jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: David H. Gracias
Source: IEEE Nanotechnology Magazine (article, 2022-01-20)
Field: Materials Science — Biomaterials

Integrated Nanotechnology 2.0: 3D, Smart, Flexible, and Dynamic [Highlights] In recent decades, the extreme miniaturization in very large-scale microchip fabrication and the development of ultrasensitive instrumentation such as scanning probe microscopy and bottom-up macromolecular chemistry, have allowed integrated nanotechnology to transform human engineering. Now, an emergent thrust seeks to move this field into new areas, such as biological interfaces, wearables, and small-scale robotics. Many of these functions are already embodied in our bodies, plants, and organisms, and they require unique attributes, including three dimensionality, heterogeneous materials integration, flexibility, motion, and shape change. Integrated nanotechnology 2.0 focuses on the design, fabrication, and assembly of nanostructured devices that could significantly impact human life through artificial intelligence, smart medicine, and robotics. In this article, a few examples, principles, and perspectives are outlined. Nanotechnology ; Robotics ; Flexibility (engineering) ; Computer science ; Wearable computer ; Miniaturization ; Artificial intelligence ; Robot ; Engineering ; Materials science Supramolecular Self-Assembly in Materials ; Modular Robots and Swarm Intelligence ; Advanced biosensing and bioanalysis techniques
Authors: Shuangqi Song | Mingbo Zhou | Yutao Rao | Ling Xu | Jianxin Song
Source: Scientia Sinica Chimica (article, 2022-05-09)
Field: Materials Science — Materials Chemistry

Development of the design, synthesis and property of porphyrin arrays and porphyrinoids <p indent="0mm">As <italic>pigments of life</italic> porphyrins are widespread in nature such as heme and chlorophyll. Porphyrinoids generally show similar structures and properties to those of porphyrins, therefore porphyrinoids exhibit wide potential applications in the field of photoelectric devices, photodynamic therapy, solar cells, biosensing and artificial intelligence. Based on the recent work of our group this account summarizes researches on the design, synthesis and properties of porphyrin arrays and porphyrinoids systematically. Furthermore, the developments in this field are predicted, aiming to provide insights for the design and synthesis of porphyrin arrays and porphyrinoids with novel structures and excellent properties. Porphyrin ; Property (philosophy) ; Computer science ; Combinatorial chemistry ; Chemistry ; Photochemistry ; Philosophy ; Epistemology Porphyrin and Phthalocyanine Chemistry ; Photosynthetic Processes and Mechanisms ; Photochromic and Fluorescence Chemistry
Authors: Rahul Islam Barbhuiya | Saipriya Ramalingam | Harsimran Kaur Kalra | Abdallah Elsayed | Winny Routray | Annamalai Manickavasagan | Ashutosh Singh
Source: Biophysica (review, 2022-06-24)
Field: Materials Science — Materials Chemistry

Application of Non-Destructive Testing Techniques (NDTT) to Characterize Nanocarriers Used for Drug Delivery: A Mini Review The synthesis of tailored and highly engineered multifunctional pharmaceutical nanocarriers is an emerging field of study in drug delivery applications. They have a high surface-area-to-volume ratio, aiding the targeted drug’s biodistribution and pharmacokinetic properties. Therefore, the characterization of nanocarriers is critical for understanding their physicochemical properties, which significantly impact their molecular and systemic functioning. To achieve specific goals, particle size, surface characteristics, and drug release properties of nanocarriers must be managed. This mini review provides an overview of the applications of non-destructive testing techniques (NDTT) to reveal the characteristics of nanocarriers, considering their surface charge, porosity, size, morphology, and crystalline organization. The compositional and microstructural characterization of nanocarriers through NDTT, such as dynamic light scattering, X-ray diffraction, confocal laser scanning microscopy, ultraviolet-visible spectroscopy, scanning electron microscopy, atomic force microscopy, and nuclear magnetic resonance spectroscopy, have been comprehensively reviewed. Furthermore, NDTT is only used to characterize physicochemical parameters related to the physiological performance of nanocarriers but does not account for nanocarrier toxicity. Hence, it is highly recommended that in the future, NDTT be developed to assess the toxicity of nanocarriers. In addition, by developing more advanced, effective, and precise techniques, such as machine vision techniques using artificial intelligence, the future of using NDTT for nanocarrier characterization will improve the evaluation of internal quality parameters. Nanocarriers ; Nanotechnology ; Drug delivery ; Materials science ; Characterization (materials science) Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Drug Solubulity and Delivery Systems
Authors: Dietmar Koch | Elisabeth Opila | Gérard L. Vignoles
Source: Open Ceramics (article, 2022-05-18)
Field: Materials Science — Ceramics and Composites

