Self-modification of Defective TiO2 under Controlled H2/Ar Gas Environment and Dynamics of Photoinduced Oxygen Vacancies.

In recent years, defective TiO2 has caught considerable research attention because of its potential to overcome the limits of low visible light absorption and fast charge recombination present in pristine TiO2 photocatalysts. Among the different synthesis conditions for defective TiO2, ambient pressure hydrogenation with the addition of Ar as inert gas for safety purposes has been established as an easy to realize process. Whether the Ar gas might still influence the resulting photocatalytic properties and defective surface layer remains an open question. Here, we reveal that the gas flow ratio between H2 and Ar has a crucial impact on the defective structure as well as the photocatalyic activity of TiO2. In particular, transmission electron microscopy (TEM) in combination with electron energy loss spectroscopy (EELS) revealed a larger width of the defective surface layer when using a H2/Ar (50%-50%) gas mixture over pure H2. Here, a possible reason could be the increase in dynamic viscosity of the gas mixture when Ar is added. Additionally, photoinduced enhanced Raman spectroscopy (PIERS) is implemented as a complementary approach to investigate the dynamics of the defective structures under ambient conditions which cannot be effortlessly realized by vacuum techniques like TEM.

Miniaturized double-wing ∆E-effect magnetic field sensors.

Magnetoelastic micro-electromechanical systems (MEMS) are integral elements of sensors, actuators, and other devices utilizing magnetostriction for their functionality. Their sensitivity typically scales with the saturation magnetostriction and inversely with magnetic anisotropy. However, large saturation magnetostriction and small magnetic anisotropy make the magnetoelastic layer highly susceptible to minuscule anisotropic stress. It is inevitably introduced during the release of the mechanical structure during fabrication and severely impairs the device's reproducibility, performance, and yield. To avoid the transfer of residual stress to the magnetic layer, we use a shadow mask deposition technology. It is combined with a free-free magnetoelectric microresonator design to minimize the influence of magnetic inhomogeneity on device performance. Magnetoelectric resonators are experimentally and theoretically analyzed regarding local stress anisotropy, magnetic anisotropy, and the ΔE-effect sensitivity in several resonance modes. The results demonstrate an exceptionally small device-to-device variation of the resonance frequency < 0.2% with large sensitivities comparable with macroscopic ΔE-effect magnetic field sensors. This development marks a promising step towards highly reproducible magnetoelastic devices and the feasibility of large-scale, integrated arrays.

Modeling of Magnetoelectric Microresonator Using Numerical Method and Simulated Annealing Algorithm.

A comprehensive understanding of the linear/nonlinear dynamic behavior of wireless microresonators is essential for micro-electromechanical systems (MEMS) design optimization. This study investigates the dynamic behaviour of a magnetoelectric (ME) microresonator, using a finite element method (FEM) and machine learning algorithm. First, the linear/nonlinear behaviour of a fabricated thin-film ME microactuator is assessed in both the time domain and frequency spectrum. Next, a data driven system identification (DDSI) procedure and simulated annealing (SA) method are implemented to reconstruct differential equations from measured datasets. The Duffing equation is employed to replicate the dynamic behavior of the ME microactuator. The Duffing coefficients such as mass, stiffness, damping, force amplitude, and excitation frequency are considered as input parameters. Meanwhile, the microactuator displacement is taken as the output parameter, which is measured experimentally via a laser Doppler vibrometer (LDV) device. To determine the optimal range and step size for input parameters, the sensitivity analysis is conducted using Latin hypercube sampling (LHS). The peak index matching (PIM) and correlation coefficient (CC) are considered assessment criteria for the objective function. The data-driven developed models are subsequently employed to reconstruct/predict mode shapes and the vibration amplitude over the time domain. The effect of driving signal nonlinearity and total harmonic distortion (THD) is explored experimentally under resonance and sub-resonance conditions. The vibration measurements reveal that as excitation levels increase, hysteresis variations become more noticeable, which may result in a higher prediction error in the Duffing array model. The verification test indicates that the first bending mode reconstructs reasonably with a prediction accuracy of about 92 percent. This proof-of-concept study demonstrates that the simulated annealing approach is a promising tool for modeling the dynamic behavior of MEMS systems, making it a strong candidate for real-world applications.

Diblock copolymer pattern protection by silver cluster reinforcement.

