jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junwei Qin | Xiaohua Chen | Yanlin Wang | Yuzhi Zhu | Shiwei Pan | Wei Zhou | Mingwen Chen | Zidong Wang
Source: Metals (article, 2022-08-28)
Field: Materials Science — Materials Chemistry

Fabrication Techniques and the Formation Mechanism of Nanoparticles and Nanoclusters in Metal Materials Continuous innovation in the design of metallic materials is essential for further progress in aerospace, automotive, construction, and shipping. Fine grain strengthening is considered to increase the strength of metals without losing plasticity. However, many fabrication techniques are restricted to very small sizes. Recently, the introduction of in situ nanoparticles with coherent or semi-coherent interfaces in the metallic matrix achieves simultaneous enhancement of the strength and ductility of metallic materials. In this review, the focus is on fabrication techniques and the formation mechanism of nanoparticles and nanoclusters in metal materials. The effects of nanoparticles on grain refinement, inhibiting segregation, second phase, and inclusion refinement are discussed, and the mechanism of simultaneous improvement in the strength and ductility of nanostructured metal materials is briefly covered. Finally, we provide a summary and outline of the possible direction for further advances in this research field. Nanoclusters ; Fabrication ; Materials science ; Ductility (Earth science) ; Nanoparticle ; Nanotechnology ; Metal ; Structural material ; Mechanism (biology) ; Nanocomposite ; Composite material ; Metallurgy Microstructure and mechanical properties ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Bahareh Bakhshi | Sudhakar Malla | Lokesh Siddalinge Gowda
Source: Indian Journal of Pharmaceutical Education and Research (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

Garlic Mediated Green Synthesis of Silver Nanoparticles as Antifungal Agents against Magnaporthe oryzae Background: Plant elements such as carbohydrates, lipids, flavonoids, polyphenols, enzymes, terpenoids, and alkaloids are used as reducing substance in the green production of silver nanoparticles. The strategy proved to be highly straightforward, cost-effective, and practical. Materials and Methods: The synthesis of nanoparticles was validated using optical inspection, in which the yellow colour solution became brown. UV-visible spectroscopy, XRD, FTIR analysis, and SEM were used to further characterise the material. Results: Transmission electron microscopy have shown that the silver nanoparticles size was between 10-46 nm (SEM). Size of silver nanoparticles was found to be between 10-46nm approximately as determined by transmission electron microscopy (SEM). Well diffusion method demonstrated the antifungal activity of AgNPs on Magnaporthe oryzae with the zone of inhibition of 5 and 11mm when 12.5 and 25g/ ml of AgNPs was used respectively. Lowest inhibitory concentration was found to be 5.2. Conclusion: The leakage of reducing sugars and proteins was used to explore the mode of action of nanoparticles' antifungal activity, indicating that AgNPs were able to reduce membrane permeability. Silver nanoparticle ; Allium sativum ; Nuclear chemistry ; Chemistry ; Transmission electron microscopy ; Agar diffusion test ; Nanoparticle ; Antifungal ; Polyphenol ; Reducing agent ; Scanning electron microscope ; Germination ; Conidium ; Materials science ; Biochemistry ; Botany ; Microbiology ; Nanotechnology ; Biology ; Organic chemistry Nanoparticles: synthesis and applications ; Essential Oils and Antimicrobial Activity ; Phytochemistry and Bioactivity Studies
Authors: Nataliia Behei | О. V. Тryhubchak | Bogdan Pryymak
Source: Pharmacia (article, 2022-08-15)
Field: Chemistry — Analytical Chemistry

