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Record W3161520988 · doi:10.1002/cjce.24189

Performance of geopolymer as adsorbent on desulphurization of heavy gas oil

2021· article· en· W3161520988 on OpenAlexaffvenue
Biswajit Saha, V. Sundaramurthy, Ajay K. Dalai

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsGeopolymerAdsorptionMesoporous materialMetakaolinMaterials scienceChemical engineeringSulfurInorganic chemistryNuclear chemistryChemistryMetallurgyFly ashOrganic chemistryCatalysisComposite material

Abstract

fetched live from OpenAlex

Abstract Geopolymer is a porous aluminosilicate material and chemically similar to zeolites. As a low‐cost construction material, its suitability for adsorptive desulphurization (ADS) was studied using a petroleum feedstock. Geopolymer was produced by alkali activation of metakaolin and characterized by BET, NH 3 –TPD, SEM, FTIR, XRD, and XPS. The XRD and SEM studies evidenced the amorphous nature of geopolymer and the existence of macro‐ and mesopores. The XPS and NH 3 –TPD studies revealed the presence of surface Na and Al, and strong acid sites, respectively, in the prepared geopolymer. These sites interact with sulphur compounds of heavy gas oil through π ‐ π and acid–base interactions. The geopolymer showed a high sulphur adsorption capacity of 38.4 mg/g. The effects of adsorption parameters such as operating temperature, amount of adsorbent, and time for absorption on the adsorption capacity were examined using the Box–Behnken design statistical model. All three operating parameters significantly influenced the sulphur adsorption capacity of geopolymer. The adsorption of sulphur compounds on the geopolymer followed pseudo‐first‐order kinetics and did not affect its structural stability. Finally, the thermodynamic study revealed that adsorption of sulphur compounds on the geopolymer was spontaneous and exothermic.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.304

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 head0.175
Teacher spread0.169 · 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

Citations11
Published2021
Admission routes2
Has abstractyes

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