jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daxu Zhang | Yu Liu | Hailong Liu | Yu‐Qi Feng | Guo Hong-bao | Zhiliang Hong | Chao Chen | Yi Zhang
Source: Composite Structures (article, 2021-07-31)
Field: Materials Science — Ceramics and Composites

Characterisation of damage evolution in plain weave SiC/SiC composites using in situ X-ray micro-computed tomography Materials science ; Composite material ; Transverse plane ; Microstructure ; Cracking ; Ultimate tensile strength ; Volume fraction ; Structural engineering Advanced ceramic materials synthesis ; Mechanical Behavior of Composites ; Advanced Surface Polishing Techniques
Authors: Hung-Jin Huang | Yu‐Hsuan Lee | Yung-Ho Hsu | Chia‐Te Liao | Yuh‐Feng Lin | Hui‐Wen Chiu
Source: International Journal of Molecular Sciences (review, 2021-04-19)
Field: Materials Science — Materials Chemistry

Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing Millions of experimental animals are widely used in the assessment of toxicological or biological effects of manufactured nanomaterials in medical technology. However, the animal consciousness has increased and become an issue for debate in recent years. Currently, the principle of the 3Rs (i.e., reduction, refinement, and replacement) is applied to ensure the more ethical application of humane animal research. In order to avoid unethical procedures, the strategy of alternatives to animal testing has been employed to overcome the drawbacks of animal experiments. This article provides current alternative strategies to replace or reduce the use of experimental animals in the assessment of nanotoxicity. The currently available alternative methods include in vitro and in silico approaches, which can be used as cost-effective approaches to meet the principle of the 3Rs. These methods are regarded as non-animal approaches and have been implemented in many countries for scientific purposes. The in vitro experiments related to nanotoxicity assays involve cell culture testing and tissue engineering, while the in silico methods refer to prediction using molecular docking, molecular dynamics simulations, and quantitative structure-activity relationship (QSAR) modeling. The commonly used novel cell-based methods and computational approaches have the potential to help minimize the use of experimental animals for nanomaterial toxicity assessments. Nanotoxicology ; In vivo ; Bioelectromagnetics ; Animal testing ; Current (fluid) ; Biology ; Computational biology ; Biochemical engineering ; Medicine ; Biotechnology ; Engineering ; Ecology ; Physics ; Internal medicine Nanoparticles: synthesis and applications ; Animal testing and alternatives ; Computational Drug Discovery Methods
Authors: Lingling Zhang | Yu Guo | Rui Hao | Yafei Shi | Hongjun You | Nan Hu | Yanzhu Dai | Danjun Liu | Dangyuan Lei | Jixiang Fang
Source: Nature Communications (article, 2021-11-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultra-rapid and highly efficient enrichment of organic pollutants via magnetic mesoporous nanosponge for ultrasensitive nanosensors Currently, owing to the single-molecule-level sensitivity and highly informative spectroscopic characteristics, surface-enhanced Raman scattering (SERS) is regarded as the most direct and effective detection technique. However, SERS still faces several challenges in its practical applications, such as the complex matrix interferences, and low sensitivity to the molecules of intrinsic small cross-sections or weak affinity to the surface of metals. Here, we show an enrichment-typed sensing strategy with both excellent selectivity and ultrahigh detection sensitivity based on a powerful porous composite material, called mesoporous nanosponge. The nanosponge consists of porous β-cyclodextrin polymers immobilized with magnetic NPs, demonstrating remarkable capability of effective and fast removal of organic micropollutants, e.g., ~90% removal efficiency within ~1 min, and an enrichment factor up to ~10<sup>3</sup>. By means of this current enrichment strategy, the limit of detection for typical organic pollutants can be significantly improved by 2~3 orders of magnitude. Consequently, the current enrichment strategy is proved to be applicable in a variety of fields for portable and fast detection, such as Raman and fluorescent sensing. Mesoporous material ; Detection limit ; Nanosensor ; Nanotechnology ; Raman scattering ; Chemistry ; Raman spectroscopy ; Porosity ; Materials science ; Chromatography ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Salifu Nanga | Ahmed Tijani Bawah | Benjamin Ansah Acquaye | Mac-Issaka Billa | Francis Delali Baeta | Nii Afotey Odai | Samuel Kwaku Obeng | Ampem Darko Nsiah
