jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dylan H. Ross | Libin Xu
Source: Analytica Chimica Acta (review, 2021-02-02)
Field: Chemistry — Spectroscopy

Determination of drugs and drug metabolites by ion mobility-mass spectrometry: A review Chemistry ; Ion-mobility spectrometry ; Mass spectrometry ; Drug ; Ion-mobility spectrometry–mass spectrometry ; Chromatography ; Pharmacology ; Tandem mass spectrometry ; Selected reaction monitoring Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Yun Zhang | Xiaojie Xu
Source: International Journal of Applied Ceramic Technology (article, 2021-01-03)
Field: Materials Science — Materials Chemistry

Machine learning the lattice constant of cubic pyrochlore compounds Abstract Pyrochlores, with a general formula A 2 B 2 X 7 , are promising candidates in many potential applications due to their wide varieties of physical properties. Different combinations of cations and anions in the crystal structure enable the tailoring of their properties and functionalities. For cubic pyrochlores, the lattice constant, a, is an integrated result of stoichiometry, ionic radii, and electronegativities of alloying elements. It also has significant impacts on stabilities, electronic structures, ionic conductivities, and thus performance of materials. Here, we develop the Gaussian process regression model to shed light on the relationship among ionic radii, electronegativities, and lattice constants for cubic pyrochlores. The dataset includes ternary pyrochlores and mixed pyrochlores with co‐doping and multi‐doping situations. One hundred and thirty‐nine samples with lattice constants between 9.287 and 11.150 Å are examined. The model is highly stable and accurate that contributes to efficient and low‐cost lattice constant estimations. Electronegativity ; Ionic radius ; Pyrochlore ; Lattice constant ; Materials science ; Ternary operation ; Ionic bonding ; Lattice (music) ; Stoichiometry ; Doping ; Condensed matter physics ; Ion ; Thermodynamics ; Physical chemistry ; Computer science ; Physics ; Optics ; Chemistry ; Quantum mechanics ; Diffraction Nuclear materials and radiation effects ; Advanced Condensed Matter Physics ; Ferroelectric and Piezoelectric Materials
Authors: Yun Zhang | Xiaojie Xu
Source: Machine Learning with Applications (article, 2021-10-25)
Field: Materials Science — Polymers and Plastics

Machine learning tensile strength and impact toughness of wheat straw reinforced composites The wheat straw reinforced composite is considered as a “green composite” as it utilizes biodegradable and recyclable food waste materials as reinforcing materials. Mechanical properties, such as tensile strength and impact toughness, are greatly dependent on the composite content and processing parameters. While compression molding is one of popular methods to fabricate the wheat straw/polypropylene composite, experimental trials to achieve targeted mechanical properties can be time-consuming and expensive. In this work, we develop the Gaussian process regression model to present the relationship among the fiber content, processing parameters of the compression molding, and mechanical performance of wheat straw reinforced polypropylene composites. The model achieves a correlation coefficient of 99.13% (95.68%), a root mean square error of 0.0857 (0.4369), and a mean absolute error of 0.0693 (0.3265) for tensile strength (impact toughness). The models are simple and fast to implement, produce predictions with high accuracy, and thus might be considered as efficient tools for mechanical property estimations. Should data become available, the model may be extended to include other descriptors, such as the wheat straw length, size distribution, and chemical treatment parameters. Ultimate tensile strength ; Materials science ; Composite material ; Polypropylene ; Straw ; Toughness ; Composite number ; Molding (decorative) ; Compression molding ; Fiber ; Izod impact strength test ; Agronomy Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Innovations in Concrete and Construction Materials
Authors: Daniil M. Polyukhov | Artem S. Poryvaev | A. S. Sukhikh | S. А. Gromilov | Matvey V. Fedin
Source: ACS Applied Materials & Interfaces (article, 2021-08-23)
Field: Chemistry — Inorganic Chemistry

