Nanoscopic imaging of oxidized graphene monolayer using tip-enhanced Raman scattering

Smolsky, Joseph; Krasnoslobodtsev, Alexey V.

doi:10.1007/s12274-018-2158-x

Cited by 14 publications

(3 citation statements)

References 68 publications

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“…Natural expandable graphite with a fineness of 200 mesh was used as raw material, treated with sulfuric acid, nitric acid, and other oxidants for 96 h, washed with dilute hydrochloric acid and deionized water, dried in vacuum, and rapidly thermally expanded in high-temperature equipment in an argon atmosphere to prepare graphene [22]. Smolsky et al oxidized graphene with sulfuric acid, nitric acid, and other oxidants, so that active groups such as carboxyl and hydroxyl groups were generated on its molecular surface and then esterified with it with coupling agent KH-560 [23]. Soleimani et al used graphene oxide at a low price, which was directly used to modify epoxy resin without chemical treatment [24].…”

Section: Study On Defect Modification Of Graphenementioning

confidence: 99%

Prediction Method of Graphene Defect Modification Based on Neural Network

Feng

Yao

et al. 2022

Mobile Information Systems

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Because of its excellent thermal, mechanical, and electrical properties, graphene has been used in a variety of functional coatings. Noncovalent bond functionalization and covalent bond functionalization are the most common graphene surface functionalization methods. Polymer modification, for example, can be used to give graphene and its derivatives new structure, morphology, and properties. The basic structure and predictive control principle of neural networks are discussed in this study, and a high thermal resistance porous graphene structure is reversely designed using machine learning. The effect of a graphene defect modification prediction model based on a GA (genetic algorithm) and improved BPNN (BP neural network) algorithm is investigated. The RMSE predicted by submodels 1–4 decreases by 13.26%, 3.86%, 11.71%, and 19.63%, respectively, according to the simulation results. The BPNN graphene defect modification prediction model optimized by GA has a better training and prediction effect than before optimization.

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Section: Study On Defect Modification Of Graphenementioning

confidence: 99%

Prediction Method of Graphene Defect Modification Based on Neural Network

Feng

Yao

et al. 2022

Mobile Information Systems

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“…Combining the utility of scanning probe microscopy and Raman scattering, tip‐enhanced Raman spectroscopy (TERS) has become an ultrasensitive detection technique, and it is widely used in the fields of atomic structure, catalytic processes, and the redox behavior of single molecules . The selective Raman enhancement has been studied through the modifications and functionalization of TERS probes and controlling of surface plasmon polarizations and light polarizations .…”

Section: Introductionmentioning

confidence: 99%

Selective excitation of one among the three peaks of tip‐enhanced Raman spectroscopy by a shaped ultrafast laser pulse

Xia

Zhao

Zhang

et al. 2019

J Raman Spectroscopy

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There are generally several neighboring Raman peaks that are excited simultaneously in tip‐enhanced Raman spectroscopy (TERS). A method to selectively excite a specific Raman peak and simultaneously depress the others is required for suppressing noise and improving detection sensitivity and accuracy. In this study, we demonstrate a scheme for selective excitation of one Raman peak among three excited states in TERS by properly shaping the pump and Stokes femtosecond laser pulses. The shaped pump and Stokes pulses are transformed into time domain, discretized and reconstructed; they are then used to irradiate the TERS structure obliquely. The impulse responses at five probe points of the TERS structure are calculated followed by a Fourier transform. In these cases, the probability of one selectively excited Raman transition increases by several hundreds to tens of thousands in amplitude, whereas the others are both depressed to zero. The probe points at different positions in the tip apex do not affect the enhanced and selective excitation of one Raman transition among the three excited states.

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“…Приведенный спектр хорошо согласуется с ранее опубликованными данными по комбинационному рассеянию и TERS-спектроскопии слоев оксида графена. Хорошо выраженные линии (отношение сигнал/шум не менее 3) при 1350 и 1590 cm −1 соответствуют D-и G-колебательным модам оксида графена [12,13].…”

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Высокоэффективные Воспроизводимые Зонды Для Спектроскопии Комбинационного Рассеяния С Усилением На Острие (TERS)

Мочалов¹,

Соловьева²,

Ефимов³

et al. 2020

Письма В Журнал Технической Физики

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The principal limitation of the use of the Tip Enhanced Raman Scattering spectroscopy (TERS) is due to the lack of reliable scanning probes. The paper presents a simple technique for the fabrication of cantilever-based TERS-probes with highly reproducible characteristics, high performance, reliable and giving sufficient enhancement. The technique is based on the modification of standard scanning probe microscope contilevers by plasma etching and the subsequent formation of a TERS-amplifying region on the tip of probe by deposition of colloidal nanoparticles by dielectrophoresis.

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Nanoscopic imaging of oxidized graphene monolayer using tip-enhanced Raman scattering

Cited by 14 publications

References 68 publications

Prediction Method of Graphene Defect Modification Based on Neural Network

Prediction Method of Graphene Defect Modification Based on Neural Network

Selective excitation of one among the three peaks of tip‐enhanced Raman spectroscopy by a shaped ultrafast laser pulse

Высокоэффективные Воспроизводимые Зонды Для Спектроскопии Комбинационного Рассеяния С Усилением На Острие (TERS)

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