jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Liang Yu | Xue Han | Hao Wang | Saif Ullah | Qibin Xia | Weiyao Li | Jiangnan Li | Iván da Silva | Pascal Manuel | Svemir Rudić | Yongqiang Cheng | Sihai Yang⧫ | Timo Thonhauser | Jing Li
Source: Journal of the American Chemical Society (article, 2021-11-15)
Field: Chemistry — Inorganic Chemistry

Pore Distortion in a Metal–Organic Framework for Regulated Separation of Propane and Propylene The development of porous solids for adsorptive separation of propylene and propane remains an important and challenging line of research. State-of-the-art sorbent materials often suffer from the trade-off between adsorption capacity and selectivity. Here, we report the regulated separation of propylene and propane in a metal-organic framework <i>via</i> designed pore distortion. The distorted pore structure of HIAM-301 successfully excludes propane and thus achieved simultaneously high selectivity (>150) and large capacity (∼3.2 mmol/g) of propylene at 298 K and 1 bar. Dynamic breakthrough measurements validated the excellent separation of propane and propylene. <i>In situ</i> neutron powder diffraction and inelastic neutron scattering revealed the binding domains of adsorbed propylene molecules in HIAM-301 as well as host-guest interaction dynamics. This study presents a new benchmark for the adsorptive separation of propylene and propane. Propane ; Chemistry ; Metal-organic framework ; Sorbent ; Adsorption ; Propene ; Selectivity ; Chemical engineering ; Selective adsorption ; Physical chemistry ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Hydrocarbon exploration and reservoir analysis
Authors: Anthony C. Yu | Huada Lian | Xian Kong | Hector Lopez Hernandez | Jian Qin | Eric A. Appel
Source: Nature Communications (article, 2021-02-02)
Field: Materials Science — Polymers and Plastics

Physical networks from entropy-driven non-covalent interactions Physical networks typically employ enthalpy-dominated crosslinking interactions that become more dynamic at elevated temperatures, leading to network softening. Moreover, standard mathematical frameworks such as time-temperature superposition assume network softening and faster dynamics at elevated temperatures. Yet, deriving a mathematical framework connecting the crosslinking thermodynamics to the temperature-dependent viscoelasticity of physical networks suggests the possibility for entropy-driven crosslinking interactions to provide alternative temperature dependencies. This framework illustrates that temperature negligibly affects crosslink density in reported systems, but drastically influences crosslink dynamics. While the dissociation rate of enthalpy-driven crosslinks is accelerated at elevated temperatures, the dissociation rate of entropy-driven crosslinks is negligibly affected or even slowed under these conditions. Here we report an entropy-driven physical network based on polymer-nanoparticle interactions that exhibits mechanical properties that are invariant with temperature. These studies provide a foundation for designing and characterizing entropy-driven physical crosslinking motifs and demonstrate how these physical networks access thermal properties that are not observed in current physical networks. Enthalpy ; Viscoelasticity ; Entropy (arrow of time) ; Superposition principle ; Physical system ; Statistical physics ; Thermodynamics ; Polymer ; Dissociation (chemistry) ; Materials science ; Chemical physics ; Physics ; Chemistry ; Physical chemistry ; Quantum mechanics Polymer composites and self-healing ; Advanced Sensor and Energy Harvesting Materials ; Polymer crystallization and properties
Authors: Oliver McNeilly | Riti Mann | Mohammad Hamidian | Cindy Gunawan
Source: Frontiers in Microbiology (review, 2021-04-16)
Field: Materials Science — Materials Chemistry

