Direct Embedding Research Articles

Catalytic properties of variously immobilized mushroom tyrosinase: A kinetic study for future development of biomimetic amperometric biosensors

Mushroom tyrosinase was immobilized by direct embedding into electrode material (modified carbon paste electrode), incorporation of cross-linked enzyme aggregates into a polymer membrane (glassy carbon electrode covered by thin layer of Nafion®), and covalent attachment using self-assembled monolayers (gold electrode with the chemically bound enzyme). Both, standard UV–Vis spectrophotometry and amperometry in a batch configuration are presented as complementary methods to study the tyrosinase enzyme kinetics, whose catecholase activity results in electroactive products (ortho-quinones). Due to higher sensitivity of amperometric detection, evident advantage in the enzyme consumption was obtained. Prepared amperometric tyrosinase biosensors were characterized using cyclic voltammetry and atomic force microscopy. The Michaelis constant values of immobilized and unbound tyrosinase (free enzyme solution) towards dopamine and catechol were compared. The apparent Michaelis constant values for immobilized tyrosinase are significantly lower than the declared value of 0.840 mmol L−1 dopamine for the unbound enzyme. The enzyme tyrosinase arranged in self-assembled monolayer serves as an efficient sensor due to low apparent Michaelis constant of 0.061 mmol L−1 dopamine and high maximum reaction velocity of 0.458 μA s−1. This fact reflects the ideal arrangement of enzyme molecules causing high availability of the binding site. Tris-glycine sodium dodecyl sulphate polyacrylamide gel electrophoresis and atomic force microscopy clarified that the protein of molecular weight 25 kDa is bound preferably on chemically modified gold electrode. A sensor prepared by the immobilization of tyrosinase on gold electrode results in higher catecholase activity towards dopamine than in case of CPE and GC electrodes, where enzyme is immobilized physically.

Direct embedding and versatile placement of electrodes in 3D printed microfluidic-devices.

In this paper, we describe how PolyJet 3D printing technology can be used to fully integrate electrode materials into microfluidic devices during the print process. This approach uses stacked printing (separate printing steps and stage drops) with liquid support to result in devices where electrodes and a capillary fluidic connection are directly integrated and ready to use when printing is complete. A key feature of this approach is the ability to directly incorporate electrode materials into the print process so that the electrode(s) can be placed anywhere in the channel (at any height). We show that this can be done with a single electrode or an electrode array (which led to increases in signal). In both cases, we found that a middle electrode configuration leads to a significant increase in the sensitivity, as opposed to more traditional bottom channel placement. Since the electrode is embedded in the device, in situ platinum black deposition was performed to aid in the detection of nitric oxide. Finally, a generator-collector configuration with an opposed counter electrode was made by placing two working electrodes ∼750 μm apart (in the middle of the channel) and a platinum counter electrode at the bottom of the channel. The utility of this configuration was demonstrated by dual electrode detection of catechol. This 3D printing approach affords robust electrochemical detection schemes with new electrode configurations being possible in a manner that also increases the ease of use and transferability of the 3D printed devices with integrated electrode materials.

Rational assembly of mussel-inspired polydopamine (PDA)-Zn (II) complex nanospheres on graphene oxide framework tailored for robust self-healing anti-corrosion coatings application

