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Record W2012704299 · doi:10.1021/ef800895v

Upper Bound for the Efficiency of a Novel Chemical Cycle of H<sub>2</sub>S Splitting for H<sub>2</sub> Production

2009· article· en· W2012704299 on OpenAlexaff
Guifen Yu, Hui Wang, Karl T. Chuang

Bibliographic record

VenueEnergy & Fuels · 2009
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsSulfuric acidHydrogen sulfideHydrogen productionThermochemical cycleHydrogenChemistryWater splittingHydrogen iodideSulfurInorganic chemistryOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

To develop sustainable hydrogen production to meet its needs in oil sands upgrading and refining, a chemical cycle of hydrogen production from splitting hydrogen sulfide, the waste product from the same industrial sector, has been proposed. There are two routes of chemical processes to conduct the chemistry. Process 1 takes 1 mol of H 2 S and converts it into H 2 and S, and process 2 takes hydrogen sulfide, oxygen, and water (1:1:2 H 2 S/O 2 /H 2 O) as a feedstock and produces 2 mol of hydrogen and 1 mol of sulfuric acid. Similar to the sulfur−iodine cycle of water splitting for hydrogen, this cycle consists of an iodine−iodide loop and a sulfur dioxide−sulfuric acid loop. This study uses the thermodynamic data of the reactions involved to calculate an upper bound of the thermal efficiency of the hydrogen sulfide splitting cycle. With the enthalpy of the H 2 S oxidation reactions as part of the energy input to the cycle, the values of the maximum thermal efficiency for processes 1 and 2 are 0.41 and 0.36, respectively. However, if only external energy that meets the requirement of heat and work for reactions and pumping is taken into account, the thermal efficiency can be higher, at 0.66 and 0.70. It is found that the separations of sulfuric acid and hydrogen iodide respectively from their aqueous solutions generated within the cycle are still the most energy-consuming processes.

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 score0.024
Threshold uncertainty score0.726

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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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

Citations18
Published2009
Admission routes1
Has abstractyes

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