jablonkagroup/rise_ai_scientists huggingface dataset:
Source:  (article, 2023-06-17)
Field: Materials Science — General Materials Science

Design and Analysis of Advanced Composite Materials for Structural Applications Due to their exceptional qualities and potential to enhance the performance of many engineering systems, innovative composite materials for structural applications have attracted a lot of attention in recent years.This paper provides a comprehensive evaluation of the design and analysis techniques utilised in the development of advanced composite materials for structural applications.The design phase includes selecting the appropriate fibre and matrix materials, understanding their mechanical properties, and modifying the composite architecture to meet specific performance requirements.Advanced composite materials usually combine high-strength fibres, such as carbon or aramid fibres, with a matrix material, such as epoxy resin, to obtain the optimum balance of mechanical, thermal, and chemical properties.Recent advancements in the design and analysis of advanced composite materials are highlighted in this work via the use of multiscale modelling, progressive damage analysis, and optimisation methodologies.Modern computational tools and simulation approaches have significantly increased the efficiency and accuracy of the design process.The report also looks at cost considerations, intricate production processes, and issues with standardisation as they relate to creating and assessing composite materials.There are now more chances to build sturdy, lightweight structures that perform better thanks to research into cutting-edge composite materials for structural applications.This research contributes to the growing body of knowledge by providing insights into the design and analytical methods that help speed up the adoption of advanced composites in several industries, including aerospace, automotive, and civil engineering. Composite number ; Materials science ; Computer science ; Composite material Material Properties and Applications ; Mechanical Behavior of Composites ; Composite Structure Analysis and Optimization
Authors: Jaydeep Patil
Source: International Journal on Recent and Innovation Trends in Computing and Communication (review, 2023-10-30)
Field: Chemistry — Analytical Chemistry

Deep Learning for Fruit Grading: A State-of-the-Art Review In the food industry, grading fruit quality is a critical responsibility. Throughout this process, fruits are sorted and categorized in by their quality. Fruit grading can be done using both machine learning and visual assessment. Visual inspection is subjective and can be influenced by human prejudice. Machine learning can produce more accurate and unbiased results. Deep learning-based methods can be used to evaluate the fruit quality by teaching a neural network to recognize various quality parameters like size, color, and defects. Deep learning methodologies for evaluating fruit quality offer further benefits. They are neutral and accurate, and they can manage enormous amounts of data. They can also save labor expenses and improve the efficiency of the grading process. Deep learning methods are useful for evaluating fruit quality, but they have several drawbacks. These include an intricate neural network, overfitting, and a lack of high-quality training data. Addressing these issues is crucial for the success of deep learning in fruit quality evaluation. In this paper, various significant deep-learning methods for evaluating fruit quality are described. The methods' advantages and disadvantages are also discussed. The study gives the researcher pointers on how to improve current strategies or create fresh ones to improve performance in terms of training effectiveness, accuracy, etc. Overfitting ; Computer science ; Artificial intelligence ; Machine learning ; Grading (engineering) ; Deep learning ; Artificial neural network ; Quality (philosophy) ; Deep neural networks ; Process (computing) ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Cemre Avşar | Suna Ertunç
Source: Chemical Industry and Chemical Engineering Quarterly (article, 2023-06-06)
Field: Materials Science — Materials Chemistry

