Erscheinungsjahr 2021

Surface-enhanced Raman scattering (SERS) is one of the most powerful analytical techniques for the identification of molecules. The substrate, on which SERS is dependent, contains regions of nanoscale gaps (hotspots) that hold the ability to concentrate incident electromagnetic fields and effectively amplify vibrational scattering signals of adsorbed analytes. While surface plasmon resonance from metal nanostructures is a central focus for the SERS effect, the support of polymers can be significantly advantageous to provide larger exposure of structured metal surfaces for efficient interactions with analytes. Characteristics of the polymer particles such as softness, flexibility, swellability, porosity, optical transparency, metal-loading ability, and high surface area can allow diffusion of analytes and penetrating light deeply that can enormously amplify sensing outcomes. As polymer-supported plasmon-active sensor particles can emerge as versatile SERS substrates, the microfluidic platform is promising for the generation of sensor particles as well as for performing sequential SERS analysis of multiple analytes. Therefore, in this perspective article, the development of multifunctional polymer-metal composite particles, and their applications as potential sensors for SERS sensing through microfluidics are presented. A detailed background from the beginning of the SERS field and perspectives for the multifunctional sensor particles for efficient SERS sensing are provided.

High-density electroencephalography (HD-EEG) is currently limited to laboratory environments since state-of-the-art electrode caps require skilled staff and extensive preparation. We propose and evaluate a 256-channel cap with dry multipin electrodes for HD-EEG. We describe the designs of the dry electrodes made from polyurethane and coated with Ag/AgCl. We compare in a study with 30 volunteers the novel dry HD-EEG cap to a conventional gel-based cap for electrode-skin impedances, resting state EEG, and visual evoked potentials (VEP). We perform wearing tests with eight electrodes mimicking cap applications on real human and artificial skin. Average impedances below 900 k[Ohm] for 252 out of 256 dry electrodes enables recording with state-of-the-art EEG amplifiers. For the dry EEG cap, we obtained a channel reliability of 84% and a reduction of the preparation time of 69%. After exclusion of an average of 16% (dry) and 3% (gel-based) bad channels, resting state EEG, alpha activity, and pattern reversal VEP can be recorded with less than 5% significant differences in all compared signal characteristics metrics. Volunteers reported wearing comfort of 3.6 ± 1.5 and 4.0 ± 1.8 for the dry and 2.5 ± 1.0 and 3.0 ± 1.1 for the gel-based cap prior and after the EEG recordings, respectively (scale 1-10). Wearing tests indicated that up to 3,200 applications are possible for the dry electrodes. The 256-channel HD-EEG dry electrode cap overcomes the principal limitations of HD-EEG regarding preparation complexity and allows rapid application by not medically trained persons, enabling new use cases for HD-EEG.

In a single-objective setting, nonconvex quadratic problems can equivalently be reformulated as convex problems over the cone of completely positive matrices. In small dimensions this cone equals the cone of matrices which are entrywise nonnegative and positive semidefinite, so the convex reformulation can be solved via SDP solvers. Considering multiobjective nonconvex quadratic problems, naturally the question arises, whether the advantage of convex reformulations extends to the multicriteria framework. In this note, we show that this approach only finds the supported nondominated points, which can already be found by using the weighted sum scalarization of the multiobjective quadratic problem, i.e. it is not suitable for multiobjective nonconvex problems.

Secondary ion mass spectroscopy, Fourier transformed infrared spectroscopy, ellipsometry, reflection high energy diffraction and transmission electron microscopy are used to gain inside into the effect of Ge on the formation of ultrathin 3C-SiC layers on Si(111) substrates. Accompanying the experimental investigations with simulations it is found that the ultrathin single crystalline 3C-SiC layer is formed on top of a gradient Si1-x-yGexCy buffer layer due to a complex alloying and alloy decomposition processes promoted by carbon and germanium interdiffusion and SiC nucleation. This approach allows tuning residual stress at very early growth stages as well as the interface properties of the 3C-SiC/Si heterostructure. Useful yields of secondary ions of Ge in Si matrix and Si dimer are estimated.