Advances in ceramic composites: manufacture, performances and applications Fiber-reinforced ceramic matrix composites (CMCs) are designed for high temperature application under severe environments. Recent achievements helped establishing non-oxide CMCs in aeroengines and all-oxide CMCs in industrial application. In parallel, research focuses on fully understanding the adjustment of properties, evaluating the processing chain and describing and modeling manufacturing routes as well as properties in application relevant environments. Furthermore, multifunctional, smart composite materials are designed and processed with support of artificial intelligence and digital twins. Also, green manufacturing and life cycle assessment will have to be considered in the near future to manufacture sustainable high-performance CMCs. Ceramic ; Composite material ; Materials science ; Ceramic composite Advanced ceramic materials synthesis ; Innovations in Concrete and Construction Materials ; Additive Manufacturing and 3D Printing Technologies
Authors: Yuan Si | Ji‐Chun Lian | Ke Yang | Tao Huang | Wei‐Qing Huang | Wangyu Hu | Gui‐Fang Huang
Source: physica status solidi (RRL) - Rapid Research Letters (article, 2022-04-15)
Field: Materials Science — Materials Chemistry

Data‐Driven Approach to Designing 2D Van der Waals Heterostructures: Misjudgment of Band Alignment Type and Its Mechanism Data‐driven approach, including database building and high‐throughput design, is rapidly applied to various fields, ranging from physics to materials. Predicting the band alignment type at interfaces, which is based on Anderson's rule (AR), is crucial for implementing the data‐driven approach in the field of 2D van der Waals heterostructures (vdWHs). The validity of the AR‐based data‐driven approach, however, is ambiguous, particularly when it involves band alignment. Based on a large‐scale calculation (1231 heterostructures), it is explicitly demonstrated that the current AR‐based data‐driven approach have prediction errors for certain band alignment type, including the inability to predict type‐other band alignment and the partial misclassification of type II band alignment as type III. Such prediction errors are quantitatively explained by geometric deformation, interlayer orbital coupling, and charge redistribution effects. As a result, the current data‐driven design approach is insufficiently reliable for type II, type III, and type‐other heterostructures, which may result in inaccurate or incomplete design results. Herein, a flaw in the current AR‐based data‐driven approach is elucidated as well as important information for reasonably designing 2D vdWHs is provided in general. Heterojunction ; Computer science ; van der Waals force ; Type (biology) ; Physics ; Condensed matter physics ; Quantum mechanics ; Biology 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: E. Teruya | Tadashi Takeuchi | Hidekazu Morita | Takayuki Hayashi | Kanta Ono
Source: Machine Learning Science and Technology (article, 2022-03-29)
Field: Materials Science — Materials Chemistry

