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Record W2345171299 · doi:10.1002/aic.15286

Novel inexpensive transition metal phosphide catalysts for upgrading of pyrolysis oil via hydrodeoxygenation

2016· article· en· W2345171299 on OpenAlexaff
Cheng Guo, K. T. Venkateswara Rao, Ehsan Reyhanitash, Zhongshun Yuan, Sohrab Rohani, Chunbao Xu, Quan He

Bibliographic record

VenueAIChE Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsDalhousie UniversityWestern University
Fundersnot available
KeywordsHydrodeoxygenationPhosphideCatalysisDeoxygenationMolybdenumChemistryTransition metalPyrolysisPyrolysis oilYield (engineering)HydrogenChemical engineeringNuclear chemistryMaterials scienceInorganic chemistryMetallurgyOrganic chemistrySelectivity

Abstract

fetched live from OpenAlex

Supported molybdenum/molybdenum‐phosphides as inexpensive catalysts for bio‐oil hydrodeoxygenation (HDO) were in‐house prepared using different support materials, i.e., Al 2 O 3 , activated carbon (AC), MgAl 2 O 4 , and Mg 6 Al 2 (CO 3 )(OH) 16 . The HDO activity of these catalysts were investigated using a 100 mL bench‐scale reactor operating at 300°C with an initial hydrogen pressure of 50 bar for 3 h with a pyrolysis oil (PO). The catalytic efficiencies for bio‐oil HDO for the catalysts were compared with the expensive but commercially available Ru/C catalyst. Addition of small amount of P to the Mo catalysts supported on either AC and Al 2 O 3 led to increased degree of deoxygenation (DOD) and oil yield compared with those without P. MoP supported on AC (MoP/AC) demonstrated bio‐oil HDO activity comparable to the Ru/C catalyst. Furthermore, three AC‐supported metal phosphides for PO HDO were compared under the same conditions, and they were found to follow the order of NiP/AC > CoP/AC > MoP/AC. © 2016 American Institute of Chemical Engineers AIChE J , 62: 3664–3672, 2016

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: none
Teacher disagreement score0.600
Threshold uncertainty score0.418

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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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

Citations25
Published2016
Admission routes1
Has abstractyes

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