Well-established metal-catalyzed vapor-liquid-solid (VLS) growth represents still undoubtedly the key technology for bottom-up synthesis of single-crystalline silicon nanowires (SiNWs). Although various SiNW applications are demonstrated, electrical and optical properties are exposed to the inherent risk of electronic deep trap state formation by metal impurities. Therefore, metal catalyst-free growth strategies are intriguing. The oxid-assisted SiNW synthesis is explored and it is shown that contamination control is absolutely crucial. Slightest metal impurities, such as iron, are sufficient to trigger SiNW growth, calling into question true metal catalyst-free SiNW synthesis. Therefore, the term contamination-assisted is rather introduced and it is shown that contamination-assisted SiNW growth is determined by the chemical surface treatment (e.g., with KOH solution), but also by the crystal orientation of a silicon substrate. SiNWs are grown in this regards in a reproducible manner, but so far with a distinct tapering, using a conventional gas-phase reactor system at temperatures of about 680 ˚C and monosilane (SiH4) as the precursor gas. The synthesized SiNWs show convincing electrical properties compared to Au-catalyzed SiNWs. Nevertheless, contamination-assisted growth of SiNWs appears to be an important step toward bottom-up synthesis of high-quality SiNWs with a lower risk of metal poisoning, such as those needed for CMOS and other technologies.

Microbatteries (MBs) and microsupercapacitors (MSCs) are primary on-chip micropower sources that drive autonomous and stand-alone microelectronic devices for implementation of the Internet of Things (IoT). However, the performance of conventional MBs and MSCs is restricted by their 2D thin-film electrode design, and these devices struggle to satisfy the increasing IoT energy demands for high energy density, high power density, and long lifespan. The energy densities of MBs and MSCs can be improved significantly through adoption of a 2D thick-film electrode design; however, their power densities and lifespans deteriorate with increased electrode thickness. In contrast, 3D architecture electrodes offer remarkable opportunities to simultaneously improve MB and MSC energy density, power density, and lifespan. To date, various 3D architecture electrodes have been designed, fabricated, and investigated for MBs and MSCs. This review provides an update on the principal superiorities of 3D architecture electrodes over 2D thick-film electrodes in the context of improved MB and MSC energy density, power density, and lifespan. In addition, the most recent and representative progress in 3D architecture electrode development for MBs and MSCs is highlighted. Finally, present challenges are discussed and key perspectives for future research in this field are outlined.

Based on WHO reports the new SARS-CoV-2 coronavirus is currently widespread all over the world. So far > 162 million cases have been confirmed, including > 3 million deaths. Because of the pandemic still spreading across the globe the accomplishment of computational methods to find new potential mechanisms of virus inhibitions is necessary. According to the fact that C60 fullerene (a sphere-shaped molecule consisting of carbon) has shown inhibitory activity against various protein targets, here the analysis of the potential binding mechanism between SARS-CoV-2 proteins 3CLpro and RdRp with C60 fullerene was done; it has resulted in one and two possible binding mechanisms, respectively. In the case of 3CLpro, C60 fullerene interacts in the catalytic binding pocket. And for RdRp in the first model C60 fullerene blocks RNA synthesis pore and in the second one it prevents binding with Nsp8 co-factor (without this complex formation, RdRp can't perform its initial functions). Then the molecular dynamics simulation confirmed the stability of created complexes. The obtained results might be a basis for other computational studies of 3CLPro and RdRp potential inhibition ways as well as the potential usage of C60 fullerene in the fight against COVID-19 disease.

Fluorescence probes have widely been used for detecting and imaging Ca2+-enriched parts of cells but more rarely for quantitative determination of concentrations. In this study we show how this can be achieved by a novel approach using hydrogel particles. In a microfluidic co-flow arrangement spherical droplets were generated from an aqueous solution of acrylamide, N,N'-methylenebisacrylamide crosslinker and photoinitiator and subsequently photo-cured in situ yielding gel particles in a sub millimeter range. These particles were separated, dried under reduced pressure and re-swollen in water containing Rhod-5N tri potassium salt as calcium ion selective fluorescence probe. After that the particles were dried again and stored for further investigations. Upon exposure of dried particles to calcium chloride solutions they swell and take up Ca2+-ions forming a strong fluorescing complex with Rhod-5N. Thus, fluorescence intensity increases with calcium ion concentration. Up to ca. 0.50 mM the enhancement effect is strong and then becomes considerably weaker. The intensity-concentration-dependence is well described by an equation derived from the equilibrium of the formation of a 1:1 Ca2+:Rhod-5N complex. The particles allow for a fast optical determination of Ca2+-concentrations up to 0.50 mM in analyte volumes down to below 10 [my]L.

