Synthetic Micro Research Articles

Identification method for micro–nano defects in Si3N4 ceramic bearing rollers

In response to the surface micro–nano defects of small Si3N4 ceramic bearing rollers during use, as well as the poor generalization ability and low accuracy of the object detection model trained on the micro and nano defect dataset, an enhanced recognition algorithm based on deep convolutional generative adversarial networks is proposed. Due to the limited size of the dataset, the DCGAN model is reconstructed to effectively expand the micro–nano defect dataset. In addition, noise generalization is applied to stabilize DCGAN model training, creating a low-dimensional manifold distribution to ensure significant overlap between the data and the original dataset, and activating Jensen–Shannon (JS) divergence for stable training. To verify the effectiveness of the enhanced dataset, synthetic micro–nano defects are used to improve the YOLO-v4-tiny object detection model. By comparing t-distributed stochastic neighbor embedding (t-SNE) and feature vectors, it can be found that the images generated by the optimized DCGAN have higher grayscale feature diversity and better visual consistency. After generations of enhancements, the micro–nano defect detection speed has reached 226FPS, and the accuracy has reached 97.41%.

Hydrovoltaic Materials and Devices: From Synthetic Materials, Macroscopic Biomaterials, to Micro Biomaterials

Much progress has been made in hydrovoltaic materials and devices. However, the power conversion efficiency of hydrovoltaic devices is still low. One of the main factors that determine the efficiency is the properties of hydrovoltaic materials. Herein, the research status of hydrovoltaic materials is summarized from the perspectives of synthetic materials, macroscopic biomaterials, and micro biomaterials, as well as the hydrovoltaic devices based on these materials.

Preparation of synthetic micro- and nano plastics for method validation studies

Microplastic (MP) pollution is a persisting global problem. Accurate analysis is essential in quantifying the effects of microplastic pollution and develop novel technologies that reliably and reproducibly measure microplastic content in various samples. The most common methods for this are FTIR and Raman spectroscopy. Coloured, standardized beads are often used for method validation tests, which limits the conclusions to a very specific case rarely observed in the natural environment. This study focuses on the preparation of reference micro- and nanoplastics via cryogenic milling and shows their use for FTIR and Raman method validation studies. MPs can now be reproducibly milled from various plastics, offering the advantages of a better representation of MPs in real environment. Moreover, this study highlights issues with the current detection methods, up to now considered as the most reliable ones for MP detection and identification. Such issues, e.g. misidentification, will need to be addressed in the future. Additionally, milled MPs were used in experiments with commercial high-resolution imaging device, enabling a possible in-situ optical detection of microplastics. These experiments represent a step forward in understanding MPs in a water sample and provide a basis for a more accurate detection and identification directly from water, which would considerably reduce the time of analysis.

The biote-bot hybrid. The ultimate biothreat merging nanobots, AI-enabled cybernetics and synthetic biology

The paper intends to warn stakeholders, by using open sources, of the possibility of extremely small, nano-/pico-aerial vehicles controlled locally or remotely by artificial intelligence mindsets to deliver, on specific hosts and tissues, either diverse bioagents produced by conventional and synthetic (micro)biology, including xenobiota or bionic microbiota or existing microbiota selected from natural reservoirs. The accuracy in delivery would leverage minute quantities of pathogens to cause mass-scale bioevents. Such hybrids (biote-bots) would increase the effectiveness of unfit but virulent pathogens, preserve the carried biota for the trip and contain bioagents' weaponization footprint to levels below the detection threshold of current regimes, while complicating immune response and denying pre-infection detection and identification. To respond, we suggest that novel diagnostics and surveillance amenities are needed, prompting cooperation of experts from Medicine, medical instruments/diagnostics, artificial Intelligence and from disciplines tackling cybernetics, remote sensing, surveying and tracking.

Detection of Antibodies against Endemic and SARS-CoV-2 Coronaviruses with Short Peptide Epitopes.

