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Record W4403924431 · doi:10.3390/molecules29215126

Effect of the Reactor Material on the Reforming of Primary Syngas

2024· article· en· W4403924431 on OpenAlexaff
Cláudia Bezerra Silva, Michael Lugo, Carlos Ceballos, Jean‐Michel Lavoie

Bibliographic record

VenueMolecules · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsSyngasSyngas to gasoline plusPrimary (astronomy)Waste managementChemical engineeringProcess engineeringChemistryEnvironmental scienceNuclear engineeringSteam reformingCatalysisEngineeringOrganic chemistryPhysicsHydrogen production

Abstract

fetched live from OpenAlex

Syngas, mostly hydrogen and carbon monoxide, has traditionally been produced from coal and natural gas, with biomass gasification later emerging as a renewable process. It is widely used in fuel synthesis through the Fischer–Tropsch (FT) process, where the H2/CO ratio is crucial in determining product efficiency and quality. In this sense, this study aimed to reform an emulated syngas resulting from the supercritical water gasification of biomass, tailoring it to meet the H2/CO ratio required for FT synthesis. Conditions resembling dry reforming were applied, using temperatures from 600 to 950 °C and steel wool as a catalyst. Additionally, the effects of Inconel and stainless steel as reactor materials on syngas reforming were investigated. When Inconel was used, H2/CO ratios ranged between 1.04 and 1.84 with steel wool and 1.28 and 1.67 without. When comparing reactions without steel wool performed either in the Inconel or the stainless steel reactors, those using Inconel consistently outperformed the stainless steel ones, achieving CH4 and CO2 conversions up to 95% and 76%, respectively, versus 0% and 39% with stainless steel. It was concluded that the Inconel reactor exhibited catalytic properties due to its high nickel content and specific oxides.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.202

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2024
Admission routes1
Has abstractyes

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