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The influence of SWCNT–metallic nanoparticle mixtures on the desorption properties of milled MgH<sub>2</sub>powders

2009· article· en· W2065671177 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNanotechnology · 2009
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceMicrostructureHydrideNanoparticleThermogravimetric analysisChemical engineeringDesorptionCarbon nanotubeMetalComposite materialNanotechnologyMetallurgyAdsorptionOrganic chemistry

Abstract

fetched live from OpenAlex

We have examined the effect of single-walled carbon nanotube (SWCNT)-metallic nanoparticle additions on the hydrogen desorption behavior of MgH(2) after high-energy co-milling. The metallic nanoparticles were the catalysts used for the SWCNT growth. The co-milling consisted of high-energy planetary milling in an inert argon environment of the hydride powder mixed with the SWCNTs. Identically milled pure MgH(2) powders were used as a baseline. The composites were tested using a combined differential scanning calorimeter and thermogravimetric analyzer, while the microstructures were examined using a variety of techniques including x-ray diffraction and transmission electron microscopy (TEM). We found that the SWCNT-nanoparticle additions do have an influence on the desorption kinetics. However, the degree to which they are effective depends on the composite's final state. The optimum microstructure for sorption, obtained after 1 h of co-milling, consists of highly defective SWCNTs in intimate contact with metallic nanoparticles and with the hydride. This microstructure is optimum, presumably because of the dense and uniform coverage of the defective SWCNTs on the MgH(2) surface. Prolonged co-milling of 7 h destroys the SWCNT structure and reduces the enhancement. Even after 72 h of co-milling, when the SWCNTs are completely destroyed, the metallic nanoparticles remain dispersed on the hydride surfaces. This indicates that the metallic nanoparticles alone are not responsible for the enhanced sorption and that there is indeed something catalytically unique about a defective SWCNT-metal combination. Cryo-stage TEM analysis of the hydride powders revealed that they are nanocrystalline and in some cases multiply twinned. To our knowledge this is the first study where the structure of milled alpha- MgH(2) has been directly imaged. Since defects are an integral component of hydride-to-metal phase transformations, such analysis sheds new insight regarding the fundamental microstructural origins of the sorption enhancement due to mechanical milling.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.001
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 score0.002
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.213
Teacher spread0.201 · 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