jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: А. В. Гончар | M. S. Anosov | D. A. Ryabov
Source: Russian Journal of Nondestructive Testing (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Estimation of Structural Degradation of the Heat Affected Zone of the Welded Joint Under Fatigue Abstract We consider the features of using modern nondestructive methods for assessing structural degradation in the process of elastic-plastic cyclic loading of normalizing (average grain size 10 μm) and overheating (average grain size 45 μm) zones of the heat affected zone of St3 steel. In this case, the approaches of digital image processing of microstructures, fractal analysis, and ultrasonic and magnetic testing were used. The features of the stages of degradation of the structure under fatigue and the corresponding behavior of the numerical microstructure parameters such as fractal dimension and the concentration and fraction of defects and slip bands are analyzed, as well as changes in the elastic properties of the material under study (determining an acoustic parameter that takes into account the complex change in the velocities of longitudinal and transverse waves) and the coercive force. It has been established that cyclic loading of samples from the sections of the heat affected zone leads to the formation of a large number of structural defects in the form of deformation bands, discontinuities, and microcracks, the occurrence of which is accompanied by a considerable change in the numerical microstructure characteristics (a decrease in the value of fractal dimension) as well as elastic (a decrease in the values of acoustic parameters) and magnetic (an increase in coercive force) properties. It is also shown that cyclic loading of the material under study leads to an increase in microhardness. However, the nature of such an increase for overheating and normalizing zones is different. The results of the research can be applied to the problems of diagnosing premature destruction and monitoring the most loaded zones of St3 steel structures to determine the residual resource. Materials science ; Microstructure ; Overheating (electricity) ; Structural material ; Grain size ; Fractal dimension ; Nondestructive testing ; Composite material ; Coercivity ; Homogenization (climate) ; Indentation hardness ; Classification of discontinuities ; Hardening (computing) ; Fractal ; Structural engineering ; Condensed matter physics ; Mathematics Material Properties and Failure Mechanisms ; Microstructure and Mechanical Properties of Steels ; Engineering Diagnostics and Reliability
Authors: Carola Gregor | Florian Grimm | Jasmin Rehman | Christian A. Wurm | Alexander Egner
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-09-13)
Field: Chemistry — Organic Chemistry

Two-color live-cell STED nanoscopy by click labeling with cell-permeable fluorophores Abstract STED nanoscopy allows for the direct observation of dynamic processes in living cells and tissues with diffraction-unlimited resolution. Although fluorescent proteins can be used for STED imaging, these labels are often outperformed in photostability by organic fluorescent dyes. This feature is especially crucial for time-lapse imaging. Unlike fluorescent proteins, organic fluorophores cannot be genetically fused to a target protein but require different labeling strategies. To achieve simultaneous imaging of more than one protein in the interior of the cell with organic fluorophores, bioorthogonal labeling techniques and cell-permeable dyes are required. In addition, the fluorophores should preferentially emit in the red spectral range to reduce potential phototoxic effects that can be induced by the STED light, which further restricts the choice of suitable markers. Here we demonstrate two-color STED imaging of living cells using various pairs of dyes that fulfill all of the above requirements. To this end, we combine click-chemistry-based protein labeling with other orthogonal and highly specific labeling methods, enabling long-term STED imaging of different target structures in living specimens. STED microscopy ; Bioorthogonal chemistry ; Fluorescence ; Live cell imaging ; Nile red ; Chemistry ; Stimulated emission ; Fluorescent labelling ; Fluorescent protein ; Biophysics ; Green fluorescent protein ; Nanotechnology ; Cell ; Click chemistry ; Materials science ; Optics ; Biology ; Biochemistry ; Physics ; Laser Click Chemistry and Applications ; Advanced Fluorescence Microscopy Techniques ; Advanced biosensing and bioanalysis techniques
Authors: Christoph Schreiber | Tim Franzen | Laura S. Hildebrand | René Stein | Bernhard Friedrich | Rainer Tietze | Rainer Fietkau | Luitpold Distel
Source: Bioengineering (article, 2022-12-15)
Field: Materials Science — Biomaterials

