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Mixed metal oxide (MMO) nanoparticles (NPs) are hybrids consisting of two or more nanoscale metal oxides. Advantages of MMO NPs over single metal oxides include improved catalytic activity, enhanced electrical and magnetic properties, and increased thermal stability due to the synergy of the different oxide components. This study presents a novel fabrication route for TiO2‐CeO2 NPs enriched with oxygen vacancies using a Haberland‐type gas aggregation cluster source. The NPs, deposited from different segmented Ti/Ce targets under varying O2 addition, were examined with respect to final composition, morphology, and Ti, Ce surface oxidation states. Particle formation mechanisms are proposed for the observed morphologies. Additionally, available O2 during deposition and its impact on the formation of defective sites were investigated. Defective sites in TiO2‐CeO2 NPs were analyzed using transfer to X‐ray photoelectron spectroscopy and transmission electron microscopy without contact to ambient oxygen. The incorporation of Ce to the target exhibits synergistic effects on the synthesis process. Segmented Ti/Ce targets enable the deposition of a broad range of mixed oxide NPs with diverse compositions and morphologies at considerably enhanced deposition rates, which is vital for practical applications. The presented fabrication approach is expected to be applicable for a broad variety of MMO NPs.

Abstract 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

An application specific integrated circuit (ASIC) and a custom-made microelectromechanical system (MEMS) sensor are presented, designed to function together as a sensor system for measuring low amplitude low frequency magnetic fields. The MEMS system comprises several free-standing double-wing magnetoelectric resonators with a size of $900~\mu $ m x $150~\mu $ m to measure alternating magnetic fields in the sub-kilohertz regime. It utilizes piezolelectric (AlN) and magnetostrictive (FeCoSiB) layers to exploit the delta-E effect for magnetic field sensing. On the ASIC a three-channel current-reuse amplifier with lateral bipolar transistors in the input stage is implemented occupying a chip area of 0.0864 mm2. Measurements demonstrate a voltage gain of 40 dB with a 3-dB bandwidth of 75 kHz and an input referred noise floor of 8 nV/ $\surd $ Hz while consuming $199~\mu $ W per channel. The sensor system is capable of detecting magnetic fields with a limit of detection (LOD) of 16 nT/ $\surd $ Hz for single sensor elements. By operating three sensor elements in parallel, one on each amplifier channel, the LOD is further reduced to 10 nT/ $\surd $ Hz. Owing to the high reproducibility of the sensor elements, this improvement in the LOD is close to the ideal value of $\surd 3$ . The results imply that the system can be scaled to large numbers of sensor elements without principle obstacles.

Abstract Thin polymer coatings are used to improve the interface between biological species and functional materials. Their interaction is significantly influenced by the functional groups and roughness of the polymer film and prediction of the interaction is thus of great interest. However, for conventional polymer films, this cannot be examined independently because of the interplay of defects, residual solvent molecules, roughness, and functional groups. Solvent‐free polymer films prepared by initiated chemical vapor deposition (iCVD) exhibit conformal, defect‐free characteristics and enable precise tailoring of the functional groups. This facilitates to isolate the contribution of functional groups on the bio‐interface performance. Consequently, in silico studies can enable a prediction of ligand interaction in anti‐viral activity for SARS‐CoV‐2 based on defined polymer and key protein structures. Furthermore, the cell viability of human fibroblasts can be traced back to the functional groups of the repeating units. For human liver cancer cell culture, it turns out that more sophisticated models are needed. The insilico‐iCVD approach can enable precise tailoring of complex polymer films optimized for the respective interfaces. In addition, this first big scan of the bio‐interface performance of iCVD films enables a solid starting point in areas like anticancer, antiviral, and biocompatibility for future studies.

Certain biomarkers in exhaled breath are indicators of diseases in the human body. The non-invasive detection of such biomarkers in human breath increases the demand for simple and cost-effective gas sensors to replace state-of-the-art gas chromatography (GC) machines. The use of metal oxide (MOX) gas sensors based on thin-film structures solves the current limitations of breath detectors. However, the response at high humidity levels, i.e., in the case of exhaled human breath, significantly decreases the sensitivity of MOX sensors, making it difficult to detect small traces of biomarkers. We have introduced, in previous work, the concept of a hybrid gas sensor, in which thin-film-based MOX gas sensors are combined with an ultra-thin (20–30 nm) polymer top layer deposited by solvent-free initiated chemical vapor deposition (iCVD). The hydrophobic top layer enables sensor measurement in high-humidity conditions as well as the precise tuning of selectivity and sensitivity. In this paper, we present a way to increase the hydrogen (H2) sensitivity of hybrid sensors through chemical modification of the polymer top layer. A poly(1,3,5,7-tetramethyl-tetravinylcyclotetrasiloxane) (PV4D4) thin film, already applied in one of our previous studies, is transformed into a silsesquioxane-containing top layer by a simple heating step. The transformation results in a significant increase in the gas response for H2 ~709% at an operating temperature of 350 °C, which we investigate based on the underlying sensing mechanism. These results reveal new pathways in the biomedical application field for the analysis of exhaled breath, where H2 indicates gastrointestinal diseases.