ARTS: autonomous research topic selection system using word embeddings and network analysis Abstract The materials science research process has become increasingly autonomous due to the remarkable progress in artificial intelligence. However, autonomous research topic selection (ARTS) has not yet been fully explored due to the difficulty of estimating its promise and the lack of previous research. This paper introduces an ARTS system that autonomously selects potential research topics that are likely to reveal new scientific facts yet have not been the subject of much previous research by analyzing vast numbers of articles. Potential research topics are selected by analyzing the difference between two research concept networks constructed from research information in articles: one that represents the promise of research topics and is constructed from word embeddings, and one that represents known facts and past research activities and is constructed from statistical information on the appearance patterns of research concepts. The ARTS system is also equipped with functions to search and visualize information about selected research topics to assist in the final determination of a research topic by a scientist. We developed the ARTS system using approximately 100 00 articles published in the Computational Materials Science journal. The results of our evaluation demonstrated that research topics studied after 2016 could be generated autonomously from an analysis of the articles published before 2015. This suggests that potential research topics can be effectively selected by using the ARTS system. The arts ; Computer science ; Selection (genetic algorithm) ; Subject (documents) ; Data science ; Process (computing) ; Word (group theory) ; Artificial intelligence ; World Wide Web ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Software Engineering Research
Authors: Jiawei Zhang | Geng Fu | Fan Xu | Bin Zhang | Ning Tian | Sichen Qin
Source: Energy Reports (article, 2022-10-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Influence of the conductivity of the magnetoelectric composites electrode under resonance frequency and its validation by laser vibrometer system Magnetic sensor based on functional dielectric with high-efficiency energy property is projected to stimulate technological advancement in the fields of energy internet, power engineering materials, energy harvesting and storage, touchless human–machine interface, and magnetoreception for artificial intelligence (AI). At present, the research and development of hypersensitive magnetic sensors remain extraordinarily challenging. The effects of magnetoelectric (ME) composite electrodes with different conductivities and their electromechanical resonance frequency of Metal/PZT/Metal laminates were investigated in this paper. The results of calculations and experiments showed that the magnetoelectric coupling in the magnetoelectric composite (Metal/PZT/Metal) was mostly caused by the piezoelectricity effect in the radial direction and Ampere force under DC bias magnetic fields. According to the radial strain measurement, it can be further indicated that the capping layer with high conductivity produced a larger magnetoelectric voltage at the range of resonance frequency. Therefore, the ME voltage coefficient of the silver electrodes was higher than that of the gold electrodes, and they were 77.8 mV/(cm Oe) and 58.3 mV/(cm Oe) at a DC magnetic field of 3000 Oe, respectively. The ME coupling in the ME composite was mostly connected to the piezoelectricity effect and Ampere force, as well as the conductivity of the electrode layer of the ME composite. The optimization of the electrode structures of magnetoelectric composites provides a vital reference for the development of energy storage, information storage and sensing technology in new power systems. Materials science ; Piezoelectricity ; Magnetoelectric effect ; Dielectric ; Composite material ; Electrode ; Composite number ; Conductivity ; Magnetic field ; Voltage ; Optoelectronics ; Electrical engineering ; Ferroelectricity Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies
Authors: Craig Burkhart | Bing Jiang | George Papakonstantopoulos | Patrycja Polińska | Hongyi Xu | Richard J. Sheridan | L. Catherine Brinson | Wei Chen
Source: Tire Science and Technology (article, 2022-06-23)
Field: Materials Science — Materials Chemistry

Data-Driven Multiscale Science for Tread Compounding ABSTRACT Tread compounding has always been faced with the simultaneous optimization of multiple performance properties, most of which have tradeoffs between the properties. The search for overcoming these conflicting tradeoffs have led many companies in the tire industry to discover and develop material physics-based platforms. This report describes some of our efforts to quantify compound structures and properties at multiple scales, and their subsequent application in compound design. Integration of experiment and simulation has been found to be critical to highlighting the levers in data-driven multiscale compound tread design. Tread ; Compounding ; Computer science ; Mechanical engineering ; Engineering ; Biochemical engineering ; Materials science ; Composite material Machine Learning in Materials Science
Authors: Jamshid Sourati | James Evans
Source: arXiv (Cornell University) (preprint, 2022-07-02)
Field: Materials Science — Materials Chemistry