Pattern fabrication by self-assembly of diblock copolymers is of significant interest due to the simplicity in fabricating complex structures. In particular, polystyrene-block-poly-4-vinylpyridine (PS-b-P4VP) is a fascinating base material as it forms an ordered micellar structure on silicon surfaces. In this work, silver (Ag) is applied using direct current magnetron sputter deposition and high-power impulse magnetron sputter deposition on an ordered micellar PS-b-P4VP layer. The fabricated hybrid materials are structurally analyzed by field emission scanning electron microscopy, atomic force microscopy, and grazing incidence small angle X-ray scattering. When applying simple aqueous posttreatment, the pattern is stable and reinforced by Ag clusters, making micellar PS-b-P4VP ordered layers ideal candidates for lithography.

Two-in-One Sensor Based on PV4D4-Coated TiO2 Films for Food Spoilage Detection and as a Breath Marker for Several Diseases.

Certain molecules act as biomarkers in exhaled breath or outgassing vapors of biological systems. Specifically, ammonia (NH3) can serve as a tracer for food spoilage as well as a breath marker for several diseases. H2 gas in the exhaled breath can be associated with gastric disorders. This initiates an increasing demand for small and reliable devices with high sensitivity capable of detecting such molecules. Metal-oxide gas sensors present an excellent tradeoff, e.g., compared to expensive and large gas chromatographs for this purpose. However, selective identification of NH3 at the parts-per-million (ppm) level as well as detection of multiple gases in gas mixtures with one sensor remain a challenge. In this work, a new two-in-one sensor for NH3 and H2 detection is presented, which provides stable, precise, and very selective properties for the tracking of these vapors at low concentrations. The fabricated 15 nm TiO2 gas sensors, which were annealed at 610 °C, formed two crystal phases, namely anatase and rutile, and afterwards were covered with a thin 25 nm PV4D4 polymer nanolayer via initiated chemical vapor deposition (iCVD) and showed precise NH3 response at room temperature and exclusive H2 detection at elevated operating temperatures. This enables new possibilities in application fields such as biomedical diagnosis, biosensors, and the development of non-invasive technology.

Tuning the Selectivity of Metal Oxide Gas Sensors with Vapor Phase Deposited Ultrathin Polymer Thin Films.

Metal oxide gas sensors are of great interest for applications ranging from lambda sensors to early hazard detection in explosive media and leakage detection due to their superior properties with regard to sensitivity and lifetime, as well as their low cost and portability. However, the influence of ambient gases on the gas response, energy consumption and selectivity still needs to be improved and they are thus the subject of intensive research. In this work, a simple approach is presented to modify and increase the selectivity of gas sensing structures with an ultrathin polymer thin film. The different gas sensing surfaces, CuO, Al2O3/CuO and TiO2 are coated with a conformal < 30 nm Poly(1,3,5,7-tetramethyl-tetravinyl cyclotetrasiloxane) (PV4D4) thin film via solvent-free initiated chemical vapor deposition (iCVD). The obtained structures demonstrate a change in selectivity from ethanol vapor to 2-propanol vapor and an increase in selectivity compared to other vapors of volatile organic compounds. In the case of TiO2 structures coated with a PV4D4 thin film, the increase in selectivity to 2-propanol vapors is observed even at relatively low operating temperatures, starting from >200 °C. The present study demonstrates possibilities for improving the properties of metal oxide gas sensors, which is very important in applications in fields such as medicine, security and food safety.

A novel method for the synthesis of core-shell nanoparticles for functional applications based on long-term confinement in a radio frequency plasma.

A novel combined setup of a Haberland type gas aggregation source and a secondary radio frequency discharge is used to generate, confine, and coat nanoparticles over much longer time scales than traditional in-flight treatment. The process is precisely monitored using localized surface plasmon resonance and Fourier-transform infrared spectroscopy as in situ diagnostics. They indicate that both untreated and treated particles can be confined for extended time periods (at least one hour) with minimal losses. During the entire confinement time, the particle sizes do not show considerable alterations, enabling multiple well-defined modifications of the seed nanoparticles in this synthesis approach. The approach is demonstrated by generating Ag@SiO2 nanoparticles with a well-defined surface coating. The in situ diagnostics provide insights into the growth kinetics of the applied coating and are linked to the coating properties by using ex situ transmission electron microscopy and energy dispersive X-ray spectroscopy. Surface coating is shown to occur in two phases: first, singular seeds appear on the particle surface which then grow to cover the entire particle surface over 3 to 5 minutes. Afterwards, deposition occurs via surface growth which coincides with lower deposition rates. Our setup offers full control for various treatment options, which is demonstrated by coating the nanoparticles with a SiO2 layer followed by the etching of the part of the applied coating using hydrogen. Thus, complex multi-step nanofabrication, e.g., using different monomers, as well as very large coating thicknesses is possible.

