jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bohayra Mortazavi | Fazel Shojaei | M. Yagmurcukardes | Alexander V. Shapeev | Xiaoying Zhuang
Source: Carbon (article, 2022-09-02)
Field: Materials Science — Materials Chemistry

Anisotropic and outstanding mechanical, thermal conduction, optical, and piezoelectric responses in a novel semiconducting BCN monolayer confirmed by first-principles and machine learning Monolayer ; Materials science ; Graphene ; Anisotropy ; Thermal expansion ; Condensed matter physics ; Piezoelectricity ; Density functional theory ; Nanotechnology ; Composite material ; Computational chemistry ; Chemistry ; Optics ; Physics Graphene research and applications ; Thermal properties of materials ; MXene and MAX Phase Materials
Authors: Mohammad Ahangari | ELHAM MAHMOODI | Nagihan Delibas | Jafar Mostafaei | Elnaz Asghari | Aligholi Niaei
Source: TURKISH JOURNAL OF CHEMISTRY (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Application of SrFeO3 perovskite as electrode material for supercapacitor and investigation of Co-doping effect on the B-site Energy storage by supercapacitors with short charging time and high power density is one of the types of electrochemical storage systems. Perovskite oxides have been significantly investigated as promising materials for energy storage in electrochemical systems. In this study, three perovskites, SrFeO3, SrCoO3, and SrCo0.5Fe0.5O3, were prepared using the sol-gel method and used as supercapacitor electrode materials. In fact, in this research, two consecutive elements (Fe, Co) from the periodic table that differ by one unit in atomic number are placed in the perovskite structure to study their electrochemical properties for use in supercapacitors. From the obtained results, it was found that Co doping with a ratio of 1/1 (Co/Fe) at B site of SrFeO3 reduced the specific capacitance from 101.687 F g-1 to 60.912 F g-1 at a scan rate of 10 mV s-1. Also, the specific capacitance of SrCoO3 decreased from 68.639 F g-1 to 60.912 F g-1 at the same substitution rate at B site. Supercapacitor ; Electrochemistry ; Capacitance ; Perovskite (structure) ; Chemistry ; Doping ; Electrode ; Energy storage ; Inorganic chemistry ; Horizontal scan rate ; Chemical engineering ; Materials science ; Optoelectronics ; Cyclic voltammetry ; Power (physics) ; Crystallography ; Physical chemistry ; Thermodynamics Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Electrocatalysts for Energy Conversion
Authors: Rupal Srivastava | Saeed Hamood Alsamhi | Niall Murray | Declan M. Devine
Source: Sensors (review, 2022-09-08)
Field: Materials Science — Materials Chemistry

Shape Memory Alloy-Based Wearables: A Review, and Conceptual Frameworks on HCI and HRI in Industry 4.0 Ever since its discovery, the applications of Shape Memory Alloys (SMA) can be found across a range of application domains, from structural design to medical technology. This is based upon the unique and inherent characteristics such as thermal Shape Memory Effect (SME) and Superelasticity (or Pseudoelasticity). While thermal SME is used for shape morphing applications wherein temperature change can govern the shape and dimension of the SMA, Superelasticity allows the alloy to withstand a comparatively very high magnitude of loads without undergoing plastic deformation at higher temperatures. These unique properties in wearables have revolutionized the field, and from fabrics to exoskeletons, SMA has found its place in robotics and cobotics. This review article focuses on the most recent research work in the field of SMA-based smart wearables paired with robotic applications for human-robot interaction. The literature is categorized based on SMA property incorporated and on actuator or sensor-based concept. Further, use-cases or conceptual frameworks for SMA fiber in fabric for ‘Smart Jacket’ and SMA springs in the shoe soles for ‘Smart Shoes’ are proposed. The conceptual frameworks are built upon existing technologies; however, their utility in a smart factory concept is emphasized, and algorithms to achieve the same are proposed. The integration of the two concepts with the Industrial Internet of Things (IIoT) is discussed, specifically regarding minimizing hazards for the worker/user in Industry 5.0. The article aims to propel a discussion regarding the multi-faceted applications of SMAs in human-robot interaction and Industry 5.0. Furthermore, the challenges and the limitations of the smart alloy and the technological barriers restricting the growth of SMA applications in the field of smart wearables are observed and elaborated. Wearable computer ; Human–computer interaction ; Alloy ; Wearable technology ; Computer science ; Psychology ; Materials science ; Embedded system ; Metallurgy Shape Memory Alloy Transformations ; Advanced Memory and Neural Computing ; Advanced Sensor and Energy Harvesting Materials
Authors: Shangjie Ge‐Zhang | Hong Yang | Haiming Ni | Hongbo Mu | Mingming Zhang
Source: Frontiers in Bioengineering and Biotechnology (review, 2022-08-05)
Field: Materials Science — Surfaces, Coatings and Films

