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Record W4205356244 · doi:10.3390/membranes12020104

Ti-40Al-10Nb-10Cr Porous Microfiltration Membrane with Hierarchical Pore Structure for Particulate Matter Capturing from High-Temperature Flue Gas

2022· article· en· W4205356244 on OpenAlexaff
Wanyuan Gui, Zhenjing Shi, Yin Zhang, Yongfeng Liang, Jingyan Qin, Yanli Wang, Junpin Lin, Benli Luan

Bibliographic record

VenueMembranes · 2022
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsWestern University
FundersFundamental Research Funds for the Central UniversitiesChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsFlue gasMaterials scienceMicrofiltrationPorosityFiltration (mathematics)Chemical engineeringAlloyParticulatesMembraneMetallurgyComposite materialWaste managementChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

TiAl-based porous microfiltration membranes are expected to be the next-generation filtration materials for potential applications in high-temperature flue gas separation in corrosive environments. Unfortunately, the insufficient high-temperature oxidation resistance severely limits their industrial applications. To tackle this issue, a Ti-40Al-10Nb-10Cr porous alloy was fabricated for highly effective high-temperature flue gas purification. Benefited from microstructural changes and the formation of two new phases, the Ti-40Al-10Nb-10Cr porous alloy demonstrated favorable high-temperature anti-oxidation performance with the incorporation of Nb and Cr high-temperature alloying elements. By the separation of a simulated high-temperature flue gas, we achieved an ultra-high PM-removal efficiency (62.242% for PM<2.5 and 98.563% for PM>2.5). These features, combined with our experimental design strategy, provide a new insight into designing high-temperature TiAl-based porous materials with enhanced performance and durability.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.208
Teacher spread0.202 · 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.

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

Citations4
Published2022
Admission routes1
Has abstractyes

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