Abstract There are increasing demands for self-healing anti-corrosion coatings with high efficiency and durability in various industrial applications. Recently, eumelanin polymers especially polydopamine (PDA)-based nanostructures have been served as a mussel-inspired strategy to provide such requirements due to the intrinsic adhesive nature of amines and catechols along with its film-forming ability. Nevertheless, direct embedment of hydrophilic PDA nanoparticles into polymer coatings could eventually trigger corrosion as a consequence of the osmotic pressure exerted beneath the coating. In the present work, we have adopted a rational design for constructing robust self-healing epoxy composites via a tailor-made procedure including oxidative polymerization of dopamine over the graphene oxide (GO) framework and subsequent loading of Zn (II) corrosion inhibitor. The triple-function of GO as a template for polymerization of dopamine; simultaneously being a nanoreservoir for storage of corrosion inhibitors and eventually as an impermeable barrier is very crucial. Curiously, the as-prepared GO-PDA and GO-PDA-Zn nanocomposites exhibit different ion-capturing/releasing properties derived from opposite charges developed on them. This property not only declines the permeation of aggressive species into the coating matrix but also facilitates the release of divalent zinc ions or PDA through an on-demand manner when local corrosion initiates at the damaged zone. The propensity of PDA to anchor ferrous or ferric ions generated from anodic reactions in one hand and Zn (II) to form zinc hydroxide compounds at cathodic sites, on the other hand, endows the GO-PDA-Zn nanocomposite with high efficiency in reducing the corrosion current as measured by polarization and EIS analyses. More importantly, the outstanding self-healing function of GO-PDA/EP and GO-PDA-Zn/EP composite coatings was proved with impedance measurements on scratched coatings immersed in saline solution.

Discriminative Neural Embedding Learning for Short-Duration Text-Independent Speaker Verification

Short duration text-independent speaker verification remains a hot research topic in recent years, and deep neural network based embeddings have shown impressive results in such conditions. Good speaker embeddings require the property of both small intra-class variation and large inter-class difference, which is critical for the ability of discrimination and generalization. Current embedding learning strategies can be grouped into two frameworks: “Cascade embedding learning” with multiple stages and “direct embedding learning” from spectral feature directly. We propose new approaches to achieve more discriminant speaker embeddings. Within the cascade framework, a neural network based deep discriminant analysis (DDA) is proposed to project i -vector to more discriminative embeddings. Within the direct embedding framework, a deep model with more advanced center loss and A-softmax loss is used, the focal loss is also investigated in this framework. Moreover, the traditional i -vector and neural embeddings are finally combined with neural network based DDA to achieve further gain. Main experiments are carried out on a short-duration text-independent speaker verification dataset generated from the SRE corpus. The results show that the newly proposed method is promising for short-duration text-independent speaker verification, and it is consistently better than traditional i -vector and neural embedding baselines. The best embeddings achieve roughly 30% relative EER reduction compared to the i -vector baseline, which could be further enhanced when combined with the i -vector system.

Biocompatible Glycine-Assisted Catalysis of the Sol-Gel Process: Development of Cell-Embedded Hydrogels.

The sol-gel process can be used for hydrogel cross-linking, thus opening an attractive route for the design of biocompatible hydrogels under soft conditions. The sol-gel process can be catalysed at basic or acidic pH values, under neutral conditions with the addition of a nucleophile. Therefore, working around pH 7 unlocks the possibility of direct cell embedment and the preparation of bioinks. We aimed to propose a generic method for sol-gel 3D bioprinting, and first screened different nucleophilic catalysts using bis-silylated polyethylene glycol (PEG) as a model hydrogel. A synergistic effect of glycine and NaF, used in low concentrations to avoid any toxicity, was observed. Biocompatibility of the approach was demonstrated by embedding primary mouse mesenchymal stem cells. The measure of viscosity as a function of time showed the impact of reaction parameters, such as temperature, complexity of the medium, pH and cell addition, on the kinetics of the sol-gel process, and allowed prediction of the gelation time.

Optimization analysis of giant spiral case with combined embedding method

For selecting reasonable embedding method of the giant spiral case, contact nonlinear finite element analysis is used to obtain optimal range of cushion laying for the combination embedding method of direct embedding and cushion in Wudongde hydropower station, comparing concrete stress, non-uniform uplifting displacement of baseplates, the bearing ratio of concrete, etc. The optimization results show that the combination scheme with cushion laying to 270°section can meet the control standards of strength, stiffness and displacement for structure, which is the best embedding method for this hydropower station.

Thiol-Gelatin-Norbornene Bioink for Laser-Based High-Definition Bioprinting.

