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Record W4412706633 · doi:10.1016/j.nxmate.2025.100973

N,S-doped carbon based on phenol formaldehyde resin precursors as an anode material for sodium-ion batteries

2025· article· en· W4412706633 on OpenAlexfundno aff
Sergey A. Urvanov, Mariem Nasraoui, Ivan S. Filimonenkov, В. З. Мордкович

Bibliographic record

VenueNext Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsnot available
FundersNational Research Council Canada
KeywordsAnodePhenolFormaldehydeSodiumDopingCarbon fibersIonPhenol formaldehyde resinMaterials scienceChemistryInorganic chemistryOrganic chemistryElectrodeComposite materialOptoelectronicsComposite numberPhysical chemistry

Abstract

fetched live from OpenAlex

This work focuses on investigating carbon materials as anodes for sodium-ion batteries, specifically utilizing a non-graphitizable carbon material based on the phenol-formaldehyde resin with varying concentrations of aniline and thiophene as heteroatoms. Four series of experiments were conducted to synthesize anode materials based on a simple phenol-formaldehyde resin precursor. Four different phenol-formaldehyde resin to aniline weight ratios were applied, namely 0:1, 1:1, 9:1, and 3:1. In the case of thiophene there were three samples and the molar ratios of reagents were as follows: 1:1, 9:1, 99:1. This study aims to provide insights into the electrochemical behavior of these novel anode materials, shedding light on the impact of aniline incorporation into phenol-formaldehyde resins for sodium-ion battery applications.

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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.264
Teacher spread0.249 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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