Bio-Based Adhesives Formulated from Tannic Acid, Chitosan, and Shellac for Packaging Materials

The aim of this study was to develop bio-based adhesives that can be used for various packaging papers. In addition to commercial paper samples, papers produced from harmful plant species in Europe, such as Japanese Knotweed and Canadian Goldenrod, were used. In this research, methods were developed to produce bio-based adhesive solutions in combinations of tannic acid, chitosan, and shellac. The results showed that the viscosity and adhesive strength of the adhesives were best in solutions with added tannic acid and shellac. The tensile strength with adhesives of tannic acid and chitosan was 30% better than with commercial adhesives and 23% for combinations of shellac and chitosan. For paper from Japanese Knotweed and Canadian Goldenrod, the most durable adhesive was pure shellac. Because the surface morphology of the invasive plant papers was more open and had numerous pores compared to the commercial papers, the adhesives penetrated the paper structure and filled the voids. There was less adhesive on the surface and the commercial papers achieved better adhesive properties. As expected, the bio-based adhesives also showed an increase in peel strength and exhibited favorable thermal stability. In summary, these physical properties support the use of bio-based adhesives use in different packaging applications.

A Multianalytical Approach for the Characterisation of Materials on Selected Artworks by Monogrammist IP

This paper presents an investigation of wooden artworks from the collection of the National Gallery Prague created by Monogrammist IP-one of the top carvers of the Salzburg-Passau region at the beginning of the 16th century. His wood reliefs were examined to gain a better understanding of the historical techniques used in medieval art workshops. The internal structure of the small relief Visitation was analysed using computed tomography. Tomographic reconstruction made it possible to distinguish wood species, observe the internal structure of the artwork in detail, study the technological procedures and identify earlier repairs, additions and damages. Tomographic investigation proved the use of four types of wood on the relief Visitation, most likely pear, lime, unspecified softwood and other different species used for joining dowels. A combination of non-invasive and micro-destructive analytical techniques was employed for the chemical characterisation of the materials in the surface layers of the artworks. Photomicrographs of the surface were taken to provide material for the initial investigation. Non-invasive material research was conducted using a portable X-ray fluorescence analyser and, in selected cases, an external reflection infrared spectrometer. The detailed analyses on the micro-samples was carried out by optical microscopy, micro-Raman spectroscopy, Fourier transform infrared spectroscopy, scanning electron microscopy coupled with energy dispersive X-ray spectrometry and gas chromatography with mass spectrometry. A glaze layer based on protein with earth pigment was identified on the relief Christ the Saviour from Death.

Application of Infrared Spectroscopy in Research on Aging of Silicone Rubber in Harsh Environment

Polymer insulators using silicone rubber materials as sheds and sheaths are widely used in power systems to replace traditional porcelain and glass insulators which are heavy, inconvenient to install, and prone to pollution flashover. However, in recent years, polymer insulators that have been operating in harsh outdoor environments for many years have experienced different degrees of aging. The aging degree and aging products of silicone rubber are the focus of research. Fourier transform infrared spectroscopy (FTIR) is a technical method to analyze the internal molecular bonds and functional groups of materials, and it is often used to study the aging degree and aging products of silicone rubber. In this paper, the aging characteristics of silicone rubber samples in a high altitude area, salt fog environment, and acid environment were studied by FTIR. The results showed that the silicone rubber in a harsh environment, such as strong radiation, salt fog, and acid fog was degraded to some extent, and its main chain was cut off, the degree of polymerization was reduced, and the content of hydrophobic functional groups was reduced. Infrared spectroscopy can be used to analyze the aging phenomenon of polymers.

