Reduced Ensemble Plasmon Line Widths and Enhanced Two-Photon Luminescence in Anodically Formed High Surface Area Au-TiO2 3D Nanocomposites

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.

Harvesting Hot Holes in Plasmon-Coupled Ultrathin Photoanodes for High-Performance Photoelectrochemical Water Splitting

The harvesting of hot carriers produced by plasmon decay to generate electricity or drive a chemical reaction enables the reduction of the thermalization losses associated with supra-band gap photons in semiconductor photoelectrochemical (PEC) cells. Through the broadband harvesting of light, hot-carrier PEC devices also produce a sensitizing effect in heterojunctions with wide-band gap metal oxide semiconductors possessing good photostability and catalytic activity but poor absorption of visible wavelength photons. There are several reports of hot electrons in Au injected over the Schottky barrier into crystalline TiO₂ and subsequently utilized to drive a chemical reaction but very few reports of hot hole harvesting. In this work, we demonstrate the efficient harvesting of hot holes in Au nanoparticles (Au NPs) covered with a thin layer of amorphous TiO₂ (a-TiO₂). Under AM1.5G 1 sun illumination, photoanodes consisting of a single layer of ∼50 nm diameter Au NPs coated with a 10 nm shell of a-TiO₂ (Au@a-TiO₂) generated 2.5 mA cm^-2 of photocurrent in 1 M KOH under 0.6 V external bias, rising to 3.7 mA cm^-2 in the presence of a hole scavenger (methanol). The quantum yield for hot-carrier-mediated photocurrent generation was estimated to be close to unity for high-energy photons (λ < 420 nm). Au@a-TiO₂ photoelectrodes produced a small positive photocurrent of 0.1 mA cm^-2 even at a bias of -0.6 V indicating extraction of hot holes even at a strong negative bias. These results together with density functional theory modeling and scanning Kelvin probe force microscope data indicate fast injection of hot holes from Au NPs into a-TiO₂ and light harvesting performed near-exclusively by Au NPs. For comparison, Au NPs coated with a 10 nm shell of Al₂O₃ (Au@Al₂O₃) generated 0.02 mA cm^-2 of photocurrent in 1 M KOH under 0.6 V external bias. These results underscore the critical role played by a-TiO₂ in the extraction of holes in Au@a-TiO₂ photoanodes, which is not replicated by an ordinary dielectric shell. It is also demonstrated here that an ultrathin photoanode (<100 nm in maximum thickness) can efficiently drive sunlight-driven water splitting.

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.

Plasmonic photocatalysis and SERS sensing using ellipsometrically modeled Ag nanoisland substrates

Silver nanoislands are key platforms for plasmonic photocatalysis, SERS sensing and optical metamaterials due to their localized surface plasmon resonances. The low intrinsic loss in Ag enables high local electromagnetic field enhancements. Solution-based fabrication techniques, while cheap, result in highly non-reproducible plasmonic substrates with wide sample-to-sample variability in geometry, optical resonances and Q-factors. Herein, we present a non-lithographic method of forming silver nanoislands based on sputter deposition of Ag films followed by elevated temperature annealing to induce spontaneous dewetting. The resulting plasmonic substrates show reproducible, well-defined LSPR resonances with high ensemble Q-factors whose optical properties could be modeled using spectroscopic ellipsometry to yield n and k values across the visible range. UV-Vis-NIR, and XRD analyses define the optical and crystallographic characteristics of the Ag nanoisland samples. FESEM was utilized to discern the geometry and architecture of the Ag nanoisland as well as their uniformity and monodispersity. Our vacuum deposited Ag nanoislands demonstrated excellent photocatalytic activity for the transformation of 4-nitrobenzenethiol (4-NBT) and 4-aminothiophenol (PATP) into p,p'-dimercaptoazobenzene (DMAB).

