MétaCan
Menu
← Back to cohort
Record W4394391175 · doi:10.6084/m9.figshare.20729262

AMORPHOUS CUSEP2/GRAPHENE COMPOSITES AS ANODE MATERIAL FOR ADVANCED POTASSIUM-ION BATTERY

2022· dataset· en· W4394391175 on OpenAlexaff
Luo Yu, Qing Liu, Lingxiao Yang, Youwei Yan

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsGrapheneAnodeMaterials scienceComposite materialBattery (electricity)Amorphous solidPotassium-ion batteryPotassiumIonNanotechnologyChemistryMetallurgyElectrodePhysicsLithium vanadium phosphate batteryCrystallography

Abstract

fetched live from OpenAlex

The ternary amorphous CuSeP2 was designed and prepared as the anode material of potassium ion battery for the first time, and its electrochemical performance was also investigated. After ball-milling with commercial graphene powder, it is used as the anode material for potassium ion battery with reversible specific capacity up to 300 mAh g-1, and the corresponding initial coulombic efficiency is close to 60%. What’s more, the specific capacity remains above 150 mAh g-1 after 100 cycles at 200 mA g-1. When increasing the current density to 1000 mA g-1, the CuSeP2/graphene composites still has a potassium storage specific capacity of 100 mAh g-1. Our results show that the potassium storage mechanism of the ternary CuSeP2 anode material is a typical conversion reaction and the introduction of Cu can not only buffer the volume expansion during the subsequent electrochemical reaction, but also effectively enhance the electrochemical reversibility of potassium ion.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.073
Threshold uncertainty score0.245

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0730.047

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 head0.018
GPT teacher head0.254
Teacher spread0.236 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueFigshare→Same topicAdvancements in Battery Materials→French-language works237,207→