Fine-Tuning Window Apertures in ZIF-8/67 Frameworks by Metal Ions and Temperature for High-Efficiency Molecular Sieving of Xylenes Separation of structurally similar components from their mixtures is one of the most promising applications of metal-organic frameworks (MOFs). A high efficiency of such molecular sieving requires fine tuning of the MOF structure. In this work, we investigate subtle metal- and temperature-induced changes in window dimensions of zeolitic imidazolate frameworks (ZIF-8(Zn) and ZIF-67(Co)) and apply such structural tuning for efficient separation of xylene isomers (<i>p</i>-, <i>m</i>-, and <i>o</i>-xylenes). The use of Co instead of Zn favorably modifies window geometry: it accelerates the diffusion of all components by a factor of 2-3 while maintaining closely the same separation efficiency as that of ZIF-8(Zn). Outstanding selectivity above 18:1 and faster isolation of demanded <i>p</i>-xylene from the ternary mixture using ZIF-67(Co) have been demonstrated at room temperature, opening new horizons for its energy-efficient xylene separation. More generally, our findings suggest the prospective ways to tune various MOFs for target liquid-state separations. Materials science ; Zeolitic imidazolate framework ; Metal-organic framework ; Ternary operation ; Xylene ; Selectivity ; Chemical engineering ; Work (physics) ; Metal ; Nanotechnology ; Adsorption ; Toluene ; Organic chemistry ; Chemistry ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Zeolite Catalysis and Synthesis
Authors: Hae Sol Lee | Nam Sun Kim | Dong‐il Kwon | Su‐Kyung Lee | Muhammad Numan | Taesung Jung | Kanghee Cho | Michal Mazur | Hae Sung Cho | Changbum Jo
Source: Advanced Materials (article, 2021-09-21)
Field: Chemistry — Inorganic Chemistry

Post‐Synthesis Functionalization Enables Fine‐Tuning the Molecular‐Sieving Properties of Zeolites for Light Olefin/Paraffin Separations Zeolite molecular sieves are widely used in gas separation and shape-selective catalysis, but these applications often require discriminating differences as little as 0.1 Å. Molecular sieving with such size selectivity demands zeolites with highly tunable pore diameters and adsorption properties, which are technically challenging to prepare. Nevertheless, it is shown that a wide range of organic functional groups can be covalently functionalized onto the interior pore walls of the zeolites, MOR, LTL, FAU, and MFI, to systematically "tune" their effective pore diameters with respect to the size of organic groups. For organic functionalization, small and aggressive organic electrophiles are used (e.g., organo-halide and -diazonium) as grafting agents, which are accessible to the intracrystalline void space, forming a C-O<sub>zeolite</sub> bond in a reaction with a bridging oxygen as proved by multiple analysis data. It is demonstrated that the post-functionalization can be used to tailor the molecular sieving action of a parent zeolite to give size-selective adsorbents for light olefin/paraffin separations. 4-Methoxybenzene-functionalized MOR separates ethylene from ethane with an ideal-adsorbed-solution-theory selectivity of ≈5873, whereas toluene-grafted MOR completely separates propylene/propane mixtures. Therefore, tailoring the molecular-sieving properties of zeolites by organic functionalization broadens their applications to challenging separations. Surface modification ; Molecular sieve ; Materials science ; Zeolite ; Selectivity ; Adsorption ; Olefin fiber ; Chemical engineering ; Selective adsorption ; Gas separation ; Catalysis ; Organic chemistry ; Membrane ; Chemistry Zeolite Catalysis and Synthesis ; Catalytic Processes in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Erfan Dashtimoghadam | Farahnaz Fahimipour | Andrew N. Keith | Foad Vashahi | P. V. Popryadukhin | Mohammad Vatankhah‐Varnosfaderani | Sergei S. Sheiko
Source: Nature Communications (article, 2021-06-25)
Field: Materials Science — Biomaterials