Fluoride removal from phosphogypsum: A study on pre-industrial scale and its mathematical analysis This study discusses fluoride removal efficiency from phosphogypsum (PG) on a lab-scale experiment matrix designed by the Box-Behnken method. Temperature, solid/liquid ratio, and time were supposed to influence fluoride removal efficiency from PG by various salt solution media. Experiment matrices were designed according to salt solution types: seawater, 5% NaCl, and 10% NaCl solutions. The factor-response analysis showed a direct proportionality between fluoride removal efficiency and temperature. The optimum fluoride removal conditions based on the experimental data obtained by the multi-variable design matrix were determined by the Design Expert v.12 software. The optimum temperature, time, and solid/liquid ratio were 80 ?C, 3 h, and 0,174 for seawater. The software predicted a 73,31% fluoride removal efficiency at the optimum conditions, whereas the experimental value was 74,99%. Since the actual vs. predicted data show high consistency, results might also be useful when industrial-scale fluoride removal to a predetermined level is required prior to a particular use of PG. PG has a high potential as an alternative raw material, and fluoride removal might be important in recycling applications. This study provides a novel pre-industrial scale fluoride removal inventory, especially for the fertilizer and cement industry. Phosphogypsum ; Fluoride ; Scale (ratio) ; Environmental science ; Environmental chemistry ; Waste management ; Engineering ; Chemistry ; Organic chemistry ; Inorganic chemistry ; Geography ; Cartography Graphite, nuclear technology, radiation studies ; Metallurgy and Cultural Artifacts ; Soil and Unsaturated Flow
Authors: Christos Gioldasis | Απόστολος Γκάμας | Othonas A. Moultos | Costas Vlahos
Source: Polymers (article, 2023-07-12)
Field: Chemistry — Organic Chemistry

Chemical Feedback in Templated Reaction-Assembly of Polyelectrolyte Complex Micelles: A Molecular Simulation Study of the Kinetics and Clustering The chemical feedback between building blocks in templated polymerization of diblock copolymers and their consecutive micellization was studied for the first time by means of coarse-grained molecular dynamics simulations. Using a stochastic polymerization model, we were able to reproduce the experimental findings on the effect of chemical feedback on the polymerization rates at low and high solution concentrations. The size and shape of micelles were computed using a newly developed software in Python conjugated with graph theory. In full agreement with the experiments, our simulations revealed that micelles formed by the templated micellization are more spherical and have a lower radius of gyration than those formed by the traditional two-step micellization method. The advantage of molecular simulation over the traditional kinetic models is that with the simulation, one studies in detail the heterogeneous polymerization in the presence of the oppositely charged template while also accounting for the incompatibility between reacted species, which significantly influences the reaction process. Micelle ; Radius of gyration ; Polymerization ; Molecular dynamics ; Copolymer ; Materials science ; Kinetics ; Hydrodynamic radius ; Chemical physics ; Polymer chemistry ; Polymer ; Chemical engineering ; Chemistry ; Computational chemistry ; Physical chemistry ; Physics Advanced Polymer Synthesis and Characterization ; Polymer Surface Interaction Studies ; Surfactants and Colloidal Systems
Authors: Ilavarasan Karthic Subramaniyan | P. K. Srividhya | J. Kesavan
Source: Revista de Metalurgia (article, 2023-12-30)
Field: Materials Science — Materials Chemistry

Study on mechanical and micro structural properties of spin arc welding in Hastelloy C-2000 Nickel-based Hastelloy C-2000 is widely used in the aerospace, chemical, and medicinal sectors. Investigating the potential efficacy of the spin arc welding process on Hastelloy C-2000 was the main focus of this study. In spin arc welding the centrifugal force has been obtained in the fusion zone, thus the weldbead quality increases. Weld current, rotating speed, and spin diameter are all separate parameters used in the welding procedure. The microstructural investigation was carried out using optical microscopy, X-Ray Diffraction (XRD), and field emission scanning electron microscopy (FESEM). The mechanical characteristics of the welded specimens were examined closely. Spin Arc Welding ultimate tensile strength (UTS), hardness value (HV), and impact experiments were compared to those of the Multi-pass Pulsed Current Gas Tungsten Arc welding method (MPCGTAW). In 27 tests, increasing the current and rotating speed resulted in greater penetration depth and weld height. The width of the weld was found to be a little high, with a spinning diameter of 2 mm. In comparison, samples 5 and 15 were found to have better hardness, tensile strength, and toughness, especially with suitable welding parameters such as current (120 I and 140 I), speed (1800 rpm), and spin diameter (2 mm and 3 mm). A microstructural study showed no grain segregation, contributing to the material’s increased hardness and tensile strength. The novel findings of the present study suggest that spin arc welding might be superior for various Hastelloy C-2000 connections that might have great applications in industries. Materials science ; Welding ; Ultimate tensile strength ; Metallurgy ; Gas tungsten arc welding ; Composite material ; Shielded metal arc welding ; Heat-affected zone ; Toughness ; Arc welding Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Ion-surface interactions and analysis
Authors: Yerlan Zharykbasov | Zhainagul Kakimova | Айтбек Какимов | Klara Zharykbasova | Gulmira Mirasheva | Nadir Ibragimov | Sandugash Toleubekova | Аlibek Muratbayev | Gulnara Tulkebayeva | Zhanibek Yessimbekov
Source: Heliyon (article, 2023-08-01)
Field: Chemistry — Analytical Chemistry

