Plasmonic photocatalysis and SERS sensing using ellipsometrically modeled Ag nanoisland substrates

Analyte-dependent Rabi splitting in solid-state plexcitonic sensors based on plasmonic nanoislands strongly coupled to J-aggregates

A key challenge in the field of plexcitonic quantum devices is the fabrication of solid-state, device-friendly plexcitonic nanostructures using inexpensive and scalable techniques. Lithography-free, bottom-up nanofabrication methods have remained relatively unexplored within the context of plexcitonic coupling. In this work, a plexcitonic system consisting of thermally dewetted plasmonic gold nanoislands (AuNI) coated with a thin film of J-aggregates was investigated. Control over nanoisland size and morphology allowed for a range of plasmon resonances with variable detuning from the exciton. The extinction spectra of the hybrid AuNI/J-aggregate films display clear splitting into upper and lower hybrid resonances, while the dispersion curve shows anti-crossing behavior with an estimated Rabi splitting of 180 eV at zero detuning. As a proof of concept for quantum sensing, the AuNI/J-aggregate hybrid was demonstrated to behave as a plexcitonic sensor for hydrochloric acid vapor analyte. This work highlights the possibility of using thermally dewetted nanoparticles as a platform for high-quality, tunable, cost-effective, and scalable plexcitonic nanostructures for sensing devices and beyond.

Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future Directions

Advances in plasmonic materials and devices have given rise to a variety of applications in photocatalysis, microscopy, nanophotonics, and metastructures. With the advent of computing power and artificial neural networks, the characterization and design process of plasmonic nanostructures can be significantly accelerated using machine learning as opposed to conventional FDTD simulations. The machine learning (ML) based methods can not only perform with high accuracy and return optical spectra and optimal design parameters, but also maintain a stable high computing efficiency without being affected by the structural complexity. This work reviews the prominent ML methods involved in forward simulation and inverse design of plasmonic nanomaterials, such as Convolutional Neural Networks, Generative Adversarial Networks, Genetic Algorithms and Encoder-Decoder Networks. Moreover, we acknowledge the current limitations of ML methods in the context of plasmonics and provide perspectives on future research directions.

Wafer-scale, highly uniform, and well-arrayed suspended nanostructures for enhancing the performance of electronic devices

Suspended nanostructures play an important role in enhancing the performance of a diverse group of nanodevices. However, realizing a good arrangement and suspension for nanostructures of various shapes remains a significant challenge. Herein, a rapid and simple method for fabricating wafer-scale, highly uniform, well-arrayed suspended nanostructures via nanowelding lithography is reported. Suspended nanostructures with various shapes (nanowires, nanoholes, nanomesh, and nanofilms) and materials (gold, silver, and palladium metals) were employed to demonstrate the applicability of our method. Moreover, gas sensors and thermoacoustic speakers with suspended nanowires outperformed those with unsuspended nanostructures. The proposed method is expected to help advance the development of future nanodevices based on suspended nanostructures.

Machine learning-based leaky momentum prediction of plasmonic random nanosubstrate

In this work, we explore the use of machine learning for constructing the leakage radiation characteristics of the bright-field images of nanoislands from surface plasmon polariton based on the plasmonic random nanosubstrate. The leakage radiation refers to a leaky wave of surface plasmon polariton (SPP) modes through a dielectric substrate which has drawn interest due to its possibility of direct visualization and analysis of SPP propagation. A fast-learning two-layer neural network has been deployed to learn and predict the relationship between the leakage radiation characteristics and the bright-field images of nanoislands utilizing a limited number of training samples. The proposed learning framework is expected to significantly simplify the process of leaky radiation image construction without the need of sophisticated equipment. Moreover, a wide range of application extensions can be anticipated for the proposed image-to-image prediction.

Hot Electrons in TiO2-Noble Metal Nano-Heterojunctions: Fundamental Science and Applications in Photocatalysis

Plasmonic photocatalysis enables innovation by harnessing photonic energy across a broad swathe of the solar spectrum to drive chemical reactions. This review provides a comprehensive summary of the latest developments and issues for advanced research in plasmonic hot electron driven photocatalytic technologies focusing on TiO2-noble metal nanoparticle heterojunctions. In-depth discussions on fundamental hot electron phenomena in plasmonic photocatalysis is the focal point of this review. We summarize hot electron dynamics, elaborate on techniques to probe and measure said phenomena, and provide perspective on potential applications-photocatalytic degradation of organic pollutants, CO2 photoreduction, and photoelectrochemical water splitting-that benefit from this technology. A contentious and hitherto unexplained phenomenon is the wavelength dependence of plasmonic photocatalysis. Many published reports on noble metal-metal oxide nanostructures show action spectra where quantum yields closely follow the absorption corresponding to higher energy interband transitions, while an equal number also show quantum efficiencies that follow the optical response corresponding to the localized surface plasmon resonance (LSPR). We have provided a working hypothesis for the first time to reconcile these contradictory results and explain why photocatalytic action in certain plasmonic systems is mediated by interband transitions and in others by hot electrons produced by the decay of particle plasmons.

