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Hydrogen energy systems: Technologies, trends, and future prospects

2024· article· en· W4399143104 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Science of The Total Environment · 2024
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsCarleton University
FundersQatar National Library
KeywordsHydrogen productionEnergy carrierHydrogen technologiesFossil fuelEnvironmental economicsHydrogen storageInefficiencyEnvironmental scienceHydrogen fuelSustainable developmentNatural resource economicsGreenhouse gasProduction (economics)Hydrogen economyHydrogenBusinessRenewable energyEngineeringWaste managementEconomicsChemistry

Abstract

fetched live from OpenAlex

This review critically examines hydrogen energy systems, highlighting their capacity to transform the global energy framework and mitigate climate change. Hydrogen showcases a high energy density of 120 MJ/kg, providing a robust alternative to fossil fuels. Adoption at scale could decrease global CO2 emissions by up to 830 million tonnes annually. Despite its potential, the expansion of hydrogen technology is curtailed by the inefficiency of current electrolysis methods and high production costs. Presently, electrolysis efficiencies range between 60 % and 80 %, with hydrogen production costs around $5 per kilogram. Strategic advancements are necessary to reduce these costs below $2 per kilogram and push efficiencies above 80 %. Additionally, hydrogen storage poses its own challenges, requiring conditions of up to 700 bar or temperatures below −253 °C. These storage conditions necessitate the development of advanced materials and infrastructure improvements. The findings of this study emphasize the need for comprehensive strategic planning and interdisciplinary efforts to maximize hydrogen's role as a sustainable energy source. Enhancing the economic viability and market integration of hydrogen will depend critically on overcoming these technological and infrastructural challenges, supported by robust regulatory frameworks. This comprehensive approach will ensure that hydrogen energy can significantly contribute to a sustainable and low-carbon future.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.183
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it