Source: Journal of Data Analysis and Information Processing (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Review of Dimension Reduction Methods Purpose: This study sought to review the characteristics, strengths, weaknesses variants, applications areas and data types applied on the various Dimension Reduction techniques. Methodology: The most commonly used databases employed to search for the papers were ScienceDirect, Scopus, Google Scholar, IEEE Xplore and Mendeley. An integrative review was used for the study where 341 papers were reviewed. Results: The linear techniques considered were Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), Latent Semantic Analysis (LSA), Locality Preserving Projections (LPP), Independent Component Analysis (ICA) and Project Pursuit (PP). The non-linear techniques which were developed to work with applications that have complex non-linear structures considered were Kernel Principal Component Analysis (KPCA), Multi-dimensional Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Self-Organizing Map (SOM), Latent Vector Quantization (LVQ), t-Stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). DR techniques can further be categorized into supervised, unsupervised and more recently semi-supervised learning methods. The supervised versions are the LDA and LVQ. All the other techniques are unsupervised. Supervised variants of PCA, LPP, KPCA and MDS have been developed. Supervised and semi-supervised variants of PP and t-SNE have also been developed and a semi supervised version of the LDA has been developed. Conclusion: The various application areas, strengths, weaknesses and variants of the DR tech- Isomap ; Dimensionality reduction ; Linear discriminant analysis ; Principal component analysis ; Kernel principal component analysis ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Learning vector quantization ; Nonlinear dimensionality reduction ; Supervised learning ; Unsupervised learning ; Machine learning ; Support vector machine ; Kernel method ; Vector quantization ; Artificial neural network Spectroscopy and Chemometric Analyses ; Face and Expression Recognition ; Neural Networks and Applications
Authors: Kianoosh Sattari | Yunchao Xie | Jian Lin
Source: Soft Matter (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Data-driven algorithms for inverse design of polymers The ever-increasing demand for novel polymers with superior properties requires a deeper understanding and exploration of the chemical space. Recently, data-driven approaches to explore the chemical space for polymer design have emerged. Among them, inverse design strategies for designing polymers with specific properties have evolved to be a significant materials informatics platform by learning hidden knowledge from materials data as well as smartly navigating the chemical space in an optimized way. In this review, we first summarize the progress in the representation of polymers, a prerequisite step for the inverse design of polymers. Then, we systematically introduce three data-driven strategies implemented for the inverse design of polymers, i.e., high-throughput virtual screening, global optimization, and generative models. Finally, we discuss the challenges and opportunities of the data-driven strategies as well as optimization algorithms employed in the inverse design of polymers. Polymer ; Inverse ; Space (punctuation) ; Algorithm ; Computer science ; Mathematical optimization ; Materials science ; Mathematics ; Composite material ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Chunhua Fang | Yuning Tao | Jianguo Wang | Can Ding | Li Huang | Mi Zhou | Yi Gu | Yali Wang
Source: Frontiers in Energy Research (article, 2021-11-12)
Field: Materials Science — Materials Chemistry

Mapping Relation of Leakage Currents of Polluted Insulators and Discharge Arc Area A fundamental parameter of polluted insulator online monitoring is the leakage current, which has already been shown to be well-related to the pollution discharge of insulators. In this article, in an effort to quantitatively reflect the discharge intensity and the discharge status by the leakage current, we carried out an experimental study on artificial pollution discharge of insulators. A high-speed photographic apparatus was utilized to capture the entire process of local arcs on a porcelain insulator surface, including the arc generation, the arc development, and the flashover, for which the associated leakage current of insulators was synchronously digitized. A comparative analysis of the relation between the two-dimensional discharge image and the leakage current waveform in the process of arc generation and development shows that if the arc area on the insulator surface is relatively small and the leakage current passes through zero, the arc might completely become extinct, whereas this phenomena will not occur if the arc area is larger. In addition, the amplitude of the discharge arc area is found to be roughly proportional to the square of leakage current over the range of leakage current amplitude from 0 to 150 mA. Our results can provide an important guidance for judgment of the discharge status and the discharge intensity on insulator surfaces using the leakage current of insulators. Arc flash ; Insulator (electricity) ; Electric arc ; Leakage (economics) ; Waveform ; Amplitude ; Electric discharge ; Materials science ; Electrical engineering ; Mechanics ; Optics ; Optoelectronics ; Voltage ; Electrode ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Non-Destructive Testing Techniques ; Infrastructure Maintenance and Monitoring
Authors: Priyanka Kumari | Kamakhya Prakash Misra | Saikat Chattopadhyay | Susruta Samanta
Source: Materials Today Proceedings (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

A brief review on transition metal ion doped ZnO nanoparticles and its optoelectronic applications Materials science ; Nanoparticle ; Nanotechnology ; Nanocrystalline material ; Doping ; Crystallinity ; Nanostructure ; Characterization (materials science) ; Band gap ; Optoelectronics ZnO doping and properties ; Copper-based nanomaterials and applications ; Gas Sensing Nanomaterials and Sensors
Authors: Qiuran Wang | Sam H. Brooks | Tianchang Liu | Neil C. Tomson
Source: Chemical Communications (review, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Tuning metal–metal interactions for cooperative small molecule activation Cluster complexes have attracted interest for decades due to their promise of drawing analogies to metallic surfaces and metalloenzyme active sites, but only recently have chemists started to develop ligand scaffolds that are specifically designed to support multinuclear transition metal cores. Such ligands not only hold multiple metal centers in close proximity but also allow for fine-tuning of their electronic structures and surrounding steric environments. This Feature Article highlights ligand designs that allow for cooperative small molecule activation at cluster complexes, with a particular focus on complexes that contain metal-metal bonds. Two useful ligand-design elements have emerged from this work: a degree of geometric flexibility, which allows for novel small molecule activation modes, and the use of redox-active ligands to provide electronic flexibility to the cluster core. The authors have incorporated these factors into a unique class of dinucleating macrocycles (<sup>n</sup>PDI<sub>2</sub>). Redox-active fragments in <sup>n</sup>PDI<sub>2</sub> mimic the weak-overlap covalent bonding that is characteristic of M-M interactions, and aliphatic linkers in the ligand backbone provide geometric flexibility, allowing for interconversion between a range of geometries as the dinuclear core responds to the requirements of various small molecule substrates. The union of these design elements appears to be a powerful combination for analogizing critical aspects of heterogeneous and metalloenzyme catalysts. Small molecule ; Molecule ; Metal ; Chemistry ; Combinatorial chemistry ; Nanotechnology ; Computational chemistry ; Materials science ; Organic chemistry ; Biochemistry Asymmetric Hydrogenation and Catalysis ; Nanomaterials for catalytic reactions ; Organometallic Complex Synthesis and Catalysis
Authors: A. Karthikeyan | U. Deva Priyakumar