(1) Background: Coronavirus proteins are quite conserved amongst endemic strains (eCoV) and SARS-CoV-2. We aimed to evaluate whether peptide epitopes might serve as useful diagnostic biomarkers to stratify previous infections and COVID-19. (2) Methods: Peptide epitopes were identified at an amino acid resolution that applied a novel statistical approach to generate data sets of potential antibody binding peptides. (3) Results: Data sets from more than 120 COVID-19 or eCoV-infected patients, as well as vaccinated persons, have been used to generate data sets that have been used to search in silico for potential epitopes in proteins of SARS-CoV-2 and eCoV. Peptide epitopes were validated with >300 serum samples in synthetic peptide micro arrays and epitopes specific for different viruses, in addition to the identified cross reactive epitopes. (4) Conclusions: Most patients develop antibodies against non-structural proteins, which are useful general markers for recent infections. However, there are differences in the epitope patterns of COVID-19, and eCoV, and the S-protein vaccine, which can only be explained by a high degree of cross-reactivity between the viruses, a pre-existing immune response against some epitopes, and even an alternate processing of the vaccine proteins.

Optical Manipulation Heats up: Present and Future of Optothermal Manipulation.

Optothermal manipulation is a versatile technique that combines optical and thermal forces to control synthetic micro-/nanoparticles and biological entities. This emerging technique overcomes the limitations of traditional optical tweezers, including high laser power, photon and thermal damage to fragile objects, and the requirement of refractive-index contrast between target objects and the surrounding solvents. In this perspective, we discuss how the rich opto-thermo-fluidic multiphysics leads to a variety of working mechanisms and modes of optothermal manipulation in both liquid and solid media, underpinning a broad range of applications in biology, nanotechnology, and robotics. Moreover, we highlight current experimental and modeling challenges in the pursuit of optothermal manipulation and propose future directions and solutions to the challenges.

Calculating porosity and permeability from synthetic micro‐CT scan images based on a hybrid artificial intelligence

AbstractNowadays, lattice Boltzmann is one of the standard and exact methods of simulation in micro‐CT images of rock. However, it has a high weakness in run time. Therefore, the effort in this article is to reach a comprehensive substitute method for permeability calculation with less run time than the lattice Boltzmann method. The other purposes are the automation of processing operations, preparation of images, and in the end, the calculation of porosity. The best way to achieve these outcomes is to use hybrid artificial intelligence. In this research work, comprehensive model architecture has been used to design a hybrid artificial intelligence to be able to calculate permeability and porosity in complex images. A thousand images were randomly generated with high complexity, which makes the model comprehensive and extensible, and image processing was applied. After that, the lattice Boltzmann method as the direct simulation was selected. Finally, the convolutional neural network and multilayer perceptron based on a new and comprehensive model were evaluated for the first time; the mean squared error resulting from the evaluation of training data is 0.01, and the test data is 0.03. Expert systems have been used as a subset of artificial intelligence for automated image processing and porosity calculation. In this way, problems related to the direct implementation of classical algorithms for image processing, models, and patterns related to machine learning and needing an expert were solved to an acceptable extent, and an error of less than 5% was achieved.