Artificial Neural Network-Based Prediction of the Optical Properties of Spherical Core-Shell Plasmonic Metastructures

The substitution of time- and labor-intensive empirical research as well as slow finite difference time domain (FDTD) simulations with revolutionary techniques such as artificial neural network (ANN)-based predictive modeling is the next trend in the field of nanophotonics. In this work, we demonstrated that neural networks with proper architectures can rapidly predict the far-field optical response of core-shell plasmonic metastructures. The results obtained with artificial neural networks are comparable with FDTD simulations in accuracy but the speed of obtaining them is between 100-1000 times faster than FDTD simulations. Further, we have proven that ANNs does not have problems associated with FDTD simulations such as dependency of the speed of convergence on the size of the structure. The other trend in photonics is the inverse design problem, where the far-field optical response of a spherical core-shell metastructure can be linked to the design parameters such as type of the material(s), core radius, and shell thickness using a neural network. The findings of this paper provide evidence that machine learning (ML) techniques such as artificial neural networks can potentially replace time-consuming finite domain methods in the future.

Asymmetric Multipole Plasmon-Mediated Catalysis Shifts the Product Selectivity of CO2 Photoreduction toward C2+ Products

Cu/TiO₂ is a well-known photocatalyst for the photocatalytic transformation of CO₂ into methane. The formation of C₂₊ products such as ethane and ethanol rather than methane is more interesting due to their higher energy density and economic value, but the formation of C-C bonds is currently a major challenge in CO₂ photoreduction. In this context, we report the dominant formation of a C₂ product, namely, ethane, from the gas-phase photoreduction of CO₂ using TiO₂ nanotube arrays (TNTAs) decorated with large-sized (80-200 nm) Ag and Cu nanoparticles without the use of a sacrificial agent or hole scavenger. Isotope-labeled mass spectrometry was used to verify the origin and identity of the reaction products. Under 2 h AM1.5G 1-sun illumination, the total rate of hydrocarbon production (methane + ethane) was highest for AgCu-TNTA with a total C_xH_2x+2 rate of 23.88 μmol g^-1 h^-1. Under identical conditions, the C_xH_2x+2 production rates for Ag-TNTA and Cu-TNTA were 6.54 and 1.39 μmol g^-1 h^-1, respectively. The ethane selectivity was the highest for AgCu-TNTA with 60.7%, while the ethane selectivity was found to be 15.9 and 10% for the Ag-TNTA and Cu-TNTA, respectively. Adjacent adsorption sites in our photocatalyst develop an asymmetric charge distribution due to quadrupole resonances in large metal nanoparticles and multipole resonances in Ag-Cu heterodimers. Such an asymmetric charge distribution decreases adsorbate-adsorbate repulsion and facilitates C-C coupling of reaction intermediates, which otherwise occurs poorly in TNTAs decorated with small metal nanoparticles.

Nonlithographic Formation of Ta2O5 Nanodimple Arrays Using Electrochemical Anodization and Their Use in Plasmonic Photocatalysis for Enhancement of Local Field and Catalytic Activity

We demonstrate the formation of Ta₂O₅ nanodimple arrays on technologically relevant non-native substrates through a simple anodization and annealing process. The anodizing voltage determines the pore diameter (25-60 nm), pore depth (2-9 nm), and rate of anodization (1-2 nm/s of Ta consumed). The formation of Ta dimples after delamination of Ta₂O₅ nanotubes occurs within a range of voltages from 7 to 40 V. The conversion of dimples from Ta into Ta₂O₅ changes the morphology of the nanodimples but does not impact dimple ordering. Electron energy loss spectroscopy indicated an electronic band gap of 4.5 eV and a bulk plasmon band with a maximum of 21.5 eV. Gold nanoparticles (Au NPs) were coated on Ta₂O₅ nanodimple arrays by annealing sputtered Au thin films on Ta nanodimple arrays to simultaneously form Au NPs and convert Ta to Ta₂O₅. Au NPs produced this way showed a localized surface plasmon resonance maximum at 2.08 eV, red-shifted by ∼0.3 eV from the value in air or on SiO₂ substrates. Lumerical simulations suggest a partial embedding of the Au NPs to explain this magnitude of the red shift. The resulting plasmonic heterojunctions exhibited a significantly higher ensemble-averaged local field enhancement than Au NPs on quartz substrates and demonstrated much higher catalytic activity for the plasmon-driven photo-oxidation of p-aminothiophenol to p,p'-dimercaptoazobenzene.