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Record W2088443921 · doi:10.1021/ef030116r

Novel Catalyst for Cracking of Biomass Tar

2004· article· en· W2088443921 on OpenAlex
Tiejun Wang, Jie Chang, Pengmei Lv, Jesse Zhu

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

VenueEnergy & Fuels · 2004
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsWestern University
Fundersnot available
KeywordsDolomitetar (computing)CatalysisCokeCrackingSpace velocityFluid catalytic crackingChemical engineeringChemistryMineralogyOrganic chemistrySelectivity

Abstract

fetched live from OpenAlex

Cracking of biomass tar was investigated over Ni/dolomite catalyst prepared by the incipient wetness method using modified dolomite as precursor. Modified dolomite was prepared by mixing Fe 2 O 3 powders with natural dolomite powders to increase Fe 2 O 3 content for higher activity of tar cracking. Four other catalysts (natural dolomite, modified dolomite, ICI-46-1, and Z409) were tested and compared with Ni/dolomite catalyst. The effects of temperature, steam-to-carbon, and space velocity on tar conversion were explored. Ni/dolomite is shown to be very active and useful for tar removal. A 97% tar removal is easily obtained at catalyst temperature of 750 °C and space velocities of 12 000 h - 1 . The minimum S / C ratio for Ni/dolomite was 2.5 at a catalyst temperature of 750 °C to prevent the formation of the coke on the catalyst. No obvious deactivation of catalyst was observed in 60 h on-stream tests. Compared with the Ni-based catalysts (ICI-46-1, Z409), Ni/dolomite catalyst is cheap and has also excellent activity and anticoke ability.

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)0.000
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 score0.070
Threshold uncertainty score0.474

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 head0.011
GPT teacher head0.209
Teacher spread0.199 · 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