Development of amlodipine and enalapril combined tablets based on quality by design and artificial neural network for confirming of qualitative composition Using approaches of Quality by design, namely dispersion analysis, random balance method, regression analysis and neural networks, the composition and technology tablets based on amlodipine besylate and enalapril maleate have been developed. Using dispersion analysis was determined the effect of 30 excipients on 10 pharmaco-technological parameters of tablets. With the help of the desirability function, the leaders of the excipients were selected from 6 functional groups. It was confirmed that the composition with the best pharmaco-technological parameters, determined by using statistical methods, coincides with the results studies the synthesized Feed-forward neural network. The quantities of preferable excipients at 3 levels were identified by the random balance method. The relationship between the studied factors and the quality of the tablets was described by regression equations. Based on the placement of equal output lines, the optimal composition of amlodipine tablets with enalapril was established. Amlodipine ; Artificial neural network ; Enalapril ; Composition (language) ; Computer science ; Mathematics ; Artificial intelligence ; Medicine ; Internal medicine ; Blood pressure ; Angiotensin-converting enzyme Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods ; Polymer Synthesis and Characterization
Authors: Jinquan Hu | Huihua Yang | Guoliang Zhao | Ruizhi Zhou
Source: Computational Intelligence and Neuroscience (article, 2022-05-14)
Field: Chemistry — Analytical Chemistry

Research on Online Rapid Sorting Method of Waste Textiles Based on Near-Infrared Spectroscopy and Generative Adversity Network In this paper, aiming at the application of online rapid sorting of waste textiles, a large number of effective high-content blending data are generated by using generative adversity network to deeply mine the combination relationship of blending spectra, and A BEGAN-RBF-SVM classification model is constructed by compensating the imbalance of negative samples in the data set. Various experiments show that the model can effectively extract the spectrum of pure textile samples. The classification model has high robustness and high speed, reaches the performance of similar products in the world, and has a broad application market. Robustness (evolution) ; Sorting ; Support vector machine ; Computer science ; Generative grammar ; Generative model ; Artificial intelligence ; Artificial neural network ; Data set ; Set (abstract data type) ; Machine learning ; Textile ; Pattern recognition (psychology) ; Data mining ; Process engineering ; Materials science ; Algorithm ; Engineering ; Chemistry Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Color Science and Applications
Authors: Oktay Göcenler | Cansu M. Yenici | Kerem Kahraman | Cengizhan Büyükdağ | Çağdaş Dağ
Source: Mugla Journal of Science and Technology (article, 2022-12-02)
Field: Materials Science — Materials Chemistry

BIOMOLECULAR SOLUTION X-RAY SCATTERING AT n2STAR BEAMLINE Small angle X-ray Scattering (SAXS) is a method for determining basic structural characteristics such as the size, shape, and surface of particles. SAXS data can be used to generate low resolution models of biomolecules faster than any other conventional experimental structural biology tool. SAXS data is mostly collected in synchrotron facilities to obtain the best scattering data possible however home source SAXS devices can also generate valuable data when properly optimized. Here, we examined sample data collection and optimization at home source SAXS beamline in terms of the concentration, purity, and duration of data acquisition. We validated that high concentration, monodisperse and ultra pure protein samples obtained by size exclusion chromatography are necessary for generating viable SAXS data using a home source beamline. At least one hour is required to generate a feasible model from SAXS data, although longer data collection times do not always translate to higher resolutions. We show that with small optimizations during data collection and analysis SAXS can characterize properties such as oligomerization, molecular mass, and overall shape of particles in solution under physiological conditions. Small-angle X-ray scattering ; Beamline ; Scattering ; Synchrotron ; Materials science ; Resolution (logic) ; Small-angle scattering ; Crystallography ; Optics ; Chemistry ; Computer science ; Physics ; Artificial intelligence ; Beam (structure) Enzyme Structure and Function ; Protein Structure and Dynamics ; Glycosylation and Glycoproteins Research
Authors: Joshua Peeples | Julie F. Jameson | Nisha M. Kotta | Jonathan M. Grasman | Whitney L. Stoppel | Alina Zare
Source: BME Frontiers (article, 2022-01-01)
Field: Materials Science — Biomaterials

Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA) for Adipose Tissue Segmentation <i>Objective</i>. We aim to develop a machine learning algorithm to quantify adipose tissue deposition at surgical sites as a function of biomaterial implantation. <i>Impact Statement</i>. To our knowledge, this study is the first investigation to apply convolutional neural network (CNN) models to identify and segment adipose tissue in histological images from silk fibroin biomaterial implants. <i>Introduction</i>. When designing biomaterials for the treatment of various soft tissue injuries and diseases, one must consider the extent of adipose tissue deposition. In this work, we analyzed adipose tissue accumulation in histological images of sectioned silk fibroin-based biomaterials excised from rodents following subcutaneous implantation for 1, 2, 4, or 8 weeks. Current strategies for quantifying adipose tissue after biomaterial implantation are often tedious and prone to human bias during analysis. <i>Methods</i>. We used CNN models with novel spatial histogram layer(s) that can more accurately identify and segment regions of adipose tissue in hematoxylin and eosin (H&E) and Masson's trichrome stained images, allowing for determination of the optimal biomaterial formulation. We compared the method, Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA), to the baseline UNET model and an extension of the baseline model, attention UNET, as well as to versions of the models with a supplemental attention-inspired mechanism (JOSHUA+ and UNET+). <i>Results</i>. The inclusion of histogram layer(s) in our models shows improved performance through qualitative and quantitative evaluation. <i>Conclusion</i>. Our results demonstrate that the proposed methods, JOSHUA and JOSHUA+, are highly beneficial for adipose tissue identification and localization. The new histological dataset and code used in our experiments are publicly available. Adipose tissue ; Biomaterial ; Fibroin ; Biomedical engineering ; Computer science ; H&E stain ; Histogram ; Tissue engineering ; Artificial intelligence ; Pathology ; Medicine ; SILK ; Immunohistochemistry ; Internal medicine Silk-based biomaterials and applications ; Periodontal Regeneration and Treatments
Authors: Špiro Ivošević | Gyöngyi Vastag | Nataša Kovač | Peter Majerič | Rebeka Rudolf
Source: Micromachines (article, 2022-06-29)
Field: Materials Science — Materials Chemistry

A Nonlinear Probabilistic Pitting Corrosion Model of Ni–Ti Alloy Immersed in Shallow Seawater The degradation of metal materials in a marine environment represents the consequence of the electrochemical corrosion of metals under the influence of the environment. The application of new materials in the maritime industry requires experimental, real-world research on the form of corrosive damage and the intensity of the corrosion. This paper analyses the pitting corrosion of a rod-shaped nickel-titanium (Ni-Ti) alloy that was produced by means of the continuous casting method. In total, three samples were posted in a real seawater environment and analysed after 6, 12, and 18 months. Pits were detected on the Ni-Ti alloy after 18 months of exposure to the marine environment. The database on pitting corrosion was created by measuring depth in mm, which was performed by means of a nonlinear method, and by the generation of an artificial database of a total of 120, gauged in critical pit areas. The data were obtained by the application of a nonlinear model, and under the assumption that corrosion starts after 12 months of exposure in the corrosive marine environment. The EDX analysis of the Ni-Ti alloy composition inside the pits and on the edges of the pits indicated that the corrosion process in the hole of the pit occurs due to the degradation of the Ni. Corrosion ; Pitting corrosion ; Seawater ; Metallurgy ; Materials science ; Alloy ; Titanium ; Nickel ; Geology Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Structural Integrity and Reliability Analysis
Authors: Christina H. Wegner | Jürgen Hubbuch
Source: Frontiers in Bioengineering and Biotechnology (article, 2022-12-14)
Field: Chemistry — Analytical Chemistry

Calibration-free PAT: Locating selective crystallization or precipitation sweet spot in screenings with multi-way PARAFAC models When developping selective crystallization or precipitation processes, biopharmaceutical modalities require empirical screenings and analytics tailored to the specific needs of the target molecule. The multi-way chemometric approach called parallel factor analysis (PARAFAC) coupled with ultraviolet visible light (UV/Vis) spectroscopy is able to predict specific concentrations and spectra from highly structured data sets without the need for calibration samples and reference analytics. These calculated models can provide exploratory information on pure species spectra and concentrations in all analyzed samples by representing one model component with one species. In this work, protein mixtures, monoclonal antibodies, and virus-like particles in chemically defined and complex solutions were investigated in three high-throughput crystallization or precipitation screenings with the aim to construct one PARAFAC model per case. Spectroscopic data sets of samples after the selective crystallization or precipitation, washing, and redissolution were recorded and arranged into a four-dimensional data set per case study. Different reference analytics and pure species spectra served as validation. Appropriate spectral preprocessing parameters were found for all case studies allowing even the application of this approach to the third case study in which quantitative concentration analytics are missing. Regardless of the modality or the number of species present in complex solutions, all models were able to estimate the specific concentration and find the optimal process condition regarding yield and product purity. It was shown that in complex solutions, species demonstrating similar phase behavior can be clustered as one component and described in the model. PARAFAC as a calibration-free approach coupled with UV/Vis spectroscopy provides a fast overview of species present in complex solution and of their concentration during selective crystallization or precipitation, washing, and redissolution. Calibration ; Biological system ; Crystallization ; Analyte ; Precipitation ; Computer science ; Preprocessor ; Chemistry ; Process engineering ; Artificial intelligence ; Chromatography ; Mathematics ; Physics ; Statistics ; Meteorology Spectroscopy and Chemometric Analyses ; Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography
Authors: Fan Ye | Yunquan Sun | Jiaping Yang | Yong Liu | Ding Xu | Dongping Ren | Hailong Zhou
Source: Mobile Information Systems (article, 2022-05-20)
Field: Chemistry — Analytical Chemistry

Automatic Grading Technology and Application of Baijiu Based on Segmented Characteristics and IIoT In order to explore the segmented relationship between the segmented rating results of Baijiu and the segmented characteristics of alcohol content and cumulative flow in the distillation process and to verify whether different liquor categories can achieve rating through the segmented characteristics of alcohol content and cumulative flow, in view of this, combined with the Industrial Internet of Things (IIoT) technology and online detection and analysis of alcohol and cumulative traffic, its accuracy can reach ±0.5%, ±1%, with the integration of Baijiu categories, sources, liquor characteristic conditions, and other multisource data, to achieve Baijiu segmented rating data reconstruction, the use of standard error and standard deviation as evaluation indicators, quantification of distilled liquor alcohol, and cumulative flow segmented characteristics to form a liquor rating strategy, so as to use the Arduino platform control motor to achieve automatic grading of Baijiu. Experiments show that the relative error between automatic rating and manual rating is less than 10%, which shows that automatic rating can be better applied to the actual brewing process. It provides a solution for the digitization and standardization of Baijiu grading. Computer science ; Alcohol content ; Standard deviation ; Grading (engineering) ; Artificial intelligence ; Process engineering ; Statistics ; Alcohol ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Currency Recognition and Detection
Authors: Qiuyu Zeng | Jun Xie | Wei Zhou | Jinbo Zhu | Liangliang Liu | Jianqiang Yin | Wenliang Zhu
Source: Minerals (article, 2022-08-05)
Field: Materials Science — Biomaterials

Study of the Crystallographic Distortion Mechanism during the Annealing of Kaolinite The distortion process of kaolinite annealed from 25 °C to 550 °C for different holding times can be characterized using a thermogravimetric/differential scanning calorimeter (TG/DSC) for thermal analysis, X-ray diffraction (XRD) for establishing the crystal structure, the Fourier transform infrared spectrum (FTIR) for identifying the functional groups, and a scanning electron microscope (SEM) for establishing the microstructure. Dehydroxylation is the main reaction during annealing from 25 °C to 550 °C and leads to kaolinite crystal distortion. A stable crystal structure during distortion was obtained by optimizing the bulk phase with quantum chemistry. Then, the crystal structure was studied by using ab initio multiple scattering calculations for X-ray absorption of the fine structure (XAFS). The results of X-ray absorption near the edge structures (XANES) determined that peak shifts and intensity phases slightly increased. The crystal structure distortion of kaolinite during annealing can be explained by the experimental and simulation results. This work provides theoretical support for identifying kaolinite with different degrees of distortion and has the potential for further developments in coal gangue separation. Kaolinite ; Materials science ; Crystal structure ; X-ray absorption fine structure ; Crystal (programming language) ; Crystallography ; Differential scanning calorimetry ; Annealing (glass) ; XANES ; Analytical Chemistry (journal) ; Chemistry ; Spectroscopy ; Physics ; Thermodynamics ; Composite material Clay minerals and soil interactions ; Mineral Processing and Grinding ; Metal Extraction and Bioleaching
Authors: Xiaopeng Wang | Noa Marom
Source: Molecular Systems Design & Engineering (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