Biomimetic superhydrophobic metal/nonmetal surface manufactured by etching methods: A mini review As an emerging fringe science, bionics integrates the understanding of nature, imitation of nature, and surpassing nature in one aspect, and it organically combines the synergistic complementarity of function and structure-function integrated materials which is of great scientific interest. By imitating the microstructure of a natural biological surface, the bionic superhydrophobic surface prepared by human beings has the properties of self-cleaning, anti-icing, water collection, anti-corrosion and oil-water separation, and the preparation research methods are increasing. The preparation methods of superhydrophobic surface include vapor deposition, etching modification, sol-gel, template, electrostatic spinning, and electrostatic spraying, which can be applied to fields such as medical care, military industry, ship industry, and textile. The etching modification method can directly modify the substrate, so there is no need to worry about the adhesion between the coating and the substrate. The most obvious advantage of this method is that the obtained superhydrophobic surface is integrated with the substrate and has good stability and corrosion resistance. In this article, the different preparation methods of bionic superhydrophobic materials were summarized, especially the etching modification methods, we discussed the detailed classification, advantages, and disadvantages of these methods, and the future development direction of the field was prospected. Nonmetal ; Materials science ; Etching (microfabrication) ; Nanotechnology ; Metal ; Surface (topology) ; Composite material ; Metallurgy ; Geometry ; Mathematics ; Layer (electronics) Surface Modification and Superhydrophobicity ; Adhesion, Friction, and Surface Interactions ; Advanced Sensor and Energy Harvesting Materials
Authors: Ryan Kingsbury | Andrew Rosen | Ayush Gupta | Jason M. Munro | Shyue Ping Ong | Anubhav Jain | Shyam Dwaraknath | Matthew K. Horton | Kristin A. Persson
Source: npj Computational Materials (article, 2022-09-12)
Field: Materials Science — Materials Chemistry

A flexible and scalable scheme for mixing computed formation energies from different levels of theory Abstract Computational materials discovery efforts are enabled by large databases of properties derived from high-throughput density functional theory (DFT), which now contain millions of calculations at the generalized gradient approximation (GGA) level of theory. It is now feasible to carry out high-throughput calculations using more accurate methods, such as meta-GGA DFT; however recomputing an entire database with a higher-fidelity method would not effectively leverage the enormous investment of computational resources embodied in existing (GGA) calculations. Instead, we propose here a general procedure by which higher-fidelity, low-coverage calculations (e.g., meta-GGA calculations for selected chemical systems) can be combined with lower-fidelity, high-coverage calculations (e.g., an existing database of GGA calculations) in a robust and scalable manner. We then use legacy PBE(+ U ) GGA calculations and new r 2 SCAN meta-GGA calculations from the Materials Project database to demonstrate that our scheme improves solid and aqueous phase stability predictions, and discuss practical considerations for its implementation. Density functional theory ; Leverage (statistics) ; Scalability ; Computer science ; Fidelity ; Scheme (mathematics) ; Mixing (physics) ; Throughput ; Statistical physics ; Computational science ; Materials science ; Theoretical computer science ; Chemistry ; Physics ; Computational chemistry ; Database ; Mathematics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Loukas Kollias | Roger Rousseau | Vassiliki‐Alexandra Glezakou | Matteo Salvalaglio
Source: Journal of the American Chemical Society (article, 2022-06-16)
Field: Chemistry — Inorganic Chemistry

