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Record W3195536050 · doi:10.1016/j.gce.2021.07.003

A novel integrated energy systems combining methanol reformed fuel cell with sodium ion battery

2021· article· en· W3195536050 on OpenAlexaff
Haiying Che, Ziyu Zhang, Xinhai Yu, John Paul Shen, Shan‐Tung Tu, Zi‐Feng Ma

Bibliographic record

VenueGreen Chemical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsPalcan Energy Corporation (Canada)
FundersScience and Technology Commission of Shanghai MunicipalityShanxi Provincial Key Research and Development ProjectNational Natural Science Foundation of China
KeywordsBattery (electricity)Fuel cellsMethanolSodiumIonMaterials scienceAutomotive engineeringNuclear engineeringComputer scienceChemistryChemical engineeringEngineeringPhysicsOrganic chemistryPower (physics)ThermodynamicsMetallurgy

Abstract

fetched live from OpenAlex

A novel integrated energy systems combining methanol reformed fuel cell with sodium ion battery Hydrogen safety in storage and transport is one of the major obstacles for the widespread adoption of hydrogen fuel cells, making it critical to assuage public concerns on the safety of compressed hydrogen storage.Methanol in bountiful supply is a promising hydrogen energy carrier.Accordingly, a novel MSR-HT-PEMFC system coupling the hydrogen production via methanol steam reforming (MSR) and energy generation via high temperature proton exchange membrane fuel cell (HT-PEMFC) was firstly introduced by Prof. Zi-Feng Ma from

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.188
Teacher spread0.179 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2021
Admission routes1
Has abstractno

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