Emerging Concern for Silver Nanoparticle Resistance in Acinetobacter baumannii and Other Bacteria The misuse of antibiotics combined with a lack of newly developed ones is the main contributors to the current antibiotic resistance crisis. There is a dire need for new and alternative antibacterial options and nanotechnology could be a solution. Metal-based nanoparticles, particularly silver nanoparticles (NAg), have garnered widespread popularity due to their unique physicochemical properties and broad-spectrum antibacterial activity. Consequently, NAg has seen extensive incorporation in many types of products across the healthcare and consumer market. Despite clear evidence of the strong antibacterial efficacy of NAg, studies have raised concerns over the development of silver-resistant bacteria. Resistance to cationic silver (Ag<sup>+</sup>) has been recognised for many years, but it has recently been found that bacterial resistance to NAg is also possible. It is also understood that exposure of bacteria to toxic heavy metals like silver can induce the emergence of antibiotic resistance through the process of co-selection. <i>Acinetobacter baumannii</i> is a Gram-negative coccobacillus and opportunistic nosocomial bacterial pathogen. It was recently listed as the "number one" critical level priority pathogen because of the significant rise of antibiotic resistance in this species. NAg has proven bactericidal activity towards <i>A. baumannii</i>, even against strains that display multi-drug resistance. However, despite ample evidence of heavy metal (including silver; Ag<sup>+</sup>) resistance in this bacterium, combined with reports of heavy metal-driven co-selection of antibiotic resistance, little research has been dedicated to assessing the potential for NAg resistance development in <i>A. baumannii</i>. This is worrisome, as the increasingly indiscriminate use of NAg could promote the development of silver resistance in this species, like what has occurred with antibiotics. Acinetobacter baumannii ; Antibiotic resistance ; Antibiotics ; Microbiology ; Bacteria ; Antimicrobial ; Silver nanoparticle ; Drug resistance ; Biology ; Nanotechnology ; Pseudomonas aeruginosa ; Nanoparticle ; Materials science ; Genetics Nanoparticles: synthesis and applications ; Healthcare and Environmental Waste Management ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: Anjana Talapatra | Blas P. Uberuaga | Christopher R. Stanek | Ghanshyam Pilania
Source: Chemistry of Materials (article, 2021-01-15)
Field: Materials Science — Materials Chemistry

A Machine Learning Approach for the Prediction of Formability and Thermodynamic Stability of Single and Double Perovskite Oxides Perovskite oxides continue to attract huge interest due to their fascinating and wide-ranging properties for diverse applications. The tunability of these properties may be further enhanced by increasing their compositional complexity via double perovskite-ordered configurations containing multiple cations. In this work, we focus on an exhaustive chemical space of single and double oxide perovskites and optimally explore this space to identify novel compositions that are likely to form stable compounds. Critically, we examine the relationship between formability, the practical ability to synthesize a compound, and stability, the thermodynamic preference to form the structure. Our formability and stability training data sets were enumerated from the available experimental literature and in-house density functional theory computations and contained 1505 and 3469 examples, respectively, representing state-of-the-art in the current open literature in perovskite and double perovskite compounds. Subsequently, cross-validated and highly accurate machine learning classification models are built using these training data sets and employed to screen for novel stable oxide perovskites. The study identifies (1) atomic features relevant to prediction of formability and stability in perovskite and double perovskite compounds, (2) the importance of including energy contributions due to local structural relaxations going beyond the high symmetry perovskite phase, and (3) 437,828 double perovskite compounds that are likely to be stable and 891,188 compounds that are likely to be formable. From the intersection of this large chemical space of formable and stable oxide perovskites, 414 compositions are identified as the most promising candidates for future experimental synthesis of novel oxide perovskites. The developed models may be generalized and have implications beyond perovskite discovery if applied to other families of compounds. Perovskite (structure) ; Formability ; Oxide ; Materials science ; Stability (learning theory) ; Chemical stability ; Thermodynamics ; Computer science ; Machine learning ; Chemistry ; Crystallography ; Physics ; Composite material ; Metallurgy Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Condensed Matter Physics
Authors: Mian Adnan Kakakhel | Fasi Wu | Wasim Sajjad | Qi Zhang | Ikram Khan | Kalim Ullah | Wanfu Wang
Source: Environmental Sciences Europe (article, 2021-02-04)
Field: Materials Science — Materials Chemistry

