jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maria Rosaria di Nunzio | Yuto Suzuki | Ichiro Hisaki | Abderrazzak Douhal
Source: International Journal of Molecular Sciences (review, 2022-02-09)
Field: Chemistry — Inorganic Chemistry

HOFs Built from Hexatopic Carboxylic Acids: Structure, Porosity, Stability, and Photophysics Hydrogen-bonded organic frameworks (HOFs) have attracted renewed attention as another type of promising candidates for functional porous materials. In most cases of HOF preparation, the applied molecular design principle is based on molecules with rigid π-conjugated skeleton together with more than three H-bonding groups to achieve 2D- or 3D-networked structures. However, the design principle does not always work, but results in formation of unexpected structures, where subtle structural factors of which we are not aware dictate the entire structure of HOFs. In this contribution, we assess recent advances in HOFs, focusing on those composed of hexatopic building block molecules, which can provide robust frameworks with a wide range of topologies and properties. The HOFs described in this work are classified into three types, depending on their H-bonded structural motifs. Here in, we focus on: (1) the chemical aspects that govern their unique fundamental chemistry and structures; and (2) their photophysics at the ensemble and single-crystal levels. The work addresses and discusses how these aspects affect and orient their photonic applicability. We trust that this contribution will provide a deep awareness and will help scientists to build up a systematic series of porous materials with the aim to control both their structural and photodynamical assets. Molecule ; Porosity ; Materials science ; Hydrogen bond ; Nanotechnology ; Conjugated system ; Computer science ; Chemistry ; Polymer ; Organic chemistry ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Supramolecular Chemistry and Complexes
Authors: Jiaqiang Yan
Source: ECS Journal of Solid State Science and Technology (article, 2022-05-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Perspective–The Elusive Quantum Anomalous Hall Effect in MnBi <sub>2</sub> Te <sub>4</sub> : Materials Observation of the quantum anomalous Hall effect (QAHE) in MnBi 2 Te 4 flakes is one of the most exciting results in the study of the intrinsic magnetic topological insulator MnBi 2 Te 4 and related compounds. Reproducing this fascinating result has been found to be challenging. The quality of starting MnBi 2 Te 4 single crystals is believed to be the key factor. An interesting and important question to address is what is the right quality to enable the QAHE. In this perspective, we present possible approaches to tuning the magnetic and topological properties of MnBi 2 Te 4 by using lattice imperfections, strain, stacking sequence, and interactions between the substrate and flakes/films. It is of critical importance to eventually identify the factor(s) responsible for the realization of QAHE. This paper is part of the JES/JSS Joint Focus Issue In Honor of John Goodenough: A Centenarian Milestone Materials science ; Perspective (graphical) ; Condensed matter physics ; Quantum Hall effect ; Engineering physics ; Hall effect ; Nanotechnology ; Quantum mechanics ; Physics ; Computer science ; Electron ; Electrical resistivity and conductivity Magnetic and transport properties of perovskites and related materials ; Topological Materials and Phenomena ; Advanced Condensed Matter Physics
Authors: Magdalena Maciejewska | Anna Sowińska‐Baranowska
Source: Nanomaterials (article, 2022-04-04)
Field: Materials Science — Polymers and Plastics