Cloud data warehouse systems lower the barrier to access data analytics. These applications often lack a database administrator and integrate data from various sources, potentially leading to data not satisfying strict constraints. Automatic schema optimization in self-managing databases is difficult in these environments without prior data cleaning steps. In this paper, we focus on constraint discovery as a subtask of schema optimization. Perfect constraints might not exist in these unclean datasets due to a small set of values violating the constraints. Therefore, we introduce the concept of a generic PatchIndex structure, which handles exceptions to given constraints and enables database systems to define these approximate constraints. We apply the concept to the environment of distributed databases, providing parallel index creation approaches and optimization techniques for parallel queries using PatchIndexes. Furthermore, we describe heuristics for automatic discovery of PatchIndex candidate columns and prove the performance benefit of using PatchIndexes in our evaluation.

In the studies presented here, the subsequent growth of graphene on hexagonal boron nitride (h-BN) is achieved by the thermal decomposition of molecular precursors and the catalytic assistance of metal substrates. The epitaxial growth of h-BN on Pt(111) is followed by the deposition of a temporary Pt film that acts as a catalyst for the fabrication of the graphene sheet. After intercalation of the intermediate Pt film underneath the boron-nitride mesh, graphene resides on top of h-BN. Scanning tunneling microscopy and density functional calculations reveal that the moiré pattern of the van-der-Waals-coupled double layer is due to the interface of h-BN and Pt(111). While on Pt(111) the graphene honeycomb unit cells uniformly appear as depressions using a clean metal tip for imaging, on h-BN they are arranged in a honeycomb lattice where six protruding unit cells enframe a topographically dark cell. This superstructure is most clearly observed at small probe-surface distances. Spatially resolved inelastic electron tunneling spectroscopy enables the detection of a previously predicted acoustic hybrid phonon of the stacked materials. Its' spectroscopic signature is visible in surface regions where the single graphene sheet on Pt(111) transitions into the top layer of the stacking.

This paper presents the technological status of robot-assisted gait self-training under real clinical environment conditions. A successful rehabilitation after surgery in hip endoprosthetics comprises self-training of the lessons taught by physiotherapists. While doing this, immediate feedback to the patient about deviations from the expected physiological gait pattern during training is important. Hence, the Socially Assistive Robot (SAR) developed for this type of training employs task-specific, user-centered navigation and autonomous, real-time gait feature classification techniques to enrich the self-training through companionship and timely corrective feedback. The evaluation of the system took place during user tests in a hospital from the point of view of technical benchmarking, considering the therapists' and patients' point of view with regard to training motivation and from the point of view of initial findings on medical efficacy as a prerequisite from an economic perspective. In this paper, the following research questions were primarily considered: Does the level of technology achieved enable autonomous use in everyday clinical practice? Has the gait pattern of patients who used additional robot-assisted gait self-training for several days been changed or improved compared to patients without this training? How does the use of a SAR-based self-training robot affect the motivation of the patients?

In the currently rapidly developing field of soft robots, smart materials with controllable properties play the central role. Thermosensitive elastomers are soft, smart materials whose material properties can be controlled by changing their temperature. The aim of this work is to investigate the mechanical properties, to analyze the surface, the inner structure, and the heat transfer within the thermosensitive elastomer materials. This should provide a knowledge base for new combinations, such as a combination of thermosensitive and the well-known magneto sensitive elastomers, in order to realize new applications. Thermoplastic polycaprolactone particles were incorporated into a flexible polydimethylsiloxane matrix to produce thermosensitive elastomer samples. With a low melting point in the range of 58-60˚C, polycaprolactone offers good application potential compared to other thermoplastic materials such as polymethamethylacrylate with a melting point above 160˚C. Test samples of different material compositions and geometries were made to examine temperature-depending material properties. Two useful effects were identified: temperature-dependent change in stiffness and the shape memory effect. In certain examinations, carbonyl iron particles were also included to find out if the two particle systems are compatible with each other and can be combined in the polydimethylsiloxane matrix without disadvantages. Changes in shore hardness before and after the influence of temperature were investigated. Micro computed tomography images and scanning electron microscopy images of the respective samples were also obtained in order to detect the temperature influence on the material internally as well as on the surface of the thermosensitive elastomers in combination with carbonyl iron particles. In order to investigate the heat transfer within the samples, heating tests were carried out and the influence of different particle concentrations of the thermosensitive elastomers with and without carbonyl iron particles was determined. Further work will focus on comprehensive investigations of thermo-magneto-sensitive elastomers, as this will enable the functional integration in the material to be implemented with increased efficiency. By means of the different investigations, the authors see future applications for this class of materials in adaptive sensor and gripper elements in soft robotics.