Two-photon polymerization (2PP) is a lithography-based 3D printing method allowing the fabrication of 3D structures with sub-micrometer resolution. This work focuses on the characterization of gelatin-norbornene (Gel-NB) bioinks which enables the embedding of cells via 2PP. The high reactivity of the thiol-ene system allows 2PP processing of cell-containing materials at remarkably high scanning speeds (1000 mm s-1 ) placing this technology in the domain of bioprinting. Atomic force microscopy results demonstrate that the indentation moduli of the produced hydrogel constructs can be adjusted in the 0.2-0.7 kPa range by controlling the 2PP processing parameters. Using this approach gradient 3D constructs are produced and the morphology of the embedded cells is observed in the course of 3 weeks. Furthermore, it is possible to tune the enzymatic degradation of the crosslinked bioink by varying the applied laser power. The 3D printed Gel-NB hydrogel constructs show exceptional biocompatibility, supported cell adhesion, and migration. Furthermore, cells maintain their proliferation capacity demonstrated by Ki-67 immunostaining. Moreover, the results demonstrate that direct embedding of cells provides uniform distribution and high cell loading independently of the pore size of the scaffold. The investigated photosensitive bioink enables high-definition bioprinting of well-defined constructs for long-term cell culture studies.

Using SCF metadynamics to extend density matrix embedding theory to excited states.

A new framework based on density matrix embedding theory (DMET) capable of directly targeting excited electronic states is proposed and implemented. DMET has previously been shown to be an effective method of calculating the ground state energies of systems exhibiting strong static correlation but has never been applied to calculate excited state energies. In this work, the Schmidt decomposition is applied directly on excited states, approximated by higher lying self-consistent field solutions. The DMET prescription is applied following this Schmidt decomposition allowing for a direct embedding of excited states. Initial results are obtained for a system of multiple hydrogen dimers and the lithium hydride dissociation. We analyze the nature of each part of the excited state DMET calculation and identify challenges. These challenges to the implementation of excited state DMET are discussed, and potential suggestions moving forward are recommended.

A new generation of highly flexible and soft tools for high precision surface finishing with embedded micro-diamonds

Fully automated and process-integrated, high-quality surface polishing is still an extremely challenging task. In particular, the final polishing of complex surfaces without affecting the part geometry often remains a problem. A newly developed technology allows the direct embedding of diamonds into the surfaces of polymer and elastomer polishing tissues and sponges without the use of any adhesives. The same technology can be applied for making micro-diamond enhanced brushes with very fine and highly flexible filaments for polishing surfaces of complex geometries as well as customized polishing tools for direct integration in processing machines. This article highlights some of the possibilities as well as limitations of a new generation of flexible, diamond-coated polishing tools.

Novel Scheme of Reversible Watermarking With a Complementary Embedding Strategy

In this paper, a new scheme of reversible watermarking is proposed using a complementary embedding strategy in the spatial domain. The proposed scheme consists of two stages: horizontal direction embedding and vertical direction embedding. A complementary embedding strategy is designed to increase the embedding capacity and decrease the distortion of the watermarked image in the vertical direction embedding. Specifically, in the horizontal direction, the proposed scheme embeds one secret data bit by increasing values of pixels in even rows and decreasing values of pixels in odd rows by one. In the vertical direction, it embeds another secret data bit by decreasing values of pixels in even rows and increasing values of pixels in odd rows by one. In addition, a histogram shrinkage technique is adopted to prevent overflow and underflow problems. Experimental results demonstrate that the proposed reversible watermarking scheme outperforms state-of-the-art methods in terms of both embedding capacity and watermarked image quality.