Understanding Metal–Organic Framework Nucleation from a Solution with Evolving Graphs A mechanistic understanding of metal-organic framework (MOF) synthesis and scale-up remains underexplored due to the complex nature of the interactions of their building blocks. In this work, we investigate the collective assembly of building units at the early stages of MOF nucleation, using MIL-101(Cr) as a prototypical example. Using large-scale molecular dynamics simulations, we observe that the choice of solvent (water and <i>N</i>,<i>N</i>-dimethylformamide), the introduction of ions (Na<sup>+</sup> and F<sup>-</sup>) and the relative populations of MIL-101(Cr) half-secondary building unit (half-SBU) isomers have a strong influence on the cluster formation process. Additionally, the shape, size, nucleation and growth rates, crystallinity, and short and long-range order largely vary depending on the synthesis conditions. We evaluate these properties as they naturally emerge when interpreting the self-assembly of MOF nuclei as the time evolution of an undirected graph. Solution-induced conformational complexity and ionic concentration have a dramatic effect on the morphology of clusters emerging during assembly. While pure solvents lead to the rapid formation of a small number of large clusters, the presence of ions in aqueous solutions results in smaller clusters and slower nucleation. This diversity is captured by the key features of the graph representation. Principle component analysis on graph properties reveals that only a small number of molecular descriptors is needed to deconvolute MOF self-assembly. Descriptors such as the average coordination number between half-SBUs and fractal dimension are of particulalr interest as they can be can be followed experimentally by techniques like by time-resolved spectroscopy. Ultimately, graph theory emerges as an approach that can be used to understand complex processes revealing molecular descriptors accessible by both simulation and experiment. Nucleation ; Chemistry ; Chemical physics ; Aqueous solution ; Molecular dynamics ; Ionic liquid ; Cluster (spacecraft) ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Computer science Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Paul Seibert | Alexander Raßloff | Karl A. Kalina | Marreddy Ambati | Markus Kästner
Source: Integrating materials and manufacturing innovation (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Microstructure Characterization and Reconstruction in Python: MCRpy Abstract Microstructure characterization and reconstruction (MCR) is an important prerequisite for empowering and accelerating integrated computational materials engineering. Much progress has been made in MCR recently; however, in the absence of a flexible software platform it is difficult to use ideas from other researchers and to develop them further. To address this issue, this work presents MCRpy as an easy-to-use, extensible and flexible open-source MCR software platform. MCRpy can be used as a program with graphical user interface, as a command line tool and as a Python library. The central idea is that microstructure reconstruction is formulated as a modular and extensible optimization problem. In this way, arbitrary descriptors can be used for characterization and arbitrary loss functions combining arbitrary descriptors can be minimized using arbitrary optimizers for reconstructing random heterogeneous media. With stochastic optimizers, this leads to variations of the well-known Yeong–Torquato algorithm. Furthermore, MCRpy features automatic differentiation, enabling the utilization of gradient-based optimizers. In this work, after a brief introduction to the underlying concepts, the capabilities of MCRpy are demonstrated by exemplarily applying it to typical MCR tasks. Finally, it is shown how to extend MCRpy by defining a new microstructure descriptor and readily using it for reconstruction without additional implementation effort. Python (programming language) ; Modular design ; Computer science ; Extensibility ; Graphical user interface ; Software ; Characterization (materials science) ; Computational science ; Software framework ; Interface (matter) ; Programming language ; Theoretical computer science ; Software development ; Component-based software engineering ; Parallel computing Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Keling Tu | Shaozhe Wen | Ying Cheng | Yanan Xu | Pan Tong | Haonan Hou | Riliang Gu | Jianhua Wang | Fengge Wang | Qun Sun
Source: Plant Methods (article, 2022-06-11)
Field: Chemistry — Analytical Chemistry

