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Record W2315827814 · doi:10.1021/acs.iecr.5b03991

Removal of Hydrogen Sulfide by Metal-Doped Nanotitanate under Gasification-Like Conditions

2016· article· en· W2315827814 on OpenAlex
David Roller, Marc Bläsing, Inge Dreger, Farzad Yazdanbakhsh, James A. Sawada, Steven M. Kuznicki, Michael Müller

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

VenueIndustrial & Engineering Chemistry Research · 2016
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsUniversity of Alberta
FundersHelmholtz-Gemeinschaft
KeywordsHydrogen sulfideScrubberSorbentScanning electron microscopeChromiumHydrogenCopperMaterials scienceCeriumAnalytical Chemistry (journal)Chemical engineeringChemistryNuclear chemistryMetallurgyEnvironmental chemistryAdsorptionSulfur

Abstract

fetched live from OpenAlex

A comparative study between nanotitanate doped with different metals (copper, copper–chromium, and cerium) is executed under gasification conditions in order to investigate their maximum H 2 S removal ability and their breakthrough behavior. Therefore, the sorbent is placed in a fixed-bed reactor and exposed to the H 2 S containing gas flow at temperatures ranging from 75 to 950 °C. Online analysis is done by a mass spectrometer. The sorbents are also tested by the offline analytical techniques X-ray diffraction (XRD) and scanning electron microscopy (SEM) after the experiments to provide detailed information about their elemental and crystalline composition. The results indicate Cu-ETS-2 as the most effective H 2 S-scrubber among the tested sorbents. The lowest H 2 S concentration in the outlet gas is always achieved in water rich gas. Additionally, the H 2 S capacity is nearly always higher for the water rich gas than in the hydrogen rich gas.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.068
GPT teacher head0.306
Teacher spread0.238 · 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