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Record W2620714767 · doi:10.14447/jnmes.v13i3.172

A Study of the Catalyst/Absorbent Effect on the Hydrogen Production by Solid Absorption Enhanced Water Gas Shift (SAEWGS)

2010· article· en· W2620714767 on OpenAlexvenueno aff
Miguel A. Escobedo-Bretado, E. López-Chipres, M.D. Delgado-Vigil, Jesús M. Salinas-Gutiérrez, Alejandro López-Ortíz, V. Collins-Martı́nez

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2010
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsnot available
Fundersnot available
KeywordsCatalysisCalcinationHydrogenHydrogen productionParticle sizePartial pressureDiffusionAbsorption (acoustics)Inorganic chemistryChemistryChemical engineeringMaterials scienceParticle (ecology)Nuclear chemistryAnalytical Chemistry (journal)ChromatographyOxygenOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

The combination of the WGS and CO2 solid absorption (SAEWGS) produce H2 and CO2 separation in one step. Experimental conditions: quartz-made fixed bed reactor at SV = 1500 h-1, feed; 5 % CO, 15 % H2O, balance He-N2 and 600 °C, 1 atm. Absorbents tested were calcined dolomite (CaO*MgO) and sodium zirconate (Na2ZrO3) employing catalyst/absorbent mixtures in 1/1 and 1/2 weight ratios. A synthesized WGS catalyst (Fe-Cr) was used. Results using the mixture catalyst/absorbent = 1/2 with CaO*MgO generated 95 % of H2 and 5 % CO2 without CO. An increase in the catalyst/absorbent weight ratio from 1/1 to 1/2 also increased hydrogen from 89 to 95 %, respectively. This was attributed to slow CO2 diffusion into the particle affecting absorption kinetics. Whereas, Na2ZrO3 produced only 70 % H2, 29 % CO2 and 1 % CO being a small CO2 partial pressure responsible for the lower H2 content. Using Na2ZrO3, the variation of the cat/abs ratio had no effect over the hydrogen content.

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)0.001
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 score0.001
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.222
Teacher spread0.216 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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