Breaking free: endocytosis and endosomal escape of extracellular vesicles

Extracellular vesicles (EVs) are natural micro-/nanoparticles that play an important role in intercellular communication. They are secreted by producer/donor cells and subsequent uptake by recipient/acceptor cells may result in phenotypic changes in these cells due to the delivery of cargo molecules, including lipids, RNA, and proteins. The process of endocytosis is widely described as the main mechanism responsible for cellular uptake of EVs, with endosomal escape of cargo molecules being a necessity for the functional delivery of EV cargo. Equivalent to synthetic micro-/nanoparticles, the properties of EVs, such as size and composition, together with environmental factors such as temperature, pH, and extracellular fluid composition, codetermine the interactions of EVs with cells, from binding to uptake, intracellular trafficking, and cargo release. Innovative assays for detection and quantification of the different steps in the EV formation and EV-mediated cargo delivery process have provided valuable insight into the biogenesis and cellular processing of EVs and their cargo, revealing the occurrence of EV recycling and degradation, next to functional cargo delivery, with the back fusion of the EV with the endosomal membrane standing out as a common cargo release pathway. In view of the significant potential for developing EVs as drug delivery systems, this review discusses the interaction of EVs with biological membranes en route to cargo delivery, highlighting the reported techniques for studying EV internalization and intracellular trafficking, EV-membrane fusion, endosomal permeabilization, and cargo delivery, including functional delivery of RNA cargo.

Multicompartment calcium alginate microreactors to reduce substrate inhibition in enzyme cascade reactions.

The formation of macromolecularly enriched condensates through associative or segregative liquid-liquid phase separation phenomena is known to play a central role in controlling various cellular functions in nature. The potential to spatially and temporally modulate multistep chemical reactions and pathways has inspired the use of phase-separated systems for the development of various synthetic colloidal micro- and nanoreactor systems. Here, we report a rational and synthetically minimal design strategy to emulate intended spatiotemporal functions in morphologically intricate and structurally defined calcium alginate hydrogel microreactors possessing multicompartmentalized internal architectures. Specifically, we implement a thermal phase separation protocol to achieve fine-control over liquid-liquid phase separation inside complex aqueous emulsion droplet templates that are loaded with hydrophilic polymer mixtures. Subsequent gelation of alginate-containing droplet templates using a novel freeze-thaw approach that can be applied to both scalable batch production or more precise microfluidic methods yields particle replicas, in which subcompartmentalized architectures can be retained. Larger active components can be enriched in the internal compartments due to their preferential solubility, and we show that selective sequestration of enzymes serves to create desired microenvironments to control and tune the reaction kinetics of a multistep enzyme cascade by reducing their mutual interference. This demonstration of mitigating substrate inhibition that is based primarily on optimizing the multicompartmentalized hydrogel particle morphology offers new opportunities for the simple and synthetically-minimal batch generation of hydrogel-based synthesis microreactors.

Long-Term Vaccine Delivery and Immunological Responses Using Biodegradable Polymer-Based Carriers.

Biodegradable polymers are largely employed in the biomedical field, ranging from tissue regeneration to drug/vaccine delivery. The biodegradable polymers are highly biocompatible and possess negligible toxicity. In addition, biomaterial-based vaccines possess adjuvant properties, thereby enhancing immune responses. This Review introduces the use of different biodegradable polymers and their degradation mechanism. Different kinds of vaccines, as well as the interaction between the carriers with the immune system, then are highlighted. Natural and synthetic biodegradable micro-/nanoplatforms, hydrogels, and scaffolds for local or targeted and controlled vaccine release are subsequently discussed.

Layer-by-Layer Delivery of Multiple Antigens Using Trimethyl Chitosan Nanoparticles as a Malaria Vaccine Candidate.