Effect of Citrate- and Gold-Stabilized Superparamagnetic Iron Oxide Nanoparticles on Head and Neck Tumor Cell Lines during Combination Therapy with Ionizing Radiation Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. They are associated with alcohol and tobacco consumption, as well as infection with human papillomaviruses (HPV). Therapeutic options include radiochemotherapy, surgery or chemotherapy. Nanoparticles are becoming more and more important in medicine. They can be used diagnostically, but also therapeutically. In order to provide therapeutic alternatives in the treatment of HNSCC, the effect of citrate-coated superparamagnetic iron oxide nanoparticles (Citrate-SPIONs) and gold-coated superparamagnetic iron oxide nanoparticles (Au-SPIONs) in combination with ionizing irradiation (IR) on two HPV positive and two HPV negative HNSCC and healthy fibroblasts and keratinocytes cell lines were tested. Effects on apoptosis and necrosis were analyzed by using flow cytometry. Cell survival studies were performed with a colony formation assay. To better understand where the SPIONs interact, light microscopy images and immunofluorescence studies were performed. The HNSCC and healthy cell lines showed different responses to the investigated SPIONs. The cytotoxic effects of SPIONs, in combination with IR, are dependent on the type of SPIONs, the dose administered and the cell type treated. They are independent of HPV status. Reasons for the different cytotoxic effect are probably the different compositions of the SPIONs and the related different interaction of the SPIONs intracellularly and paramembranously, which lead to different strong formations of double strand breaks. Cancer research ; Head and neck squamous-cell carcinoma ; Cytotoxic T cell ; Flow cytometry ; Radiation therapy ; Cell culture ; Cell ; Iron oxide nanoparticles ; Chemistry ; Medicine ; Pathology ; Head and neck cancer ; Nanoparticle ; Materials science ; Immunology ; In vitro ; Nanotechnology ; Internal medicine ; Biology ; Biochemistry Nanoparticle-Based Drug Delivery ; Radiation Therapy and Dosimetry ; Head and Neck Cancer Studies
Authors: Xiaodan Liu | Yonghui Yu | Xiulin Bai | Xiaolong Li | Jun Zhang | Dun Wang
Source: Processes (article, 2022-12-29)
Field: Chemistry — Analytical Chemistry

Rapid Identification of Insecticide- and Herbicide-Tolerant Genetically Modified Maize Using Mid-Infrared Spectroscopy Genetically modified (GM) technology is of great significance for increasing crop production, protecting biodiversity, and reducing environmental pollution. However, with the frequent occurrence of safety events regarding GM foods, more and more disputes have arisen over the potential safety of transgenic technology. It is particularly necessary to find a fast and accurate method for transgenic product identification. In this research, mid-infrared spectroscopy, coupled with chemometric methods, was applied to discriminate GM maize from its non-GM parent. A total of 120 GM maize and 120 non-GM maize samples were prepared, and the spectral information in the range of 400–4000 cm−1 was collected. After acquiring the spectra, wavelet transform (WT) was used to preprocess the data, and k-means was carried out to split all samples into calibration and prediction sets in the ratio of 2:1. Principal component analysis (PCA) was then conducted to qualitatively distinguish the two types of samples, and an apparent cluster was observed. Since the full spectrum covered a large amount of data and redundant information, we adopted the successive projections algorithm (SPA) to select optimal wavelengths for further analysis. Chemometrics, including partial least squares-discriminant analysis (PLS-DA), the k-nearest neighbor algorithm (KNN), and the extreme learning machine (ELM), were performed to establish classification models based on full spectra and optimal wavelengths. The overall results indicated that ELM models based on full spectra and optimal spectra showed better accuracy and reliability, with a 100% recognition rate in the calibration set and a 98.75% recognition rate in the prediction set. It has been confirmed that mid-infrared spectroscopy, combined with chemometric methods, can be a novel approach to identify transgenic maize. Chemometrics ; Principal component analysis ; Genetically modified maize ; Calibration ; Partial least squares regression ; Linear discriminant analysis ; Extreme learning machine ; Pattern recognition (psychology) ; Biological system ; Wavelet ; Artificial intelligence ; Computer science ; Mathematics ; Statistics ; Chemistry ; Genetically modified crops ; Machine learning ; Biology ; Artificial neural network ; Transgene Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Jan Pinc | Jiří Kubásek | Jan Drahokoupil | Jaroslav Čapek | Dalibor Vojtěch | Andrea Školáková
Source: Materials (article, 2022-12-06)
Field: Materials Science — Biomaterials