Studying the concentration of xenobiotics in milk and developing the biosensor method for their rapid determination In this article the content of toxic xenobiotics (heavy metals and pesticides) in cow milk collected from 5 districts of Eastern Kazakhstan was examined and their cumulative properties were determined. The content of organochlorine pesticides (HCCH, DDT) was not detected in the analyzed milk. The content of mercury and arsenic in milk samples does not exceed the maximum allowable concentration (0.005 and 0.05 mg/kg, respectively). The content of cadmium above the maximum allowable concentration (0.03 mg/kg) was found in milk sampled from Shemonaikha and Katon-Karagai districts. The content of lead and zinc above the maximum allowable concentration (0.1 and 5.0 mg/kg, respectively) was found in milk samples taken from all 5 studied districts. The content of copper above the maximum allowable concentration (1.0 mg/kg) was found in milk samples collected from 4 districts under study (Borodulikha, Beskaragai, Shemonaikha and Katon-Karagai). Based on the analysis of information data the need to develop an accelerated method of determining toxic xenobiotics in milk was substantiated. The basic directions of modernization of the biosensor for determination of cadmium and lead salts in milk and dairy products were selected. A new approach to the process of immobilization of the enzyme on the surface of a substrate for cadmium and lead salts determination in milk has been developed. The efficiency of using a polymeric plate with a graphite conducting layer as a basis for the enzyme biosensor was established. Cadmium ; Xenobiotic ; Chemistry ; Pesticide ; Environmental chemistry ; Pesticide residue ; Mercury (programming language) ; Cow milk ; Arsenic ; Zinc ; Contamination ; Food science ; Enzyme ; Biology ; Biochemistry ; Agronomy Dye analysis and toxicity
Authors: Yuhan Sun | Huanzi Wang | Yuan Gan | Yudie Qing | Tianli Yue | Yahong Yuan | Yiheng Shi
Source: Food Frontiers (article, 2023-06-09)
Field: Chemistry — Analytical Chemistry

Fast recognition model of <i>Fusarium</i> in agaric based on hyperspectral imaging Abstract Agaric is a dual‐purpose fungus for medicine and food, which is considered a healthcare product. However, in cultivation and production, agaric is contaminated by a variety of molds, resulting in a decline in quality and yield. Fusarium , as one of the poisonous filamentous fungi, seriously threatens the healthy development of the agaric industry and even affects human health. Therefore, it is necessary to establish timely and accurate identification and rapid detection of pathogens and crops. In this study, the visible near infrared hyperspectral imaging was used to obtain the image and spectral information, and the best classification model was selected and established to quickly distinguish Fusarium oxysporum from Fusarium verticillioides , which was also successfully applied for the preliminary identification of agaric infection. This approach could provide a nondestructive manner for rapid identification and detection of pathogenic fungi contamination in agaric. Hyperspectral imaging ; Fusarium ; Fusarium oxysporum ; Identification (biology) ; Fungus ; Biotechnology ; Biology ; Microbiology ; Artificial intelligence ; Computer science ; Botany Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Víctor Leborán | F. Rivadulla
Source: Journal of Chemical Education (article, 2023-09-21)
Field: Chemistry — Spectroscopy

