sorghum, and grapevine pruning waste were analyzed.In their design, the hydrogen production system is integrated with an organic Rankine cycle to utilize the high temperature waste heat from the biomass gasifier for electricity generation.An optimal solution predicts a hydrogen yield of 39.31 mol/kg and electricity generation of 0.99 kWh/kg using wood chips as the biomass feedstock, with the hydrogen yield and electricity generation equally important in the optimization.Chuayboon et al. carried out an experimental study on syngas and hydrogen coproduction from methane partial oxidation and water splitting, respectively in a solar-driven thermochemical redox cycle.The ceria-based reticulated porous ceramics were used as the oxygen
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.031 | 0.034 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".