Formation of aqua and tetraammine Cu(II) complexes inside the cavities of cucurbit[6,8]urils: A DFT forecast

AbstractResults of DFT calculations of the structure and thermodynamics of formation of aqua and tetraammine Cu(II) complexes inside CB[n] (n = 6,8) are presented in this study. Formation thermodynamics of the complexes in the cavitands was evaluated by taking into account the most probable number of water molecules inside CB[n]. In this methodology, the complexation was first considered as a substitution reaction in which the guest complex displaces partially or completely the water molecules that are located inside the cavity. The water molecules present in the cavitand were shown to play an important role in the fixation of the guest complex inside the cavity due to the hydrogen bonds with the oxygen portals. The hydration of Cu(II) ion inside CB[6] leads to the formation of an inclusion compound with the formula {[Cu(H2O)4]2+·2H2O}@CB[6] while in CB[8] {[Cu(H2O)6]2+·4H2O}@CB[8] is formed. For the binding of tetraammine Cu(II) complex, CB[8] was determined to be a significantly more suitable “container” than CB[6]. Both a direct embedding of this complex into the CB[8] and another mechanism in which ammonia molecules replace the water molecules in the Cu(II) aqua complex, preexisting in CB[8] were determined to be thermodynamically possible. Both these lead to the formation of the resultant inclusion compound described by the formula {[Cu(NH3)4(H2O)2]2+·4H2O}@CB[8].

Composite (PVA/Cu nano) films: Two yield points, embedding mechanism and thermal properties

Direct embedding of Cu nanoparticles into the PVA surface is detected by AFM and a fresh embedding mechanism submitted via the change of the annealing temperatures (Tannealing). When the Tannealing goes above the glass transition temperature of PVA, Cu nanoparticles are rapidly sinked by a thin wetting layer of PVA. In order to it makes capillary pressure on a Cu nanoparticles, suggested, this wetting layer is accountable for Cu embedding. Also, study of the thermal and mechanical properties of the nanocomposites during the embedding of Cu into the PVA. The XRD patterns show that the presence of the Cu nanoparticles in the PVA matrix. The DSC studies show an increase in the glass temperature (Tg) from 344.4 K for the green PVA to 354.2 K for the annealing temperature 423 K. Also shows two melting points for the composites (PVA/Cu nano) with thermal annealing temperature below the glass temperature (Tg) and one melting point with annealing above the glass temperature (Tg). The study shows that Young’s modulus (Y) increases with increasing the thermal annealing temperatures from 0.20 × 109 Pa for the green PVA to 3.75 × 109 Pa for the annealing temperature 423 K. In addition to, the strength at the break increases, while the elongation at break decreases with increasing the thermal annealing temperatures.

3D Printed and Additively Manufactured RoboSiC™ for Space, Cryogenic, Laser and Nuclear Environments

Abstract Goodman Technologies has been directly responsive to, and focused on, 3D printing and additive manufacturing techniques, and what it takes to manufacture in zero-gravity. During a NASA Phase I SBIR project, using a small multi-printhead machine, we showed that it was possible to formulate and 3D print silicon carbide into shapes appropriate for lightweight mirrors and structures at the production rate of 1.2 square-meter/day. Gradient lattice coupons with feature sizes on the order of 0.8mm were printed and were easily machined to very fine tolerances, ten-thousandths of an inch by Coastline Optics in Camarillo, CA. To further elaborate on the list of achievements, in Phase I, Team GT demonstrated three different ceramization techniques for 3D printing low areal cost, ultra-lightweight Silicon Carbide (SiC) mirrors and structures, radiation shielding, and electronics, several of which could be employed in microgravity The Goodman Technologies briefing presented at 2017 Mirror Technology Days “3D Printed Silicon Carbide Scalable to Meter-Class Segments for Far-Infrared Surveyor: NASA Contract NNX17CM29P along with sample coupons resulted in extreme interest from both Government and the Contractor communities. Our materials, which we call RoboSiC™, is suited for many other applications including heat sinks and radiation shielding for space electronics, and we have already started to make the first parts for these applications. The successful Phase I project suggests that we will meet or exceed all NASA requirements for the primary mirror of a Far-IR Surveyor such as the Origins Space Telescope (OST) and have a high probability solution for the LUVOIR Surveyor in time for the 2020 Decadal Survey. Results indicate that printing on the ground will achieve an areal density of 7.75 kg/square-meter (~39% of a James Webb Space Telescope (JWST) beryllium segment), a cost to print of $60K/segment, and an optical surface that has nanometer-scale tolerances. Printing in the microgravity environment of space we have the potential to achieve an areal density of 1.0–2.0 kg/square meter (<10% of a JWST beryllium segment), with a cost to print of ~$10K/segment. The areal density is 2–15 times better than the NASA goal of 15 kg/square meter, and the costs are substantially better than the NASA goal of $100K/square meter. The encapsulated gradient lattice construction provides a uniform CTE throughout the part for dimensional stability, incredible specific stiffness, and the added benefit of cryo-damping. For the extreme wavefront control required by the Large UV/Optical/IR Surveyor (LUVOIR) the regularly spaced lattice construction should also provide deterministic mapping of any optical distortions directly to the regular actuator spacing of a deformable mirror (DM). Some of our processes will also allow for direct embedding of electronics for active structures and segments. Encapsulation of the lattice structures will allow for actively cooling with helium for unprecedented low emissivity and thermal control. Several decades of experience and testing with SiC have shown that our materials will survive, nay thrive in, the most extreme Space, Cryogenic, Laser and Nuclear Environments.