Numerical and Experimental Study of Colored Magnetic Particle Mapping via Magnetoelectric Sensors.

Colored imaging of magnetic nanoparticles (MNP) is a promising noninvasive method for medical applications such as therapy and diagnosis. This study investigates the capability of the magnetoelectric sensor and projected gradient descent (PGD) algorithm for colored particle detection. In the first step, the required circumstances for image reconstruction are studied via a simulation approach for different signal-to-noise ratios (SNR). The spatial accuracy of the reconstructed image is evaluated based on the correlation coefficient (CC) factor. The inverse problem is solved using the PGD method, which is adapted according to a nonnegativity constraint in the complex domain. The MNP characterizations are assessed through a magnetic particle spectrometer (MPS) for different types. In the experimental investigation, the real and imaginary parts of the MNP's response are used to detect the spatial distribution and particle type, respectively. The experimental results indicate that the average phase difference for CT100 and ARA100 particles is 14 degrees, which is consistent with the MPS results and could satisfy the system requirements for colored imaging. The experimental evaluation showed that the magnetoelectric sensor and the proposed approach could be potential candidates for color bio-imaging applications.

Brain-like critical dynamics and long-range temporal correlations in percolating networks of silver nanoparticles and functionality preservation after integration of insulating matrix.

Random networks of nanoparticle-based memristive switches enable pathways for emulating highly complex and self-organized synaptic connectivity together with their emergent functional behavior known from biological neuronal networks. They therefore embody a distinct class of neuromorphic hardware architectures and provide an alternative to highly regular arrays of memristors. Especially, networks of memristive nanoparticles (NPs) poised at the percolation threshold are promising due to their capabilities of showing brain-like activity such as critical dynamics or long-range temporal correlation (LRTC), which are closely connected to the computational capabilities in biological neuronal networks. Here, we adapt this concept to networks of Ag-NPs poised at the electrical percolation threshold, where the memristive properties are governed by electro-chemical metallization. We show that critical dynamics and LRTC are preserved although the nature of individual memristive gaps throughout the network is fundamentally changed by filling the gaps with an insulating matrix. The results in this work generate important contributions towards the practical applicability of critical dynamics and LRTC in percolating NP networks by elucidating the consequences of NP network encapsulation, which is considered as an important step towards device integration.

Nanoscale Synergetic Effects on Ag-TiO2 Hybrid Substrate for Photoinduced Enhanced Raman Spectroscopy (PIERS) with Ultra-Sensitivity and Reusability.

Here, a 4N-in-1 hybrid substrate concept (nanocolumnar structures, nanocrack network, nanoscale mixed oxide phases, and nanometallic structures) for ultra-sensitive and reliable photo-induced-enhanced Raman spectroscopy (PIERS), is proposed. The use of the 4N-in-1 hybrid substrate leads to an ≈50-fold enhancement over the normal surface-enhanced Raman spectroscopy, which is recorded as the highest PIERS enhancement to date. In addition to an improved Raman signal, the 4N-in-1 hybrid substrate provides a high detection sensitivity which may be attributed to the activation possibility at extremely low UV irradiation dosage and prolonged relaxation time (long measurement time). Moreover, the 4N-in-1 hybrid substrate exhibits a superior photocatalytic degradation performance of analytes, allowing its reuse at least 18 times without any loss of PIERS activity. The use of the 4N-in-1 concept can be adapted to biomedicine, forensic, and security fields easily.

Sparse CNT networks with implanted AgAu nanoparticles: A novel memristor with short-term memory bordering between diffusive and bipolar switching.