Bromide and Chloride Ionic Liquids Applied to Enhance the Vulcanization and Performance of Natural Rubber Biocomposites Filled with Nanosized Silica In this study, the possibility of using ionic liquids (ILs) as auxiliary substances improving the vulcanization and physicochemical properties of natural rubber (NR) biocomposites filled with nanosized silica was investigated. Hence, the influence of ILs with bromide and chloride anions and various cations, i.e., alkylimidazolium, alkylpyrrolidinium and alkylpiperidinium cation, on the curing characteristics and crosslink density of NR compounds was determined. Furthermore, the effect of nanosized silica and ILs on the functional properties of the obtained vulcanizates, including mechanical properties under static and dynamic conditions, hardness, thermal stability and resistance to thermo-oxidative aging, were explored. Applying nanosized silica improved the processing safety of NR compounds but significantly increased the optimal vulcanization time compared to the unfilled rubber. ILs significantly improved the cure characteristics of NR compounds by increasing the rate of vulcanization and the crosslink density of NR biocomposites. Consequently, the tensile strength and hardness of the vulcanizates significantly increased compared to that without ILs. Moreover, the use of nanosized silica and ILs had a favorable impact on the thermal stability of the vulcanizates and their resistance to prolonged thermo-oxidation. Vulcanization ; Ionic liquid ; Materials science ; Natural rubber ; Thermal stability ; Curing (chemistry) ; Ultimate tensile strength ; Bromide ; Precipitated silica ; Chemical engineering ; Chloride ; Composite material ; Polymer chemistry ; Organic chemistry ; Catalysis ; Chemistry ; Metallurgy Polymer Nanocomposites and Properties ; biodegradable polymer synthesis and properties ; Lignin and Wood Chemistry
Authors: Goutam Kumar Kole | Marta Košćak | Anissa Amar | Dragomira Majhen | Ksenija Božinović | Zlatko Brkljača | Matthias Ferger | Evripidis Michail | Sabine Lorenzen | Alexandra Friedrich | Ivo Krummenacher | Michael Moos | Holger Braunschweig | Abdou Boucekkine | Christoph Lambert | Jean‐François Halet | Ivo Piantanida | Klaus Müller‐Buschbaum | Todd B. Marder
Source: Chemistry - A European Journal (article, 2022-05-03)
Field: Materials Science — Materials Chemistry

Methyl Viologens of Bis‐(4’‐Pyridylethynyl)Arenes – Structures, Photophysical and Electrochemical Studies, and their Potential Application in Biology A series of bis-(4'-pyridylethynyl)arenes (arene=benzene, tetrafluorobenzene, and anthracene) were synthesized and their bis-N-methylpyridinium compounds were investigated as a class of π-extended methyl viologens. Their structures were determined by single crystal X-ray diffraction, and their photophysical and electrochemical properties (cyclic voltammetry), as well as their interactions with DNA/RNA were investigated. The dications showed bathochromic shifts in emission compared to the neutral compounds. The neutral compounds showed very small Stokes shifts, which are a little larger for the dications. All of the compounds showed very short fluorescence lifetimes (<4 ns). The neutral compound with an anthracene core has a quantum yield of almost unity. With stronger acceptors, the analogous bis-N-methylpyridinium compound showed a larger two-photon absorption cross-section than its neutral precursor. All of the dicationic compounds interact with DNA/RNA; while the compounds with benzene and tetrafluorobenzene cores bind in the grooves, the one with an anthracene core intercalates as a consequence of its large, condensed aromatic linker moiety, and it aggregates within the polynucleotide when in excess over DNA/RNA. Moreover, all cationic compounds showed highly specific CD spectra upon binding to ds-DNA/RNA, attributed to the rare case of forcing the planar, achiral molecule into a chiral rotamer, and negligible toxicity toward human cell lines at ≤10 μM concentrations. The anthracene-analogue exhibited intracellular accumulation within lysosomes, preventing its interaction with cellular DNA/RNA. However, cytotoxicity was evident at 1 μM concentration upon exposure to light, due to singlet oxygen generation within cells. These multi-faceted features, in combination with its two-photon absorption properties, suggest it to be a promising lead compound for development of novel light-activated theranostic agents. Anthracene ; Chemistry ; Photochemistry ; Bathochromic shift ; Moiety ; DNA ; Molecule ; Stereochemistry ; Crystallography ; Fluorescence ; Organic chemistry Photochromic and Fluorescence Chemistry ; Synthesis and Properties of Aromatic Compounds ; Photochemistry and Electron Transfer Studies
Authors: Claudia Matschegewski | Stefanie Kohse | Jana Markhoff | Michael Teske | Katharina Wulf | Niels Grabow | Klaus‐Peter Schmitz | Sabine Illner
Source: Materials (article, 2022-03-09)
Field: Materials Science — Biomaterials