Microstructural and Mechanical Characterization of Newly Developed Zn-Mg-CaO Composite In this study, the Zn-0.8Mg-0.28CaO wt.% composite was successfully prepared using different conditions of ball milling (rotations and time) followed by a direct extrusion process. These materials were characterized from the point of view of microstructure and compressive properties, and the correlation between those characteristics was found. Microstructures of individual materials possessed differences in grain size, where the grain size decreased with the intensified conditions (milling speed and time). However, the mutual relation between grain size and compressive strength was not linear. This was caused by the effect of other factors, such as texture, intermetallic phases, and pores. Material texture affects the mechanical properties by a different activity ratio between basal and pyramidal <c + a> slips. The properties of intermetallic particles and pores were determined in material volume using micro-computed tomography (µCT), enhancing the precision of our assumptions compared with commonly applied methods. Based on that, and the analysis after the compressive tests, we were able to determine the influence of aspect ratio, feret diameters, and volume content of intermetallic phases and pores on mechanical behavior. The influence of the aspects on mechanical behavior is described and discussed. Intermetallic ; Materials science ; Microstructure ; Grain size ; Compressive strength ; Extrusion ; Composite number ; Ball mill ; Composite material ; Texture (cosmology) ; Metallurgy Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Mohammed Dukhi Almutairi | Sultan Saleh Alnahdi | Muhammad Khan
Source: Polymers (article, 2022-07-30)
Field: Materials Science — Polymers and Plastics

Strain Release Behaviour during Crack Growth of a Polymeric Beam under Elastic Loads for Self-Healing The response of polymeric beams made of Acrylonitrile butadiene styrene (ABS) and thermoplastic polyurethane (TPU) in the form of 3D printed beams is investigated to test their elastic and plastic responses under different bending loads. Two types of 3D printed beams were designed to test their elastic and plastic responses under different bending loads. These responses were used to develop an origami capsule-based novel self-healing mechanism that can be triggered by crack propagation due to strain release in a structure. Origami capsules of TPU in the form of a cross with four small beams, either folded or elastically deformed, were embedded in a simple ABS beam. Crack propagation in the ABS beam released the strain, and the TPU capsule unfolded with the arms of the cross in the direction of the crack path, and this increased the crack resistance of the ABS beam. This increase in the crack resistance was validated in a delamination test of a double cantilever specimen under quasi-static load conditions. Repeated test results demonstrated the effect of self-healing on structural crack growth. The results show the potential of the proposed self-healing mechanism as a novel contribution to existing practices which are primarily based on external healing agents. Materials science ; Composite material ; Bending ; Beam (structure) ; Self-healing ; Crack closure ; Delamination (geology) ; Thermoplastic polyurethane ; Fracture mechanics ; Three point flexural test ; Structural engineering ; Elastomer Polymer composites and self-healing ; Advanced Sensor and Energy Harvesting Materials ; Additive Manufacturing and 3D Printing Technologies
Authors: Alyaa Awad | Khalid A. Sukkar | Dalia Jaed
Source: Journal of Applied Sciences and Nanotechnology (article, 2022-09-01)
Field: Chemistry — Analytical Chemistry

Deep Understanding of the Mechanism and Thermophysical Properties of Prepared Nanofluids Lube Oil Stock-60 with Al2O3 NPs Iraqi petroleum refineries produce large quantities of base lubricating oils (lube oils). Managing the influence of nano-additives on the lube oil nanofluids is required deep understanding to explain the resulting new specifications of produced nano-lubricants. The present study investigated the effect of Al2O3 NPs addition on the thermal properties of lube oil stock-60. Different mass additions of 0.25, 0.65, 1.05, 1.45, and 1.85 wt.% of Al2O3 NPs at operating temperatures of 20-50°C were evaluated. Also, the thermal conductivity coefficient of the prepared nanofluid was studied at the full range of the experimental temperatures (20-50°C). It was noted that the addition of Al2O3 NPs improved the thermal properties of the prepared nano-lubricant due to the high thermal conductivity of the added Al2O3 NPs. Moreover, the greatest improvement in the thermal conductivity of modified nano-lubricating oil was 13.02% at added Al2O3 mass fraction of 1.85%. The results indicated that the viscosity index of the prepared nano-lubricant was improved dramatically with Al2O3 NPs addition increase at measured standard temperatures of 40 and 100°C. The viscosity index of lubricant nanofluid is increased up to 2.46% at a weight fraction of 1.85%. The flashpoint increased by 1.33, 3.54, 5.75, 7.52, and 9.73% for mass fraction of 0.25, 0.65, 1.05, 1.45, and 1.85 wt.%, respectively. Furthermore, the highest flashpoint value was 248oC of prepared nanofluid lube oil with 1.85 wt.% of Al2O3 NPs. Finally, the produced nano-lubricating oil has high operating quality with economic feasibility. Furthermore, an accurate correlation for predicting the viscosity of both types of nano-lubricants was provided. Nanofluid ; Mass fraction ; Thermal conductivity ; Lubricant ; Materials science ; Base oil ; Viscosity index ; Chemical engineering ; Viscosity ; Fraction (chemistry) ; Nano- ; Mass concentration (chemistry) ; Composite material ; Nanoparticle ; Chromatography ; Chemistry ; Nanotechnology Petroleum Processing and Analysis ; Lubricants and Their Additives ; Power Transformer Diagnostics and Insulation
Authors: Li Duan | Lewis Boyd | Gerardo Aragón-Camarasa
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

Learning Physics Property Parameters of Fabrics and Garments With a Physics Similarity Neural Network Predicting the physics properties of deformable objects such as garments and fabrics is a challenge in robotic research. Directly measuring their physics properties in a real environment is difficult [1]. Therefore, learning and predicting the physics property parameters of garments and fabrics can be conducted in simulated environments. However, garments have collars, sleeves, pockets and buttons that change the way garments deform and simulating these is time-consuming. Therefore, in this paper, we propose to predict the physics parameters of real fabrics and garments by learning the physics similarities between simulated fabrics via a Physics Similarity Network (PhySNet). For this, we estimate wind speeds generated by an electric fan and area weights to predict the bending stiffness parameters of real fabrics and garments. We found that PhySNet coupled with a Bayesian optimiser can predict physics property parameters and improve state-of-art by 34.0% for fabrics and 68.1% for garments. Property (philosophy) ; Clothing ; Artificial neural network ; Similarity (geometry) ; Artificial intelligence ; Computer science ; Machine learning ; Physics ; Engineering drawing ; Engineering Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; 3D Shape Modeling and Analysis
Authors: Jun‐Li Xu | Ana Herrero‐Langreo | Sakshi Lamba | Mariateresa Ferone | Anastasia Swanson | Vicky Caponigro | Amalia G. M. Scannell | Aoife Gowen
Source: Scientific Reports (article, 2022-09-14)
Field: Chemistry — Analytical Chemistry

Exploring the identification of multiple bacteria on stainless steel using multi-scale spectral imaging from microscopic to macroscopic This work investigates non-contact reflectance spectral imaging techniques, i.e. microscopic Fourier transform infrared (FTIR) imaging, macroscopic visible-near infrared (VNIR), and shortwave infrared (SWIR) spectral imaging, for the identification of bacteria on stainless steel. Spectral images of two Gram-positive (GP) bacteria (Bacillus subtilis (BS) and Lactobacillus plantarum (LP)), and three Gram-negative (GN) bacteria (Escherichia coli (EC), Cronobacter sakazakii (CS), and Pseudomonas fluorescens (PF)), were collected from dried suspensions of bacterial cells dropped onto stainless steel surfaces. Through the use of multiple independent biological replicates for model validation and testing, FTIR reflectance spectral imaging was found to provide excellent GP/GN classification accuracy (&gt; 96%), while the fused VNIR-SWIR data yielded classification accuracy exceeding 80% when applied to the independent test sets. However, classification within gram type was far less reliable, with lower accuracies for classification within the GP (&lt; 75%) and GN (≤ 51%) species when calibration models were applied to the independent test sets, underlining the importance of independent model validation when dealing with samples of high biological variability. VNIR ; Remote sensing ; Materials science ; Fourier transform infrared spectroscopy ; Pseudomonas fluorescens ; Bacteria ; Spectral imaging ; Enterobacter ; Escherichia coli ; Biological system ; Hyperspectral imaging ; Microbiology ; Optics ; Biology ; Physics ; Geology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Listeria monocytogenes in Food Safety
Authors: Elsayed Ahmed Elnashar
Source: Trends in Textile Engineering & Fashion Technology (article, 2022-02-01)
Field: Materials Science — Polymers and Plastics

