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Record W3128267273 · doi:10.1038/s41529-020-00147-0

High efficiency hierarchical porous composite microfiltration membrane for high-temperature particulate matter capturing

2021· article· en· W3128267273 on OpenAlexaff
Yuhai Qu, Yongfeng Liang, Yanli Wang, Hui Zhang, Benli Luan, Junpin Lin

Bibliographic record

Venuenpj Materials Degradation · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsWestern University
FundersFundamental Research Funds for the Central UniversitiesScience Fund for Creative Research GroupsChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsMaterials scienceMicrofiltrationPorosityMembraneFiltration (mathematics)IntermetallicComposite numberChemical engineeringDiffusionParticulatesFabricationComposite materialChemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract Porous intermetallic membrane with extensive interconnected pores are potential candidates as functional materials for high-temperature particulate matter (PM) capturing. However, fabrication of intermetallic membrane with a combined performance of high filtration efficiency and high-temperature oxidation resistance remains a challenge. To tackle this issue, a hierarchical micro-/nano-dual-scale sized pores was constructed on the inner cell walls of a porous support through mutual diffusion and chemical reaction. Benefited from its hierarchical micro/nano-dual-scaled pore structural features, the high Nb containing TiAl-based porous composite microfiltration membrane demonstrates ultrahigh PM >2.5 removal efficiency (99.58%) and favorable oxidation/sulfidation performance at high temperature. These features, combined with our experimental design strategy, provide insight into designing high-temperature PM filtration membrane 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003

Distilled classifier scores by category (both heads)

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 head0.235
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2021
Admission routes1
Has abstractyes

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