Preparation of immobilized Lactobacillus plantarum agent for silage

Lactic acid bacteria and cellulose degrading bacteria play an important role in fermentation process of silage, because they can prevent the rancidity and increase the nutritive value of silage. But the propagation of lactic acid bacteria will inhibit the activity of cellulose degrading bacteria in the silage fermentation system. This problem can be solved by releasing lactic acid bacteria and cellulose degrading bacteria in different time. Therefore, we immobilized lactic acid bacteria as a microbial agent for sustained release. Firstly, the optimal balling concentration of the composite immobilized carrier and composite immobilized carrier were obtained by immobilization of blank balls and corncob adsorbed Lactobacillus plantarum S1 respectively. The best immobilization condition of L. plantarum S1 was obtained by comparing the immobilized rate and balling effect of two kinds of balls, which were embedded by sodium alginate (SA), CMC-Na and embedded-crosslinked by SA, CMC-Na, polyvinyl alcohol (PVA). The results showed that the best balling concentration was achieved by using 6% PVA+0.4% SA+0.3% CMC-Na for embedding-crosslinking and 1.2% SA+0.5% CMC-Na for direct embedding respectively. In addition, comparing with the mechanical strength and embedding rate of five kinds of immobilization process, the best immobilized process was obtained by adding of the mixture of immobilized carriers (1.2%SA+ 0.5%CMC-Na) and corncob adsorbed L. plantarum S1 slowly into 4% CaCl₂ for 24 hours. The corncob adsorption and SA embedding methodology can effectively increase the embedding efficiency of Lactobacillus plantarum S1.

Non-destructive ghost authentication for single-pixel imaging in mass user environment

A secured ghost imaging system can be constructed in a big data environment by generating many ciphertext sequences (including both correct and incorrect ones) from different object images and only allowing authorized users to retrieve the target correct ones. In such schemes, the identification of the truth of the ciphertext is a critical issue. In previous works, a ciphertext authentication scheme for a ghost imaging system was proposed by embedding ciphertext fractions generated from hidden images into the target object ciphertext sequence. However, the substituted ciphertext elements will cause severe degradation in the reconstructed object image quality, especially when a large number of hidden images are employed in a mass user environment. In this paper, we propose a non-destructive ciphertext authentication scheme by recording the positions of the ciphertext elements with similar intensities to form the authentication key, rather than direct embedding. Our proposed scheme has a significant advantage over the previous destructive authentication scheme by preserving the fidelity of the reconstructed object image when the ciphertext is authenticated by many users simultaneously.