The field of optical lithography is subject to intense research and has gained enormous improvement. However, the effort necessary for creating structures at the size of 20 nm and below is considerable using conventional technologies. This effort and the resulting financial requirements can only be tackled by few global companies and thus a paradigm change for the semiconductor industry is conceivable: custom design and solutions for specific applications will dominate future development (Fritze in: Panning EM, Liddle JA (eds) Novel patterning technologies. International society for optics and photonics. SPIE, Bellingham, 2021. https://doi.org/10.1117/12.2593229). For this reason, new aspects arise for future lithography, which is why enormous effort has been directed to the development of alternative fabrication technologies. Yet, the technologies emerging from this process, which are promising for coping with the current resolution and accuracy challenges, are only demonstrated as a proof-of-concept on a lab scale of several square micrometers. Such scale is not adequate for the requirements of modern lithography; therefore, there is the need for new and alternative cross-scale solutions to further advance the possibilities of unconventional nanotechnologies. Similar challenges arise because of the technical progress in various other fields, realizing new and unique functionalities based on nanoscale effects, e.g., in nanophotonics, quantum computing, energy harvesting, and life sciences. Experimental platforms for basic research in the field of scale-spanning nanomeasuring and nanofabrication are necessary for these tasks, which are available at the Technische Universität Ilmenau in the form of nanopositioning and nanomeasuring (NPM) machines. With this equipment, the limits of technical structurability are explored for high-performance tip-based and laser-based processes for enabling real 3D nanofabrication with the highest precision in an adequate working range of several thousand cubic millimeters.

We propose a novel automatic segmentation algorithm that separates the components of human skin cells from the rest of the tissue in fluorescence data of three-dimensional scans using non-invasive multiphoton tomography. The algorithm encompasses a multi-stage merging on preprocessed superpixel images to ensure independence from a single empirical global threshold. This leads to a high robustness of the segmentation considering the depth-dependent data characteristics, which include variable contrasts and cell sizes. The subsequent classification of cell cytoplasm and nuclei are based on a cell model described by a set of four features. Two novel features, a relationship between outer cell and inner nucleus (OCIN) and a stability index, were derived. The OCIN feature describes the topology of the model, while the stability index indicates segment quality in the multi-stage merging process. These two new features, combined with the local gradient magnitude and compactness, are used for the model-based fuzzy evaluation of the cell segments. We exemplify our approach on an image stack with 200 × 200 × 100 [my]m^3, including the skin layers of the stratum spinosum and the stratum basale of a healthy volunteer. Our image processing pipeline contributes to the fully automated classification of human skin cells in multiphoton data and provides a basis for the detection of skin cancer using non-invasive optical biopsy.

This research presents a hybrid approach to generate sample data for future machine learning applications for the prediction of mechanical properties in directed energy deposition-arc (DED-Arc) using the GMAW process. DED-Arc is an additive manufacturing process which offers a cost-effective way to generate 3D metal parts, due to its high deposition rate of up to 8 kg/h. The mechanical properties additively manufactured wall structures made of the filler material G4Si1 (ER70 S-6) are shown in dependency of the t8/5 cooling time. The numerical simulation is used to link the process parameters and geometrical features to a specific t8/5 cooling time. With an input of average welding power, welding speed and geometrical features such as wall thickness, layer height and heat source size a specific temperature field can be calculated for each iteration in the simulated welding process. This novel approach allows to generate large, artificial data sets as training data for machine learning methods by combining experimental results to generate a regression equation based on the experimentally measured t8/5 cooling time. Therefore, using the regression equations in combination with numerically calculated t8/5 cooling times an accurate prediction of the mechanical properties was possible in this research with an error of only 2.6%. Thus, a small set of experimentally generated data set allows to achieve regression equations which enable a precise prediction of mechanical properties. Moreover, the validated numerical welding simulation model was suitable to achieve an accurate calculation of the t8/5 cooling time, with an error of only 0.3%.

