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Record W2325748008 · doi:10.1021/jz200836h

Free-Standing Layer-By-Layer Hybrid Thin Film of Graphene-MnO<sub>2</sub> Nanotube as Anode for Lithium Ion Batteries

2011· article· en· W2325748008 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

VenueThe Journal of Physical Chemistry Letters · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGrapheneMaterials scienceAnodeLithium (medication)ElectrodeLayer (electronics)Thin filmChemical engineeringNanotechnologyComposite numberCurrent collectorNanotubeCarbon nanotubeComposite materialChemistryElectrolyte

Abstract

fetched live from OpenAlex

Free-standing layer-by-layer assembled hybrid graphene-MnO 2 nanotube (NT) thin films were prepared by an ultrafiltration technique and studied as anodes for lithium ion batteries. Each thin layer of graphene provides not only conductive pathways accelerating a conversion reaction of MnO 2 but also buffer layers to maintain electrical contact with MnO 2 NT during lithium insertion/extraction. In addition, the unique structures of the thin film provide porous structures that enhance Li ion diffusion into the structure. The graphene-MnO 2 NT films as anode present excellent cycle and rate capabilities with a reversible specific capacity based on electrode composite mass of 495 mAh/g at 100 mA/g after 40 cycles with various current rates from 100 to 1600 mA/g. On the contrary, graphene-free MnO 2 NT electrodes demonstrate only 140 mAh/g at 80 mA/g after 10 cycles. Furthermore, at a high current rate of 1600 mA/g, the charge capacity of graphene-MnO 2 NT film reached 208 mAh/g.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.872

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 science0.0010.000
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 head0.015
GPT teacher head0.225
Teacher spread0.209 · 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