Source: Journal of Chemical Sciences (article, 2021-12-21)
Field: Materials Science — Materials Chemistry

Artificial intelligence: machine learning for chemical sciences Computer science ; Artificial intelligence ; Perspective (graphical) ; Natural science ; Natural (archaeology) ; Relevance (law) ; Artificial neural network ; Machine learning ; Data science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Mahmoud Elsisi | Minh‐Quang Tran | Karar Mahmoud | Diaa‐Eldin A. Mansour | Matti Lehtonen | Mohamed M. F. Darwish
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Towards Secured Online Monitoring for Digitalized GIS Against Cyber-Attacks Based on IoT and Machine Learning Recently, the Internet of Things (IoT) has an important role in the growth and development of digitalized electric power stations while offering ambitious opportunities, specifically real-time monitoring and cybersecurity. In this regard, this paper introduces a novel IoT architecture for the online monitoring of the gas-insulated switchgear (GIS) status instead of the traditional observation methods. The proposed IoT architecture is derived from the concept of the cyber-physic system (CPS) in Industry 4.0. However, the cyber-attacks and the classification of the GIS insulation defects represent the main challenges against the implementation of IoT topology for the online monitoring and tracking of the GIS status. For this purpose, advanced machine learning techniques are utilized to detect cyber-attacks to conduct the paradigm and verification. Different test scenarios on various defects in GIS are performed to demonstrate the effectiveness of the proposed IoT architecture. Partial discharge pulse sequence features are extracted for each defect to represent the inputs for IoT architecture. The results confirm that the proposed IoT architecture based on the machine learning technique, that is the extreme gradient boosting (XGBoost), can visualize all defects in the GIS with different alarms, besides showing the cyber-attacks on the networks effectively. Furthermore, the defects of GIS and the fake data due to the cyber-attacks are recognized and presented on the dashboard of the proposed IoT platform with high accuracy and more clarified visualization to enhance the decision-making about the GIS status. Computer science ; Architecture ; Switchgear ; Visualization ; Internet of Things ; Situation awareness ; Dashboard ; Computer security ; Embedded system ; Artificial intelligence ; Database ; Engineering High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Smart Grid Security and Resilience
Authors: Gabriel dos Passos Gomes | Robert Pollice | Alán Aspuru‐Guzik
Source: Trends in Chemistry (article, 2021-01-14)
Field: Materials Science — Materials Chemistry

Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning Catalysis ; Homogeneous catalysis ; Population ; Homogeneous ; Ab initio ; Field (mathematics) ; Biochemical engineering ; Chemistry ; Artificial intelligence ; Computer science ; Machine learning ; Physics ; Engineering ; Mathematics ; Statistical physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Mohammad Reza Saeb | Navid Rabiee | Masoud Mozafari | Francis Verpoort | Leonid G. Voskressensky | Rafael Luque
Source: Materials (article, 2021-11-28)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Frameworks (MOFs) for Cancer Therapy MOFs exhibit inherent extraordinary features for diverse applications ranging from catalysis, storage, and optics to chemosensory and biomedical science and technology. Several procedures including solvothermal, hydrothermal, mechanochemical, electrochemical, and ultrasound techniques have been used to synthesize MOFs with tailored features. A continued attempt has also been directed towards functionalizing MOFs via "post-synthetic modification" mainly by changing linkers (by altering the type, length, functionality, and charge of the linkers) or node components within the MOF framework. Additionally, efforts are aimed towards manipulating the size and morphology of crystallite domains in the MOFs, which are aimed at enlarging their applications window. Today's knowledge of artificial intelligence and machine learning has opened new pathways to elaborate multiple nanoporous complex MOFs and nano-MOFs (NMOFs) for advanced theranostic, clinical, imaging, and diagnostic purposes. Successful accumulation of a photosensitizer in cancerous cells was a significant step in cancer therapy. The application of MOFs as advanced materials and systems for cancer therapy is the main scope beyond this perspective. Some challenging aspects and promising features in MOF-based cancer diagnosis and cancer therapy have also been discussed. Metal-organic framework ; Nanotechnology ; Cancer therapy ; Materials science ; Nanoporous ; Cancer treatment ; Cancer ; Medicine ; Chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Advanced Nanomaterials in Catalysis
