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1.
Nano Lett ; 24(12): 3670-3677, 2024 Mar 27.
Artigo em Inglês | MEDLINE | ID: mdl-38483128

RESUMO

Functionalization of metallic surfaces by molecular monolayers is a key process in fields such as nanophotonics or biotechnology. To strongly enhance light-matter interaction in such monolayers, nanoparticle-on-a-mirror (NPoM) cavities can be formed by placing metal nanoparticles on such chemically functionalized metallic monolayers. In this work, we present a novel functionalization process of gold surfaces using 5-amino-2-mercaptobenzimidazole (5-A-2MBI) molecules, which can be used for upconversion from THz to visible frequencies. The synthesized surfaces and NPoM cavities are characterized by Raman spectroscopy, atomic force microscopy (AFM), and advancing-receding contact angle measurements. Moreover, we show that NPoM cavities can be efficiently integrated on a silicon-based photonic chip performing pump injection and Raman-signal extraction via silicon nitride waveguides. Our results open the way for the use of 5-A-2MBI monolayers in different applications, showing that NPoM cavities can be effectively integrated with photonic waveguides, enabling on-chip enhanced Raman spectroscopy or detection of infrared and THz radiation.

2.
Nanoscale ; 16(7): 3549-3559, 2024 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-38287770

RESUMO

In this paper, we present the surface modification of multilayer graphene electrodes with platinum (Pt) nanoparticles (NPs) using spark ablation. This method yields an individually selective local printing of NPs on an electrode surface at room temperature in a dry process. NP printing is performed as a post-process step to enhance the electrochemical characteristics of graphene electrodes. The NP-printed electrode shows significant improvements in impedance, charge storage capacity (CSC), and charge injection capacity (CIC), versus the equivalent electrodes without NPs. Specifically, electrodes with 40% NP surface density demonstrate 4.5 times lower impedance, 15 times higher CSC, and 4 times better CIC. Electrochemical stability, assessed via continuous cyclic voltammetry (CV) and voltage transient (VT) tests, indicated minimal deviations from the initial performance, while mechanical stability, assessed via ultrasonic vibration, is also improved after the NP printing. Importantly, NP surface densities up to 40% maintain the electrode optical transparency required for compatibility with optical imaging and optogenetics. These results demonstrate selective NP deposition and local modification of electrochemical properties in graphene electrodes for the first time, enabling the cohabitation of graphene electrodes with different electrochemical and optical characteristics on the same substrate for neural interfacing.

3.
ACS Appl Mater Interfaces ; 15(2): 3558-3565, 2023 Jan 18.
Artigo em Inglês | MEDLINE | ID: mdl-36538469

RESUMO

Controlled integration of metallic nanoparticles (NPs) onto photonic nanostructures enables the realization of complex devices for extreme light confinement and enhanced light-matter interaction. For instance, such NPs could be massively integrated on metal plates to build nanoparticle-on-mirror (NPoM) nanocavities or photonic integrated waveguides (WGs) to build WG-driven nanoantennas. However, metallic NPs are usually deposited via drop-casting, which prevents their accurate positioning. Here, we present a methodology for precise transfer and positioning of individual NPs onto different photonic nanostructures. Our method is based on soft lithography printing that employs elastomeric stamp-assisted transfer of individual NPs onto a single nanostructure. It can also parallel imprint many individual NPs with high throughput and accuracy in a single step. Raman spectroscopy confirms enhanced light-matter interactions in the resulting NPoM-based nanophotonic devices. Our method mixes top-down and bottom-up nanofabrication techniques and shows the potential of building complex photonic nanodevices for multiple applications ranging from enhanced sensing and spectroscopy to signal processing.

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