In recent years, semiconductor photocatalysts such as zinc oxide (ZnO) and titanium dioxide (TiO2) have been studied to utilize solar light for energy production and environmental remediation. However, their wide band gap energy and fast charge recombination limit their practical applications. So far, various approaches, such as noble metal incorporation, metal oxide coupling, and the design of Z-scheme photocatalyst systems, among others, have been explored to enhance the lifetime of photogenerated charge carriers in ZnO and TiO2. In this study, a novel TiO2-Ag-ZnO nanocomposite photocatalyst has been successfully synthesized by a combination of wet chemistry, photochemistry, and plasma-based processes. Simple spin coating of ZnO thin films was coupled with subsequent photodeposition of metallic Ag nanoparticles (NPs). Afterward, well-uniform TiO2 NPs (roughly 18 nm) were deposited on this unique Ag-ZnO surface via a gas cluster aggregation source (GAS) process. The results showed that the activity of the ZnO photocatalyst under mixed UV-visible light was significantly increased by the TiO2 and Ag contributions. A single mechanism to be responsible for the photocatalytic activity can be excluded. Instead, a critical assessment of the possible mechanisms corresponding to the different interfaces present at the TiO2-Ag-ZnO surface including a Z–scheme is provided.

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.

Increasing the efficiency of solar thermal collectors is extremely important as they are essential for many applications, ranging from the UV up to the NIR spectral range, from water heating systems up to micro-electromechanical systems. In this work, a plasmonic multilayer nanocomposite thin-film system that efficiently absorbs solar radiation across an extended spectral range was simulated and experimentally tested. Novel to our approach, copper nanoparticles in an alumina matrix were chosen as the nanocomposite material. Compared to other plasmonic materials such as gold or silver, copper is more abundant and economic. The alumina matrix provides high thermal stability, good optical properties, and corrosion protection. Using a multiscale-modeling approach, we inspect on computational grounds the effect of the nanoparticle filling factor, the angle of incidence, and the thin-film thicknesses on the absorber performance. We found that an optimally designed device absorbs up to 90% light energy from 200 nm to 1800 nm. To validate the simulations, two promising absorber layouts are experimentally realized. Their performance compares very well with simulations.

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.

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.

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.

Magnetoelectric thin-film sensors based on the delta-E effect have widely been reported for the detection of low frequency and small amplitude magnetic fields. Such sensors are usually fabricated with microelectromechanical system technology, where aluminum nitride (AlN) is the established piezoelectric material. Here, we present aluminum scandium nitride (AlScN) for delta-E effect sensors instead and compare it with AlN using two sensors of identical design. The sensors are experimentally and theoretically analyzed regarding sensitivity, noise, limit of detection (LOD), and resonator linearity. We identify the influence of the dominating piezoelectric coefficients dij and other material parameters. Simulations and measurements demonstrate that, in contrast to the conventional direct operation of magnetoelectric sensors, a sensitivity increase ∝dij2 and a LOD improvement ∝dij−1 can be achieved if thermal–mechanical noise is dominant. In the present case, an 8× improved sensitivity and LOD are measured with AlScN at small excitation amplitudes. This factor decreases with increasing amplitude and resonator nonlinearities. The overall minimum LOD does not change due to an earlier onset of magnetic noise in the AlScN sensor. All in all, this study reveals the influence of the piezoelectric material on the signal and noise of delta-E effect sensors and the potential of AlScN to significantly improve sensitivity.

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.

In this study, Ag-doped ZnO (ZnO:Ag) nanostructured films were functionalized with silver nanoparticles (Ag NPs), silver-platinum bimetallic nanoparticles (AgPt NPs) and silver-gold bimetallic NPs (AgAu NPs) using a gas phase PVD process based on a Haberland type gas aggregation cluster source and unipolar DC planar magnetron sputtering. Ultraviolet (UV) sensing investigations showed arespectable time constants reduction for rising and decaying photocurrents, as well as an increase for the UV response. Compared to a pristine nanostructured film the surface functionalization with Ag, AgPt and AgAu increased the UV response by factors of 2.7, 3.5 and 4, respectively. The increased performances of the here presented ZnO:Ag nanostructured films functionalized with monometallic and bimetallic NPs based photodetectors are explained by the increased lifetime of photogenerated electron –hole pairs, as well as the formation of nanoscale Schottky barriers at the interface of Au/ZnO:Ag and Pt/ZnO:Ag.

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.

Abstract Nanoparticles composed of multiple silver cores and a plasma polymer shell (multicore@shell) were prepared in a single step with a gas aggregation cluster source operating with Ar/hexamethyldisiloxane mixtures and optionally oxygen. The size distribution of the metal inclusions as well as the chemical composition and the thickness of the shells were found to be controlled by the composition of the working gas mixture. Shell matrices ranging from organosilicon plasma polymer to nearly stoichiometric SiO2 were obtained. The method allows facile fabrication of multicore@shell nanoparticles with tailored functional properties, as demonstrated here with the optical response.