In der klimapolitischen Debatte stehen meist einzelne Instrumente und Maßnahmen im Vordergrund, wohingegen die Frage der ökonomischen Rationalität der Klimapolitik als solcher häufig vernachlässigt wird. Eine Klimapolitik wäre dann ökonomisch rational, wenn politisch vorgegebene Klimaziele mit geringstmöglichen Kosten realisiert werden würden. Notwendig hierfür ist eine umfassende und einheitliche Bepreisung der Treibhausgasemissionen. Dieser rationalen Klimapolitik wird die aktuelle deutsche und europäische Politik gegenübergestellt, die sich als ineffektiv und ineffizient erweist. Ursächlich hierfür sind die Anreize, denen die politischen Akteure unterliegen und die zu einem Widerspruch zwischen ökonomischer Rationalität und politischer Opportunität führen.

Fluorogenic labeling strategies have emerged as powerful tools for in vivo and in vitro imaging applications for diagnostic and theranostic purposes. Free organic chromophores (fluorescent dyes) are bright but rapidly degrade. Inorganic nanoparticles (e.g., quantum dots) are photostable but toxic to biological systems. Alternatively, dye-doped polymer particles are promising for labeling and imaging due to their properties that overcome limitations of photodegradation and toxicity. This progress report, therefore, presents various synthesis techniques for the generation of dye-doped fluorescent polymer particles. Polymer particles are relatively soft compared to inorganic nanoparticles and can be synthesized with characteristics like biocompatibility and stimuli responsiveness. Also, their ability of loading fluorophores through various interactions reveals brightness. Here, a multiscale-multicolor library of bright and soft fluorescent polymer particles is generated hierarchically. Various microfluidic supported strategies have been applied where fluorophores can be linked to polymeric networks noncovalently and covalently in the interior, and at the surface of nanoparticles (60-550 nm). Besides, microfluidic strategies for hydrophilic and hydrophobic fluorescent polymer microparticles (20-800 [my]m) have been performed for systematic tuning in size and color combination. Furthermore, soft and bright particulate assemblies are enabled through interfacial interactions at the intermediate scale (600 nm-3 [my]m) between the nanometer and micrometer lengthscale.

We study the reachability problem for queue automata and lossy queue automata. Concretely, we consider the set of queue contents which are forwards resp. backwards reachable from a given set of queue contents. Here, we prove the preservation of regularity if the queue automaton loops through some special sets of transformation sequences. This is a generalization of the results by Boigelot et al. and Abdulla et al. regarding queue automata looping through a single sequence of transformations. We also prove that our construction is possible in polynomial time.

The biomechanical parameters of muscle soleus contraction in rats and their blood biochemical indicators after the intramuscular administration of water-soluble C60 fullerene at doses of 0.5, 1, and 2 mg/kg 1 h before the onset of muscle ischemia were investigated. In particular, changes in the contraction force of the ischemic muscle soleus, the integrated power of the muscle, the time to achieve the maximum force response, the dynamics of fatigue processes, and the parameters of the transition from dentate to smooth tetanus, levels of creatinine, creatine kinase, lactate and lactate dehydrogenase, and parameters of prooxidant-antioxidant balance (thiobarbituric acid reactive substances, hydrogen peroxide, and reduced glutathione and catalase) were analyzed. The positive therapeutic changes in the studied biomechanical and biochemical markers were revealed, which indicate the possibility of using water-soluble C60 fullerenes as effective prophylactic nanoagents to reduce the severity of pathological conditions of the muscular system caused by ischemic damage to skeletal muscles.

As standard concepts for precision positioning within a machine reach their limits with increasing measurement volumes, inverse concepts are a promising approach for addressing this problem. The inverse principle entails other limitations, as for high-precision positioning of a sensor head within a large measurement volume, three four-beam interferometers are required in order to measure all necessary translations and rotations of the sensor head and reconstruct the topography of the reference system consisting of fixed mirrors in the x-, y-, and z-directions. We present the principle of a passive heterodyne laser interferometer with consequently separated beam paths for the individual heterodyne frequencies. The beam path design is illustrated and described, as well as the design of the signal-processing and evaluation algorithm, which is implemented using a System-On-a-Chip with an integrated FPGA, CPU, and A/D converters. A streamlined bench-top optical assembly was set up and measurements were carried out to investigate the remaining non-linearities. Additionally, reference measurements with a commercial homodyne interferometer were executed.