Developing a safe and effective malaria vaccine is critical to reducing the spread and resurgence of this deadly disease, especially in children. In recent years, vaccine technology has seen expanded development of subunit protein, peptide, and nucleic acid vaccines. This is due to their inherent safety, the ability to tailor their immune response, simple storage requirements, easier production, and lower expense compared to using attenuated and inactivated organism-based approaches. However, these new vaccine technologies generally have low efficacy. Subunit vaccines, due to their weak immunogenicity, often necessitate advanced delivery vectors and/or the use of adjuvants. A new area of vaccine development involves design of synthetic micro- and nano-particles and adjuvants that can stimulate immune cells directly through their physical and chemical properties. Further, the unique and complex life cycle of the Plasmodium organism, with multiple stages and varying epitopes/antigens presented by the parasite, is another challenge for malaria vaccine development. Targeting multistage antigens simultaneously is therefore critical for an effective malaria vaccine. Here, we rationally design a layer-by-layer (LbL) antigen delivery platform (we called LbL NP) specifically engineered for malaria vaccines. A biocompatible modified chitosan nanoparticle (trimethyl chitosan, TMC) was synthesized and utilized for LbL loading and release of multiple malaria antigens from pre-erythrocytic and erythrocytic stages. LbL NP served as antigen/protein delivery vehicles and were demonstrated to induce the highest Plasmodium falciparum Circumsporozoite Protein (PfCSP) specific T-cell responses in mice studies as compared to multiple controls. From immunogenicity studies, it was concluded that two doses of intramuscular injection with a longer interval (4 weeks) than traditional malaria vaccine candidate dosing would be the vaccination potential for LbL NP vaccine candidates. Furthermore, in PfCSP/Py parasite challenge studies we demonstrated protective efficacy using LbL NP. These LbL NP provided a significant adjuvant effect since they may induce innate immune response that led to a potent adaptive immunity to mediate non-specific anti-malarial effect. Most importantly, the delivery of CSP full-length protein stimulated long-lasting protective immune responses even after the booster immunization 4 weeks later in mice.

The Influence of Bi2O3 Nanoparticle Content on the γ-ray Interaction Parameters of Silicon Rubber.

In this study, synthetic silicone rubber (SR) and Bi2O3 micro- and nanoparticles were purchased. The percentages for both sizes of Bi2O3 were 10, 20 and 30 wt% as fillers. The morphological, mechanical and shielding properties were determined for all the prepared samples. The Linear Attenuation Coefficient (LAC) values of the silicon rubber (SR) without Bi2O3 and with 5, 10, 30 and 30% Bi2O3 (in micro and nano sizes) were experimentally measured using different radioactive point sources in the energy range varying from 0.06 to 1.333 MeV. Additionally, we theoretically calculated the LAC for SR with micro-Bi2O3 using XCOM software. A good agreement was noticed between the two methods. The NaI (Tl) scintillation detector and four radioactive point sources (Am-241, Ba-133, Cs-137 and Co-60) were used in the measurements. Other shielding parameters were calculated for the prepared samples, such as the Half Value Layer (HVL), Mean Free Path (MFP) and Radiation Protection Efficiency (RPE), all of which proved that adding nano-Bi2O3 ratios of SR produces higher shielding efficiency than its micro counterpart.

Collective Behavior of Urease pH Clocks in Nano- and Microvesicles Controlled by Fast Ammonia Transport.

The transmission of chemical signals via an extracellular solution plays a vital role in collective behavior in cellular biological systems and may be exploited in applications of lipid vesicles such as drug delivery. Here, we investigated chemical communication in synthetic micro- and nanovesicles containing urease in a solution of urea and acid. We combined experiments with simulations to demonstrate that the fast transport of ammonia to the external solution governs the pH–time profile and synchronizes the timing of the pH clock reaction in a heterogeneous population of vesicles. This study shows how the rate of production and emission of a small basic product controls pH changes in active vesicles with a distribution of sizes and enzyme amounts, which may be useful in bioreactor or healthcare applications.

Performance Investigation of a Copper Azide Micro Detonator Using Experiments and Simulations