An energetics assessment of benzo[<i>a</i>]tetracene and benzo[<i>a</i>]pyrene as triplet–triplet annihilation emitters Optical upconversion (UC) of low energy photons into high energy photons enables solar cells to harvest photons with energies below the band gap of the absorber, reducing the transmission loss. Tetracene ; Benzo(a)pyrene ; Annihilation ; Pyrene ; Energetics ; Physics ; Chemistry ; Photochemistry ; Anthracene ; Nuclear physics ; Thermodynamics ; Organic chemistry Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Organic Electronics and Photovoltaics
Authors: Abdelaziz Lekrine | Ahmed Belaadi | Messaouda Boumaaza | Hassan Alshahrani | Mostefa Bourchak
Source: Research Square (preprint, 2022-04-18)
Field: Materials Science — Polymers and Plastics

Water absorption of industrial high-density polyethylene biocomposites reinforced with Washingtonia filifera fibers: optimization using Fick’s, RSM, and ANN models <title>Abstract</title> The present study aims to investigate the phenomenon of water absorption scattering mechanisms and biocomposite kinetics by the immersion of different HDPE matrix reinforced with different amounts of <italic>Washingtonia filifera</italic> (WF) fibers (10, 20, and 30% by mass) in distilled water at room temperature. The response surface methodology (RSM) and artificial neural network (ANN) models were examined by considering WF fiber content and immersion time in the water absorption of HDPE/WF biocomposite. In this study, the central composite design (CCD) model of RSM was used to perform test design, modelling, and optimization. The process of water absorption was revealed to be tracking the diffusion mode of Fickian. The results obtained show that the addition of WF fibers to HDPE matrix reduced diffusivity. The results also reveal that ANN models were highly accurate in the prediction of water absorption with the training, validation, and test correlation coefficients of 0.9955, 0.9999, and 0.9915, respectively. The optimal conditions for maximum absorption were a fiber content of 29.88% and an immersion time of 752 hours. Moreover, a highly appropriate model to predict HDPE/WF biocomposites water absorption suitable for various industrial applications is proposed. High-density polyethylene ; Absorption of water ; Biocomposite ; Materials science ; Response surface methodology ; Distilled water ; Composite material ; Immersion (mathematics) ; Absorption (acoustics) ; Thermal diffusivity ; Composite number ; Polyethylene ; Chemistry ; Mathematics ; Chromatography ; Thermodynamics ; Physics Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Electrospun Nanofibers in Biomedical Applications
Authors: Alexander R. Bricco | Iliya Miralavy | Shaowei Bo | Or Perlman | Christian T. Farrar | Michael T. McMahon | Wolfgang Banzhaf | Assaf A. Gilad
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-03-06)
Field: Materials Science — Materials Chemistry

Protein Optimization Evolving Tool (POET) based on Genetic Programming ABSTRACT Proteins are used by scientists to serve a variety of purposes in clinical practice and laboratory research. To optimize proteins for greater function, a variety of techniques have been developed. For the development of reporter genes used in Magnetic Resonance Imaging (MRI) based on Chemical Exchange Saturation Transfer (CEST), these techniques have encountered a variety of challenges. Here we develop a mechanism of protein optimization using a computational approach known as “genetic programming”. We developed an algorithm called Protein Optimization Evolving Tool (POET). Starting from a small library of literature values, use of this tool allowed us to develop proteins which produce four times more MRI contrast than what was previously state-of-the-art. Next, we used POET to evolve peptides that produced CEST-MRI contrast at large chemical shifts where no other known peptides have previously demonstrated contrast. This demonstrated the ability of POET to evolve new functions in proteins. Interestingly, many of the peptides produced using POET were dramatically different with respect to their sequence and chemical environment than existing CEST producing peptides, and challenge prior understandings of how those peptides function. This suggests that unlike existing algorithms for protein engineering that rely on divergent evolution, POET relies on convergent evolution. Variety (cybernetics) ; Computer science ; Genetic programming ; Protein engineering ; Computational biology ; Artificial intelligence ; Function (biology) ; Protein function ; Chemistry ; Biology ; Gene ; Genetics ; Biochemistry ; Enzyme Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies