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Record W2421757936 · doi:10.1021/jacs.5b03505

Simple and Efficient System for Combined Solar Energy Harvesting and Reversible Hydrogen Storage

2015· article· en· W2421757936 on OpenAlexafffund
Lü Li, Xiaoyue Mu, Wenbo Liu, Zetian Mi, Chao‐Jun Li

Bibliographic record

VenueJournal of the American Chemical Society · 2015
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsMcGill UniversityCentre in Green Chemistry and Catalysis
FundersArmy Research OfficeFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsDehydrogenationChemistryHydrogenEnergy transformationHydrogen storageSolar energyEnergy storageArtificial photosynthesisCatalysisChemical energyChemical engineeringHydrogen fuelPhotochemistryProcess engineeringOrganic chemistryThermodynamicsPhotocatalysisElectrical engineering

Abstract

fetched live from OpenAlex

Solar energy harvesting and hydrogen economy are the two most important green energy endeavors for the future. However, a critical hurdle to the latter is how to safely and densely store and transfer hydrogen. Herein, we developed a reversible hydrogen storage system based on low-cost liquid organic cyclic hydrocarbons at room temperature and atmospheric pressure. A facile switch of hydrogen addition (>97% conversion) and release (>99% conversion) with superior capacity of 7.1 H2 wt % can be quickly achieved over a rationally optimized platinum catalyst with high electron density, simply regulated by dark/light conditions. Furthermore, the photodriven dehydrogenation of cyclic alkanes gave an excellent apparent quantum efficiency of 6.0% under visible light illumination (420-600 nm) without any other energy input, which provides an alternative route to artificial photosynthesis for directly harvesting and storing solar energy in the form of chemical fuel.

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)0.000
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 score0.020
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.014
GPT teacher head0.253
Teacher spread0.239 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations74
Published2015
Admission routes2
Has abstractyes

Explore more

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