In situ Electron Energy Loss Spectroscopy (EELS) Studies of Laser-induced Graphene Oxide Reduction in a Dynamic Transmission Electron Microscope (DTEM)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».