A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery

Research has become increasingly more interdisciplinary over the past few years. Artificial intelligence and its sub-fields have proven valuable for interdisciplinary research applications, especially physical sciences. Recently, machine learning-based mechanisms have been adapted for material science applications, meeting traditional experiments' challenges in a time and cost-efficient manner. The scientific community focuses on harnessing varying mechanisms to process big data sets extracted from material databases to derive hidden knowledge that can successfully be employed in technical frameworks of material screening, selection, and recommendation. However, a plethora of underlying aspects of the existing material discovery methods needs to be critically assessed to have a precise and collective analysis that can serve as a baseline for various forthcoming material discovery problems. This study presents a comprehensive survey of state-of-the-art benchmark data sets, detailed pre-processing and analysis, appropriate learning model mechanisms, and simulation techniques for material discovery. We believe that such an in-depth analysis of the mentioned aspects provides promising directions to the young interdisciplinary researchers from computing and material science fields. This study will help devise useful modeling in the materials discovery to positively contribute to the material industry, reducing the manual effort involved in the traditional material discovery. Moreover, we also present a detailed analysis of experimental and computation-based artificial intelligence mechanisms suggested by the existing literature.

Comparative Analysis of Polyurethane Drive Belts with Different Cross-Section Using Thermomechanical Tests for Modeling the Hot Plate Welding Process

The paper presents a comparative analysis of the circular and flat cross-section belts using measurements of a set of thermomechanical parameters, contributing to research about hot plate welding of drive belts. On the basis of thermogravimetric and spectrophotometric tests, information about the same chemical composition of the two belts was obtained. Dynamic thermomechanical analysis and scanning differential calorimetry provided information about a small difference between belts, which disappeared when the material was placed in a state of increased temperature and mechanical stress. On the basis of the analysis of the specific heat, thermal diffusion, density, and hardness, the values of the selected thermal properties of the belt were obtained, and a large similarity between the belts was identified. On the basis of the novel performed test cycle, it has been hypothesized that circular and flat belts made from thermoplastic polyurethane elastomer could be used interchangeably for butt-welding testing. It has also been proven that cyclic thermomechanical loads unify the properties of both materials so that multiple mechanical and thermal loads do not result in any change in the material properties of the two belts. As a consequence, changes in the weld properties after welding, compared to a solid belt, are not expected.

Learning Medical Materials From Radiography Images

Deep learning models have been shown to be effective for material analysis, a subfield of computer vision, on natural images. In medicine, deep learning systems have been shown to more accurately analyze radiography images than algorithmic approaches and even experts. However, one major roadblock to applying deep learning-based material analysis on radiography images is a lack of material annotations accompanying image sets. To solve this, we first introduce an automated procedure to augment annotated radiography images into a set of material samples. Next, using a novel Siamese neural network that compares material sample pairs, called D-CNN, we demonstrate how to learn a perceptual distance metric between material categories. This system replicates the actions of human annotators by discovering attributes that encode traits that distinguish materials in radiography images. Finally, we update and apply MAC-CNN, a material recognition neural network, to demonstrate this system on a dataset of knee X-rays and brain MRIs with tumors. Experiments show that this system has strong predictive power on these radiography images, achieving 92.8% accuracy at predicting the material present in a local region of an image. Our system also draws interesting parallels between human perception of natural materials and materials in radiography images.

Midinfrared Multispectral Detection for Real-Time and Noninvasive Analysis of the Structure and Composition of Materials

In situ material identification and object tracking have been demonstrated using a mid-infrared (mid-IR) robotic scanning system. This detection method is capable of inspecting materials noninvasively because the mid-IR spectrum overlaps with numerous characteristic absorption bands corresponding to various chemical function groups. The scanning system consisted of a fiber probe connected to a mid-IR tunable laser with a wavelength tuning range of λ = 2.45-3.75 μm. For the high-speed performance of the scanning system to be evaluated, a testing platform was constructed with an object plate rapidly rotating at ω = 231 rpm. The objects on the plate were SU-8 epoxy-based resin and polydimethylsiloxane, which were mid-IR absorptive while visibly transparent. Applying mid-IR multispectral scanning, the system was able to simultaneously track the object position and identify the composition by interpreting the spectral and spatial intensity variation. The mid-IR robotic scanning method thus provides a visualization system critical for process inspection in automatic manufacturing and high-throughput biomedical screening.