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Record W4410419494 · doi:10.1016/j.carbon.2025.120444

Detailed in situ TEM/EELS analysis of laser-induced reduction of graphene oxide

2025· article· en· W4410419494 on OpenAlexafffund
Israt Ali, Huiyu Lei, Shuhui Sun, Kenneth R. Beyerlein

Bibliographic record

VenueCarbon · 2025
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationMinistère de l'Économie, de la Science et de l'Innovation - Québec
KeywordsGrapheneIn situOxideMaterials scienceLaserReduction (mathematics)NanotechnologyChemical engineeringChemistryMetallurgyOpticsPhysics

Abstract

fetched live from OpenAlex

Laser-induced reduction of graphene oxide (GO) represents a highly promising approach to graphene synthesis due to local control of the treated area, avoidance of hazardous chemical reagents, and environmentally benign ambient conditions. In this study, we present a comprehensive investigation of the reduction of GO induced by multiple laser pulses at a wavelength of 532 nm by in situ transmission electron microscopy. By systematically varying the laser fluence from 6.36 mJ/cm 2 to 15.88 mJ/cm 2 and employing electron energy loss spectroscopy (EELS), we achieved precise quantification and identification of the removal of distinct oxygen functional groups. The kinetics of the process are modelled using a double-exponential function to determine photoreduction rates as a function of laser fluence. We estimate laser fluences in this range raise the film temperature from 342 °C to 823 °C, and correlate this with observed functional group removal and established thermal decomposition thresholds. Our work offers a detailed understanding of the laser-induced reduction process and highlights the potential for tailoring the chemical composition of graphitic oxides through controlled laser processing.

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 score0.071
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.018
GPT teacher head0.293
Teacher spread0.275 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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