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Record W4396750307 · doi:10.3390/molecules29102195

Mercury Adsorption and Oxidation Performance of an Iron-Based Oxygen Carrier during Coal Chemical Looping Process

2024· article· en· W4396750307 on OpenAlexaboutno aff
Guochao Hu, Shuju Zhao, Minggang Gao, Yongzhuo Liu

Bibliographic record

VenueMolecules · 2024
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsnot available
FundersKey Technology Research and Development Program of ShandongNational Natural Science Foundation of China
KeywordsMercury (programming language)Chemical looping combustionOxygenChemistryAdsorptionCoalChemisorptionBituminous coalInorganic chemistryEnvironmental chemistryChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

During chemical looping combustion (CLC) and chemical looping gasification (CLG) of coal, the release, migration, and speciation of mercury in coal are significantly influenced by oxygen-carrier materials; however, the underlying mechanism remains inadequately addressed. In this work, the effect of a typical iron-based oxygen carrier on the release behavior of mercury from a bituminous coal and a lignite was investigated based on the Ontario-Hydro method. It is found that the effect of the iron-based oxygen carrier is attributed to three aspects: the enhanced release rate of mercury from coal, the adsorption of the released mercury, and the oxidization of gaseous Hg0 into Hg2+. With the increasing temperature, the adsorbance of mercury by the iron-based oxygen carrier decreases, while the oxidation of mercury enhances. Even at 900 °C, the adsorbance of mercury by the oxygen carrier remained at 0.1687 g/g, with a relative content of Hg2+ at 22.55%. Additionally, it was observed that iron-based oxygen carriers can physically absorb both Hg0 and Hg2+, while chemisorption refers to complex-compound formation between the iron-based oxygen carrier and mercury.

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 score0.174
Threshold uncertainty score0.469

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 head0.005
GPT teacher head0.209
Teacher spread0.204 · 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
Published2024
Admission routes1
Has abstractyes

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