An Operator Product Expansion for Form Factors II. Born level

Form factors in planar $$ \mathcal{N} $$ N = 4 Super-Yang-Mills theory admit a type of non-perturbative operator product expansion (OPE), as we have recently shown in [1]. This expansion is based on a decomposition of the dual periodic Wilson loop into elementary building blocks: the known pentagon transitions and a new object that we call form factor transition, which encodes the information about the local operator. In this paper, we compute the two-particle form factor transitions for the chiral part of the stress-tensor supermultiplet at Born level; they yield the leading contribution to the OPE. To achieve this, we explicitly construct the Gubser-Klebanov-Polyakov two-particle singlet states. The resulting transitions are then used to test the OPE against known perturbative data and to make higher-loop predictions.

Lüscher-corrections for 1-particle form-factors in non-diagonally scattering integrable quantum field theories

In this paper we derive from field theory a Lüscher-formula, which gives the leading exponentially small in volume corrections to the 1-particle form-factors in non-diagonally scattering integrable quantum field theories. Our final formula is expressed in terms of appropriate expressions of 1- and 3-particle form-factors, and can be considered as the generalization of previous results obtained for diagonally scattering bosonic integrable quantum field theories. Since our formulas are also valid for fermions and operators with non-zero Lorentz-spin, we demonstrated our results in the Massive Thirring Model, and checked our formula against 1-loop perturbation theory finding perfect agreement.

Branch point twist field form factors in the sine-Gordon model I: Breather fusion and entanglement dynamics

The quantum sine-Gordon model is the simplest massive interacting integrable quantum field theory whose two-particle scattering matrix is generally non-diagonal. As such, it is a model that has been extensively studied, especially in the context of the bootstrap program. In this paper we compute low particle-number form factors of a special local field known as the branch point twist field, whose correlation functions are building blocks for measures of entanglement. We consider the attractive regime where the theory possesses a particle spectrum consisting of a soliton, an antisoliton (of opposite U(1) charges) and several (neutral) breathers. In the breather sector we exploit the fusion procedure to compute form factors of heavier breathers from those of lighter ones. We apply our results to the study of the entanglement dynamics after a small mass quench and for short times. We show that in the presence of two or more breathers the von Neumann and Rényi entropies display undamped oscillations in time, whose frequencies are proportional to the even breather masses and whose amplitudes are proportional to the breather's one-particle form factor.

Antimicrobial Effect of Additive Silver Nanoparticles to Paints for Reducing the Risk of Cross-Contamination

Paints are mainly used to protect metal structures from rusting and object from adverse effects of weather and sun, in addition to decoration. Most paints are either oil-based or water-based and both have distinct advantages. It can be applied as a solid, a gaseous suspension (aerosol) or a liquid. The increasing demand for new antimicrobial paints is rising recently due to the important need to avoid the spreading of infections mainly caused by harmful microorganisms. The antimicrobial additive can be defined as the additive compound that can resist or prevents the growth of harmful microbes. In this connection, a number of critical factors should be considered in selecting the additive antimicrobials to paints. These factors include safe from adverse impacts on human health and environment, antimicrobial efficiency, achieve a broad spectrum of microbial control, low percentage of the antimicrobial additive, ease of handling, fast and long-acting, migration capability, chemical stability, cost-effective and maintaining the properties of the product and its components. In the case of edible coatings which provide a unique opportunity to control microbial and oxidative changes in human ready-to-use food products, suitable safe materials and active agents for different cases should be applied. To make the traditional paints resistant to pathogenic microorganisms, it is of importance to include several antimicrobial additives, such as silver and zinc ions during the manufacturing process. Silver is a widely used technology in the world, especially in its nano-particle form due to its suitability for deployment in a broad range of materials and applications and its broad spectrum performance. This durable treatment will provide to a large extent effective protection against harmful fungi, bacteria, viruses and consequently helping to minimize staining and material degradation on any surface it is applied to. These antimicrobial paints (APs) can be used in places that harbor pathogenic microorganisms such as hospitals, schools, care homes, kitchen areas, dental and veterinary practices and food production factories. In these places, APs can be applied to contact surfaces within these environments, such as door handles, light switches, flooring, elevator buttons, and bathroom in order to reduce the risk of cross-contamination.