Long-term exposure to high-concentration silver nanoparticles induced toxicity, fatality, bioaccumulation, and histological alteration in fish (Cyprinus carpio) Abstract Background Currently, nanotechnology and nanoparticles have quickly emerged and have gained the attention of scientists due to their massive applications in environmental sectors. However, these environmental applications of silver nanoparticles potentially cause serious effects on terrestrial and aquatic organisms. In the current study, freshwater fish C. carpio were exposed to blood-mediated silver nanoparticles for toxicity, mortality, bioaccumulation, and histological alterations. Silver nanoparticles were fabricated using animal blood serum and their toxic effect was studied against common carp fish at different concentrations levels (0.03, 0.06, and 0.09 mg/L). Results The findings have revealed a little influence of blood-induced silver nanoparticles on fish behavior at the highest concentration (0.09 mg/L). However, bioaccumulation of blood-mediated silver nanoparticles was reported in different organs of fish. Maximum bioaccumulation of silver nanoparticles was reported in the liver, followed by the intestine, gills, and muscles. Furthermore, the findings have shown that the bioaccumulation of silver nanoparticles led to histopathological alterations; including damaged structure of gill tissue and have caused necrosis. It is summarized that histopathological alteration in gill and intestine mostly occurred by the highest concentration of blood-induced silver nanoparticles (0.09 mg/L). Conclusion This study provides evidence of the silver nanoparticles influence on aquatic life; however, further systematic studies are crucial to access the effects of AgNPs on aquatic life. Bioaccumulation ; Gill ; Silver nanoparticle ; Cyprinus ; Toxicity ; Silver carp ; Ecotoxicology ; Nanoparticle ; Environmental chemistry ; Biology ; Chemistry ; Toxicology ; Fish <Actinopterygii> ; Fishery ; Nanotechnology ; Materials science Nanoparticles: synthesis and applications
Authors: Mizraín Solares-Briones | Guadalupe Coyote-Dotor | José C. Páez-Franco | Miriam Rosario Zermeño-Ortega | Carmen Myriam de la O Contreras | Daniel Canseco‐González | Alcives Avila‐Sorrosa | David Morales‐Morales | Juan Manuel Germán-Acacio
Source: Pharmaceutics (review, 2021-05-25)
Field: Chemistry — Physical and Theoretical Chemistry

Mechanochemistry: A Green Approach in the Preparation of Pharmaceutical Cocrystals Mechanochemistry is considered an alternative attractive greener approach to prepare diverse molecular compounds and has become an important synthetic tool in different fields (e.g., physics, chemistry, and material science) since is considered an ecofriendly procedure that can be carried out under solvent free conditions or in the presence of minimal quantities of solvent (catalytic amounts). Being able to substitute, in many cases, classical solution reactions often requiring significant amounts of solvents. These sustainable methods have had an enormous impact on a great variety of chemistry fields, including catalysis, organic synthesis, metal complexes formation, preparation of multicomponent pharmaceutical solid forms, etc. In this sense, we are interested in highlighting the advantages of mechanochemical methods on the obtaining of pharmaceutical cocrystals. Hence, in this review, we describe and discuss the relevance of mechanochemical procedures in the formation of multicomponent solid forms focusing on pharmaceutical cocrystals. Additionally, at the end of this paper, we collect a chronological survey of the most representative scientific papers reporting the mechanochemical synthesis of cocrystals. Mechanochemistry ; Biochemical engineering ; Nanotechnology ; Chemistry ; Green chemistry ; Organic chemistry ; Catalysis ; Materials science ; Engineering ; Reaction mechanism Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Phenothiazines and Benzothiazines Synthesis and Activities
Authors: Juraj Mavračić | Callum J. Court | Taketomo Isazawa | Stephen R. Elliott | Jacqueline M. Cole
Source: Journal of Chemical Information and Modeling (article, 2021-09-16)
Field: Materials Science — Materials Chemistry