Applications of Mechatronics Opportunities in Textiles Trends in Textile Engineering & Fashion Technology Applications of Mechatronics Opportunities in Textiles Elsayed Ahmed Elnashar* Kaferelsheikh University, Professor of Textiles & Apparel, Egypt/p> *Corresponding author:Elsayed Ahmed Elnashar, Professor of Textiles & Apparel, Egypt Submission: January 06, 2022; Published: February 01, 2022 DOI: 10.31031/TTEFT.2022.06.000640 ISSN 2578-0271 Volume 6 Issue 3 Mechatronics ; Clothing ; Textile ; Engineering ; History ; Electrical engineering ; Archaeology Textile materials and evaluations
Authors: Song Li | Pan Tan | Jun Li | Min Tang | Liang Hong
Source: Physical Review Research (article, 2022-04-05)
Field: Materials Science — Materials Chemistry

Many-body effect renders universal subdiffusion to water on different proteins Diffusion of interfacial water is crucial for the function and stability of enclosed protein molecule. By combining neutron scattering and molecular dynamics simulation results, we found that the interfacial water on different proteins including intrinsically disordered proteins exhibit a universal subdiffusive motion with a common power law. Further analysis of the simulation trajectories and analytical modeling reveal that it is the many-body effect, i.e., water prefers to jump between shallow trapping sites on the protein as the deep ones are mostly occupied, that overrides the surface differences among proteins to render the interfacial water universal subdiffusion. Molecular dynamics ; Chemical physics ; Statistical physics ; Physics ; Stability (learning theory) ; Chemistry ; Materials science ; Computer science ; Computational chemistry Material Dynamics and Properties ; Theoretical and Computational Physics ; Spectroscopy and Quantum Chemical Studies
Authors: Beilong Zhang | Jun Hua | Liping Cai | Yunbo Gao | Yilin Li
Source: Journal of Wood Science (article, 2022-11-28)
Field: Materials Science — Polymers and Plastics

Multi-objective optimization of particle gluing operating parameters in particleboard production based on improved machine learning algorithms Abstract Particle gluing operating parameters in particleboard (PB) production have an important influence on the mechanical properties of PBs. This study developed a multi-objective optimization model based on support vector regression (SVR) optimized by the non-dominated sorted genetic algorithm-II (NSGA2) to realize the multi-objective accurate prediction of PB mechanical properties (modulus of elasticity (MOE), modulus of rupture (MOR), and internal bonding (IB) strength) by adjusting particle gluing operating parameters. The NSGA2-SVR multi-objective prediction model was trained by 496 groups of experimental data of particle gluing operating parameters and PB mechanical properties. The prediction results of the NSGA2-SVR multi-objective prediction model were evaluated by 124 groups of experimental data and compared with the prediction results of the back propagation neural network (BPNN) model, general regression neural network (GRNN) model, and SVR model. The mean absolute percentage errors (MAPEs) of the NSGA2-SVR model were 49.11%, 33.64%, and 24.20% lower than that of the BPNN model, GRNN model, and SVR model, respectively. The Theil’s inequality coefficients (TICs) of the NSGA2-SVR model were 40.93%, 27.39%, and 18.58% lower than that of the BPNN model, GRNN model, and SVR model, respectively. The results showed that the multi-objective prediction model based on NSGA2-SVR has a superior fitting and higher prediction accuracy for the prediction performance of particle gluing operating parameters, and the NSGA2-SVR model can be applied to the multi-objective synchronous prediction of particle gluing operating parameters in the PB production line. Support vector machine ; Artificial neural network ; Algorithm ; Machine learning ; Particle swarm optimization ; Computer science ; Mathematics ; Artificial intelligence Natural Fiber Reinforced Composites ; Agricultural Engineering and Mechanization