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Record W4413272199 · doi:10.1021/acsaem.5c01163

Construction of Stable Zn Anode by 3D Flamed Reduced Graphene Oxide with Long Cycling Life

2025· article· en· W4413272199 on OpenAlexaff
Ziwei Gan, Lei Hu, Mengxuan Sun, Nengze Wang, Xiaohe Ren, Chunyang Jia, Zhijie Li, Xiaojun Yao

Bibliographic record

VenueACS Applied Energy Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersFundamental Research Funds for the Central Universities
KeywordsCyclingGrapheneAnodeOxideMaterials scienceNanotechnologyMetallurgyElectrodeChemistry

Abstract

fetched live from OpenAlex

Aqueous zinc-ion capacitors (ZICs) have been regarded as an emerging energy storage device due to their excellent safety, low cost, environmental friendliness, and high energy density. However, poor cycling stability due to fatal defects of the zinc anode such as dendrite growth, corrosion, and passivation limits the better development of ZICs. Here, we propose a dual strategy of 3D host design and surface chemical modification. Zn foam is chosen as the anode skeleton to expand the 2D zinc metal into a 3D porous structure, providing abundant active sites and enhancing the electrical conductivity. Meanwhile, the flamed reduced graphene oxide (FRGO) coating is used as a protective and modulating layer for the 3D skeleton to enhance zinc deposition and effectively inhibit interfacial side reactions. As a verification, the prepared FRGO coated Zn foam (FRGO@Zn foam) in a symmetrical cell shows a stable cycle life of 1600 h at 1 mA cm –2 and 1 mAh cm –2 . Furthermore, when FRGO@Zn foam was used as the anode and dense rGO/FRGO graphene film used as the cathode, the energy density of the ZICs is as high as 118.4 Wh L –1 (124.6 Wh kg –1 ), and the capacitance retention rate is still 93.6% after 30,000 cycles, indicating that the anode has good application prospects in the ZIC field.

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 score0.030
Threshold uncertainty score0.875

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 head0.006
GPT teacher head0.216
Teacher spread0.210 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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