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Record W2563844855 · doi:10.1515/ijcre-2015-0107

Adsorptive Removal of Nitrogen and Sulfur Containing Compounds by SBA15 Supported Nickel (II) and Tungsten Phosphides and the Adsorption Mechanisms

2016· article· en· W2563844855 on OpenAlexaff
Syed Shahriar, Hongfei Lin, Ying Zheng

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsAdsorptionSulfurChemistryQuinolineInorganic chemistryNitrogenHydrodenitrogenationMonolayerFreundlich equationNickelTungstenHydrodesulfurizationOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract SBA15 supported transition metal phosphides Ni 2 P/SBA15 and WP/SBA15 have been identified as promising adsorbents especially for removing neutral nitrogen-containing compounds. Adsorption of model nitrogen- and sulfur-containing compounds as well as light cycled oil (LCO) was performed and applied for the evaluation of kinetics and isotherms. The pseudo second-order kinetic model was well fitted to both nitrogen and sulfur adsorption data. Molecular size of the adsorbates plays an important role in the adsorption. Despite of higher initial adsorption rates, the adsorption capacities for carbazole and DBT were lower than those for indole and quinoline due to their larger molecular size. Monolayer adsorption was observed for quinoline due to the acid-base interaction between the basic nitrogen adsorbate and the weak acidic support. The Freundlich model was suitable in describing the adsorption of nitrogen- and sulfur-containing compounds from LCO. Cooperative adsorption took place when replacing the model compound DBT by the sulfur-containing compounds in LCO.

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 score0.008
Threshold uncertainty score0.345

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 head0.200
Teacher spread0.194 · 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
Published2016
Admission routes1
Has abstractyes

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