ChemDataExtractor 2.0: Autopopulated Ontologies for Materials Science The ever-growing abundance of data found in heterogeneous sources, such as scientific publications, has forced the development of automated techniques for data extraction. While in the past, in the physical sciences domain, the focus has been on the precise extraction of individual properties, attention has recently been devoted to the extraction of higher-level relationships. Here, we present a framework for an automated population of ontologies. That is, the direct extraction of a larger group of properties linked by a semantic network. We exploit data-rich sources, such as tables within documents, and present a new model concept that enables data extraction for chemical and physical properties with the ability to organize hierarchical data as nested information. Combining these capabilities with automatically generated parsers for data extraction and forward-looking interdependency resolution, we illustrate the power of our approach via the automatic extraction of a crystallographic hierarchy of information. This includes 18 interrelated submodels of nested data, extracted from an evaluation set of scientific articles, yielding an overall precision of 92.2%, across 26 different journals. Our method and associated toolkit, ChemDataExtractor 2.0, offers a key step toward the seamless integration of primary literature sources into a data-driven scientific framework. Computer science ; Hierarchy ; Information retrieval ; Data mining ; Information extraction ; Set (abstract data type) ; Population ; Data science ; Knowledge extraction ; Exploit ; Parsing ; Focus (optics) ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Biomedical Text Mining and Ontologies
Authors: Guohao Li | Brian C. Wyatt | Fei Song | Changqiang Yu | Zhenjun Wu | Xiuqiang Xie | Babak Anasori | Nan Zhang
Source: Advanced Functional Materials (article, 2021-08-08)
Field: Materials Science — Materials Chemistry

2D Titanium Carbide (MXene) Based Films: Expanding the Frontier of Functional Film Materials Abstract 2D titanium carbide (Ti 3 C 2 T x ) MXene films, with their well‐defined microstructures and chemical functionality, provide a macroscale use of nano‐sized Ti 3 C 2 T x flakes. Ti 3 C 2 T x films have attractive physicochemical properties favorable for device design, such as high electrical conductivity (up to 20 000 S cm –1 ), impressive volumetric capacitance (1500 F cm –3 ), strong in‐plane mechanical strength (up to 570 MPa), and a high degree of flexibility. Here, the appealing features of Ti 3 C 2 T x ‐based films enabled by the layer‐to‐layer arrangement of nanosheets are reviewed. We devote attention to the key strategies for actualizing desirable characteristics in Ti 3 C 2 T x ‐based functional films, such as high and tunable electrical conductivity, outstanding mechanical properties, enhanced oxidation‐resistance and shelf life, hydrophilicity/hydrophobicity, adjustable porosity, and convenient processability. This review further discusses fundamental aspects and advances in the applications of Ti 3 C 2 T x ‐based films with a focus on illuminating the relationship between the structural features and the resulting performances for target applications. Finally, the challenges and opportunities in terms of future research, development, and applications of Ti 3 C 2 T x ‐based films are suggested. A comprehensive understanding of these competitive features and challenges shall provide guidelines and inspiration for the further development of Ti 3 C 2 T x ‐based functional films, and contribute to the advances in MXene technology. Materials science ; MXenes ; Microstructure ; Porosity ; Nanotechnology ; Flexibility (engineering) ; Carbide ; Layer (electronics) ; Titanium carbide ; Composite material MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Huaibo Zhao | Alastair J. McMillan | Timothée Constantin | Rory C. Mykura | Fabio Juliá | Daniele Leonori
Source: Journal of the American Chemical Society (article, 2021-09-01)
Field: Chemistry — Organic Chemistry

Merging Halogen-Atom Transfer (XAT) and Cobalt Catalysis to Override E2-Selectivity in the Elimination of Alkyl Halides: A Mild Route toward <i>contra</i>-Thermodynamic Olefins We report here a mechanistically distinct tactic to carry E2-type eliminations on alkyl halides. This strategy exploits the interplay of α-aminoalkyl radical-mediated halogen-atom transfer (XAT) with desaturative cobalt catalysis. The methodology is high-yielding, tolerates many functionalities, and was used to access industrially relevant materials. In contrast to thermal E2 eliminations where unsymmetrical substrates give regioisomeric mixtures, this approach enables, by fine-tuning of the electronic and steric properties of the cobalt catalyst, to obtain high olefin positional selectivity. This unprecedented mechanistic feature has allowed access to <i>contra</i>-thermodynamic olefins, elusive by E2 eliminations. Chemistry ; Alkyl ; Steric effects ; Selectivity ; Halide ; Catalysis ; Halogen ; Cobalt ; Olefin fiber ; Combinatorial chemistry ; Organic chemistry Catalytic C–H Functionalization Methods ; CO2 Reduction Techniques and Catalysts ; Fluorine in Organic Chemistry
Authors: Dheeraj Gunwant | Ajitanshu Vedrtnam
Source: Journal of Alloys and Compounds (review, 2021-05-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Microwave absorbing properties of carbon fiber based materials: A review and prospective Microwave ; Materials science ; Reflection loss ; Absorption (acoustics) ; Composite material ; Carbon fibers ; Polarization (electrochemistry) ; Nanotechnology ; Composite number ; Computer science ; Telecommunications ; Chemistry Electromagnetic wave absorption materials ; Fiber-reinforced polymer composites ; Natural Fiber Reinforced Composites
Authors: Katherine Develos-Bagarinao | Tomohiro Ishiyama | Haruo Kishimoto | Hiroyuki Shimada | Katsuhiko Yamaji
Source: Nature Communications (article, 2021-06-25)
Field: Materials Science — Materials Chemistry

Nanoengineering of cathode layers for solid oxide fuel cells to achieve superior power densities Solid oxide fuel cells (SOFCs) are power-generating devices with high efficiencies and considered as promising alternatives to mitigate energy and environmental issues associated with fossil fuel technologies. Nanoengineering of electrodes utilized for SOFCs has emerged as a versatile tool for significantly enhancing the electrochemical performance but needs to overcome issues for integration into practical cells suitable for widespread application. Here, we report an innovative concept for high-performance thin-film cathodes comprising nanoporous La<sub>0.6</sub>Sr<sub>0.4</sub>CoO<sub>3</sub><sub>-</sub><sub>δ</sub> cathodes in conjunction with highly ordered, self-assembled nanocomposite La<sub>0.6</sub>Sr<sub>0.4</sub>Co<sub>0.2</sub>Fe<sub>0.8</sub>O<sub>3</sub><sub>-</sub><sub>δ</sub> (lanthanum strontium cobalt ferrite) and Ce<sub>0.9</sub>Gd<sub>0.1</sub>O<sub>2</sub><sub>-</sub><sub>δ</sub> (gadolinia-doped ceria) cathode layers prepared using pulsed laser deposition. Integration of the nanoengineered cathode layers into conventional anode-supported cells enabled the achievement of high current densities at 0.7 V reaching ~2.2 and ~4.7 A/cm<sup>2</sup> at 650 °C and 700 °C, respectively. This result demonstrates that tuning material properties through an effective nanoengineering approach could significantly boost the electrochemical performance of cathodes for development of next-generation SOFCs with high power output. Nanoengineering ; Materials science ; Cathode ; Anode ; Oxide ; Electrochemistry ; Nanotechnology ; Pulsed laser deposition ; Cobalt oxide ; Power density ; Chemical engineering ; Electrode ; Thin film ; Power (physics) ; Electrical engineering ; Metallurgy ; Chemistry Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials
Authors: Jonathan Schmidt | Love Pettersson | Claudio Verdozzi | Silvana Botti | Miguel A. L. Marques
Source: Science Advances (article, 2021-12-03)
Field: Materials Science — Materials Chemistry

Crystal graph attention networks for the prediction of stable materials Graph neural networks for crystal structures typically use the atomic positions and the atomic species as input. Unfortunately, this information is not available when predicting new materials, for which the precise geometrical information is unknown. We circumvent this problem by replacing the precise bond distances with embeddings of graph distances. This allows our networks to be applied directly in high-throughput studies based on both composition and crystal structure prototype without using relaxed structures as input. To train these networks, we curate a dataset of over 2 million density functional calculations of crystals with consistent calculation parameters. We apply the resulting model to the high-throughput search of 15 million tetragonal perovskites of composition ABCD<sub>2</sub>. As a result, we identify several thousand potentially stable compounds and demonstrate that transfer learning from the newly curated dataset reduces the required training data by 50%. Crystal (programming language) ; Computer science ; Graph ; Materials science ; Theoretical computer science ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions