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Design and synthesis of thin palladium membranes on porous metal substrate for hydrogen extraction

2009· article· en· W2161896186 on OpenAlexaff
Zheng-Zheng Shi, Jerzy A. Szpunar, Shanqiang Wu

Bibliographic record

VenueJournal of Physics Conference Series · 2009
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsMcGill University
Fundersnot available
KeywordsMembraneMaterials scienceHydrogenPalladiumChemical engineeringPorosityPermeationSubstrate (aquarium)MetalThin filmComposite materialNanotechnologyCatalysisMetallurgyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Membrane separation is regarded nowadays as a preferred method for production of purified hydrogen. Palladium (Pd) is an attractive membrane material due to its ability to dissociate molecular hydrogen into atoms. It is usually deposited on the porous substrate that can provide good mechanical support and reduce the thickness of the membrane for maximizing hydrogen permeability. Pd membrane used for hydrogen separation must be thin enough to increase hydrogen flux and reduce cost while remaining thick enough to retain adhesion, attrition resistance and mechanical integrity during high temperature cycles. In this paper, the progress of electroless deposition of Pd around the pore area at surface of porous stainless steel was recorded and a bridge structure that was formed during the membrane deposition around the pore area of the substrate was illustrated. After that, the porous substrate was modified using micro-or nano-size metal or metal oxide particles in order to reduce pore size in the substrate surface. The experimental results obtained from hydrogen permeation through the Pd membranes having the thickness from 400 nm to 18 μm built on both modified and original porous stainless steel substrates demonstrate that these thin membranes are solid and they can be used at the temperature of 550°C and hydrogen pressure difference of 3.447x10 5 Pa. The proposed processing will allow optimizing the design and fabrication of thin Pd membranes on different porous substrates for hydrogen separation.

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 score0.000
Threshold uncertainty score0.002

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 head0.022
GPT teacher head0.241
Teacher spread0.218 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

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