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Record W2326359515 · doi:10.1021/ja204976z

Ti-Doped LiAlH<sub>4</sub> for Hydrogen Storage: Synthesis, Catalyst Loading and Cycling Performance

2011· article· en· W2326359515 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of the American Chemical Society · 2011
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryHydrogen storageCatalysisYield (engineering)MetalMolar ratioDopingCyclingHydrogenChemical engineeringMetallurgyOrganic chemistryOptoelectronicsMaterials science

Abstract

fetched live from OpenAlex

The direct synthesis of LiAlH(4) from commercially available LiH and Al powders in the presence of TiCl(3) and Me(2)O has been achieved for the first time. The effects of TiCl(3) loadings (Ti/Al = 0, 0.01, 0.05, 0.2, 0.5, 1.0 and 2.0%) and various other additives (TiCl(3)/Al(2)O(3), metallic Ti, Nb(2)O(5), and NbCl(5)) on the formation and stability of LiAlH(4) have been systematically investigated. The yield of LiAlH(4) initially increases, and then decreases, with increasing TiCl(3) loadings. LiH + Al → LiAlH(4) yields above 95% were obtained when the molar ratios of Ti/Al were 0.05 and 0.2%. In the presence of a very tiny amount of TiCl(3) (Ti/Al = 0.01%), LiAlH(4) is still generated, but the yield is lower. In the complete absence of TiCl(3), LiAlH(4) does not form. Addition of metallic Ti, Nb(2)O(5), and NbCl(5) to commercial LiH and Al does not result in the formation of LiAlH(4). Preliminary tests show that TiCl(3)-doped LiAlH(4) can be cycled, making it a suitable candidate for hydrogen storage.

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)0.001
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 score0.004
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.019
GPT teacher head0.237
Teacher spread0.217 · 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