A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning This strategy has potential for wide adoption in large-scale variety genuineness detection operations for internal quality control or governmental regulatory agencies, or for accelerating the breeding of new varieties. Besides, it could easily be extended to other target varieties and other crops. Hyperspectral imaging ; Robustness (evolution) ; Artificial intelligence ; Support vector machine ; Variety (cybernetics) ; Machine learning ; Pattern recognition (psychology) ; Computer science ; Biotechnology ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Filipa Lebre | Nivedita Chatterjee | Stefania de Freitas Zefirino Costa | Eli Fernández-de-Gortari | Carla Lopes | João Meneses | Luís Ortiz | Ana R. Ribeiro | Vânia Vilas‐Boas | Ernesto Alfaro‐Moreno
Source: Nanomaterials (review, 2022-05-25)
Field: Materials Science — Materials Chemistry

Nanosafety: An Evolving Concept to Bring the Safest Possible Nanomaterials to Society and Environment The use of nanomaterials has been increasing in recent times, and they are widely used in industries such as cosmetics, drugs, food, water treatment, and agriculture. The rapid development of new nanomaterials demands a set of approaches to evaluate the potential toxicity and risks related to them. In this regard, nanosafety has been using and adapting already existing methods (toxicological approach), but the unique characteristics of nanomaterials demand new approaches (nanotoxicology) to fully understand the potential toxicity, immunotoxicity, and (epi)genotoxicity. In addition, new technologies, such as organs-on-chips and sophisticated sensors, are under development and/or adaptation. All the information generated is used to develop new in silico approaches trying to predict the potential effects of newly developed materials. The overall evaluation of nanomaterials from their production to their final disposal chain is completed using the life cycle assessment (LCA), which is becoming an important element of nanosafety considering sustainability and environmental impact. In this review, we give an overview of all these elements of nanosafety. Risk analysis (engineering) ; Sustainability ; Nanotoxicology ; Life-cycle assessment ; Cosmetics ; Biochemical engineering ; Computer science ; Nanotechnology ; Engineering ; Production (economics) ; Business ; Chemistry ; Materials science Nanoparticles: synthesis and applications ; Healthcare and Environmental Waste Management ; Air Quality and Health Impacts
Authors: Dae-Hyun Jung | Jeong‐Do Kim | Ho‐Youn Kim | Taek Sung Lee | Hyoung Seok Kim | Soo Hyun Park
Source: Frontiers in Plant Science (article, 2022-03-11)
Field: Chemistry — Analytical Chemistry

A Hyperspectral Data 3D Convolutional Neural Network Classification Model for Diagnosis of Gray Mold Disease in Strawberry Leaves Gray mold disease is one of the most frequently occurring diseases in strawberries. Given that it spreads rapidly, rapid countermeasures are necessary through the development of early diagnosis technology. In this study, hyperspectral images of strawberry leaves that were inoculated with gray mold fungus to cause disease were taken; these images were classified into healthy and infected areas as seen by the naked eye. The areas where the infection spread after time elapsed were classified as the asymptomatic class. Square regions of interest (ROIs) with a dimensionality of 16 × 16 × 150 were acquired as training data, including infected, asymptomatic, and healthy areas. Then, 2D and 3D data were used in the development of a convolutional neural network (CNN) classification model. An effective wavelength analysis was performed before the development of the CNN model. Further, the classification model that was developed with 2D training data showed a classification accuracy of 0.74, while the model that used 3D data acquired an accuracy of 0.84; this indicated that the 3D data produced slightly better performance. When performing classification between healthy and asymptomatic areas for developing early diagnosis technology, the two CNN models showed a classification accuracy of 0.73 with regards to the asymptomatic ones. To increase accuracy in classifying asymptomatic areas, a model was developed by smoothing the spectrum data and expanding the first and second derivatives; the results showed that it was possible to increase the asymptomatic classification accuracy to 0.77 and reduce the misclassification of asymptomatic areas as healthy areas. Based on these results, it is concluded that the proposed 3D CNN classification model can be used as an early diagnosis sensor of gray mold diseases since it produces immediate on-site analysis results of hyperspectral images of leaves. Convolutional neural network ; Asymptomatic ; Hyperspectral imaging ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Smoothing ; Computer vision ; Medicine ; Pathology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Plant Pathogens and Fungal Diseases
Authors: Yige Li | Yangnan Hu | Hao Wei | Wei Cao | Yanru Qi | Shan Zhou | Panpan Zhang | Huawei Li | Geng‐Lin Li | Renjie Chai
Source: Journal of Nanobiotechnology (article, 2022-08-31)
Field: Materials Science — Materials Chemistry

