jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mukaddes Karataş | Ercan Aydoğmuş
Source: International Journal of Advanced Natural Sciences and Engineering Researches (article, 2023-04-22)
Field: Materials Science — Polymers and Plastics

Use of Inorganic Wastes as Fillers in Production of Polyester Composites and Evaluation of Properties of Obtained Composite The use of industrial factory wastes as a filler in polymer composite materials is becoming more and more common. In this way, these wastes that cause environmental pollution are eliminated and new composite materials are developed. Polymer composites with low cost and high thermal stability are preferred in many sectors. In this research, some physical and chemical properties of inorganic waste reinforced polyester composites have been evaluated. The use of industrial inorganic wastes as fillers in polyester composites develops some thermophysical properties of composites. In particular, such industrial wastes are dried before being used in the composite, the particle size is reduced, and it is ensured to have a homogeneous structure. This type of waste, which is used as filler, is used in the polyester composite in optimum proportions. The use of high inorganic fillers in polyester both weakens the mechanical strength of the composites and negatively affects the matrix structure. Besides, inorganic waste reinforcement raises the surface hardness of polyester composites. Such fillers are improved both the thermal stability and non-flammability of the polyester composite. High-density inorganic fillers are used to obtain a high-density and economical polyester composite. Low-density inorganic industrial wastes also reduce the density of composites. However, the reinforcement of inorganic fillers into polyester at a high rate adversely affects both the surface morphology and the workability of the produced composites. Materials science ; Composite material ; Polyester ; Filler (materials) ; Composite number ; Polyester resin ; Thermal stability ; Polymer ; Chemical engineering Natural Fiber Reinforced Composites ; Polymer Nanocomposites and Properties ; Tribology and Wear Analysis
Authors: Andy Schneider | Jan Bergsch | Georg Lipps
Source: Nucleic Acids Research (article, 2023-04-26)
Field: Materials Science — Materials Chemistry

The monomeric archaeal primase from <i>Nanoarchaeum equitans</i> harbours the features of heterodimeric archaeoeukaryotic primases and primes sequence-specifically The marine thermophilic archaeon Nanoarchaeum equitans possesses a monomeric primase encompassing the conserved domains of the small catalytic and the large regulatory subunits of archaeoeukaryotic heterodimeric primases in one protein chain. The recombinant protein primes on templates containing a triplet with a central thymidine, thus displaying a pronounced sequence specificity typically observed with bacterial type primases only. The N. equitans primase (NEQ395) is a highly active primase enzyme synthesizing short RNA primers. Termination occurs preferentially at about nine nucleotides, as determined by HPLC analysis and confirmed with mass spectrometry. Possibly, the compact monomeric primase NEQ395 represents the minimal archaeoeukaryotic primase and could serve as a functional and structural model of the heterodimeric archaeoeukaryotic primases, whose study is hindered by engagement in protein assemblies and rather low activity. Primase ; Biology ; Biochemistry ; Peptide sequence ; DNA ; Recombinant DNA ; Computational biology ; Genetics ; Gene ; RNA Enzyme Structure and Function ; Bacteriophages and microbial interactions ; Genomics and Phylogenetic Studies
Authors: Ashish Sharma | V. S. Rangra
Source: Journal of Physics Condensed Matter (article, 2023-12-05)
Field: Materials Science — Materials Chemistry

Electronic and elastic correlations in AlB<sub>2</sub>-type two-dimensional hexagonal MBenes With the advent of MXenes as two-dimensional (2D) materials beyond graphene, non carbonic 2D materials analogically referred as MBenes have significantly attracted researchers' attention. Such 2D MBenes remains largely unexplored. Here, we systematically investigate electronic and elastic properties of 2D transition metal (TM) based AlB<sub>2</sub>-type hexagonal MBenes consisting of a honeycomb networked graphene like boron layer embedded with diverse TM atoms at center. First we determine the thermodynamic, dynamic, thermal, and mechanical stability of MBenes, considering a wide range of 3d, 4d, and 5d TM elements. Electronic and elastic calculations are performed for stable MBenes in order to parameterize and investigate the interdependence of properties. Elastic calculations predicts the brittle-ductile nature and bond character of MBenes while unraveling the in-plane auxetic behavior. Our electronic calculations predict the metallic band nature for 2D VB<sub>2</sub>, NbB<sub>2</sub>, TaB<sub>2</sub>, and WB<sub>2</sub>along with previously reported dirac points in 2D TiB<sub>2</sub>, FeB<sub>2</sub>, ZrB<sub>2</sub>, and HfB<sub>2</sub>. The elastic and electronic calculations clearly indicates the non-directional metallic bonds and intrinsically ductile nature of 2D-FeB<sub>2</sub>distinct from other MBenes. Subsequently we performed a covariance analysis to assess the correlation amongst the observables of interest and further establish the interdependence of the properties. Our calculations for elastic correlations also suggests that mechanical brittle-ductile nature and auxetic behavior of MBenes can be tuned by strain engineering of the elastic constants. Our results further suggests that strong correlations between Poisson ratio and d state electrons can be utilized to tune the auxetic behavior by careful doping of the materials. Our work demonstrates the weak elastic-electronic correlations, suggesting that the strain engineering can be utilized for the tailored behavior of MBenes for practical applications. Thus, our systematic analysis of the mechano-elastic and electronic properties of 2D hexagonal MBenes and their correlations advance our understandings of emergent 2D family. Materials science ; Graphene ; Brittleness ; Condensed matter physics ; Electronic structure ; Honeycomb ; Phonon ; Auxetics ; Nanotechnology ; Physics ; Composite material MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene research and applications
Authors: Rafael C. Castro | Ricardo N.M.J. Páscoa | M. Lúcia M.F.S. Saraiva | João L.M. Santos | David S.M. Ribeiro
Source: Biosensors (article, 2023-03-30)
Field: Chemistry — Analytical Chemistry

Kinetic Determination of Acetylsalicylic Acid Using a CdTe/AgInS2 Photoluminescence Probe and Different Chemometric Models The combination of multiple quantum dots (QDs) in a multi-emitter nanoprobe can be envisaged as a promising sensing scheme, as it enables obtaining a collective response of individual emitters towards a given analyte and allows for achieving specific analyte-response profiles. The processing of these profiles using adequate chemometric methods empowers a more sensitive, reliable and selective determination of the target analyte. In this work, we developed a kinetic fluorometric method consisting of a dual CdTe/AgInS<sub>2</sub> quantum dots photoluminescence probe for the determination of acetylsalicylic acid (ASA). The fluorometric response was acquired as second-order time-based excitation/emission matrices that were subsequently processed using chemometric methods seeking to assure the second-order advantage. The data obtained in this work are considered second-order data as they have a three-dimensional size, I × J × K (where I represents the samples' number, J the fluorescence emission wavelength while K represents the time). In order to select the most adequate chemometric method regarding the obtained data structure, different chemometric models were tested, namely unfolded partial least squares (U-PLS), N-way partial least squares (N-PLS), multilayer feed-forward neural networks (MLF-NNs) and radial basis function neural networks (RBF-NNs). Analyte ; Partial least squares regression ; Photoluminescence ; Quantum dot ; Cadmium telluride photovoltaics ; Fluorescence ; Chemometrics ; Chemistry ; Biological system ; Analytical Chemistry (journal) ; Materials science ; Nanotechnology ; Chromatography ; Computer science ; Optoelectronics ; Machine learning ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Electrochemical sensors and biosensors ; Electrochemical Analysis and Applications
Authors: Alina Y. Morkina | Rita I. Babicheva | Elena A. Korznikova | Nariman A. Enikeev | Kaveh Edalati | Sergey V. Dmitriev
Source: Metals (article, 2023-09-14)
Field: Materials Science — Materials Chemistry

A Molecular Dynamics Simulation to Shed Light on the Mechanical Alloying of an Al-Zr Alloy Induced by Severe Plastic Deformation In a recent experimental work, as a result of severe plastic deformation, a non-equilibrium solid solution was obtained despite the very limited solubility of zirconium (Zr) in aluminum (Al). This opens up a new path in the development of heat-treatable alloys with improved electrical and mechanical properties, where mechanically dissolved elements can form intermetallic particles that contribute to precipitation strengthening. In the present study, molecular dynamics simulations were performed to better understand the process of mechanical dissolution of Zr within an Al model, with Zr atoms segregated along its grain boundaries. Stress–strain curves, radial distribution functions, and mechanisms of plastic deformation and dissolution of Zr in Al were analyzed. It is revealed that orientation of the grain boundary with segregation normal to the shear direction promotes more efficient mixing of alloy components compared to its parallel arrangement. This happens because in the second case, grain boundary sliding is the main deformation mechanism, and Zr tends to remain within the interfaces. In contrast, the involvement of dislocations in the case of normal orientation of grain boundaries with Zr segregation significantly contributes to deformation and facilitates better dissolution of Zr in the Al matrix. The findings obtained can provide new insights considering the role of texture during mechanical alloying of strongly dissimilar metals. Materials science ; Intermetallic ; Grain boundary ; Dissolution ; Alloy ; Molecular dynamics ; Zirconium ; Deformation (meteorology) ; Precipitation ; Deformation mechanism ; Severe plastic deformation ; Metallurgy ; Strengthening mechanisms of materials ; Work (physics) ; Composite material ; Microstructure ; Thermodynamics ; Chemical engineering ; Computational chemistry ; Chemistry Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Nuclear Materials and Properties
Authors: Bridget V. Granados-Vega | Carlos Maldonado-Flores | Camila S. Gómez-Navarro | Walter M. Warren‐Vega | Armando Campos-Rodríguez | Luis A. Romero‐Cano
Source: Foods (article, 2023-10-19)
Field: Chemistry — Analytical Chemistry

Development of a Low-Cost Artificial Vision System as an Alternative for the Automatic Classification of Persian Lemon: Prototype Test Simulation In the present research work, an algorithm of artificial neural network (ANN) has been developed based on the processing of digital images of Persian lemons with the aim of optimizing the quality control of the product. For this purpose, the physical properties (weight, thickness of the peel, diameter, length, and color) of 90 lemons selected from the company Esperanza de San José Ornelas SPR de RL (Jalisco, Mexico) were studied, which were divided into three groups (Category "extra", Category I, and Category II) according to their characteristics. The parameters of weight (26.50 ± 3.00 g), diameter/length (0.92 ± 0.08) and thickness of the peel (1.50 ± 0.29 mm) did not present significant differences between groups. On the other hand, the color (determined by the RGB and HSV models) presents statistically significant changes between groups. Due to the above, the proposed ANN correctly classifies 96.60% of the data obtained for each of the groups studied. Once the ANN was trained, its application was tested in an automatic classification process. For this purpose, a prototype based on the operation of a stepper motor was simulated using Simulink from Matlab, which is connected to three ideal switches powered by three variable pulse generators that receive the information from an ANN and provide the corresponding signal for the motor to turn to a specific position. Manual classification is a process that requires expert personnel and is prone to human error. The scientific development presented shows an alternative for the automation of the process using low-cost computational tools as a potential alternative. Artificial intelligence ; Test (biology) ; Computer science ; Machine learning ; Pattern recognition (psychology) ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Frederic Kanter | Jan Lellmann | Herbert Thiele | Steve E. Kalloger | David F. Schaeffer | Axel Wellmann | Oliver Klein
Source: Cancers (article, 2023-01-22)
Field: Chemistry — Spectroscopy

Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks Despite numerous diagnostic and therapeutic advances, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, and is the fourth leading cause of cancer death in developing countries. Besides its increasing prevalence, pancreatic malignancies are characterized by poor prognosis. Omics technologies have potential relevance for PDAC assessment but are time-intensive and relatively cost-intensive and limited by tissue heterogeneity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can obtain spatially distinct peptide-signatures and enables tumor classification within a feasible time with relatively low cost. While MALDI-MSI data sets are inherently large, machine learning methods have the potential to greatly decrease processing time. We present a pilot study investigating the potential of MALDI-MSI in combination with neural networks, for classification of pancreatic ductal adenocarcinoma. Neural-network models were trained to distinguish between pancreatic ductal adenocarcinoma and other pancreatic cancer types. The proposed methods are able to correctly classify the PDAC types with an accuracy of up to 86% and a sensitivity of 82%. This study demonstrates that machine learning tools are able to identify different pancreatic carcinoma from complex MALDI data, enabling fast prediction of large data sets. Our results encourage a more frequent use of MALDI-MSI and machine learning in histopathological studies in the future. Pancreatic cancer ; Pancreatic ductal adenocarcinoma ; MALDI imaging ; Adenocarcinoma ; Medicine ; Mass spectrometry imaging ; Computer science ; Artificial intelligence ; Cancer ; Matrix-assisted laser desorption/ionization ; Oncology ; Internal medicine ; Mass spectrometry ; Chemistry ; Desorption Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Molecular Biology Techniques and Applications
Authors: Lina Zhang | Xinfeng Tan | Jianguo Jiao | Dan Guo | Jianbin Luo
Source: Friction (article, 2023-04-26)
Field: Materials Science — Materials Chemistry

Comparative analysis of frictional behavior and mechanism of molybdenum ditelluride with different structures Abstract Two-dimensional (2D) transition metal dichalcogenides (TMDCs) have layered structures with excellent tribological properties. Since the energy difference between hexagonal-molybdenum ditelluride (2H-MoTe 2 ) and distorted octahedral-molybdenum ditelluride (1T’-MoTe 2 ) is very small among the transition metal dichalcogenides (TMDCs), MoTe 2 becomes one of the most promising candidates for phase engineering. In our experiment, we found that the friction force and friction coefficient (COF) of 2H-MoTe 2 were an order of magnitude smaller than those of 1T’-MoTe 2 by the atomic force microscope (AFM) experiments. The friction difference between 1T’-MoTe 2 and 2H-MoTe 2 was further verified in molecular dynamics (MD) simulations. The density functional theory (DFT) calculations suggest that the friction contrast is related to the difference in sliding energy barrier of the potential energy surface (PES) for a tip sliding across the surface. The PES obtained from the DFT calculation indicates that the maximum energy barrier and the minimum energy path (MEP) energy barrier of 2H-MoTe 2 are both smaller than those of 1T’-MoTe 2 , which means that less energy needs to be dissipated during the sliding process. The difference in energy barrier of the PES could be ascribed to its larger interlayer spacing and weaker Mo–Te interatomic interactions within the layers of 2H-MoTe 2 than those of 1T’-MoTe 2 . The obvious friction difference between 1T’-MoTe 2 and 2H-MoTe 2 not only provides a new non-destructive means to detect the phase transition by the AFM, but also provides a possibility to tune friction by controlling the phase transition, which has the potential to be applied in extreme environments such as space lubrication. Molybdenum ; Transition metal ; Octahedron ; Materials science ; Molecular dynamics ; Density functional theory ; Surface energy ; Nanotechnology ; Condensed matter physics ; Crystallography ; Chemistry ; Composite material ; Computational chemistry ; Physics ; Crystal structure ; Metallurgy 2D Materials and Applications ; Adhesion, Friction, and Surface Interactions ; MXene and MAX Phase Materials
Authors: Bohayra Mortazavi
Source: Applied Sciences (article, 2023-01-28)
Field: Materials Science — Materials Chemistry