Complementary artificial intelligence designed to augment human discovery Neither artificial intelligence designed to play Turing's imitation game, nor augmented intelligence built to maximize the human manipulation of information are tuned to accelerate innovation and improve humanity's collective advance against its greatest challenges. We reconceptualize and pilot beneficial AI to radically augment human understanding by complementing rather than competing with human cognitive capacity. Our approach to complementary intelligence builds on insights underlying the wisdom of crowds, which hinges on the independence and diversity of crowd members' information and approach. By programmatically incorporating information on the evolving distribution of scientific expertise from research papers, our approach follows the distribution of content in the literature while avoiding the scientific crowd and the hypotheses cognitively available to it. We use this approach to generate valuable predictions for what materials possess valuable energy-related properties (e.g., thermoelectricity), and what compounds possess valuable medical properties (e.g., asthma) that complement the human scientific crowd. We demonstrate that our complementary predictions, if identified by human scientists and inventors at all, are only discovered years further into the future. When we evaluate the promise of our predictions with first-principles equations, we demonstrate that increased complementarity of our predictions does not decrease and in some cases increases the probability that the predictions possess the targeted properties. In summary, by tuning AI to avoid the crowd, we can generate hypotheses unlikely to be imagined or pursued until the distant future and promise to punctuate scientific advance. By identifying and correcting for collective human bias, these models also suggest opportunities to improve human prediction by reformulating science education for discovery. Computer science ; Human intelligence ; Data science ; Collective intelligence ; Artificial intelligence ; Complementarity (molecular biology) ; Cognitive science ; Psychology Machine Learning in Materials Science
Authors: Kazuma Kobayashi | Dinesh Kumar | Syed Bahauddin Alam
Source: arXiv (Cornell University) (preprint, 2022-11-24)
Field: Materials Science — Materials Chemistry

AI-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology In response to the urgent need to establish AI/ML-integrated Digital Twin (DT) technology within next-generation nuclear systems, advancements in modeling methods and simulation codes are necessary. The increased complexity of models demands significant computational resources to quantify their uncertainties. To address this challenge, a data-driven non-intrusive uncertainty quantification method via polynomial chaos expansion is introduced as an efficient strategy within the finite element analysis-based fuel performance code BISON. Models of and fuels, alongside SiC/SiC cladding material, were prepared to demonstrate the proposed method. The impact of four independent uncertain input variables on the system output was quantified, requiring fewer than 100 BISON simulations for each model. This approach not only accelerates the modeling and simulation task but also enhances the reliability in the development of DT-enabling technologies. Unavailability ; Adaptability ; Uncertainty quantification ; Computer science ; Systems engineering ; Data science ; Engineering ; Reliability engineering ; Machine learning Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fault Detection and Control Systems
Authors: N. Sakhnenko | І. І. Stepanova | A. N. Korogodskaya | Ann Karakurkchi | Olena Skrypnyk | Anatoly Dzheniuk | Oleksandr Halak
Source: Eastern-European Journal of Enterprise Technologies (article, 2022-12-30)
Field: Chemistry — Inorganic Chemistry

Patterns in the electrochemical synthesis of thin-film photocatalytic materials based on titanium heterooxide compounds This paper reports a study the into processes of plasma-electrolyte formation of heterooxidic coatings on titanium alloys for the photocatalytic destruction of natural and man-made toxicants. The synthesis of coatings was carried out from aqueous solutions of diphosphates under a galvanostatic mode. For a quantitative description of photocatalytic reactions, the constants of the reaction rate kf from the linearized dependences ln(Ct/C0), where Ct is the current, C0 is the initial concentration of the reagent, were calculated. The morphology of the coating surface was investigated by atomic force microscopy, and the results were visualized by reconstruction of the relief in the form of 2D and 3D topographic maps. Morphological features of coatings made of titanium oxide (IV) and heterooxidic composites containing oxides of transition metals were analyzed. It is shown that the specific surface area remains an effective factor in regulating the photocatalytic activity of coatings. The establishment of the morphology of heterooxidic composites, as well as methods for controlling this parameter of the target product, is a constant component of the systematic study of such materials in determining their functional properties. It has been established that compared to oxide-titanium coatings whose surface layers are characterized by a toroidal mesostructure, heterooxidic compositions have a more developed surface, which has a positive effect on their functional properties. Subsequent heat treatment similarly affects the properties of the coating. The speed constants of the photocatalytic decomposition of the methyl yellow-hot azo dye were used to rank coatings of different composition according to their functional properties. Coatings from TiO2·ZnO showed the highest catalytic activity among the materials studied – kf is 5.26 10-3 min-1, which is several times higher than the corresponding value for TiO2 Photocatalysis ; Materials science ; Titanium ; Coating ; Reagent ; Titanium oxide ; Oxide ; Conversion coating ; Chemical engineering ; Electrolyte ; Electrochemistry ; Aqueous solution ; Decomposition ; Composite material ; Metallurgy ; Catalysis ; Electrode ; Physical chemistry ; Chemistry ; Organic chemistry Pigment Synthesis and Properties ; TiO2 Photocatalysis and Solar Cells ; Catalysis and Hydrodesulfurization Studies
Authors: S Ankit | Shilpa Thakur | Surbhi Sharma
Source: International Journal of Radiology & Radiation Therapy (article, 2022-10-14)
Field: Materials Science — Materials Chemistry