Authors: Casper W. Andersen | Rickard Armiento | Evgeny Blokhin | Gareth J. Conduit | Shyam Dwaraknath | Matthew L. Evans | Ádám Fekete | Abhijith Gopakumar | Saulius Gražulis | Andrius Merkys | Fawzi Mohamed | Corey Oses | Giovanni Pizzi | Gian-Marco Rignanese | Markus Scheidgen | Leopold Talirz | Cormac Toher | Donald Winston | Rossella Aversa | Kamal Choudhary | Pauline Colinet | Stefano Curtarolo | Davide Di Stefano | Claudia Draxl | Suleyman Er | Marco Esters | Marco Fornari | Matteo Giantomassi | Marco Govoni | Geoffroy Hautier | Vinay Hegde | Matthew K. Horton | Patrick Huck | Georg Huhs | Jens Hummelshøj | Ankit Kariryaa | Boris Kozinsky | Snehal Kumbhar | Mohan Liu | Nicola Marzari | Andrew J. Morris | Arash A. Mostofi | Kristin A. Persson | Guido Petretto | Thomas Purcell | Francesco Ricci | Frisco Rose | Matthias Scheffler | Daniel Speckhard | Martin Uhrin | Antanas Vaitkus | Pierre Villars | David Waroquiers | Chris Wolverton | Michael Wu | Xiaoyu Yang
Source: Scientific Data (article, 2021-08-12)
Field: Materials Science — Materials Chemistry

OPTIMADE, an API for exchanging materials data The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification. Application programming interface ; Interface (matter) ; Software ; User interface ; Database application ; Data integration ; Data access Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Material Selection and Properties
Authors: Hanna Pazniak | Alexey S. Varezhnikov | Dmitry A. Kolosov | Ilya A. Plugin | Alessia Di Vito | Olga E. Glukhova | Polina M. Sheverdyaeva | M. Spasova | Igor Kaikov | Еvgeny Kolesnikov | Paolo Moras | Alexey M. Bainyashev | Maksim A. Solomatin | I. Kiselev | Ulf Wiedwald | Victor V. Sysoev
Source: Advanced Materials (article, 2021-10-04)
Field: Materials Science — Materials Chemistry

2D Molybdenum Carbide MXenes for Enhanced Selective Detection of Humidity in Air 2D transition metal carbides and nitrides (MXenes) open up novel opportunities in gas sensing with high sensitivity at room temperature. Herein, 2D Mo<sub>2</sub> CT<sub>x</sub> flakes with high aspect ratio are successfully synthesized. The chemiresistive effect in a sub-µm MXene multilayer for different organic vapors and humidity at 10<sup>1</sup> -10<sup>4</sup> ppm in dry air is studied. Reasonably, the low-noise resistance signal allows the detection of H<sub>2</sub> O down to 10 ppm. Moreover, humidity suppresses the response of Mo<sub>2</sub> CT<sub>x</sub> to organic analytes due to the blocking of adsorption active sites. By measuring the impedance of MXene layers as a function of ac frequency in the 10<sup>-2</sup> -10<sup>6</sup> Hz range, it is shown that operation principle of the sensor is dominated by resistance change rather than capacitance variations. The sensor transfer function allows to conclude that the Mo<sub>2</sub> CT<sub>x</sub> chemiresistance is mainly originating from electron transport through interflake potential barriers with heights up to 0.2 eV. Density functional theory calculations, elucidating the Mo<sub>2</sub> C surface interaction with organic analytes and H<sub>2</sub> O, explain the experimental data as an energy shift of the density of states under the analyte's adsorption which induces increasing electrical resistance. MXenes ; Materials science ; Analyte ; Adsorption ; Humidity ; Capacitance ; Carbide ; Nitride ; Relative humidity ; Molybdenum ; Nanotechnology ; Analytical Chemistry (journal) ; Chemical engineering ; Layer (electronics) ; Composite material ; Chemistry ; Environmental chemistry ; Organic chemistry ; Electrode ; Physical chemistry ; Thermodynamics MXene and MAX Phase Materials ; Gas Sensing Nanomaterials and Sensors ; 2D Materials and Applications