With this work we introduce a novel memristor in a lateral geometry whose resistive switching behaviour unifies the capabilities of bipolar switching with decelerated diffusive switching showing a biologically plausible short-term memory. A new fabrication route is presented for achieving lateral nano-scaled distances by depositing a sparse network of carbon nanotubes (CNTs) via spin-coating of a custom-made CNT dispersion. Electrochemical metallization-type (ECM) resistive switching is obtained by implanting AgAu nanoparticles with a Haberland-type gas aggregation cluster source into the nanogaps between the CNTs and shows a hybrid behaviour of both diffusive and bipolar switching. The resistance state resets to a high resistive state (HRS) either if the voltage is removed with a retention time in the second- to sub-minute scale (diffusive) or by applying a reverse voltage (bipolar). Furthermore, the retention time is positively correlated to the duration of the Set voltage pulse. The potential for low-voltage operation makes this approach a promising candidate for short-term memory applications in neuromorphic circuits. In addition, the lateral fabrication approach opens the pathway towards integrating sensor-functionality and offers a general starting point for the scalable fabrication of nanoscaled devices.

Adaptive Model for Magnetic Particle Mapping Using Magnetoelectric Sensors.

Imaging of magnetic nanoparticles (MNPs) is of great interest in the medical sciences. By using resonant magnetoelectric sensors, higher harmonic excitations of MNPs can be measured and mapped in space. The proper reconstruction of particle distribution via solving the inverse problem is paramount for any imaging technique. For this, the forward model needs to be modeled accurately. However, depending on the state of the magnetoelectric sensors, the projection axis for the magnetic field may vary and may not be known accurately beforehand. As a result, the projection axis used in the model may be inaccurate, which can result in inaccurate reconstructions and artifact formation. Here, we show an approach for mapping MNPs that includes sources of uncertainty to both select the correct particle distribution and the correct model simultaneously.

Correlating Optical Reflectance with the Topology of Aluminum Nanocluster Layers Growing on Partially Conjugated Diblock Copolymer Templates.

Large-scale fabrication of metal cluster layers for usage in sensor applications and photovoltaics is a huge challenge. Physical vapor deposition offers large-scale fabrication of metal cluster layers on templates and polymer surfaces. In the case of aluminum (Al), only little is known about the formation and interaction of Al clusters during sputter deposition. Complex polymer surface morphologies can tailor the deposited Al cluster layer. Here, a poly(methyl methacrylate)-block-poly(3-hexylthiophen-2,5-diyl) (PMMA-b-P3HT) diblock copolymer template is used to investigate the nanostructure formation of Al cluster layers on the different polymer domains and to compare it with the respective homopolymers PMMA and P3HT. The optical properties relevant for sensor applications are monitored with ultraviolet-visible (UV-vis) measurements during the sputter deposition. The formation of Al clusters is followed in situ with grazing-incidence small-angle X-ray scattering (GISAXS), and the chemical interaction is revealed by X-ray photoelectron spectroscopy (XPS). Furthermore, atomic force microscopy (AFM) and field emission scanning electron microscopy (FESEM) yield topographical information about selective wetting of Al on the P3HT domains and embedding in the PMMA domains in the early stages, followed by four distinct growth stages describing the Al nanostructure formation.

Modeling and Parallel Operation of Exchange-Biased Delta-E Effect Magnetometers for Sensor Arrays.

Recently, Delta-E effect magnetic field sensors based on exchange-biased magnetic multilayers have shown the potential of detecting low-frequency and small-amplitude magnetic fields. Their design is compatible with microelectromechanical system technology, potentially small, and therefore, suitable for arrays with a large number N of sensor elements. In this study, we explore the prospects and limitations for improving the detection limit by averaging the output of N sensor elements operated in parallel with a single oscillator and a single amplifier to avoid additional electronics and keep the setup compact. Measurements are performed on a two-element array of exchange-biased sensor elements to validate a signal and noise model. With the model, we estimate requirements and tolerances for sensor elements using larger N. It is found that the intrinsic noise of the sensor elements can be considered uncorrelated, and the signal amplitude is improved if the resonance frequencies differ by less than approximately half the bandwidth of the resonators. Under these conditions, the averaging results in a maximum improvement in the detection limit by a factor of N. A maximum N≈200 exists, which depends on the read-out electronics and the sensor intrinsic noise. Overall, the results indicate that significant improvement in the limit of detection is possible, and a model is presented for optimizing the design of delta-E effect sensor arrays in the future.

Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network.

The treatment of refractory epilepsy via closed-loop implantable devices that act on seizures either by drug release or electrostimulation is a highly attractive option. For such implantable medical devices, efficient and low energy consumption, small size, and efficient processing architectures are essential. To meet these requirements, epileptic seizure detection by analysis and classification of brain signals with a convolutional neural network (CNN) is an attractive approach. This work presents a CNN for epileptic seizure detection capable of running on an ultra-low-power microprocessor. The CNN is implemented and optimized in MATLAB. In addition, the CNN is also implemented on a GAP8 microprocessor with RISC-V architecture. The training, optimization, and evaluation of the proposed CNN are based on the CHB-MIT dataset. The CNN reaches a median sensitivity of 90% and a very high specificity over 99% corresponding to a median false positive rate of 6.8 s per hour. After implementation of the CNN on the microcontroller, a sensitivity of 85% is reached. The classification of 1 s of EEG data takes t=35 ms and consumes an average power of P≈140 µW. The proposed detector outperforms related approaches in terms of power consumption by a factor of 6. The universal applicability of the proposed CNN based detector is verified with recording of epileptic rats. This results enable the design of future medical devices for epilepsy treatment.

TiO2/Cu2O/CuO Multi-Nanolayers as Sensors for H2 and Volatile Organic Compounds: An Experimental and Theoretical Investigation.

TiO2/Cu2O/CuO multi-nanolayers highly sensitive toward volatile organic compounds (VOCs) and H2 have been grown in various thicknesses by a cost-effective and reproducible combined spray-sputtering-annealing approach. The ultrathin TiO2 films were deposited by spray pyrolysis on top of sputtered-annealed Cu2O/CuO nanolayers to enhance their gas sensing performance and improve their protection against corrosion at high operating temperatures. The prepared heterostructures were investigated using scanning electron microscopy (SEM), X-ray diffraction (XRD), and ultraviolet visible (UV-vis) and micro-Raman spectroscopy. The gas sensing properties were measured at several operating temperatures, where the nanolayered sensors with oxide thicknesses between 20 and 30 nm (Cu2O/CuO nanolayers) exhibited a high response and an excellent selectivity to ethanol vapor after thermal annealing the samples at 420 °C. The results obtained at an operating temperature of 350 °C demonstrate that the CuO/Cu2O nanolayers with thicknesses between 20 and 30 nm are sensitive mainly to ethanol vapor, with a response of ∼150. The response changes from ethanol vapors to hydrogen gas as the thickness of the CuO/Cu2O nanolayers changes from 50 to 20 nm. Density functional theory-based calculations were carried out for the geometries of the CuO(1̅11)/Cu2O(111) and TiO2(111)/CuO(1̅11)/Cu2O(111) heterostructures and their sensing mechanism toward alcohols of different chain lengths and molecular hydrogen. The reconstructed hexagonal Cu2O(111) surface and the reconstructed monoclinic CuO(1̅11) and TiO2(111) facets, all of which terminate in an O layer, lead to the lowest surface energies for each isolated material. We studied the formation of the binary and ternary heteroepitaxial interfaces for the surface planes with the best-matching lattices. Despite the impact of the Cu2O(111) substrate in lowering the atomic charges of the CuO(1̅11) adlayer in the binary sensor, we found that it is the different surface structures of the CuO(1̅11)/Cu2O(111) and TiO2(111)/CuO(1̅11)/Cu2O(111) devices that are fundamental in driving the change in the sensitivity response observed experimentally. The experimental data, supported by the computational results, are important in understanding the use of the multi-nanolayered films tested in this work as reliable, accurate, and selective sensor structures for the tracking of gases at low concentrations.

Revealing the growth of copper on polystyrene-block-poly(ethylene oxide) diblock copolymer thin films with in situ GISAXS.

Copper (Cu) as an excellent electrical conductor and the amphiphilic diblock copolymer polystyrene-block-poly(ethylene oxide) (PS-b-PEO) as a polymer electrolyte and ionic conductor can be combined with an active material in composite electrodes for polymer lithium-ion batteries (LIBs). As interfaces are a key issue in LIBs, sputter deposition of Cu contacts on PS-b-PEO thin films with high PEO fraction is investigated with in situ grazing-incidence small-angle X-ray scattering (GISAXS) to follow the formation of the Cu layer in real-time. We observe a hierarchical morphology of Cu clusters building larger Cu agglomerates. Two characteristic distances corresponding to the PS-b-PEO microphase separation and the Cu clusters are determined. A selective agglomeration of Cu clusters on the PS domains explains the origin of the persisting hierarchical morphology of the Cu layer even after a complete surface coverage is reached. The spheroidal shape of the Cu clusters growing within the first few nanometers of sputter deposition causes a highly porous Cu-polymer interface. Four growth stages are distinguished corresponding to different kinetics of the cluster growth of Cu on PS-b-PEO thin films: (I) nucleation, (II) diffusion-driven growth, (III) adsorption-driven growth, and (IV) grain growth of Cu clusters. Percolation is reached at an effective Cu layer thickness of 5.75 nm.