This paper describes a standing-wave interferometer with two laser sources of different wavelengths, diametrically opposed and emitting towards each other. The resulting standing wave has an intensity profile which is moving with a constant velocity, and is directly detected inside the laser beam by two thin and transparent photo sensors. The first sensor is at a fixed position, serving as a phase reference for the second one which is moved along the optical axis, resulting in a frequency shift, proportional to the velocity. The phase difference between both sensors is evaluated for the purpose of interferometric length measurements.

This study demonstrates the fabrication of self-aligning three-dimensional (3D) platinum bridges for ammonia gas sensing using gas-phase electrodeposition. This deposition scheme can guide charged nanoparticles to predetermined locations on a surface with sub-micrometer resolution. A shutter-free deposition is possible, preventing the use of additional steps for lift-off and improving material yield. This method uses a spark discharge-based platinum nanoparticle source in combination with sequentially biased surface electrodes and charged photoresist patterns on a glass substrate. In this way, the parallel growth of multiple sensing nodes, in this case 3D self-aligning nanoparticle-based bridges, is accomplished. An array containing 360 locally grown bridges made out of 5 nm platinum nanoparticles is fabricated. The high surface-to-volume ratio of the 3D bridge morphology enables fast response and room temperature operated sensing capabilities. The bridges are preconditioned for ˜ 24 h in nitrogen gas before being used for performance testing, ensuring drift-free sensor performance. In this study, platinum bridges are demonstrated to detect ammonia (NH3) with concentrations between 1400 and 100 ppm. The sensing mechanism, response times, cross-sensitivity, selectivity, and sensor stability are discussed. The device showed a sensor response of ˜ 4% at 100 ppm NH3 with a 70% response time of 8 min at room temperature.

In outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate's performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.

Transition metal oxides have a great potential in sodium-ion capacitors (SICs) due to their pronouncedly higher capacity and low cost. However, their poor conductivity and fragile structure hinder their development. Herein, core-shell-like nickel-cobalt oxysulfide (NCOS) nanowires are synthesized and demonstrated as an advanced SICs anode. The bimetallic oxysulfide with multiple cation valence can promote the sodium ion adsorption and redox reaction, massive defects enable accommodation of the volume change in the sodiation/desodiation process, meanwhile the core-shell-like structure provides abundant channels for fast transfer of sodium ions, thereby synergistically making the NCOS electrode exhibit a high reversible sodium ion storage capacity (1468.5 mAh g^-1 at 0.1 A g^-1) and an excellent cyclability (90.5% capacity retention after 1000 cycles). The in-situ X-ray diffraction analysis unravels the insertion and conversion mechanism for sodium storage in NCOS, and the enhanced capability of NCOS is further verified by the kinetic analysis and theoretical calculations. Finally, SICs consisting of the NCOS anode and a boron-nitrogen co-doped carbon nanotubes cathode deliver an energy density of 205.7 Wh kg^-1, a power density of 22.5 kW kg^-1, and an outstanding cycling lifespan. These results indicate an efficient strategy in designing a high-performance anode for sodium storage based on bimetallic dianion compounds.

Since the first realization of two-photon direct laser writing (DLW) in Maruo et al. (Opt Lett 22:132-134, 1997), the manufacturing using direct laser writing techniques spread out in many laboratories all over the world. Photosensitive materials with different material properties open a new field for micro- and nanofabrication. The achievable structuring resolution using this technique is reported to be sub-100 nm (Paz et al. in J. Laser Appl. 24:042004, 2012), while a smallest linewidth of 25 nm could be shown in Tan et al. (Appl Phys Lett 90:071106, 2007). In our approach, the combination of DLW with the nanopositioning and nanomeasuring machine NMM-1 offers an improvement of the technique from the engineering side regarding the ultra-precise positioning (Weidenfeller et al. in Adv Fabr Technol Micro/Nano Opt Photon XI 10544:105440E, 2018). One big benefit besides the high positioning resolution of 0.1 nm is offered by the positioning range of 25 mm × 25 mm × 5 mm (Jäger et al. in Technisches Messen 67:319-323, 2000; Manske et al. in Meas Sci Technol 18:520-527, 2007). Thus, a trans-scale fabrication without any stitching or combination of different positioning systems is necessary. The immense synergy between the highly precise positioning and the DLW is demonstrated by the realization of resist lines and trenches whose center-to-center distance undergoes the modified diffraction limit for two-photon processes. The precise positioning accuracy enables a defined distance between illuminated lines. Hence, with a comparable huge width of the trenches of 1.655 [my]m due to a low effective numerical aperture of 0.16, a resist line of 30 nm between two written trenches could be achieved. Although the interrelationships for achieving such narrow trenches have not yet been clarified, much smaller resist lines and trench widths are possible with this approach in the near future.