First Theoretical Realization of a Stable Two-Dimensional Boron Fullerene Network Successful experimental realizations of two-dimensional (2D) C60 fullerene networks have been among the most exciting latest advances in the rapidly growing field of 2D materials. In this short communication, on the basis of the experimentally synthesized full boron B40 fullerene lattice, and by structural minimizations of extensive atomic configurations via density functional theory calculations, we could, for the first time, predict a stable B40 fullerene 2D network, which shows an isotropic structure. Acquired results confirm that the herein predicted B40 fullerene network is energetically and dynamically stable and also exhibits an appealing thermal stability. The elastic modulus and tensile strength are estimated to be 125 and 7.8 N/m, respectively, revealing strong bonding interactions in the predicted nanoporous nanosheet. Electronic structure calculations reveal metallic character and the possibility of a narrow and direct band gap opening by applying the uniaxial loading. This study introduces the first boron fullerene 2D nanoporous network with an isotropic lattice, remarkable stability, and a bright prospect for the experimental realization. Fullerene ; Materials science ; Isotropy ; Nanosheet ; Nanoporous ; Boron ; Chemical physics ; Thermal stability ; Nanotechnology ; Density functional theory ; Lattice (music) ; Condensed matter physics ; Computational chemistry ; Physics ; Chemistry Boron and Carbon Nanomaterials Research ; Graphene research and applications ; Fullerene Chemistry and Applications
Authors: Chih‐Yu Lee | Chuanyu Lian | Hongyi Sun | Yi‐Siou Huang | Niloy Acharjee | Ichiro Takeuchi | Carlos Rı́os
Source: Journal of the American Ceramic Society (article, 2023-11-07)
Field: Materials Science — Materials Chemistry

Spatial and temporal control of glassy‐crystalline domains in optical phase change materials Abstract Chalcogenide phase change materials (PCMs) have become one of the most promising material platforms for the Optics and Photonics community. The unparalleled combination of nonvolatility and large optical property modulation promises devices with low‐energy consumption and ultra‐compact form factors. At the core of all these applications lies the difficult task of precisely controlling the glassy amorphous and crystalline domains that compose the PCM microstructure and dictate the optical response. A spatially controllable glassy‐crystalline domain distribution is desired for intermediate optical response (vs. binary response between fully amorphous and crystalline states), and temporally resolved domains are sought after for repeatable reconfiguration. In this perspective, we briefly review the fundamentals of PCM phase transition in various reconfiguring approaches for optical devices. We discuss each method's underpinning mechanisms, design, advantages, and downsides. Finally, we lay out current challenges and future directions in this field. Amorphous solid ; Chalcogenide ; Materials science ; Control reconfiguration ; Photonics ; Optoelectronics ; Phase (matter) ; Engineering physics ; Nanotechnology ; Computer science ; Physics ; Chemistry Phase-change materials and chalcogenides ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Zhenjia Zeng | Lei Wang | Yiran Wu | Zhipeng Hu | Julian Evans | Xinhua Zhu | Gaoao Ye | Sailing He
Source: Nanomaterials (article, 2023-10-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Utilizing Mixed Training and Multi-Head Attention to Address Data Shift in AI-Based Electromagnetic Solvers for Nano-Structured Metamaterials When designing nano-structured metamaterials with an iterative optimization method, a fast deep learning solver is desirable to replace a time-consuming numerical solver, and the related issue of data shift is a subtle yet easily overlooked challenge. In this work, we explore the data shift challenge in an AI-based electromagnetic solver and present innovative solutions. Using a one-dimensional grating coupler as a case study, we demonstrate the presence of data shift through the probability density method and principal component analysis, and show the degradation of neural network performance through experiments dealing with data affected by data shift. We propose three effective strategies to mitigate the effects of data shift: mixed training, adding multi-head attention, and a comprehensive approach that combines both. The experimental results validate the efficacy of these approaches in addressing data shift. Specifically, the combination of mixed training and multi-head attention significantly reduces the mean absolute error, by approximately 36%, when applied to data affected by data shift. Our work provides crucial insights and guidance for AI-based electromagnetic solvers in the optimal design of nano-structured metamaterials. Solver ; Computer science ; Paradigm shift ; Metamaterial ; Artificial neural network ; Deep learning ; Artificial intelligence ; Machine learning Metamaterials and Metasurfaces Applications ; Microwave Engineering and Waveguides ; Animal Vocal Communication and Behavior