jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daijun Fu | Qianxin Zhang | Ping Chen | Xiaoshan Zheng | Jun Hao | Peiying Mo | Haijin Liu | Guoguang Liu | Wenying Lv
Source: RSC Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Efficient removal of bisphenol pollutants on imine-based covalent organic frameworks: adsorption behavior and mechanism The extensive use of bisphenol analogues in industry has aggravated the contamination of the water environment, and how to effectively remove them has become a research hotspot. This study presents two imine-based covalent organic frameworks with different pore sizes (COFs) [TAPB (1,3,5-tris(4-aminophenyl)benzene)-Dva (2,5-divinylterephthaldehyde)-PDA (terephthalaldehyde) (COF-1), and TAPB (1,3,5-tris(4-aminophenyl)benzene)-Dva (2,5-divinylterephthaldehyde)-BPDA (biphenyl dialdehyde) (COF-2)], which have achieved the efficient adsorption of bisphenol S (BPS) and bisphenol A (BPA). The maximum adsorption capacity of COF-2 for BPS and BPA obtained from Langmuir isotherms were calculated as 200.00 mg g<sup>-1</sup> and 149.25 mg g<sup>-1</sup>. Both hydrogen bonding and π-π interactions might have been responsible for the adsorption of BPS and BPA on the COFs, where the high adsorption capacity of COFs was due to their unique pore dimensions and structures. Different types of pharmaceutical adsorption studies indicated that COF-2 exhibited a higher adsorption performance for different types of pharmaceuticals than COF-1, and the adsorption capacity was ranked as follows: bisphenol pharmaceuticals > anti-inflammatory pharmaceuticals > sulfa pharmaceuticals. These results confirmed that COFs with larger pore sizes were more conducive to the adsorption of pollutants with smaller molecular dimensions. Moreover, COF-1 and COF-2 possessed excellent pH stability and recyclability, which suggested strong potential applications for these novel adsorbents in the remediation of organic pollutants in natural waterways and aqueous ecosystems. Bisphenol A ; Imine ; Mechanism (biology) ; Adsorption ; Pollutant ; Covalent bond ; Chemistry ; Chemical engineering ; Environmental chemistry ; Photochemistry ; Organic chemistry ; Epoxy ; Catalysis ; Engineering Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Ayman Abo Elmaaty | Maha M. Abdel‐Fattah | Muhammad I. Ismail | Eslam B. Elkaeed | Hamada S. Abulkhair | Muhammad Khattab | Ahmed A. Al‐Karmalawy
Source: Molecules (article, 2021-06-21)
Field: Chemistry — Organic Chemistry

Computational Insights on the Potential of Some NSAIDs for Treating COVID-19: Priority Set and Lead Optimization The discovery of drugs capable of inhibiting SARS-CoV-2 is a priority for human beings due to the severity of the global health pandemic caused by COVID-19. To this end, repurposing of FDA-approved drugs such as NSAIDs against COVID-19 can provide therapeutic alternatives that could be utilized as an effective safe treatment for COVID-19. The anti-inflammatory activity of NSAIDs is also advantageous in the treatment of COVID-19, as it was found that SARS-CoV-2 is responsible for provoking inflammatory cytokine storms resulting in lung damage. In this study, 40 FDA-approved NSAIDs were evaluated through molecular docking against the main protease of SARS-CoV-2. Among the tested compounds, sulfinpyrazone <b>2</b>, indomethacin <b>3</b>, and auranofin <b>4</b> were proposed as potential antagonists of COVID-19 main protease. Molecular dynamics simulations were also carried out for the most promising members of the screened NSAID candidates (<b>2</b>, <b>3</b>, and <b>4</b>) to unravel the dynamic properties of NSAIDs at the target receptor. The conducted quantum mechanical study revealed that the hybrid functional B3PW91 provides a good description of the spatial parameters of auranofin <b>4</b>. Interestingly, a promising structure-activity relationship (SAR) was concluded from our study that could help in the future design of potential SARS-CoV-2 main protease inhibitors with expected anti-inflammatory effects as well. NSAIDs may be used by medicinal chemists as lead compounds for the development of potent SARS-CoV-2 (M<sup>pro</sup>) inhibitors. In addition, some NSAIDs can be selectively designated for treatment of inflammation resulting from COVID-19. Repurposing ; Coronavirus disease 2019 (COVID-19) ; Drug repositioning ; Protease ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Cytokine storm ; Pharmacology ; Medicine ; 2019-20 coronavirus outbreak ; Auranofin ; Computational biology ; Chemistry ; Drug ; Virology ; Infectious disease (medical specialty) ; Rheumatoid arthritis ; Biology ; Disease ; Internal medicine ; Enzyme ; Biochemistry Synthesis and biological activity ; Computational Drug Discovery Methods ; Phenothiazines and Benzothiazines Synthesis and Activities
Authors: Tao Zhong | Ji-Tao Yi | Zhida Chen | Quan-Can Zhuang | Yong-Zhao Li | Gui Lu | Jiang Weng
Source: Chemical Science (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

