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Record W4414223954 · doi:10.1021/acsomega.5c05566

Ultrahigh Temperature Purification of Graphite for the Development of a Continuous Process

2025· article· en· W4414223954 on OpenAlex
Yewen Tan, Marc Duchesne, Anna Doninger, Igor Barsukov

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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueACS Omega · 2025
Typearticle
Languageen
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsNatural Resources Canada
FundersNatural Resources Canada
KeywordsGraphiteImpurityAnalytical Chemistry (journal)Residence time (fluid dynamics)Elemental analysis

Abstract

fetched live from OpenAlex

This work presents a study of ultrahigh temperature purification of natural Canadian graphite flakes. The concentrated natural graphite flakes were purified using two test facilities, an ultrahigh temperature fixed bed furnace and an ultrahigh temperature fast-heating counterflow reactor. With the fixed bed furnace, the natural graphite flakes were purified at 2500 or 2800 °C for 15-120 min. With the counterflow reactor, the residence time was ∼20-25 min, with an average temperature of 2700 °C and higher local temperatures due to electric arcing. The heat-treated samples were characterized by using several different analysis techniques. The results showed that the samples treated with the fast-heating counterflow reactor reached a very high purity above 99.9 wt % carbon. The samples treated at 2800 °C in the fixed bed furnace reached a similar purity. At the lower temperature of 2500 °C, a similar purity could only be achieved with a duration of at least 60 min. Four elemental analysis techniques to quantify impurities in graphite were evaluated in this work, with a focus on elements that disrupt the performance of Li-ion batteries, such as magnesium, aluminum, iron, copper, and silicon. The analysis results with the original graphite flakes and the heat-treated graphite flakes showed that significant differences exist among the various analysis techniques. For some critical elements, such as iron and silicon, the detected concentrations could differ by more than 1 order of magnitude.

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 score0.009
Threshold uncertainty score0.262

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 head0.005
GPT teacher head0.223
Teacher spread0.217 · 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