Comparative Study of the Antimicrobial Activity of Selenium Nanoparticles With Different Surface Chemistry and Structure

Although selenium nanoparticles (SeNPs) have gained attention in the scientific community mostly through investigation of their anticancer activity, a great potential of this nanomaterial was recognized recently regarding its antimicrobial activity. The particle form, size, and surface chemistry have been recognized as crucial parameters determining the interaction of nanomaterials with biological entities. Furthermore, considering a narrow boundary between beneficial and toxic effects for selenium per se, it is clear that investigations of biomedical applications of SeNPs are very demanding and must be done with great precautions. The goal of this work is to evaluate the effects of SeNPs surface chemistry and structure on antimicrobial activity against several common bacterial strains, including Staphylococcus aureus (ATCC 6538), Enterococcus faecalis (ATCC 29212), Bacillus subtilis (ATCC 6633), and Kocuria rhizophila (ATCC 9341), as well as Escherichia coli (ATCC 8739), Salmonella Abony (NCTC 6017), Klebsiella pneumoniae (NCIMB 9111) and Pseudomonas aeruginosa (ATCC 9027), and the standard yeast strain Candida albicans (ATCC 10231). Three types of SeNPs were synthesized by chemical reduction approach using different stabilizers and reducing agents: (i) bovine serum albumin (BSA) + ascorbic acid, (ii) chitosan + ascorbic acid, and (iii) with glucose. A thorough physicochemical characterization of the obtained SeNPs was performed to determine the effects of varying synthesis parameters on their morphology, size, structure, and surface chemistry. All SeNPs were amorphous, with spherical morphology and size in the range 70–300 nm. However, the SeNPs obtained under different synthesis conditions, i.e. by using different stabilizers as well as reducing agents, exhibited different antimicrobial activity as well as cytotoxicity which are crucial for their applications. In this paper, the antimicrobial screening of the selected systems is presented, which was determined by the broth microdilution method, and inhibitory influence on the production of monomicrobial and dual-species biofilm was evaluated. The potential mechanism of action of different systems is proposed. Additionally, the cytotoxicity of SeNPs was examined on the MRC-5 cell line, in the same concentration interval as for antimicrobial testing. It was shown that formulation SeNPs-BSA expressed a significantly lower cytotoxic effect than the other two formulations.

Quantifying Nanoparticle Assembly States in Polymer Matrix Through Deep Learning

<p>There is great interest in controlling the spatial dispersion of inorganic nanoparticles (NPs) in an organic polymer matrix, because this centrally underpins the property enhancements obtained from these hybrid materials. Currently, qualitative information on NP spatial distribution is obtained by visual inspection of transmission electron microscopy (TEM) images. Quantitative information is only indirectly obtained through the use of scattering probes such as small angle X-ray/neutron scattering. While the main challenge, that scattering probes operate in reciprocal space, can be remedied by Fourier inverting the data into real space, a much harder issue is deconvolving the contribution of the particle form factor (which is affected by the details of the NP size and shape) from the structure factor which contains information on the NP spatial distribution. These problems become acute when we deal with the popular topic of NPs grafted with polymer chains, because the polymeric corona, and hence the particle form factor, becomes context dependent and hard to quantify. To make progress, we develop and apply a deep-learning based image analysis method to quantify the distribution of spherical NPs in a polymer matrix directly from their real-space TEM images. A dataset of NP detection (DOPAD) is built by manually labeling particle positions on experimental TEM images of diverse polymer composite systems. A convolutional neural network (CNN) object detection model is then trained on DOPAD. Together with sliding-window and merging algorithms, an automated pipeline is established, which takes a large TEM image as input and extracts NP locations and sizes. We validate the structural information resulting from this method against SAXS derived structural information for NPs ordered by polymer crystallization, and then use it to distinguish between different states of the assembly of polymer grafted NPs in a polymer matrix achieved by using their surfactancy. We show that this data-rich protocol allows us to draw critical facets of experimental behavior which have previously not been accessible. The DOPAD dataset, Python source code and trained model are shared on GitHub.</p>