Interactive Learning of Diffusion and the Diffusion Equation with Mathematical Software The process of diffusion is intimately related to random molecular displacement and is central to many problems in chemistry. Here, we provide an intuitive derivation of the diffusion equation based on the analysis of the random trajectories of particles generated and plotted by simple MATLAB and Python scripts. The codes are very simple and profusely commented on, so they can be modified by students and instructors with little experience with these software packages. The codes plot the trajectories in 1D, 2D, and 3D of a number of particles during a number of steps, both specified by the operator. Advanced versions of the codes with animated evolution of the diffusion process are also provided. The interactive generation and analysis of the trajectories of the particles in different scenarios allows a faster and more complete understanding of the effect of the number of particles, diffusion time, or dimensionality of the system, on the basic aspects of chemical diffusion, like mean square displacement, through the statistical nature of the process. A series of activities for the instructor and students was also suggested. Python (programming language) ; Scripting language ; Computer science ; Diffusion ; Software ; Curse of dimensionality ; Statistical physics ; Plot (graphics) ; Diffusion equation ; Diffusion process ; MATLAB ; Anomalous diffusion ; Theoretical computer science ; Mathematics ; Physics ; Artificial intelligence ; Programming language ; Statistics ; Quantum mechanics Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Various Chemistry Research Topics
Authors: Marco Ruggiero
Source: Preprints.org (preprint, 2023-11-16)
Field: Materials Science — Biomaterials

Design and Development of Novel Compounds Aimed at Optimizing the Function of Microtubules and Tubulin in the Immune System and the Brain This study describes the design and development of novel compounds aimed at optimizing the function of microtubules and tubulin in the immune system and the brain as they relate to the Orch OR theory of consciousness. These compounds are defined by the following molecular formulas:C65H100CoN15O22P(C6H12O5);C65H100CoN15O22P(C5H10NO4);(C65H100CoN15O22P)(C6H12N2O)n;(C65H100CoN15O22P)(C6H12O5)(C6H12N2O)n;(C6H12N2O)n(C6H12O5)m;Zn(C6H12O5)2;These molecules, formulated under the form of drugs, health functional foods, or food supplements, have the potential to positively affect a number of areas ranging from prevention and therapy of neurological diseases to supporting the immune system. Microtubule ; Immune system ; Tubulin ; Brain function ; Function (biology) ; Neuroscience ; Consciousness ; Biology ; Computational biology ; Chemistry ; Cell biology ; Immunology Supramolecular Self-Assembly in Materials
Authors: Adibah Shakri | Muhammad Ayman Ikmal Shawaludin | Izzat Fahimuddin Mohamed Suffian | Irna Farikhah | M P Khairunnisa
Source: Journal of Research in Nanoscience and Nanotechnology (article, 2023-12-08)
Field: Materials Science — Materials Chemistry

