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Record W1544002740 · doi:10.5539/mas.v9n7p107

Preparation and Characterization of CuO/r-Al2O3 for Adsorption of SO2 in Flue Gas

2015· article· en· W1544002740 on OpenAlexvenueno aff
Yuono Yuono, David Bahrin, Herri Susanto

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsnot available
Fundersnot available
KeywordsAdsorptionCalcinationFlue gasMaterials scienceEffluentInertInert gasStoichiometryVolume (thermodynamics)Nuclear chemistryChemistryChemical engineeringCatalysisWaste managementComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Adsorbent CuO/r-Al2O3 was successfully prepared by the dry impregnation using Cu(NO3)2.3H2O solution oncommercial r-Al2O3. Impregnation and calcination process was be done repeatedly to get a desired CuO contenton the support. The impregnations were done at 50oC for 8 hours or 120°C for 5 hours. Calcinations were carriedout at a temperature of 400oC for 8 hours. Adsorbent obtained from this preparation contained Cu: 7.93% (named8Cu), 14.76% (15Cu) and 28.98% (30Cu). Pore characteristic indicated that the surface area decreased with theincrease in Cu content in the adsorbent (from 207 in original support to 124 m2/g in 8Cu). Similar tendency wasfound for the pore volume (from 0.47 to 0.28 mL/g).In batch adsorption test, the amount of adsorbed SO2 was calculated from the increase in mass of adsorbent.Adsorbent 8Cu had the best adsorption capacity in term of mol ratio, ie. 0.78 SO2/CuO (close to stoichiometry).Original support of r-Al2O3 was found to be inert to SO2. In semi-continue test, the adsorption was carried undera flow of gas containing 2.5% SO2. The amount of adsorbed SO2 was calculated from the different of SO2content between influent to effluent. It was found again that the best adsorbent was 8Cu with the adsorptioncapacity of 0.97 mol/mol CuO.

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)0.002
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 score0.105
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.287
Teacher spread0.263 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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