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Enhanced Biomass-Derived Glycerol Conversion to Syngas in the CO<sub>2</sub> Reforming Process: Influence of the Nickel Loading Method on Physicochemical Properties and Catalytic Performance

2024· article· en· W4392285397 on OpenAlexaff
Zahra Pirzadi, Fereshteh Meshkani, Dai‐Viet N. Vo

Bibliographic record

VenueEnergy & Fuels · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSyngasNickelCatalysisBiomass (ecology)GlycerolProcess (computing)Chemical engineeringChemistryCatalytic reformingMaterials scienceOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

In the current study, renewable syngas production from glycerol has been investigated through a catalytic dry reforming process using the Ni/CaO·Al 2 O 3 catalyst. Calcium aluminate support has been synthesized via a one-step sol–gel technique. The active metal of Ni attached to the CaO·Al 2 O 3 is supported by wet impregnation, simultaneous sol–gel, and EDTA-assisted impregnation. Investigation of different methods’ impact on Ni species attachment onto carrier revealed that the EDTA-assisted impregnation method improves the reducibility catalytic activity (57% at 750 °C) of the catalyst in the dry reforming of glycerol (dry-RG) process. It also affects carbon formation on the catalyst surface. Besides, the impact of Ni content (5–20 wt %) in the catalyst has been evaluated. Increasing the nickel loading amount increased the active phase’s reducibility and reduced the pore size. Ni loading experiments’ results indicate that glycerol conversion improves with increasing Ni loading up to 10 wt %, impregnated by chelated Ni species [Ni (EDTA) 2– ] onto calcium aluminate support. Based on the characterization results, the impact of the nickel loading method and amount on the dry-RG reaction was discussed.

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 score0.005
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.243
Teacher spread0.230 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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