Poly Lactic-co-Glycolic Acid Nanoparticles: Flow Rates and Gauge Sizes Influence the Droplet Surface Tension and Particle Sizing Electrospray is a novel and versatile approach to the synthesis of nanosized particles. In this paper, the effects of electrospray process parameters such as gauge size (18 G-25 G) and flow rate (0.9-1.5 ml hr-1) on droplet surface tension and electrospray particle size were evaluated. 25 mg Poly Lactic-co-Glycolic Acid (PLGA) were dissolved in 100 ml acetone before being subjected to electrospray. Tate’s Law was used to calculate the surface tension while the Malvern nanosizer was used to measure the particle size. Based on Tate’s Law calculation, when the gauze size increased from 18 G-25 G, the droplet surface tension increased from 10.07 Nm-1 to 18.17 Nm-1 showing a direct pattern. At the same time, the flowrate is inversely proportional to droplet surface tension. When the flow rate increased from 0.9-1.5 ml hr-1, the droplet surface tension was reduced. This is due to the increasing ratio of vicious force and surface tension. For particle size, as surface tension increased from 10.07 Nm-1 to 18.17 Nm-1, particle size increased from 205.57 nm to 612 nm owing to the corona discharge producing larger progeny droplets that chain into smaller particles through coulomb fission as more charge is required to overcome the surface tension. In conclusion, flow rate and gauge size influenced the surface tension thus affecting the nanosized particles. Surface tension ; Glycolic acid ; Sizing ; Nanoparticle ; Materials science ; Particle (ecology) ; Particle size ; Lactic acid ; Tension (geology) ; Flow properties ; Chemical engineering ; Nanotechnology ; Chemistry ; Composite material ; Mechanics ; Thermodynamics ; Organic chemistry ; Physical chemistry ; Physics ; Bacteria Pickering emulsions and particle stabilization ; Innovative Microfluidic and Catalytic Techniques Innovation ; Algal biology and biofuel production
Authors: Junho Jung | Heesoo Kim
Source: Korean Journal of Metals and Materials (article, 2023-05-04)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Prediction of Grain Size from Colored Microstructure We constructed a convolutional neural network to estimate average grain size from microstructure images. In the previous study from our research group, the network was trained using GB-type images in which the grain matrix and grain boundary were represented in white and black, respectively. The model well estimated the same GB-type images, but did not properly predict CL-type images where grain boundaries were defined by color contrast between grains. In the present study, the convolutional neural network was trained using CL-type microstructure images, and evaluated the average grain size, for comparison with the previous GB-type model. The relationship between the microstructure image and the average grain size was determined using regression rather than classification. Then, the results were compared with the previous ones. Finally, the proposed approach was used for actual microstructural image analysis. Mid-layer images were extracted to examine how the network recognizes the characteristics of microstructures, such as grain color and grain boundary. Like the previous GB-type model, the present CL-type model seems to estimate the average grain size from the curvature of the grain boundary through edge detection of the grain boundaries. However, the GB- and CL-type models only properly predicted the grain size from the same kind of images as the training data, because the definitions of the grain boundaries of the two models were different. Grain size ; Microstructure ; Materials science ; Grain boundary ; Artificial intelligence ; Convolutional neural network ; Curvature ; Pattern recognition (psychology) ; Mathematics ; Computer science ; Geometry ; Metallurgy Machine Learning in Materials Science ; Mineral Processing and Grinding ; X-ray Diffraction in Crystallography
Authors: Ziqi Guo | Zherui Han | Dudong Feng | Guang Lin | Xiulin Ruan
Source: arXiv (Cornell University) (preprint, 2023-11-21)
Field: Materials Science — Materials Chemistry

Sampling-accelerated First-principles Prediction of Phonon Scattering Rates for Converged Thermal Conductivity and Radiative Properties First-principles prediction of thermal conductivity and radiative properties is crucial. However, computing phonon scattering, especially for four-phonon scattering, could be prohibitively expensive, and the thermal conductivity even for silicon was still under-predicted and not converged in the literature. Here we propose a method to estimate scattering rates from a small sample of scattering processes using maximum likelihood estimation. The computational cost of estimating scattering rates and associated thermal conductivity and radiative properties is dramatically reduced by over 99%. This allows us to use an unprecedented q-mesh of 32*32*32 for silicon and achieve a converged thermal conductivity value that agrees much better with experiments. The accuracy and efficiency of our approach make it ideal for the high-throughput screening of materials for thermal and optical applications. Thermal conductivity ; Scattering ; Phonon ; Radiative transfer ; Phonon scattering ; Thermal ; Computational physics ; Materials science ; Physics ; Optics ; Condensed matter physics ; Thermodynamics ; Composite material Thermal properties of materials ; Radiative Heat Transfer Studies ; Machine Learning in Materials Science