Crude oil frequently contains stable radicals that allow detection by means of EPR spectroscopy. On the other hand, most sands and soils possess significant amounts of iron, manganese or other metallic species that often provide excessively broad EPR signatures combined with well-defined sharp features by quartz defects. In this study, we demonstrate the feasibility to identify oil contamination in natural environments that are subject to oil spillage during production on land, as well as beachside accumulation of marine oil spillage. Straightforward identification of oil is enabled by the radical contributions of asphaltenes, in particular by vanadyl multiplets that are absent from natural soils. This potentially allows for high-throughput soil analysis or the application of mobile EPR scanners.

The majority of nanopositioning and nanomeasuring machines (NPMMs) are based on three independent linear movements in a Cartesian coordinate system. This in combination with the specific nature of sensors and tools limits the addressable part geometries. An enhancement of an NPMM is introduced by the implementation of rotational movements while keeping the precision in the nanometer range. For this purpose, a parameter-based dynamic evaluation system with quantifiable technological parameters has been set up and employed to identify and assess general solution concepts and adequate substructures. Evaluations taken show high potential for three linear movements of the object in combination with two angular movements of the tool. The influence of the additional rotation systems on the existing structure of NPMMs has been investigated further on. Test series on the repeatability of an NPMM enhanced by a chosen combination of a rotary stage and a goniometer setup are realized. As a result of these test series, the necessity of in situ position determination of the tool became very clear. The tool position is measured in situ in relation to a hemispherical reference mirror by three Fabry-Pérot interferometers. FEA optimization has been used to enhance the overall system structure with regard to reproducibility and long-term stability. Results have been experimentally investigated by use of a retroreflector as a tool and the various laser interferometers of the NPMM. The knowledge gained has been formed into general rules for the verification and optimization of design solutions for multiaxial nanopositioning machines.

We elaborate on the deviation of the Jordan structures of two linear relations that are finite-dimensional perturbations of each other. We compare their number of Jordan chains of length at least n. In the operator case, it was recently proved that the difference of these numbers is independent of n and is at most the defect between the operators. One of the main results of this paper shows that in the case of linear relations this number has to be multiplied by n+1 and that this bound is sharp. The reason for this behavior is the existence of singular chains. We apply our results to one-dimensional perturbations of singular and regular matrix pencils. This is done by representing matrix pencils via linear relations. This technique allows for both proving known results for regular pencils as well as new results for singular ones.

Nickel-cobalt borides (denoted as NCBs) have been considered as a promising candidate for aqueous supercapacitors due to their high capacitive performances. However, most reported NCBs are amorphous that results in slow electron transfer and even structure collapse during cycling. In this work, a nanocrystallized NCBs-based supercapacitor is successfully designed via a facile and practical microimpinging stream reactor (MISR) technique, composed of a nanocrystallized NCB core to facilitate the charge transfer, and a tightly contacted Ni-Co borates/metaborates (NCBi) shell which is helpful for OH^- adsorption. These merits endow NCBNCBi a large specific capacity of 966 C g^-1 (capacitance of 2415 F g^-1) at 1 A g^-1 and good rate capability (633.2 C g^-1 at 30 A g^-1), as well as a very high energy density of 74.3 Wh kg^-1 in an asymmetric supercapacitor device. More interestingly, it is found that a gradual in situ conversion of core NCBs to nanocrystallized Ni-Co (oxy)-hydroxides inwardly takes place during the cycles, which continuously offers large specific capacity due to more electron transfer in the redox reaction processes. Meanwhile, the electron deficient state of boron in metal-borates shells can make it easier to accept electrons and thus promote ionic conduction.