Collagen is a popular component of edible coatings that protect food products during storage. This article introduces a new antioxidant and antibacterial edible coating made from broiler chicken skin as a source of collagen. The coating was tested on meat and dairy products with high moisture and fat content, namely sausage, jellied beef, and soft cheese. A set of standard research methods made it possible to determine the physicochemical, sensory, and microbiological properties of broiler chicken skin and the experimental edible coating, as well as to compare the coated and uncoated meat and dairy products. A digital micrometer and a DVT Devotrans GPUG model revealed the structural and mechanical properties of the test products. The protein fraction was described using a method based on the extraction of sarcoplasmic proteins from muscle tissue in a low-ionic-strength buffer solution to produce fractions of water-soluble, salt-soluble, and alkali-soluble proteins. The water activity coefficient was obtained using an Aqualab 4TE analyzer with a dielectric humidity sensor. Alcalase (Novozymes, Danmark) was chosen as the optimal enzymic preparation available on the Russian market for the hydrolysis of collagen-containing raw materials. Its optimal concentration was 0.3% of the raw material weight after preliminary swelling in water. The optimal hydrolysis conditions were as follows: heating medium temperature-52 degrees C, exposure time-5 h. The research resulted in a production algorithm and a formulation modeled in the MultiMit automated expert system. The new technology for edible coating from chicken skin collagen can be used in the meat and dairy industries to extend the shelf-life of final products due to antibacterial and antioxidant effects.
Current robotic systems for environmental perception, target localization, and 3D reconstruction often suffer from high costs, complex calibration procedures, and limited deployment flexibility. To address these issues, we propose a novel single snapshot calibration and 3D reconstruction method for monocular omnidirectional vision-laser systems. To overcome the low detection accuracy of distant checkerboard corners in omnidirectional images, we employ image enhancement and adaptive sub-pixel localization techniques to improve corner extraction precision. On this basis, a novel calibration target and corresponding algorithm are designed to simultaneously estimate the omnidirectional camera pose, laser plane parameters, and their relative spatial relationship from a single snapshot, significantly simplifying the calibration process. Furthermore, utilizing the calibration results, we achieve absolute target position recovery and indoor 3D reconstruction, successfully validating these capabilities through obstacle avoidance experiments on a robotic platform. The experimental results demonstrate that the proposed method enables effective single-image calibration of the omnidirectional vision-laser system, improving the accuracy of long-range camera extrinsic calibration by 78.4% compared with the conventional calibration method. It also exhibits good 3D reconstruction capability and environment perception performance: compared with reconstruction based on conventional calibration, the reconstruction error in reconstruction tasks is reduced by 73.85%. Moreover, the calibration and reconstruction results have been successfully applied to a robotic platform, providing a low-cost, easily deployable solution for indoor near-field robotic visual perception that requires only a single calibration snapshot.
Metal-organic frameworks (MOFs) are a class of soft porous crystals that possess extensive capabilities for regulating and inducing morphological transitions between different crystalline phases under external influences. According to the contemporary perspective, transitions between metastable structural phases occur cooperatively throughout the material, thereby preserving its ideal crystalline structure. A phase of the metal-organic framework DUT-8(Ni), which is poorly investigated and is a transient metastable phase between phases with completely open and closed pores, was studied by using Raman spectroscopy. In this Letter, we present experimental evidence for the coexistence of two structural phases with different pore sizes within a single microcrystal using the hyperspectral Raman mapping technique. The focused light of the laser beam triggered the structural phase change of the microcrystals. The treatment spot was tiny compared to that of the transition region. The long-term stability of the microcrystal phase after the transition is demonstrated. Changes in the reflectance spectra, which characterize the crystal's color, also confirm the observed changes. The coexistence of different phases within one crystal, on the one hand, changes the existing understanding of the phase transition mechanism between open and closed pore phases. And on the other hand, it is a very illustrative example of Raman mapping capabilities as not only the isolated Raman spectrum matters but also the whole data set obtained from the microcrystal surface.
Thiolate-functionalized gold nanoclusters, in particular Au25(SCH3)18, are of considerable interest as catalysts for the hydrogen evolution reaction. In this work, the DFT method was used to investigate the effects of copper and palladium doping, as well as the role of the thiolate shell, on the atomic and electronic structures of the nanoclusters. It is shown that the low-symmetric configurations, in which the dopant atom (Cu or Pd) occupies the β position in the outer icosahedral layer of the cluster, are the most energetically favorable. For thiolate-functionalized clusters, stabilization of the central position (γ) is confirmed for Pd, whereas for Cu, substitution at the β position remains preferred. The electronic structure analysis shows that doping leads to a narrowing of the band gap, which may affect the chemical activity of the clusters. A machine-learning potential based on the DPA-2 descriptor was developed for modeling cluster dynamics, providing high accuracy (RMSE 3 meV/atom) when reproducing DFT data. It is shown that for unfunctionalized clusters, the molecular dynamics optimization agrees well with the DFT results (RMSD < 0.15 Å), whereas for thiolate-functionalized systems, significant structural deviations (RMSD > 0.50 Å) related to the conformational flexibility of the ligands are observed.
The results of ab initio modeling of the interaction between hydrogen and the ferrite/cementite interface in pearlitic steel are presented for three crystallographic orientations: Bagaryatskii, Isaichev, and Pitsch–Petch. It was found that phase boundaries act as effective traps for hydrogen, with a binding energy of up to –0.30 eV, in agreement with experimental data reported in the literature. The minimum hydrogen solution energy and maximum binding energy are observed at the interface, whereas hydrogen binding in bulk ferrite and cementite is weaker. Structural analysis, including interatomic distances, magnetic moments, and interstitial geometry, reveals a strong correlation between crystal lattice structure and hydrogen trapping ability. The interface with Isaichev orientation provides the strongest hydrogen trapping due to favorable structural alignment of adjacent crystal lattices. The obtained results were used to develop a thermodynamic model of ferrite–cementite interaction; calculations of hydrogen absorption based on the derived trapping energies show that fine lamellar mixtures of ferrite and cementite with interlamellar spacing below 0.1 μm exhibit significant hydrogen adsorption capacity at temperatures below 400 K.
