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Record W4405740596 · doi:10.1016/j.jmgm.2024.108934

A new type of two-dimensional carbon-based monolayers namely irida-graphene as an anode material for magnesium-ion batteries

2024· article· en· W4405740596 on OpenAlex
Maher Ali Rusho, Abdulrahman T. Ahmed, Prakash Kanjariya, Asha Rajiv, Aman Shankhyan, Pushpa Negi Bhakuni, Hashim Elshafie

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

VenueJournal of Molecular Graphics and Modelling · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsLockheed Martin (Canada)
FundersKing Khalid University
KeywordsAnodeGrapheneMagnesiumMaterials scienceCarbon fibersMonolayerIonNanotechnologyChemical engineeringMetallurgyComposite materialChemistryComposite numberElectrodeEngineeringOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

The DFT was employed to assess the ion-storage capability of an irida-graphene monolayer (IGM) in Mg-ion batteries (MIBs). The IGM had a mechanically stable structure. The IGM also exhibited great conductance based on the DOS calculations. The energy density of the IGM for MIBs was 3139.60 mWh g −1 and its storage capacity was 1643.21 mAh g −1 . Moreover, the Mg ions migrated easily across the IGM surface throughout cycle, as indicated by the increased rate of diffusion (1.58 x 10 −5 cm 2 s −1 ) and the small energy barrier (0.068 eV). In addition, the obtained OCV for MIBs was 0.18 V, which was in line with the requirements for commercial designing. The current theoretical study demonstrated the possibility of using the IGM as an electrode in future MIBs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score0.703

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 head0.019
GPT teacher head0.262
Teacher spread0.243 · 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