AbstractIn this paper, to study the output capacity of the micro detonator in MEMS flyer initiation sequence, we studied the driving Ti flyer process of micro detonator filled with “in‐situ” synthetic copper azide micro charge by combining experimental test, thermodynamic calculation, and hydrodynamic simulation. On the one hand, the flight process of the flyer is tested by an all‐fiber photonic Doppler velocimeter (AFPDV), and a complete flyer velocity curve is obtained. On this basis, the effects of different micro charge thickness, micro charge density, and accelerating chamber length on the flight process of the flyer are studied and analyzed. Thus, valuable experimental data are provided for the research of related micro detonators. On the other hand, we study the simulation of the copper azide micro charge driving flyer process. Firstly, we use the detonation thermodynamic program to calculate and fit the detonation velocity (D), detonation CJ pressure (PCJ), and JWL equation of state (EOS) parameters of detonation products of copper azide for simulation. For the gaseous detonation product, we compared the effects of three virial EOS: VLW EOS, VHL EOS, and VPL EOS on the calculation values of detonation parameters for copper azide. For the solid detonation product, a new EOS that can accurately describe the high temperature and high‐pressure state of copper was introduced to the program. Using the detonation parameters of copper azide calculated by thermodynamics, the process of driving the Ti flyer by micro charge is simulated by the 2D ALE method and compared with the experimental data under corresponding conditions.

A synthetic population dataset for estimating small area health and socio-economic outcomes in Great Britain

In order to understand the health outcomes for distinct sub-groups of the population or across different geographies, it is advantageous to be able to build bespoke groupings from individual level data. Individuals possess distinct characteristics, exhibit distinct behaviours and accumulate their own unique history of exposure or experiences. However, in most disciplines, not least public health, there is a lack of individual level data available outside of secure settings, especially covering large portions of the population. This paper provides detail on the creation of a synthetic micro dataset for individuals in Great Britain who have detailed attributes which can be used to model a wide range of health and other outcomes. These attributes are constructed from a range of sources including the United Kingdom Census, survey and administrative datasets. It provides a rationale for the need for this synthetic population, discusses methods for creating this dataset and provides some example results of different attribute distributions for distinct sub-population groups and over different geographical areas.

Neural Cell Membrane-Coated Nanoparticles for Targeted and Enhanced Uptake by Central Nervous System Cells.

Targeted drug delivery to specific neural cells within the central nervous system (CNS) plays important roles in treating neurological disorders, such as neurodegenerative (e.g., targeting neurons) and demyelinating diseases [e.g., targeting oligodendrocytes (OLs)]. However, the presence of many other cell types within the CNS, such as microglial and astrocytes, may lead to nonspecific uptake and subsequent side effects. As such, exploring an effective and targeted drug delivery system is of great necessity. Synthetic micro-/nanoparticles that have been coated with biologically derived cellular membranes have emerged as a new class of drug delivery vehicles. However, the use of neural cell-derived membrane coatings remains unexplored. Here, we utilized this technique and demonstrated the efficacy of targeted delivery by using four types of cell membranes that were derived from the CNS, namely, microglial, astrocytes, oligodendrocyte progenitor cells (OPCs), and cortical neurons. A successful cell membrane coating over poly(ε-caprolactone) nanoparticles (NPs) was confirmed using dynamic light scattering, zeta potential measurements, and transmission electron microscopy. Subsequently, an extensive screening of these cell membrane-coated NPs was carried out on various CNS cells. Results suggested that microglial and OLs were the most sensitive cell types toward cell membrane-coated NPs. Specifically, cell membrane-coated NPs significantly enhanced the uptake efficiency of OLs (p < 0.001). Additionally, a temporal uptake study indicated that the OLs took up microglial membrane-coated NPs (DPP-PCL-M Mem) most efficiently. Besides that, coating the NPs with four types of the CNS cell membrane did not result in obvious specific uptake in microglial but reduced the activation of microglial, especially for DPP-PCL-M Mem (p < 0.01). Taken together, DPP-PCL-M Mem were uptaken most efficiently in OLs and did not induce significant microglial activation and may be most suitable for CNS drug delivery applications.

Эффективность дисперсного полиармирования бетона низкомодульными волокнами

The article describes the results of experimental studies of the effect of low-modulus synthetic micro- and macro-fibers, taken separately and in combinations, on strength and durability of fiber-reinforced concrete. On the basis of the obtained data in fiber-reinforced concrete specimens testing for tension in bending, there were constructed diagrams of the deformation, according to which the influence of individual types of fibers and their combinations during hybrid reinforcement on the type of the destruction of composites was determined, the values of their power and energy characteristics were calculated. The effective ratios between the fibers during hybrid fiber-reinforcement have been determined, which makes it possible to obtain fiber-reinforced concrete with high strength and durability indicators.

A case study on homogeneous and heterogeneous reservoir porous media reconstruction by using generative adversarial networks

The advancing of modern X-ray computer tomography technology provides a powerful tool for us to illustrate the details inside the reservoir rock in three-dimensional space. Pore-scale rock characterization, modeling, and related fluid flow simulation can be challenging due to the high complexity of various rock samples. Conventional pore scale structure modeling methods such as various stochastic methods were developed for reservoir rock 3D microscopic structure reconstruction in order to generate representative realizations for numerical simulations and property upscaling approaches. In this work, generative adversarial networks (GANs) is used for generating the synthetic micro representations of porous rock by acquiring non-linear statistical information from the real 3D rock images in an unsupervised learning scheme. The related 3D image pre-processing, network training and adjusting as well as data post-processing procedures are addressed. The network prediction results from a homogeneous Berea sandstone and a heterogeneous Estaillades carbonate demonstrated the capability of GANs for high-resolution porous rock image representations reconstruction, generated and real images are compared via various visualizations and inspections. The study also illustrated the importance of the training image preprocessing, which indicating the data augmentation techniques can be one of the promising improvements in terms of capturing the sparsely distributed features from heterogenous 3D images and reconstructing the synthetic realizations, meanwhile, the robustness of the model during training process is enhanced when limited real data is available.

Collective dynamics of self-propelled nanomotors in chemically oscillating media

The collective behavior of synthetic micro- and nano-scale motors powered by catalytic reactions has been an active research area for understanding the fundamental principles in active matter as well as their prominent applications. In many situations, small motors should operate in out-of-equilibrium complex chemically reacting media. By making use of particle-based simulations, we study the collective dynamics of chemically powered sphere-dimer motors in a chemically oscillating medium which can supply fuels to the motors and remove the products they produce. In collections of such motors, the interactions among individual motors that arise from concentration gradients and hydrodynamic coupling, are greatly influenced by the properties of the oscillation. It is shown how oscillations of the concentrations in chemical species in the environment give rise to a periodic dispersion-aggregation transition of motor clusters. The susceptibility of the transition to the oscillation is found to be changed by the manner of fuel supplies, from the time-delay response in a harmonic oscillating medium to the synchronous response in a bistable medium. The dynamical process of clusters formation, which exhibits different regimes involving propulsive and diffusive behaviors, as well as the structure of transiently formed clusters, is analyzed. The dependence of dispersion-aggregation transition on the dimer density and the frequency of periodic oscillation is presented in a phase diagram. The results presented here will contribute to the applications when an ensemble of dimers performs their tasks in complex chemical media.

Autonomous flight with robust visual odometry under dynamic lighting conditions

Sensitivity to light conditions poses a challenge when utilizing visual odometry (VO) for autonomous navigation of small aerial vehicles in various applications. We present an illumination-robust direct visual odometry for a stable autonomous flight of an aerial robot under unpredictable light condition. The proposed stereo VO achieves robustness with respect to the light-changing environment by employing the patch-based affine illumination model to compensate abrupt, irregular illumination changes during direct motion estimation. We furthermore incorporate a motion prior from feature-based stereo visual odometry in the optimization, resulting in higher accuracy and more stable motion estimate. Thorough analyses of convergence rate and linearity index for the feature-based and direct VO methods support the effectiveness of the usage of the motion prior knowledge. We extensively evaluate the proposed algorithm on synthetic and real micro aerial vehicle datasets with ground-truth. Autonomous flight experiments with an aerial robot show that the proposed method successfully estimates 6-DoF pose under significant illumination changes.