MétaCan
Menu
Back to cohort
Record W2591243296 · doi:10.1002/cnma.201700023

Ruthenium‐Functionalized Hierarchical Carbon Nanocages as Efficient Catalysts for Li‐O<sub>2</sub> Batteries

2017· article· en· W2591243296 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

VenueChemNanoMat · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMinistry of Education and Child Care
FundersMinistry of Education, IndiaMinistry of Earth Sciences
KeywordsNanocagesCathodeCatalysisMaterials scienceElectrolyteChemical engineeringNanoparticleCarbon fibersRutheniumLithium (medication)Current densityNanotechnologyChemistryElectrodePhysical chemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Developing an efficient cathode is essential to obtain high rate capability and rechargeable lithium oxygen (Li‐O 2 ) batteries. Herein, ruthenium (Ru)‐functionalized hierarchical carbon nanocages (hCNCs) are synthesized and employed as a cathode catalyst for Li‐O 2 batteries. The as‐prepared cathode exhibits high discharge capacity, low charge potential (8135 mA h g −1 with 3.85 V charge potential at a current density of 0.08 mA cm −2 ), outstanding rate capability (3416 mA h g −1 at a current density of 0.48 mA cm −2 ) and good stability up to 78 cycles at a limited capacity of 500 mA h g −1 . Such excellent battery performance is ascribed to the synergistic effect of the interconnected hierarchically porous structure of hCNCs, which can facilitate effective electrolyte immersion and efficient Li + /O 2 mass transport, and the high catalytic activity of Ru nanoparticles.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score1.000

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 head0.013
GPT teacher head0.245
Teacher spread0.232 · 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