Robust stego-key directed LSB substitution scheme based upon cuckoo search and chaotic map

High capacity image steganography using Least Significant Bit (LSB) substitution greatly impaires visual quality of the stego image. In addition, most of steganographic schemes consider direct embedding of secret message without considering its security concern over public channel transmission. To address this problem, we have introduced a novel stego-key directed LSB substitution scheme which not only offers high embedding capacity but also resilient against stego image distortion. The aim of this work is to consider this problem as search and optimization. High embedding capacity is achieved through optimal utilization of search domain using proposed LUDO scan scheme. Optimal Stego-key is determined through cuckoo search optimization where Lévy flight is used for obtaining new Stego-key. In addition, secret message is encrypted before embedding using chaotic map generated through random key. Large key space of Stego-key offer high resistance against various crypt analytic attacks. Proposed steganography scheme is evaluated over nine standard test images and competitive results are obtained. On an average, we achieved a Peak Signal-to-Noise Ratio (PSNR) of 44.09 and Structural Similarity Matrix (SSIM) of 0.97. Results demonstrate that the proposed steganography scheme not only outperforms state-of-the-art high embedding steganography schemes but also offers high payload capabilities with immunity against visual degradation.

Introducing the Special Issue: Clausal and Nominal Complements in Monolingual and Bilingual Grammars

To introduce this Special Issue entitled Clausal and Nominal Complements in Monolingual and Bilingual Grammars, we begin by explaining what originally motivated this Special Issue [...]

Design of Direct Embedment Foundations for Poles Under Moment Loads in Granular Soils

Introduction: The design of direct embedment pole foundations involves calculating the ultimate moment capacity and deflections/rotations of pole foundations at groundline. Methods: A theoretical model based on rigid piles is used as a basis for developing equations for the ultimate moment capacity of direct embedment pole foundation installed in granular soils. Results and Conclusion: The steps involved in the development are presented in this paper. Prototype test data are used to compare the predictions of the model and to validate the proposed method.

Semi-supervised local multi-manifold Isomap by linear embedding for feature extraction

In this paper, we mainly propose a semi-supervised local multi-manifold Isomap learning framework by linear embedding, termed SSMM-Isomap, that can apply the labeled and unlabeled training samples to perform the joint learning of neighborhood preserving local nonlinear manifold features and a linear feature extractor. The formulation of SSMM-Isomap aims at minimizing pairwise distances of intra-class points in the same manifold and maximizing the distances over different manifolds. To enhance the performance of nonlinear manifold feature learning, we also incorporate the neighborhood reconstruction error to preserve local topology structures between both labeled and unlabeled samples. To enable our SSMM-Isomap to extract local manifold features from the outside new data, we also add a feature approximation error that correlates manifold features with embedded features by the jointly learnt feature extractor. Thus, the learnt linear extractor can extract the local manifold features from the new data efficiently by direct embedding. To optimize the proposed objective function, two effective schemes are presented, i.e., Scaling by MAjorizing a Complicated Function and Eigen-decomposition. Notice that the comparison of the proposed two solvers is also described. We mainly evaluate SSMM-Isomap for manifold feature learning, data clustering and classification. Extensive simulation results verify the effectiveness of our SSMM-Isomap algorithm, compared with other related feature learning techniques.

Graph embedding and extensions: a general framework for dimensionality reduction.

Over the past few decades, a large family of algorithms - supervised or unsupervised; stemming from statistics or geometry theory - has been designed to provide different solutions to the problem of dimensionality reduction. Despite the different motivations of these algorithms, we present in this paper a general formulation known as graph embedding to unify them within a common framework. In graph embedding, each algorithm can be considered as the direct graph embedding or its linear/kernel/tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set, with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided. Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms. By utilizing this framework as a tool, we propose a new supervised dimensionality reduction algorithm called Marginal Fisher Analysis in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. We show that MFA effectively overcomes the limitations of the traditional Linear Discriminant Analysis algorithm due to data distribution assumptions and available projection directions. Real face recognition experiments show the superiority of our proposed MFA in comparison to LDA, also for corresponding kernel and tensor extensions.