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Record W1987595906 · doi:10.1002/adma.200306557

Recent Advances in Hydrogen Storage in Metal‐Containing Inorganic Nanostructures and Related Materials

2004· article· en· W1987595906 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

VenueAdvanced Materials · 2004
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMaterials scienceHydrogen storageCarbon nanotubeBoron nitrideNanotechnologyNanostructureEnergy storageLithium (medication)PorositySorptionCarbon fibersGraphitic carbon nitrideComposite numberChemical engineeringComposite materialAdsorptionCatalysisOrganic chemistryAlloy

Abstract

fetched live from OpenAlex

Abstract An overview of recent advances in the application of non‐carbonaceous nanostructured and composite materials in hydrogen storage is presented in this review. The main focus is on complex hydrides, non‐graphitic nanotubes, and other porous composite and framework materials, since carbon nanotubes have been the subject of numerous other reviews. Recent advances in the area of alanates show a promising reversible absorption capability of up to 5 %, closing in on the projected Department of Energy (DOE) target of 6 %. Non‐carbon nanotubes mainly showed a sorption capacity of 1–3 wt.‐%, although a promising level of 4.2 wt.‐% is shown by boron nitride nanotubes after collapse of their walls. Other interesting materials included here are lithium nitride and porous metallo‐organic frameworks.

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.

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)0.002
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 score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.246
Teacher spread0.238 · 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