Photoredox-catalyzed aminofluorosulfonylation of unactivated olefins The development of efficient approaches to access sulfonyl fluorides is of great significance because of the widespread applications of these structural motifs in many areas, among which the emerging sulfur(vi) fluoride exchange (SuFEx) click chemistry is the most prominent. Here, we report the first three-component aminofluorosulfonylation of unactivated olefins by merging photoredox-catalyzed proton-coupled electron transfer (PCET) activation with radical relay processes. Various aliphatic sulfonyl fluorides featuring a privileged 5-membered heterocyclic core have been efficiently afforded under mild conditions with good functional group tolerance. The synthetic potential of the sulfonyl fluoride products has been examined by diverse transformations including SuFEx reactions and transition metal-catalyzed cross-coupling reactions. Mechanistic studies demonstrate that amidyl radicals, alkyl radicals and sulfonyl radicals are involved in this difunctionalization transformation. Catalysis ; Photoredox catalysis ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Photocatalysis Sulfur-Based Synthesis Techniques ; Radical Photochemical Reactions ; Fluorine in Organic Chemistry
Authors: Oleksandr Shyshov | Shyamkumar V. Haridas | Luca Pesce | Haoyuan Qi | Andrea Gardin | Davide Bochicchio | Ute Kaiser | Giovanni M. Pavan | Max von Delius
Source: Nature Communications (article, 2021-05-25)
Field: Materials Science — Biomaterials

Living supramolecular polymerization of fluorinated cyclohexanes The development of powerful methods for living covalent polymerization has been a key driver of progress in organic materials science. While there have been remarkable reports on living supramolecular polymerization recently, the scope of monomers is still narrow and a simple solution to the problem is elusive. Here we report a minimalistic molecular platform for living supramolecular polymerization that is based on the unique structure of all-cis 1,2,3,4,5,6-hexafluorocyclohexane, the most polar aliphatic compound reported to date. We use this large dipole moment (6.2 Debye) not only to thermodynamically drive the self-assembly of supramolecular polymers, but also to generate kinetically trapped monomeric states. Upon addition of well-defined seeds, we observed that the dormant monomers engage in a kinetically controlled supramolecular polymerization. The obtained nanofibers have an unusual double helical structure and their length can be controlled by the ratio between seeds and monomers. The successful preparation of supramolecular block copolymers demonstrates the versatility of the approach. Supramolecular chemistry ; Polymerization ; Supramolecular polymers ; Monomer ; Copolymer ; Living polymerization ; Polymer chemistry ; Materials science ; Polymer ; Chemistry ; Nanotechnology ; Radical polymerization ; Organic chemistry ; Molecule Supramolecular Self-Assembly in Materials ; Luminescence and Fluorescent Materials ; Supramolecular Chemistry and Complexes
Authors: Sagnik Banerjee | Uddipan Nath | Purba Dutta | Amitkumar V. Jha | Bhargav Appasani | Nicu Bizon
Source: Inventions (article, 2021-11-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Theoretical Terahertz Metamaterial Absorber Structure with a High Quality Factor Using Two Circular Ring Resonators for Biomedical Sensing Metamaterial absorbers, on account of their inherent property of electromagnetic radiation absorption, have become a center of attraction for many researchers in recent times. This paper proposes a unique design of a terahertz metamaterial absorber that can be used to sense biomedical samples. The proposed design consists of two identical circular ring resonators (CRRs) made of aluminum on top of a gallium arsenide (GaAs) substrate. On account of its high field confinement in the sensing regime, a near-to-perfect absorption rate of 99.50% is achieved at a frequency of 2.64 THz, along with a large quality factor (Q-Factor) of 44. The design is highly sensitive to the refractive index changes in the encompassing medium. Hence, the proposed absorber can be used as a refractive index sensor exhibiting a reasonable sensitivity of 1500 GHz/RIU and a figure of merit (FoM) of 25. The refractive index range has been varied in the range of 1.34 to 1.39. As many biomedical samples, including cancerous cells, reside within this range, the proposed sensor can be used for biomedical sensing applications. Terahertz radiation ; Metamaterial ; Resonator ; Split-ring resonator ; Metamaterial absorber ; Optoelectronics ; Refractive index ; Materials science ; Figure of merit ; Substrate (aquarium) ; Absorption (acoustics) ; Optics ; Q factor ; Physics Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Terahertz technology and applications
Authors: Alec I. Saville | Adam Creuziger | Emily B. Mitchell | Sven C. Vogel | Jake T. Benzing | Jonah Klemm-Toole | Kester D. Clarke | Amy J. Clarke
Source: Integrating materials and manufacturing innovation (article, 2021-08-19)
Field: Materials Science — Materials Chemistry

