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Record W4401002952 · doi:10.1093/mam/ozae044.817

In situ Electron Energy Loss Spectroscopy (EELS) Studies of Laser-induced Graphene Oxide Reduction in a Dynamic Transmission Electron Microscope (DTEM)

2024· article· en· W4401002952 on OpenAlexaff
Israt Ali, Kenneth R. Beyerlein

Bibliographic record

VenueMicroscopy and Microanalysis · 2024
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsGrapheneElectron energy loss spectroscopyMaterials scienceTransmission electron microscopyElectron microscopeSpectroscopyLaserIn situElectronScanning transmission electron microscopyOxideOptoelectronicsOpticsNanotechnologyChemistryPhysics

Abstract

fetched live from OpenAlex

Graphene is a single layer of sp2 hybridized carbon atoms and is widely applied in electronics, photonics, and energy-based industries because of its exceptional physical and chemical properties [1]. Graphene-based materials for large-scale production can be obtained from graphene oxide (GO) through effective treatments to reduce the number of oxygen functional groups. GO conversion to reduced GO (rGO) can be done by many methods, including wet chemical reduction, thermal reduction, and photoreduction. Chemical reduction of GO requires hydrazine, sodium borohydride, and other potent reducing agents, limiting this method’s applicability in large-scale production [2]. Further, graphene is a potential candidate for biomedical applications, including drug delivery, cancer therapy, and antimicrobials, but chemically reduced GO has shown potential toxicity to normal cells [3]. Similarly, thermal heating requires temperatures exceeding 1000 °C and complex procedures for the high-quality production of graphene [4]. Photoreduction of GO with a laser is fast and avoids the use of toxic chemicals. Furthermore, the ability to pattern the illumination and raster the target in the beam allows the capability to write rGO circuits in films of GO. To prepare high-quality graphene requires understanding the detailed photoreduction mechanism. We conducted in situ experiments using a dynamic transmission electron microscope (DTEM) watching the photoreduction process of GO sheets. The DTEM at INRS-EMT is based on a JEOL 2100Plus and has been modified to couple laser light into the column through an optical port and focus it onto the sample. We used this configuration to track the reduction in oxygen concentration in the sample upon irradiation by nanosecond laser light pulses. The GO sample was prepared using a modified Hummer’s method [5] and drop cast on a lacey carbon grid. The second harmonic from a Nd:YAG nanosecond pulsed laser (wavelength = 532 nm, repetition rate = 10 Hz, pulse duration = 11 ns) was aligned into the microscope and focused to a spot size of 100 μm on the sample. Laser pulse energy densities ranging from 3.18 to 15.9 mJ/cm² were systematically applied to investigate their impact on the reduction process. We utilized electron energy loss spectroscopy (EELS) to quantify the change in oxygen reduction with a collection angle of 12.7 mrad. The EELS acquisition time was 0.01 s for low-loss spectra and 5 s for the core-loss spectra. Multiple EELS spectra of Carbon and Oxygen K-edges were collected before and after laser irradiation to determine relative composition atomic percentages. Further, the log ratio method was used to determine the changes in the thickness of graphene oxide. Results from the experiments indicated a direct correlation between laser fluence and the reduction of oxygen atomic composition in GO. Higher laser fluence led to higher removal of oxygen, suggesting the importance of controlling this parameter for tailored graphene production. Further studies will be required to identify an optimal laser fluence for substantially reducing oxygen content in GO sheets without inducing ablation. In this presentation, we will showcase the trends observed while measuring the reduction of oxygen concentration and sample thickness as a function of laser fluence. We will also demonstrate how these quantities evolve as a function of laser exposure time which allows us to measure the kinetics of the GO photoreduction process. We will discuss the optimal laser parameters to produce high-quality rGO by photoreduction while minimizing material ablation. This information is important for the growing graphene manufacturing and application industries. Furthermore, this presentation exemplifies the kind of information that can be obtained by in situ laser irradiation TEM experiments.

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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 score0.002
Threshold uncertainty score0.006

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.319
Teacher spread0.308 · 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".

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Citations0
Published2024
Admission routes1
Has abstractno

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