Modeling self-organization of thin strained metallic overlayers from atomic to micron scales

A computational study of the self-organization of heteroepitaxial ultrathin metal films is presented. By means of a continuum complex field model, the relationship of the equilibrium surface patterns of the film to the adsorbate-substrate adhesion energy, as well as to the mismatch between the adsorbate and the substrate bulk lattice parameters, are obtained in both the tensile and the compressive regimes. Our approach captures pattern periodicities over large length scales, up to several hundreds of nm, retaining atomistic resolution. Thus, the results can be directly compared with experimental data, in particular for systems such as Cu/Ru(0001) and Ag/Cu(111). Three nontrivial, stable superstructures for the overlayer, namely, stripe, honeycomb, and triangular, are identified that closely resemble those observed experimentally. Simulations in nonequilibrium conditions are performed as well to identify metastable structural configurations and the dynamics of ordering of the overlayer.Peer reviewe

Patterning of Heteroepitaxial Overlayers from Nano to Micron Scales

Thin heteroepitaxial overlayers have been proposed as templates to generate stable, self-organized nanostructures at large length scales, with a variety of important technological applications. However, modeling strain-driven self-organization is a formidable challenge due to different length scales involved. In this Letter, we present a method for predicting the patterning of ultrathin films on micron length scales with atomic resolution. We make quantitative predictions for the type of superstructures (stripes, honeycomb, triangular) and length scale of pattern formation of two metal-metal systems, Cu on Ru(0001) and Cu on Pd(111). Our findings are in excellent agreement with previous experiments and call for future experimental investigations of such systems.Peer reviewe

Development of methodologies for virus detection in soybean and wheat seeds.

Seeds that contain large amounts of oil, starch, fibers and phenols are the most difficult tissues for RNA extraction. Currently, there are some reports of virus detection in seeds using commercial kits for RNA extraction. However, individual seeds were used, which may not be always suitable for analyses that deal with large amounts of seeds. Sangha [1] described a simple, quick and efficient protocol for RNA extraction and downstream applications in a group of seeds of jatropha (Jatropha curcas), mustard (Brassica sp.) and rice (Oryza sativa). We tested this protocol for soybean (Glycine max), maize (Zea mays), wheat (Triticum aestivum) and triticale (×Triticosecale) seeds and further reverse transcription PCR (RT-PCR)/quantitative real-time PCR (qPCR) in order to have a faster and more practical method for virus detection from seeds than the traditional scheme of seed planting and subsequent Elisa/RT-PCR from leaves. The essential points in the method are: • Some modifications in the protocol [1] were done in order to increase performance: Wheat and triticale seeds are incubated with water prior to maceration. An amount of 1.2 g of dry soybean seeds is used to maceration. • RT-PCR is used for detection of Wheat streak mosaic virus from wheat seeds and RT-qPCR for detection of Soybean mosaic virus from soybean seeds. • The method may be tested for other viruses, however, pre-validation will be needed

Identification of seminal parameters predictive of conception rates in Angus and Nelore bulls used in TAI.

The ability to predict male fertility is highly desirable for bulls used in AI. Timed artificial insemination (TAI) represents a breakthrough in the use of AI in Brazil and other countries. Numerous causes contribute to the wide range of results and/or unsatisfactory pregnancy rates in TAI programs, highlighting the factors inherent in the bovine female in addition to several factors inherent to quality of semen used. Regarding the quality of semen used in AI programs, differences reported in fertility could be attributed to variation in sperm qualitative characteristics. Consequently, the success of bovine AI programs largely depends on the use of good quality semen. When only high fertility bulls are used, better conception rates are achieved, reducing costs of reproductive programs. Thus, some authors have shown that semen used in TAI has great impact on pregnancy rates, and various biomarkers of sperm quality are required to predict the fertility of bull spermatozoa (Oliveira et al., 2013, Holden et al., 2017). Our goal is to correlate different methods of post-thaw semen evaluation with the P/AI of Nelore (zebu) cows subjected to TAI to identify the candidate predictors of conception rate.Proceedings of the international Bull Fertility Conference, 2018, Westport

Lem benchmark database for tropical agricultural remote sensing application.

Abstract: The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic?s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data

Surface Modification of Bioresorbable Phosphate Glasses for Controlled Protein Adsorption

The traditional silicate bioactive glasses exhibit poor thermal processability, which inhibits fiber drawing or sintering into scaffolds. The composition of the silicate glasses has been modified to enable hot processing. However, the hot forming ability is generally at the expense of bioactivity. Metaphosphate glasses, on the other hand, possess excellent thermal processability, congruent dissolution, and a tailorable degradation rate. However, due to the layer-by-layer dissolution mechanism, cells do not attach to the material surface. Furthermore, the congruent dissolution leads to a low density of OH groups forming on the glass surface, limiting the adsorption of proteins. It is well regarded that the initial step of protein adsorption is critical as the cells interact with this protein layer, rather than the biomaterial itself. In this paper, we explore the possibility of improving protein adsorption on the surface of phosphate glasses through a variety of surface treatments, such as washing the glass surface in acidic (pH 5), neutral, and basic (pH 9) buffer solutions followed or not by a treatment with (3-aminopropyl)triethoxysilane (APTS). The impact of these surface treatments on the surface chemistry (contact angle, ζ-potential) and glass structure (FTIR) was assessed. In this manuscript, we demonstrate that understanding of the material surface chemistry enables to selectively improve the adsorption of albumin and fibronectin (used as model proteins). Furthermore, in this study, well-known silicate bioactive glasses (i.e., S53P4 and 13-93) were used as controls. While surface treatments clearly improved proteins adsorption on the surface of both silicate and phosphate glasses, it is of interest to note that protein adsorption on phosphate glasses was drastically improved to reach similar protein grafting ability to the silicate bioactive glasses. Overall, this study demonstrates that the limited cell/phosphate glass biological response can easily be overcome through deep understanding and control of the glass surface chemistry