Biomedical applications of single-particle based material: quantum dots Quantum dots are artificial "droplets" of charge that might include a single electron or a group of thousands. Their normal sizes range from nanometres to a few microns, and employing cutting-edge nanofabrication technology, it is possible to precisely regulate their size, shape, and interactions. These special qualities have drawn a lot of interest in the biomedical community recently because they make it possible for real-time tissue imaging (bioimaging), diagnostics, single molecule probes, and medication administration, among many other applications. Due to their high brightness, photo bleach resistance, multiplexing ability, and high surface-to-volume ratio, quantum dots are ideal candidates for intracellular tracking, diagnostics, in vivo imaging, and therapeutic delivery. The optical properties of quantum dots can be tuned by size and composition. In the current paper, we will review properties, preparation, characteristics as well as biomedical applications. In addition, some issues along with future aspects. Furthermore, several commercially accessible alternatives are technically contrasted with QDs. Finally, we suggest technical factors that must be considered to enhance the clinical outcome of QDs. Quantum dot ; Nanotechnology ; Materials science ; Computer science ; Brightness ; Nanolithography ; Particle (ecology) ; Fabrication ; Physics ; Optics ; Medicine Quantum Dots Synthesis And Properties
Authors: Lorentz Jäntschi
Source: Foundations (article, 2022-09-15)
Field: Chemistry — Organic Chemistry

Energetics of C8B8N8, N12B12, and C24 Macrocycles and Two [4]Catenanes Polyynes are alternations of single and triple bonds between carbon atoms, while cumulenes are successions of double bonds. Since the triple bond is the strongest bond between two carbon atoms, recent preoccupations included synthesizing and condensing cyclic polyynes and cumulenes and their clusters. Density functional theory calculations predicted stable monocyclic rings formation for a number of C atoms equal to or higher than 16. Alternative to the series of Carbon atoms are alternations of Boron and Nitrogen. Large rings (such as those of 24 atoms) can be crossed and thus small clusters can be formed. Patterns of three crosses seem to further stabilize the atomic ensemble. Clusters of 4C24 and 4B12N12 (96 atoms) as well as 4C26 (104 atoms) have been designed, and their conformation has been studied here. Energetics ; Catenane ; Triple bond ; Carbon fibers ; Chemistry ; Density functional theory ; Crystallography ; Boron ; Computational chemistry ; Double bond ; Molecule ; Materials science ; Physics ; Polymer chemistry ; Organic chemistry ; Thermodynamics Fullerene Chemistry and Applications ; Boron and Carbon Nanomaterials Research ; Energetic Materials and Combustion
Authors: Jiawang Hong | Qi Ren | Yongheng Li | Ziyan Gao | Jianming Deng | Yingzhuo Lun | Xueyun Wang | Gang Tang
Source: Research Square (preprint, 2022-07-06)
Field: Materials Science — Materials Chemistry

High-throughput Screening of Plastic Deformable Inorganic Semiconductors Throughput ; High-throughput screening ; Semiconductor ; Materials science ; Optoelectronics ; Computer science ; Nanotechnology ; Biology ; Telecommunications ; Wireless ; Bioinformatics ZnO doping and properties ; Nanowire Synthesis and Applications ; Advanced Sensor and Energy Harvesting Materials