Quantifying Nanoparticle Assembly States in Polymer Matrix Through Deep Learning

<p>There is great interest in controlling the spatial dispersion of inorganic nanoparticles (NPs) in an organic polymer matrix, because this centrally underpins the property enhancements obtained from these hybrid materials. Currently, qualitative information on NP spatial distribution is obtained by visual inspection of transmission electron microscopy (TEM) images. Quantitative information is only indirectly obtained through the use of scattering probes such as small angle X-ray/neutron scattering. While the main challenge, that scattering probes operate in reciprocal space, can be remedied by Fourier inverting the data into real space, a much harder issue is deconvolving the contribution of the particle form factor (which is affected by the details of the NP size and shape) from the structure factor which contains information on the NP spatial distribution. These problems become acute when we deal with the popular topic of NPs grafted with polymer chains, because the polymeric corona, and hence the particle form factor, becomes context dependent and hard to quantify. To make progress, we develop and apply a deep-learning based image analysis method to quantify the distribution of spherical NPs in a polymer matrix directly from their real-space TEM images. A dataset of NP detection (DOPAD) is built by manually labeling particle positions on experimental TEM images of diverse polymer composite systems. A convolutional neural network (CNN) object detection model is then trained on DOPAD. Together with sliding-window and merging algorithms, an automated pipeline is established, which takes a large TEM image as input and extracts NP locations and sizes. We validate the structural information resulting from this method against SAXS derived structural information for NPs ordered by polymer crystallization, and then use it to distinguish between different states of the assembly of polymer grafted NPs in a polymer matrix achieved by using their surfactancy. We show that this data-rich protocol allows us to draw critical facets of experimental behavior which have previously not been accessible. The DOPAD dataset, Python source code and trained model are shared on GitHub.</p>

Towards the use of acrylic acid graft-copolymerized plant biofiber in sustainable fortified composites: Manufacturing and characterization

In this study, the impact of particle form of the Cannabis indica plant biofibers and the fiber’s surface tailoring on the physical, thermal, dielectric, and mechanical properties of unsaturated polyester composite specimens manufactured utilizing nonconventional materials were investigated. The mechanical properties such as compressive, flexural, and tensile strengths of the composite specimens were noticed to increase after functionalization of biofiber with acrylic acid and maximum enhancement was found at 20% of biofiber sacking. The physical characterization was concentrated on the assurance of the dielectric constant, dielectric strength, dielectric loss, moisture absorption, chemical resistance, percentage of swelling, limiting oxygen index, and biodegradation of polymer composites under red soil. An increase in dielectric strength from 28 to 29 kV, limiting oxygen index values from 19% to 23%, and moisture/water absorption behavior was noted for resulted bio-composites after surface tailoring of biofiber. To assess the deterioration of the polymeric materials with the temperature, differential scanning calorimetric and the thermogravimetric tests were carried out and enhancement in thermal stability was noted after fortification of polyester composites with functionalized biofiber.

The formation mechanism of authigenic chlorite in tight sandstone and its effect on tight oil adsorption during hydrocarbon filling

Authigenic chlorite, which is frequently found in sandstone, has a controlling effect on the reservoirs in which tight oil is adsorbed during hydrocarbon filling. In this study, the content, occurrence state, timing, mechanism and influence of authigenic chlorite on the micro-occurrence states of tight oil were studied using Thin Section (TS), Fluorescent Thin Section (FTS), X-Ray Diffraction (XRD), Field Emission-Scanning Electron Microscopy (FE-SEM), Environmental Scanning Electron Microscopy (ESEM), and Energy Dispersive Spectroscopy (EDS). The results indicate: (1) a spatial coupling between chlorite development, a brackish water delta front facies depositional environment, and biotite-rich arkosic sandstone. (2) Authigenic chlorite can be divided into three types: grain-coating chlorite, pore-lining chlorite, and rosette chlorite. Chlorite forms after early compaction but before other diagenetic phases, and grows via precipitation from pore waters that contain products released during the dissolution of volcanic rock fragments and biotites. Porewater is also pressure-released from feldspars and mudstone. (3) The micro-occurrence states of tight oil can be divided into five types: emulsion form, cluster form, throat form, thin-film form, and the isolated or agglomerated particle form. (4) During hydrocarbon filling, tight oil mainly occurs on the surface of grain-coating and pore-lining chlorite in the form of a thin film, the granular or agglomerated forms are mainly enriched within the intercrystalline pores within the authigenic chlorite, and the cluster forms are mainly enriched in dissolution pores. Isolated or agglomerated particles of tight oil primarily occur in the intercrystalline pores of the rosette chlorite. (5) The specific surface area and the authigenic chlorite’s adsorption potential of authigenic chlorite control the micro-occurrence of tight oil on the surface of the chlorite and in intercrystalline pores. The adsorption capacity of chlorite lies in the following order: pore-lining chlorite intercrystalline pores > rosette chlorite > chlorite in feldspar dissolution pores > pore-lining chlorite surface > grain-coating chlorite intercrystalline pores > grain-coating chlorite surface.