New strategy to obtain high surface area anatase nanotube/AuNP photocatalyst

Anatase nanotubes with high surface area (ca. 350 m² g^-1), containing gold nanoparticles, were successfully obtained from trititanate nanotubes, prepared by a template-free hydrothermal method, and calcined at 450 °C. The high surface area and tubular morphology were attained due to the presence of ionic silsesquioxane, which acts as anti-sintering agent for titania during calcination process, by forming a thin silica coating between anatase nanotubes. Additionally, the ionic silsesquioxane also acts as stabilizing and adhesion agent for gold nanoparticles on the surface of anatase nanotubes. The influence of the ionic silsesquioxane on the morphological and textural properties of anatase nanotubes was studied in three different moments during the synthesis: before, after and before/after nanotubes were rolled up. The photocatalytic activity of the nanotube samples was evaluated by hydrogen generation showing remarkable enhancement in hydrogen production and stability of catalyst when compare with the bare anatase sample and commercial P-25.

Electron Tomography of Plasmonic Au Nanoparticles Dispersed in a TiO2 Dielectric Matrix

Plasmonic Au nanoparticles (AuNPs) embedded into a TiO₂ dielectric matrix were analyzed by combining two-dimensional and three-dimensional electron microscopy techniques. The preparation method was reactive magnetron sputtering, followed by thermal annealing treatments at 400 and 600 °C. The goal was to assess the nanostructural characteristics and correlate them with the optical properties of the AuNPs, particularly the localized surface plasmon resonance (LSPR) behavior. High-angle annular dark field-scanning transmission electron microscopy results showed the presence of small-sized AuNPs (quantum size regime) in the as-deposited Au-TiO₂ film, resulting in a negligible LSPR response. The in-vacuum thermal annealing at 400 °C induced the formation of intermediate-sized nanoparticles (NPs), in the range of 10-40 nm, which led to the appearance of a well-defined LSPR band, positioned at 636 nm. Electron tomography revealed that most of the NPs are small-sized and are embedded into the TiO₂ matrix, whereas the larger NPs are located at the surface. Annealing at 600 °C promotes a bimodal size distribution with intermediate-sized NPs embedded in the matrix and big-sized NPs, up to 100 nm, appearing at the surface. The latter are responsible for a broadening and a redshift, to 645 nm, in the LSPR band because of increase of scattering-to-absorption ratio. Beyond differentiating and quantifying the surface and embedded NPs, electron tomography also provided the identification of "hot-spots". The presence of NPs at the surface, individual or in dimers, permits adsorption sites for LSPR sensing and for surface-enhanced spectroscopies, such as surface-enhanced Raman scattering.

A convenient and efficient precursor transformation route to well-dispersed, stable, and highly accessible supported Au nanocatalysts with excellent catalytic hydrogenation performances

A new, convenient, and efficient precursor transformation route for the synthesis of supported Au nanocatalysts was reported. In this strategy, [Au(en)₂]³⁺-riched titanate nanospheres (en: ethylenediamine) with hierarchical flower-like architecture were pre-synthesized via “ammonia etching-ion exchange” processes and then used as the precursors of the objective catalysts. Direct pyrolysis of these precursors, varying in amount of [Au(en)₂]³⁺, led to the formation of Au nanoparticles (AuNPs) with different contents uniformly supported on highly crystalline titania nanoflowers (fTiO₂). The fTiO₂-supported AuNPs nanocomposites possessed highly open porous structures with large surface areas (142.3–149.3 m² g⁻¹), which could allow guest molecules to diffuse in and out easily. More interestingly, the formed AuNPs with small size (∼3.8 nm) were well-dispersed and partially embedded into the nanosheets of fTiO₂, which was beneficial for achieving high activity while avoiding their detachment from the support during application. Accordingly, the AuNPs/TiO₂ catalysts exhibited superior catalytic properties for 4-nitrophenol hydrogenation with significantly higher catalytic efficiencies than many previously reported heterogeneous catalysts. Moreover, the catalytic activity could remain almost unchanged after being recycled several times, demonstrating their high stability. These findings open up a new possibility for rational design and synthesis of supported catalysts for diverse catalytic applications.

Synthesis of well-dispersed, stable, and highly accessible supported Au nanocatalysts was achieved via a new and efficient precursor transformation route.