Injectable non-leaching tissue-mimetic bottlebrush elastomers as an advanced platform for reconstructive surgery Current materials used in biomedical devices do not match tissue's mechanical properties and leach various chemicals into the body. These deficiencies pose significant health risks that are further exacerbated by invasive implantation procedures. Herein, we leverage the brush-like polymer architecture to design and administer minimally invasive injectable elastomers that cure in vivo into leachable-free implants with mechanical properties matching the surrounding tissue. This strategy allows tuning curing time from minutes to hours, which empowers a broad range of biomedical applications from rapid wound sealing to time-intensive reconstructive surgery. These injectable elastomers support in vitro cell proliferation, while also demonstrating in vivo implant integrity with a mild inflammatory response and minimal fibrotic encapsulation. Elastomer ; Biomedical engineering ; In vivo ; Reconstructive surgery ; Materials science ; Implant ; Tissue engineering ; Surgery ; Medicine ; Biology ; Composite material Electrospun Nanofibers in Biomedical Applications ; Hydrogels: synthesis, properties, applications ; biodegradable polymer synthesis and properties
Authors: Naseer Muhammad Khan | Jing Lin | Muhammad Rizwan Younas | Xukun Liu | Liming Shen
Source: Cancer Nanotechnology (article, 2021-07-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synthesis of gold nanorods and their performance in the field of cancer cell imaging and photothermal therapy Abstract Cancer is one of the most common incident in the world, with malignant tumors having a death rate of up to 19%. A new method of treating cancer cells effectively with minimal cytotoxicity is needed. In the field of biomedicine with unique shape-dependent optical properties, gold nanorods (GNRs) have attracted worldwide interest. These nanorods have two distinct plasmon bands. One is transverse plasmon band in the area of visible light, and the other is longitudinal band of plasmons in near infrared region. These specific characters provide promise for the design of new optically active reagents that simultaneously perform light-mediated imaging and photothermal cancer treatment. We begin our review by summarizing the latest developments in gold nanorods synthesis with a focus on seed-mediated growth method. Nanorods spontaneous self-assembly, polymer-based alignment and its applications as a novel agent for simultaneous bioimaging and photothermal cancer therapy are listed in particular. Nanorod ; Photothermal therapy ; Nanotechnology ; Materials science ; Plasmon ; Photothermal effect ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Biosensors and Analytical Detection
Authors: Yuchen Xiao | Huajun Yang | Xianhui Bu | Pingyun Feng
Source: Carbon (article, 2021-02-07)
Field: Chemistry — Inorganic Chemistry

ZIF-8 derived carbon materials with multifunctional selective adsorption abilities Propane ; Zeolitic imidazolate framework ; Carbonization ; Adsorption ; Materials science ; Chemical engineering ; Carbon fibers ; Selectivity ; Methane ; Selective adsorption ; Hydrocarbon ; Porosity ; Specific surface area ; Salt (chemistry) ; Metal-organic framework ; Inorganic chemistry ; Organic chemistry ; Chemistry ; Catalysis ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies
Authors: Jesús G. Ovejero | Ilaria Armenia | David Serantes | S. Veintemillas‐Verdaguer | Nicoll Zeballos | Fernando López‐Gallego | Cordula Grüttner | Jesús M. de la Fuente | M. P. Morales | Valeria Grazú
Source: Nano Letters (article, 2021-08-19)
Field: Materials Science — Biomaterials

