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Record W2753289113 · doi:10.14447/jnmes.v20i3.320

A Potential Hydrogen Sources from Milled Silicon Powder Activated by Lithium, and Aluminum Chloride

2017· article· en· W2753289113 on OpenAlexvenueno aff
Tianchu Yin, Hongwei Shentu, Chengqiao Xi, Xin Chen, Wenzhen Zou, Meiqiang Fan

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2017
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsnot available
Fundersnot available
KeywordsSiliconHydrogenHydrolysisLithium (medication)AluminiumHydrogen productionMaterials scienceChemical engineeringChlorideInorganic chemistryComposite numberMicrostructureHydrogen chlorideChemistryNuclear chemistryMetallurgyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

A potential hydrogen source generated from milled Li-Si-AlCl3 composite was evaluated in this paper. The composite exhibits good hydrogen generation performance in water at 313–343 K, whereas pure silicon powder cannot continuously react with water under similar conditions. The hydrogen yield reaches 1300 mL hydrogen/g within 20 min, and the highest hydrogen generation rate is higher than 1200 mL hydrogen/g min within the first minute of hydrolysis. The hydrogen generation performance increases with increasing concentrations of lithium and aluminum chloride. Microstructure analysis indicates that silicon activity increases due to decreased particle size and distribution of lithium and aluminum chloride into silicon matrix during milling. The hydrolysis of the additives generates heat and alkaline hydrolysis byproducts, thereby stimulating the hydrolysis rate of silicon in the micro area. Therefore, the hydrolysis of silicon in water may act as a potential hydrogen source for portable micro fuel cells.

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 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 categoriesMeta-epidemiology (narrow), Scholarly communication
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 score1.000

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 communication0.0010.001
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 head0.010
GPT teacher head0.244
Teacher spread0.233 · 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 teacher head, not a consensus.

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

Citations4
Published2017
Admission routes1
Has abstractyes

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