MAUD Rietveld Refinement Software for Neutron Diffraction Texture Studies of Single- and Dual-Phase Materials Rietveld refinement ; Electron backscatter diffraction ; Software ; Neutron diffraction ; Texture (cosmology) ; Diffraction ; Computer science ; Neutron ; Phase (matter) ; Process (computing) ; Materials science ; Optics ; Artificial intelligence ; Physics ; Nuclear physics ; Programming language ; Image (mathematics) X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Werner Schlemmer | Julian Selinger | Mathias Hobisch | Stefan Spirk
Source: Carbohydrate Polymers (review, 2021-04-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Polysaccharides for sustainable energy storage – A review The increasing amount of electric vehicles on our streets as well as the need to store surplus energy from renewable sources such as wind, solar and tidal parks, has brought small and large scale batteries into the focus of academic and industrial research. While there has been huge progress in performance and cost reduction in the past years, batteries and their components still face several environmental issues including safety, toxicity, recycling and sustainability. In this review, we address these challenges by showcasing the potential of polysaccharide-based compounds and materials used in batteries. This particularly involves their use as electrode binders, separators and gel/solid polymer electrolytes. The review contains a historical section on the different battery technologies, considerations about safety on batteries and requirements of polysaccharide components to be used in different types of battery technologies. The last sections cover opportunities for polysaccharides as well as obstacles that prevent their wider use in battery industry. Renewable energy ; Battery (electricity) ; Sustainability ; Sustainable energy ; Energy storage ; Renewable resource ; Wind power ; Business ; Environmental economics ; Environmental science ; Engineering ; Electrical engineering ; Ecology Supercapacitor Materials and Fabrication ; Advanced Battery Materials and Technologies ; Advanced Battery Technologies Research
Authors: Nikunj Sharma | Daris P. Simon | Aracely Maribel Diaz-Garza | Elisa Fantino | Anis Messaabi | Fatma Meddeb‐Mouelhi | Hugo Germain | Isabel Desgagné‐Penix
Source: Frontiers in Marine Science (article, 2021-02-23)
Field: Materials Science — Biomaterials

Diatoms Biotechnology: Various Industrial Applications for a Greener Tomorrow The benefits of the complex microscopic and industrially important group of microalgae such as diatoms is not hidden and have lately surprised the scientific community with their industrial potential. The ability to survive in harsh conditions and the presence of different pore structures and defined cell walls have made diatoms ideal cell machinery to produce a variety of industrial products. The prospect of using a diatom cell for industrial application has increased significantly in synch with the advances in microscopy, metabarcoding, analytical and genetic tools. Furthermore, it is well noted that the approach of industry and academia to the use of genetic tools has changed significantly, resulting in a well-defined characterization of various molecular components of diatoms. It is possible to conduct the primary culturing, harvesting, and further downstream processing of diatom culture in a cost-effective manner. Diatoms hold all the qualities to become the alternative raw material for pharmaceutical, nanotechnology, and energy sources leading to a sustainable economy. In this review, an attempt has been made to gather important progress in the different industrial applications of diatoms such as biotechnology, biomedical, nanotechnology, and environmental technologies. Diatom ; Sustainability ; Frustule ; Nanotechnology ; Biotechnology ; Biochemical engineering ; Industrial biotechnology ; Business ; Engineering ; Biology ; Ecology ; Materials science Diatoms and Algae Research ; Algal biology and biofuel production ; Microbial Community Ecology and Physiology
Authors: Jiayan Xu | Xiaoming Cao | P. Hu
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis. Perspective (graphical) ; Computer science ; Catalysis ; Machine learning ; Artificial intelligence ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Andrij Vasylenko | Jacinthe Gamon | Benjamin B. Duff | Vladimir V. Gusev | Luke M. Daniels | Marco Zanella | J. Felix Shin | Paul M. Sharp | Alexandra Morscher | Ruiyong Chen | Alex R. Neale | Laurence J. Hardwick | John B. Claridge | Frédéric Blanc | Michael W. Gaultois | Matthew S. Dyer | Matthew J. Rosseinsky
Source: Nature Communications (article, 2021-09-21)
Field: Materials Science — Materials Chemistry

Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry The selection of the elements to combine delimits the possible outcomes of synthetic chemistry because it determines the range of compositions and structures, and thus properties, that can arise. For example, in the solid state, the elemental components of a phase field will determine the likelihood of finding a new crystalline material. Researchers make these choices based on their understanding of chemical structure and bonding. Extensive data are available on those element combinations that produce synthetically isolable materials, but it is difficult to assimilate the scale of this information to guide selection from the diversity of potential new chemistries. Here, we show that unsupervised machine learning captures the complex patterns of similarity between element combinations that afford reported crystalline inorganic materials. This model guides prioritisation of quaternary phase fields containing two anions for synthetic exploration to identify lithium solid electrolytes in a collaborative workflow that leads to the discovery of Li<sub>3.3</sub>SnS<sub>3.3</sub>Cl<sub>0.7.</sub> The interstitial site occupancy combination in this defect stuffed wurtzite enables a low-barrier ion transport pathway in hexagonal close-packing. Wurtzite crystal structure ; Computer science ; Workflow ; Selection (genetic algorithm) ; Lithium (medication) ; Nanotechnology ; Materials science ; Chemistry ; Hexagonal crystal system ; Machine learning ; Crystallography ; Biology ; Database Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Venkat Venkatasubramanian | Vipul Mann
Source: Current Opinion in Chemical Engineering (article, 2021-10-22)
Field: Materials Science — Materials Chemistry

Artificial intelligence in reaction prediction and chemical synthesis A priori and a posteriori ; Computer science ; Artificial intelligence ; Field (mathematics) ; Domain (mathematical analysis) ; Machine learning ; Categorization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Chunguang Shen | Chenchong Wang | Minghao Huang | Ning Xu | Sybrand van der Zwaag | Wei Xu
Source: Journal of Material Science and Technology (article, 2021-05-10)
Field: Materials Science — Metals and Alloys

A generic high-throughput microstructure classification and quantification method for regular SEM images of complex steel microstructures combining EBSD labeling and deep learning Electron backscatter diffraction ; Artificial intelligence ; Materials science ; Microstructure ; Ground truth ; Scanning electron microscope ; Robustness (evolution) ; Computer science ; Pattern recognition (psychology) ; Visualization ; Composite material ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Rebecca L. Greenaway | Kim E. Jelfs
Source: Advanced Materials (review, 2021-02-09)
Field: Materials Science — Materials Chemistry

Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery Organic materials find application in a range of areas, including optoelectronics, sensing, encapsulation, molecular separations, and photocatalysis. The discovery of materials is frustratingly slow however, particularly when contrasted to the vast chemical space of possibilities based on the near limitless options for organic molecular precursors. The difficulty in predicting the material assembly, and consequent properties, of any molecule is another significant roadblock to targeted materials design. There has been significant progress in the development of computational approaches to screen large numbers of materials, for both their structure and properties, helping guide synthetic researchers toward promising materials. In particular, artificial intelligence techniques have the potential to make significant impact in many elements of the discovery process. Alongside this, automation and robotics are increasing the scale and speed with which materials synthesis can be realized. Herein, the focus is on demonstrating the power of integrating computational and experimental materials discovery programmes, including both a summary of key situations where approaches can be combined and a series of case studies that demonstrate recent successes. Materials science ; Workflow ; Nanotechnology ; Engineering physics ; Systems engineering ; Computer science ; Database ; Engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Mohammad Ilyas Khan | Mohammed Khaloufa Almesfer | Abubakr Elkhaleefa | Ihab Shigidi | Mohammed Zubair M. Shamim | Ismat H. Ali | Mohammad Rehan
Source: Polymers (review, 2021-11-04)
Field: Materials Science — Polymers and Plastics

Conductive Polymers and Their Nanocomposites as Adsorbents in Environmental Applications Proper treatment and disposal of industrial pollutants of all kinds are a global issue that presents significant techno-economical challenges. The presence of pollutants such as heavy metal ions (HMIs) and organic dyes (ODs) in wastewater is considered a significant problem owing to their carcinogenic and toxic nature. Additionally, industrial gaseous pollutants (GPs) are considered to be harmful to human health and may cause various environmental issues such as global warming, acid rain, smog and air pollution, etc. Conductive polymer-based nanomaterials have gained significant interest in recent years, compared with ceramics and metal-based nanomaterials. The objective of this review is to provide detailed insights into different conductive polymers (CPs) and their nanocomposites that are used as adsorbents for environmental remediation applications. The dominant types of CPs that are being used as adsorbent materials include polyaniline (PANI), polypyrrole (Ppy), and polythiophene (PTh). The various adsorption mechanisms proposed for the removal of ODs, HMIs, and other GPs by the different CPs are presented, together with their maximum adsorption capacities, experimental conditions, adsorption, and kinetic models reported. Adsorption ; Pollutant ; Polyaniline ; Environmental remediation ; Conductive polymer ; Nanomaterials ; Materials science ; Nanocomposite ; Environmental science ; Pollution ; Metal ions in aqueous solution ; Environmental chemistry ; Polymer ; Nanotechnology ; Chemical engineering ; Metal ; Chemistry ; Organic chemistry ; Composite material ; Contamination ; Metallurgy ; Polymerization Conducting polymers and applications ; Electrochemical sensors and biosensors ; Gas Sensing Nanomaterials and Sensors