Two-dimensional Ti3C2Tx MXene promotes electrophysiological maturation of neural circuits These results expand our understanding of interaction between Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXene and NSCs and provide a critical line of evidence for using Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXene in neural interface or scaffold in stem cell therapy. Neural stem cell ; Neurite ; Neuroscience ; Electrophysiology ; Multielectrode array ; Biological neural network ; Neuron ; Patch clamp ; Neural cell ; Chemistry ; Nanotechnology ; Stem cell ; Materials science ; Biology ; Cell ; Cell biology ; Microelectrode ; In vitro ; Electrode ; Biochemistry MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; Neuroscience and Neural Engineering
Authors: Changsong Xu | Xueyang Li | Peng Chen | Yun Zhang | Hongjun Xiang | L. Bellaïche
Source: Advanced Materials (article, 2022-01-13)
Field: Materials Science — Materials Chemistry

Assembling Diverse Skyrmionic Phases in Fe<sub>3</sub>GeTe<sub>2</sub> Monolayers Skyrmionic magnetic states are promising in advanced spintronics. This topic is experiencing recent progress in 2D magnets, with, for example, a near 300 K Curie temperature observed in Fe<sub>3</sub> GeTe<sub>2</sub> . However, despite previous studies reporting skyrmions in Fe<sub>3</sub> GeTe<sub>2</sub> , such a system remains elusive, since it has been reported to host either Néel-type or Bloch-type textures, while a net Dzyaloshinskii-Moriya interaction (DMI) cannot occur in this compound for symmetry reasons. It is thus desirable to develop an accurate model to deeply understand Fe<sub>3</sub> GeTe<sub>2</sub> . Here, a newly developed method adopting spin invariants is applied to build a first-principle-based Hamiltonian, which predicts colorful topological defects assembled from the unit of Bloch lines, and reveals the critical role of specific forms of fourth-order interactions in Fe<sub>3</sub> GeTe<sub>2</sub> . Rather than the DMI, it is the multiple fourth-order interactions, with symmetry and spin-orbit couplings considered, that stabilize both Néel-type and Bloch-type skyrmions, as well as antiskyrmions, without any preference for clockwise versus counterclockwise spin rotation. This study also demonstrates that spin invariants can be used as a general approach to study complex magnetic interactions. Skyrmion ; Spintronics ; Condensed matter physics ; Hamiltonian (control theory) ; Point reflection ; Monolayer ; Spin (aerodynamics) ; Curie temperature ; Physics ; Ferromagnetism ; Materials science ; Nanotechnology ; Mathematics 2D Materials and Applications ; Topological Materials and Phenomena ; Magnetic properties of thin films
Authors: Sergio Moles Quintero | Michael M. Haley | Miklós Kertész | Juan Casado
Source: Angewandte Chemie International Edition (review, 2022-08-20)
Field: Chemistry — Organic Chemistry

Polycyclic Hydrocarbons from [4<i>n</i>]Annulenes: Correlation versus Hybridization Forces in the Formation of Diradicaloids The conceptual connections between [4n] Hückel antiaromaticity, disjoint orbitals, correlation energy, pro-aromaticity and diradical character for a variety of extended π-conjugated systems, including some salient recent examples of nanographenes and polycyclic aromatic radicals, are provided based on their [4n]annulene peripheries. The realization of such structure-property relationships has led to a beneficial pedagogic exercise establishing design guidelines for diradicaloids. The antiaromatic fingerprint of the [4n]annulene peripheries upon orbital interactions due to internal covalent connectors gives insights into the diradicaloid property of a diversity of π-conjugated molecules that have fascinated chemists recently. Antiaromaticity ; Annulene ; Diradical ; Aromaticity ; Conjugated system ; Computational chemistry ; Graphyne ; Chemistry ; Molecule ; Chemical physics ; Density functional theory ; Physics ; Organic chemistry ; Polymer ; Quantum mechanics Synthesis and Properties of Aromatic Compounds ; Fullerene Chemistry and Applications ; Synthesis and characterization of novel inorganic/organometallic compounds