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Shaping Porous Materials for Hydrogen Storage Applications: A Review

2014· review· en· W1975483233 on OpenAlexvenueno aff
Jianwei Ren, Brian C. North

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2014
Typereview
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsnot available
FundersDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsHydrogen storagePorosityMaterials scienceHydrogenProcess engineeringEnvironmental scienceEngineeringMetallurgyChemistryComposite material

Abstract

fetched live from OpenAlex

Development of safe and effective hydrogen storage systems becomes a critical factor for further implementation of fuel cell and hydrogen technologies. Among new approaches aimed at improving the performance of such systems, the concept of porous materials-based adsorptive hydrogen storage is now considered as a long-term solution due to the reversibility, good kinetics and absence of thermal management issues. However, the low packing densities associated with the porous materials such as carbon structure materials, zeolites, metal-organic frameworks lead to the compromised volumetric capacity, potential pipe contaminations and difficulties in handling, when introducing the powdered adsorbents into hydrogen storage systems. Some efforts have been devoted to solve this problem by shaping the porous materials into beads, pellets or monoliths and achieve higher storage densities at more moderate temperatures and pressures. This review will firstly state the essential properties of shaped structures for hydrogen adsorption, and then highlight the recent attributes that potentially can be utilized to shape porous materials into specific configurations for hydrogen storage applications. Later, several testing techniques on structured porous material will be also discussed.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
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 head0.031
GPT teacher head0.304
Teacher spread0.273 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations26
Published2014
Admission routes1
Has abstractyes

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