Marine Algae Incorporated Polylactide Acid Patch: Novel Candidate for Targeting Osteosarcoma Cells without Impairing the Osteoblastic Proliferation.

Biodegradable collagen-based materials have been preferred as scaffolds and grafts for diverse clinical applications in density and orthopedy. Besides the advantages of using such bio-originated materials, the use of collagen matrices increases the risk of infection transmission through the cells or the tissues of the graft/scaffold. In addition, such collagen-based solutions are not counted as economically feasible approaches due to their high production cost. In recent years, incorporation of marine algae in synthetic polymers has been considered as an alternative method for preparation grafts/scaffolds since they represent abundant and cheap source of potential biopolymers. Current work aims to propose a novel composite patch prepared by blending Sargassum vulgare powders (SVP) to polylactide (PLA) as an alternative to the porcine-derived membranes. SVP-PLA composite patches were produced by using a modified solvent casting method. Following detailed material characterization to assess the cytocompatibility, human osteoblasts (HOBs) and osteosarcoma cells (SaOS-2) were seeded on neat PLA and SVP-PLA patches. MTT and BrdU assays indicated a greater cytocompatibility and higher proliferation for HOBs cultured on SVP-PLA composite than for those cultured on neat PLA. SaOS-2 cells cultured on SVP-PLA exhibited a significant decrease in cell proliferation. The composite patch described herein exhibits an antiproliferative effect against SaOS-2 cells without impairing HOBs' adhesion and proliferation.

Magnetoelastic Coupling and Delta-E Effect in Magnetoelectric Torsion Mode Resonators.

Magnetoelectric resonators have been studied for the detection of small amplitude and low frequency magnetic fields via the delta-E effect, mainly in fundamental bending or bulk resonance modes. Here, we present an experimental and theoretical investigation of magnetoelectric thin-film cantilevers that can be operated in bending modes (BMs) and torsion modes (TMs) as a magnetic field sensor. A magnetoelastic macrospin model is combined with an electromechanical finite element model and a general description of the delta-E effect of all stiffness tensor components Cij is derived. Simulations confirm quantitatively that the delta-E effect of the C66 component has the promising potential of significantly increasing the magnetic sensitivity and the maximum normalized frequency change Δfr. However, the electrical excitation of TMs remains challenging and is found to significantly diminish the gain in sensitivity. Experiments reveal the dependency of the sensitivity and Δfr of TMs on the mode number, which differs fundamentally from BMs and is well explained by our model. Because the contribution of C11 to the TMs increases with the mode number, the first-order TM yields the highest magnetic sensitivity. Overall, general insights are gained for the design of high-sensitivity delta-E effect sensors, as well as for frequency tunable devices based on the delta-E effect.

Tailoring the Optical Properties of Sputter-Deposited Gold Nanostructures on Nanostructured Titanium Dioxide Templates Based on In Situ Grazing-Incidence Small-Angle X-ray Scattering Determined Growth Laws.

Gold/titanium dioxide (Au/TiO2) nanohybrid materials have been widely applied in various fields because of their outstanding optical and photocatalytic performance. By state-of-the-art polymer templating, it is possible to make uniform nanostructured TiO2 layers with potentially large-scale processing methods. We use customized polymer templating to achieve TiO2 nanostructures with different morphologies. Au/TiO2 hybrid thin films are fabricated by sputter deposition. An in-depth understanding of the Au morphology on the TiO2 templates is achieved with in situ grazing-incidence small-angle X-ray scattering (GISAXS) during the sputter deposition. The resulting Au nanostructure is largely influenced by the TiO2 template morphology. Based on the detailed understanding of the Au growth process, characteristic distances can be selected to achieve tailored Au nanostructures at different Au loadings. For selected sputter-deposited Au/TiO2 hybrid thin films, the optical response with a tailored localized surface plasmon resonance is demonstrated.