Potential of Progressive and Disruptive Innovation-Driven Cost Reductions of Green Hydrogen Production.

Thorin Daniel, Lei Xing, Qiong Cai, Lirong Liu, Jin Xuan

Abstract read

In one paragraph

Article in Energy & fuels : an American Chemical Society journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Ammonia at the net-zero crossroads.Nature communications · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Thorin DanielSchool of Chemistry and Chemical Engineering, University of Surrey, Guildford GU2 7XH, U.K.

Lei XingSchool of Chemistry and Chemical Engineering, University of Surrey, Guildford GU2 7XH, U.K.

Qiong CaiSchool of Chemistry and Chemical Engineering, University of Surrey, Guildford GU2 7XH, U.K.ORCID https://orcid.org/0000-0002-1677-0515

Lirong LiuCentre for Environment and Sustainability, University of Surrey, Guildford GU2 7XH, U.K.ORCID https://orcid.org/0000-0003-2872-0870

Jin XuanSchool of Chemistry and Chemical Engineering, University of Surrey, Guildford GU2 7XH, U.K.ORCID https://orcid.org/0000-0002-6718-9018

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Green hydrogen from water electrolysis is a key driver for energy and industrial decarbonization. The prediction of the future green hydrogen cost reduction is required for investment and policy-making purposes but is complicated due to a lack of data, incomplete accounting for costs, and difficulty justifying trend predictions. A new AI-assisted data-driven prediction model is developed for an in-depth analysis of the current and future levelized costs of green hydrogen, driven by both progressive and disruptive innovations. The model uses natural language processing to gather data and generate trends for the technological development of key aspects of electrolyzer technology. Through an uncertainty analysis, green hydrogen costs have been shown to likely reach the key target of <$2.5 kg

Identifiers

PMID38863683

DOI10.1021/acs.energyfuels.4c01247

PMCPMC11163429

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.