Accelerated Endothelialization of Nanofibrous Scaffolds for Biomimetic Cardiovascular Implants Nanofiber nonwovens are highly promising to serve as biomimetic scaffolds for pioneering cardiac implants such as drug-eluting stent systems or heart valve prosthetics. For successful implant integration, rapid and homogeneous endothelialization is of utmost importance as it forms a hemocompatible surface. This study aims at physicochemical and biological evaluation of various electrospun polymer scaffolds, made of FDA approved medical-grade plastics. Human endothelial cells (EA.hy926) were examined for cell attachment, morphology, viability, as well as actin and PECAM 1 expression. The appraisal of the untreated poly-L-lactide (PLLA L210), poly-ε-caprolactone (PCL) and polyamide-6 (PA-6) nonwovens shows that the hydrophilicity (water contact angle > 80°) and surface free energy (<60 mN/m) is mostly insufficient for rapid cell colonization. Therefore, modification of the surface tension of nonpolar polymer scaffolds by plasma energy was initiated, leading to more than 60% increased wettability and improved colonization. Additionally, NH3-plasma surface functionalization resulted in a more physiological localization of cell−cell contact markers, promoting endothelialization on all polymeric surfaces, while fiber diameter remained unaltered. Our data indicates that hydrophobic nonwovens are often insufficient to mimic the native extracellular matrix but also that they can be easily adapted by targeted post-processing steps such as plasma treatment. The results achieved increase the understanding of cell−implant interactions of nanostructured polymer-based biomaterial surfaces in blood contact while also advocating for plasma technology to increase the surface energy of nonpolar biostable, as well as biodegradable polymer scaffolds. Thus, we highlight the potential of plasma-activated electrospun polymer scaffolds for the development of advanced cardiac implants. Biomimetic materials ; Nanofiber ; Biomedical engineering ; Materials science ; Nanotechnology ; Medicine Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Yong Xu | Yanliang Huang | Fanfan Cai | Dongzhu Lu | Zhengquan Wang | Xiutong Wang
Source: npj Materials Degradation (article, 2022-12-05)
Field: Materials Science — Metals and Alloys

Study on corrosion, hydrogen permeation, and stress corrosion cracking behaviours of AISI 4135 steel in the tidal zone Abstract High-strength steels are widely used in marine engineering; however, they suffer from the risks of corrosion, hydrogen permeation, and stress corrosion cracking (SCC) in wet–dry cyclic marine environments. In this study, the corrosion, hydrogen permeation, and SCC behaviours of AISI 4135 steel in the tidal zone were investigated using electrochemical corrosion, electrochemical hydrogen permeation, and slow strain rate tests, respectively, via field exposure. The results showed that the AISI 4135 steel sample placed at the high tide level had high SCC susceptibility because of severe pitting corrosion and hydrogen permeation, whereas the steel samples placed at the middle and low tide levels had low SCC susceptibilities. The dry/wet time ratio was crucial in determining the SCC behaviour and mechanism of the steel in the tidal zone. With increasing time ratio, the SCC mechanism changed from micro-void coalescence control to localised anodic dissolution and hydrogen embrittlement in tandem. Corrosion ; Stress corrosion cracking ; Materials science ; Metallurgy ; Hydrogen embrittlement ; Permeation ; Dissolution ; Hydrogen ; Intergranular corrosion ; Chemical engineering ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques
Authors: Amir Abbas Kazemzadeh Farizhandi | Omar Betancourt | Mahmood Mamivand
Source: Scientific Reports (article, 2022-03-16)
Field: Materials Science — Materials Chemistry

Deep learning approach for chemistry and processing history prediction from materials microstructure Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. While the simulation methods based on physical concepts such as the phase-field method can predict the spatio-temporal evolution of the materials' microstructure, they are not efficient techniques for predicting processing and chemistry if a specific morphology is desired. In this study, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. As a case study, we used a dataset from spinodal decomposition simulation of Fe-Cr-Co alloy created by the phase-field method. The mixed dataset, which includes both images, i.e., the morphology of Fe distribution, and continuous data, i.e., the Fe minimum and maximum concentration in the microstructures, are used as input data, and the spinodal temperature and initial chemical composition are utilized as the output data to train the proposed deep neural network. The proposed convolutional layers were compared with pretrained EfficientNet convolutional layers as transfer learning in microstructure feature extraction. The results show that the trained shallow network is effective for chemistry prediction. However, accurate prediction of processing temperature requires more complex feature extraction from the morphology of the microstructure. We benchmarked the model predictive accuracy for real alloy systems with a Fe-Cr-Co transmission electron microscopy micrograph. The predicted chemistry and heat treatment temperature were in good agreement with the ground truth. Data science ; Computer science ; Microstructure ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Mineral Processing and Grinding ; Metallurgical Processes and Thermodynamics
Authors: Martin Seifrid | Riley J. Hickman | Andrés Aguilar‐Granda | Cyrille Lavigne | Jenya Vestfrid | Tony Wu | Théophile Gaudin | Emily J. Hopkins | Alán Aspuru‐Guzik
Source: ACS Central Science (article, 2022-01-06)
Field: Materials Science — Materials Chemistry

Routescore: Punching the Ticket to More Efficient Materials Development Self-driving laboratories, in the form of automated experimentation platforms guided by machine learning algorithms, have emerged as a potential solution to the need for accelerated science. While new tools for automated analysis and characterization are being developed at a steady rate, automated synthesis remains the bottleneck in the chemical space accessible to self-driving laboratories. Combining automated and manual synthesis efforts immediately significantly expands the explorable chemical space. To effectively direct the different capabilities of automated (higher throughput and less labor) and manual synthesis (greater chemical versatility), we describe a protocol, the RouteScore, that quantifies the cost of combined synthetic routes. In this work, the RouteScore is used to determine the most efficient synthetic route to a well-known pharmaceutical (structure-oriented optimization) and to simulate a self-driving laboratory that finds the most easily synthesizable organic laser molecule with specific photophysical properties from a space of ∼3500 possible molecules (property-oriented optimization). These two examples demonstrate the power and flexibility of our approach in mixed synthetic planning and optimization and especially in downselecting promising candidates from a large chemical space via an <i>a priori</i> estimation of the synthetic costs. Computer science ; Chemical space ; Bottleneck ; Flexibility (engineering) ; Synthetic data ; Throughput ; A priori and a posteriori ; Artificial intelligence ; Embedded system ; Drug discovery ; Bioinformatics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Asmaa A. H. El-Bassuony | W. M. Gamal | H. K. Abdelsalam
Source: JOM (article, 2022-05-12)
Field: Chemistry — Inorganic Chemistry

Influence of Silver Nanoferrite and Nanochromite on Physical Properties for High-Frequency and Biomedical Applications Abstract In this work, the effects of silver nanoferrite and nanochromite (Ag 2 Fe 2 O 2 , Ag 2 Cr 2 O 4 ) were studied using the flash auto-combustion technique. Numerous physical properties were clarified from the different structures determined from the x-ray examination such as lattice parameter the cation dispersion, oxygen parameter, hopping length, and interatomic distance. Among antimicrobial properties, Ag addition to both samples showed high efficacy against both types of tested bacteria (gram-positive and gram-negative); however, they showed moderate effect against Candida albicans fungus. No activity appeared against Aspergillus flavus fungus for both samples. The switching field and high-frequency response of Ag 2 Fe 2 O 4 and Ag 2 Cr 2 O 4 nanoparticles were studied from the magnetic properties. Ag 2 Cr 2 O 4 nanoparticles are expected to be used for high-frequency applications more than Ag 2 Fe 2 O 4 ones. As a result of all previous analyses, the applications of this study promote the use of Ag 2 Fe 2 O 4 nanoferrite and Ag 2 Cr 2 O 4 nanochromite for high-frequency, switching field, and biomedical applications as antimicrobial nanomaterials. Materials science ; Nanomaterials ; Nanoparticle ; Nanotechnology ; Nuclear chemistry ; Analytical Chemistry (journal) ; Chemical engineering ; Chemistry ; Chromatography Pigment Synthesis and Properties ; Magnetic Properties and Synthesis of Ferrites ; Nanomaterials for catalytic reactions
Authors: Abdulmohsen Alsaui | Saad M. Alqahtani | Faisal Mumtaz | Alsayoud G. Ibrahim | Mohammed Alghadeer | Ali H. Muqaibel | Sergey N. Rashkeev | Ahmer A.B. Baloch | Fahhad H. Alharbi
Source: Scientific Reports (article, 2022-01-28)
Field: Materials Science — Materials Chemistry