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.

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.

Memristive devices are highly promising for implementing neuromorphic functionalities in future electronic hardware, and direct insights into memristive phenomena on the nanoscale are of fundamental importance to reaching this. Conductive atomic force microscopy (cAFM) has proven to be an essential tool for probing memristive action locally on the nanoscale, but the significance of the acquired data frequently suffers from the nonlocality associated with the thermal drift of the tip in ambient conditions. Furthermore, comparative studies of different configurations of filamentary devices have proven to be difficult, because of an immanent variability of the filament properties between different devices. Herein, these problems are addressed by constraining the memristive action directly at the apex of the probe through functionalization of a cAFM tip with an archetypical memristive stack, which is comprised of Ag/Si3N4. The design of such functionalized cantilevers (entitled here as “memtips”) allowed the capture of the long-term intrinsic current response, identifying temporal correlations between switching events, and observing emerging spiking dynamics directly at the nanoscale. Utilization of an identical memtip for measurements on different counter electrodes made it possible to directly compare the impact of different device configurations on the switching behavior of the same filament. Such an analytical approach in ambient conditions will pave the way towards a deeper understanding of filamentary switching phenomena on the nanoscale.

A surface-acoustic-wave (SAW) magnetic-field sensor utilizing fundamental, first- and second-order Love-wave modes is investigated. A 4.5 μ m SiO2 guiding layer on an ST-cut quartz substrate is coated with a 200 n m (Fe90Co10)78Si12B10 magnetostrictive layer in a delay-line configuration. Love-waves are excited and detected by two interdigital transducers (IDT). The delta-E effect in the magnetostrictive layer causes a phase change with applied magnetic field. A sensitivity of 1250 ° / m T is measured for the fundamental Love mode at 263 M Hz . For the first-order Love mode a value of 45 ° / m T is obtained at 352 M Hz . This result is compared to finite-element-method (FEM) simulations using one-dimensional (1D) and two-and-a-half-dimensional (2.5 D) models. The FEM simulations confirm the large drop in sensitivity as the first-order mode is close to cut-off. For multi-mode operation, we identify as a suitable geometry a guiding layer to wavelength ratio of h GL / λ ≈ 1.5 for an IDT pitch of p = 12 μ m . For this layer configuration, the first three modes are sufficiently far away from cut-off and show good sensitivity.

High free-volume copolymers were prepared via polycondensation with 2,3,5,6,-tetrafluoroterephthalonitrile (TFTPN) in which a portion of the 3,3,3',3'-tetramethyl-1,1'-spirobisindane (TTSBI) of PIM-1 was replaced with dibutyl anthracene maleimide (4bIII). An investigation of free volume using positron annihilation lifetime spectroscopy (PALS), and gas permeation measurements was carried out for the thin film composite copolymer membranes and compared to PIM-1. The average free volume hole size and the gas permeance of the copolymer membranes increased with decreasing TTSBI content in the copolymer.

The development of new fabrication techniques of plasmonic nanocomposites with specific properties is an ongoing issue in the plasmonic and nanophotonics community. In this paper we report detailed investigations on the modifications of the microstructural and plasmonic properties of metal–titania nanocomposite films induced by swift heavy ions. Au–TiO2 and Ag–TiO2 nanocomposite thin films with varying metal volume fractions were deposited by co-sputtering and were subsequently irradiated by 100 MeV Ag8+ ions at various ion fluences. The morphology of these nanocomposite thin films before and after ion beam irradiation has been investigated in detail by transmission electron microscopy studies, which showed interesting changes in the titania matrix. Additionally, interesting modifications in the plasmonic absorption behavior for both Au–TiO2 and Ag–TiO2 nanocomposites were observed, which have been discussed in terms of ion beam induced growth of nanoparticles and structural modifications in the titania matrix.

In recent years the delta-E effect has been used for detecting low frequency and low amplitude magnetic fields. Delta-E effect sensors utilize a forced mechanical resonator that is detuned by the delta-E effect upon application of a magnetic field. Typical frequencies of operation are from several kHz to the upper MHz regime. Different models have been used to describe the delta-E effect in those devices, but the frequency dependency has mainly been neglected. With this work we present a simple description of the delta-E effect as a function of the differential magnetic susceptibility χ of the magnetic material. We derive an analytical expression for χ that permits describing the frequency dependency of the delta-E effect of the Young’s modulus and the magnetic sensitivity. Calculations are compared with measurements on soft-magnetic ( Fe 90 Co 10 ) 78 Si 12 B 10 thin films. We show that the frequency of operation can have a strong influence on the delta-E effect and the magnetic sensitivity of delta-E effect sensors. Overall, the delta-E effect reduces with increasing frequency and results in a stiffening of the Young’s modulus above the ferromagnetic resonance frequency. The details depend on the Gilbert damping. Whereas for large Gilbert damping the sensitivity continuously decreases with frequency, typical damping values result in an amplification close to the ferromagnetic resonance frequency.