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Record W7164386981 · doi:10.52843/cassyni.0msyrt

Session 2A: Hydrogen from Natural Gas (Methane)

2025· article· W7164386981 on OpenAlexaff
Zhiwei Sun, D. Chester Upham, Adam Boies, Chetan Deep Singh, Murray J. Thomson, Hai Wang

Bibliographic record

Venuenot available
Typearticle
Language
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsSteam reformingMethaneHydrogen productionPyrolysisHydrogenCarbon fibersNatural gasCatalysis

Abstract

fetched live from OpenAlex

Methane pyrolysis for hydrogen production is critical for decarbonization efforts, provided that the co-produced solid carbon can be separated affordably and valorized at scale. Studies demonstrate the potential of molten metal alloy catalysts operating in bubble column reactors, achieving industrial-relevant productivities (10⁻⁵ to 10⁻⁶ mol/s/cm³). Key catalytic mechanisms involve surface segregation and surface charge distribution, with electronic effects significantly influencing C–H and C–O bond activation rates. For instance, copper-indium alloys are shown to catalyze the formation of multi-walled carbon nanotubes (CNTs) from nanodroplets, with subsequent heat treatment enabling effective catalyst removal. Another approach, utilizing floating-catalyst gas-phase reactors with iron and sulfur precursors and gas recycling, has demonstrated net hydrogen production (84.5 vol% H₂) alongside CNTs. Significant advances in post-processing have led to CNT fibers with tensile strengths exceeding 8 GPa and recent reports of electrical conductivity surpassing copper and aluminum, making them competitive with high-end carbon fibers. Reactor productivity has increased 100-fold, though further optimization of catalyst selectivity and residence time is needed to prevent undesirable radial growth and ensure high-quality product. For industrial-scale application, a non-catalytic methane pyrolysis process aims for cost parity with steam methane reforming (SMR) coupled with carbon capture, utilization, and storage (CCUS). While initial Gen 1 reactors demonstrated hydrogen and carbon yields, achieving consistent commercial-grade carbon black requires improved reactor temperature and retention time control. New Gen 2 reactor architectures are being developed to increase hydrogen capacity and ensure consistent carbon quality. Overall, the economic viability and scalability of methane pyrolysis are contingent on developing robust carbon valorization pathways, with new markets beyond traditional carbon black, such as in construction materials, advanced conductors, and aerospace composites, being essential for accommodating the substantial volumes of carbon produced. Considerations for the long-term fate of carbon and the reduction of upstream fugitive methane emissions are also critical for the overall environmental impact. Welcome from the Chair Molten metal catalyzed carbon nanotube production Pyrolysis of Methane to Bulk CNT Materials: Manufacturing Carbons that Are Useful for Manufacturing EKONA: Our Journey to Develop Low-Cost, Clean Hydrogen from Methane Pyrolysis Discussion

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.247
Teacher spread0.240 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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