Highly accurate machine learning prediction of crystal point groups for ternary materials from chemical formula One of the most challenging problems in condensed matter physics is to predict crystal structure just from the chemical formula of the material. In this work, we present a robust machine learning (ML) predictor for the crystal point group of ternary materials (A[Formula: see text]B[Formula: see text]C[Formula: see text]) - as first step to predict the structure - with very small set of ionic and positional fundamental features. From ML perspective, the problem is strenuous due to multi-labelity, multi-class, and data imbalance. The resulted prediction is very reliable as high balanced accuracies are obtained by different ML methods. Many similarity-based approaches resulted in a balanced accuracy above 95% indicating that the physics is well captured by the reduced set of features; namely, stoichiometry, ionic radii, ionization energies, and oxidation states for each of the three elements in the ternary compound. The accuracy is not limited by the approach; but rather by the limited data points and we should expect higher accuracy prediction by having more reliable data. Ternary operation ; Crystal (programming language) ; Group (periodic table) ; Point (geometry) ; Computer science ; Machine learning ; Chemistry ; Mathematics ; Organic chemistry ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Dmitry Chernyavsky | Jeroen van den Brink | Gyu Hyeon Park | Kornelius Nielsch | Andy Thomas
Source: Advanced Theory and Simulations (article, 2022-10-03)
Field: Materials Science — Materials Chemistry

Sustainable Thermoelectric Materials Predicted by Machine Learning Abstract Using datasets from several sources, a list of more than 450 materials is generated and related them with their thermoelectric properties. This is obtained by generating a set of features using only the molecular formula. Subsequently, a machine learning algorithm classifies the materials in specific, binary classes, for example, possessing high or low Seebeck coefficients or electrical conductivity. After adjusting the threshold values and grouping the materials into clusters, the thermoelectric performance of more than 25k materials is predicted. Finally, the results are filtered to obtain only the sustainable materials, that is, neither toxic nor critical, (ideally) inexpensive, and isotropic with regard to their transport properties to simplify the preparation procedure. Thermoelectric effect ; Thermoelectric materials ; Isotropy ; Binary number ; Materials science ; Set (abstract data type) ; Seebeck coefficient ; Computer science ; Electrical resistivity and conductivity ; Engineering physics ; Mathematics ; Thermodynamics ; Physics ; Electrical engineering ; Engineering ; Optics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Cody Allen | Shiva Aryal | Tuyen Do | Rishav Gautum | Md Mahmudul Hasan | Bharat K. Jasthi | Etienne Z. Gnimpiéba | Venkataramana Gadhamshetty
Source: Frontiers in Microbiology (article, 2022-12-22)
Field: Materials Science — Materials Chemistry

Deep learning strategies for addressing issues with small datasets in 2D materials research: Microbial Corrosion Protective coatings based on two dimensional materials such as graphene have gained traction for diverse applications. Their impermeability, inertness, excellent bonding with metals, and amenability to functionalization renders them as promising coatings for both abiotic and microbiologically influenced corrosion (MIC). Owing to the success of graphene coatings, the whole family of 2D materials, including hexagonal boron nitride and molybdenum disulphide are being screened to obtain other promising coatings. AI-based data-driven models can accelerate virtual screening of 2D coatings with desirable physical and chemical properties. However, lack of large experimental datasets renders training of classifiers difficult and often results in over-fitting. Generate large datasets for MIC resistance of 2D coatings is both complex and laborious. Deep learning data augmentation methods can alleviate this issue by generating synthetic electrochemical data that resembles the training data classes. Here, we investigated two different deep generative models, namely variation autoencoder (VAE) and generative adversarial network (GAN) for generating synthetic data for expanding small experimental datasets. Our model experimental system included few layered graphene over copper surfaces. The synthetic data generated using GAN displayed a greater neural network system performance (83-85% accuracy) than VAE generated synthetic data (78-80% accuracy). However, VAE data performed better (90% accuracy) than GAN data (84%-85% accuracy) when using XGBoost. Finally, we show that synthetic data based on VAE and GAN models can drive machine learning models for developing MIC resistant 2D coatings. Artificial intelligence ; Autoencoder ; Materials science ; Deep learning ; Artificial neural network ; Graphene ; Computer science ; Generative adversarial network ; Machine learning ; Hexagonal boron nitride ; Nanotechnology Machine Learning in Materials Science ; Corrosion Behavior and Inhibition