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A multi-spot (16 spots) light-addressable potentiometric sensor (MLAPS) consisting of an Al–p-Si–SiO2 structure modified with a weak polyelectrolyte layer of PAH (poly(allylamine hydrochloride)) was applied for the label-free electrical detection of DNA (deoxyribonucleic acid) immobilization and hybridization by the intrinsic molecular charge for the first time. To achieve a preferentially flat orientation of DNA strands and thus, to reduce the distance between the DNA charge and MLAPS surface, the negatively charged probe single-stranded DNAs (ssDNA) were electrostatically adsorbed onto the positively charged PAH layer using a simple layer-by-layer (LbL) technique. In this way, more DNA charge can be positioned within the Debye length, yielding a higher sensor signal. The surface potential changes in each spot induced due to the surface modification steps (PAH adsorption, probe ssDNA immobilization, hybridization with complementary target DNA (cDNA), non-specific adsorption of mismatched ssDNA) were determined from the shifts of photocurrent–voltage curves along the voltage axis. A high sensor signal of 83 mV was registered after immobilization of probe ssDNA onto the PAH layer. The hybridization signal increases from 5 mV to 32 mV with increasing the concentration of cDNA from 0.1 nM to 5 μM. In contrast, a small signal of 5 mV was recorded in the case of non-specific adsorption of fully mismatched ssDNA (5 μM). The obtained results demonstrate the potential of the MLAPS in combination with the simple and rapid LbL immobilization technique as a promising platform for the future development of multi-spot light-addressable label-free DNA chips with direct electrical readout.
Light-addressable potentiometric sensors (LAPS) consisting of a p-Si-SiO2 and p-Si-SiO2-Au structure, respectively, have been tested for a label-free electrical detection of DNA (deoxyribonucleic acid) hybridization. Three different strategies for immobilizing single-stranded probe DNA (ssDNA) molecules on a LAPS surface have been studied and compared: (a) immobilization of thiol-modified ssDNA on the patterned Au surface via gold-thiol bond, (b) covalent immobilization of amino-modified ssDNA onto the SiO2 surface functionalized with 3-aminopropyltriethoxysilane and (c) layer-by-layer adsorption of negatively charged ssDNA on a positively charged weak polyelectrolyte layer of poly(allylamine hydrochloride).
Kyphoplasty of Osteoporotic Fractured Vertebrae: A Finite Element Analysis about Two Types of Cement
(2019)
Frequency mixing magnetic detection (FMMD) is a sensitive and selective technique to detect magnetic nanoparticles (MNPs) serving as probes for binding biological targets. Its principle relies on the nonlinear magnetic relaxation dynamics of a particle ensemble interacting with a dual frequency external magnetic field. In order to increase its sensitivity, lower its limit of detection and overall improve its applicability in biosensing, matching combinations of external field parameters and internal particle properties are being sought to advance FMMD. In this study, we systematically probe the aforementioned interaction with coupled Néel–Brownian dynamic relaxation simulations to examine how key MNP properties as well as applied field parameters affect the frequency mixing signal generation. It is found that the core size of MNPs dominates their nonlinear magnetic response, with the strongest contributions from the largest particles. The drive field amplitude dominates the shape of the field-dependent response, whereas effective anisotropy and hydrodynamic size of the particles only weakly influence the signal generation in FMMD. For tailoring the MNP properties and parameters of the setup towards optimal FMMD signal generation, our findings suggest choosing large particles of core sizes dc > 25 nm nm with narrow size distributions (σ < 0.1) to minimize the required drive field amplitude. This allows potential improvements of FMMD as a stand-alone application, as well as advances in magnetic particle imaging, hyperthermia and magnetic immunoassays.
K0 production in e+e− annihilations at 30 GeV center of mass energy. TASSO Collaboration
(1980)
Effective training requires high muscle forces potentially leading to training-induced injuries. Thus, continuous monitoring and controlling of the loadings applied to the musculoskeletal system along the motion trajectory is required. In this paper, a norm-optimal iterative learning control algorithm for the robot-assisted training is developed. The algorithm aims at minimizing the external knee joint moment, which is commonly used to quantify the loading of the medial compartment. To estimate the external knee joint moment, a musculoskeletal lower extremity model is implemented in OpenSim and coupled with a model of an industrial robot and a force plate mounted at its end-effector. The algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee. The results show that the algorithm is able to minimize the external knee joint moment in all three cases and converges after less than seven iterations.
Isomeric states in ¹³⁴ Ba
(1980)
Isomeric states in ¹³⁴ Ba
(1980)
Isomeric state in ¹³⁶ La
(1981)