Selective Magnetic Nanoheating: Combining Iron Oxide Nanoparticles for Multi-Hot-Spot Induction and Sequential Regulation The contactless heating capacity of magnetic nanoparticles (MNPs) has been exploited in fields such as hyperthermia cancer therapy, catalysis, and enzymatic thermal regulation. Herein, we propose an advanced technology to generate multiple local temperatures in a single-pot reactor by exploiting the unique nanoheating features of iron oxide MNPs exposed to alternating magnetic fields (AMFs). The heating power of the MNPs depends on their magnetic features but also on the intensity and frequency conditions of the AMF. Using a mixture of diluted colloids of MNPs we were able to generate a multi-hot-spot reactor in which each population of MNPs can be selectively activated by adjusting the AMF conditions. The maximum temperature reached at the surface of each MNP was registered using independent fluorescent thermometers that mimic the molecular link between enzymes and MNPs. This technology paves the path for the implementation of a selective regulation of multienzymatic reactions. Magnetic nanoparticles ; Nanoparticle ; Iron oxide nanoparticles ; Magnetic hyperthermia ; Iron oxide ; Materials science ; Induction heating ; Population ; Nanotechnology ; Chemistry ; Chemical engineering ; Electromagnetic coil Nanoparticle-Based Drug Delivery ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanomaterials for catalytic reactions
Authors: Bowen Ding | Gunwoo Kim | Young-Seok Kim | Flurin Eisner | Edgar Gutiérrez‐Fernández | Jaime Martín | Myung‐Han Yoon | Martin Heeney
Source: Angewandte Chemie International Edition (article, 2021-07-06)
Field: Materials Science — Polymers and Plastics

Influence of Backbone Curvature on the Organic Electrochemical Transistor Performance of Glycolated Donor–Acceptor Conjugated Polymers Two new glycolated semiconducting polymers PgBT(F)2gT and PgBT(F)2gTT of differing backbone curvatures were designed and synthesised for application as p-type accumulation mode organic electrochemical transistor (OECT) materials. Both polymers demonstrated stable and reversible oxidation, accessible within the aqueous electrochemical window, to generate polaronic charge carriers. OECTs fabricated from PgBT(F)2gT featuring a curved backbone geometry attained a higher volumetric capacitance of 170 F cm<sup>-3</sup> . However, PgBT(F)2gTT with a linear backbone displayed overall superior OECT performance with a normalised peak transconductance of 3.00×10<sup>4</sup> mS cm<sup>-1</sup> , owing to its enhanced order, expediting the charge mobility to 0.931 cm<sup>2</sup> V<sup>-1</sup> s<sup>-1</sup> . Transistor ; Materials science ; Electrochemistry ; Acceptor ; Transconductance ; Polymer ; Conjugated system ; Capacitance ; Chemistry ; Nanotechnology ; Electrode ; Physical chemistry Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Perovskite Materials and Applications
Authors: Mauro Och | Marie‐Blandine Martin | Bruno Dlubak | Pierre Sénéor | Cecilia Mattevi
Source: Nanoscale (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Synthesis of emerging 2D layered magnetic materials van der Waals atomically thin magnetic materials have been recently discovered. They have attracted enormous attention as they present unique magnetic properties, holding potential to tailor spin-based device properties and enable next generation data storage and communication devices. To fully understand the magnetism in two-dimensions, the synthesis of 2D materials over large areas with precise thickness control has to be accomplished. Here, we review the recent advancements in the synthesis of these materials spanning from metal halides, transition metal dichalcogenides, metal phosphosulphides, to ternary metal tellurides. We initially discuss the emerging device concepts based on magnetic van der Waals materials including what has been achieved with graphene. We then review the state of the art of the synthesis of these materials and we discuss the potential routes to achieve the synthesis of wafer-scale atomically thin magnetic materials. We discuss the synthetic achievements in relation to the structural characteristics of the materials and we scrutinise the physical properties of the precursors in relation to the synthesis conditions. We highlight the challenges related to the synthesis of 2D magnets and we provide a perspective for possible advancement of available synthesis methods to respond to the need for scalable production and high materials quality. Materials science ; Nanotechnology 2D Materials and Applications ; MXene and MAX Phase Materials ; Multiferroics and related materials
Authors: Chang Liu | Erina Fujita | Yukari Katsura | Yuki Inada | Asuka Ishikawa | Ryuji Tamura | Kaoru Kimura | Ryo Yoshida
Source: Advanced Materials (article, 2021-07-19)
Field: Materials Science — Materials Chemistry

Machine Learning to Predict Quasicrystals from Chemical Compositions Quasicrystals have emerged as the third class of solid-state materials, distinguished from periodic crystals and amorphous solids, which have long-range order without periodicity exhibiting rotational symmetries that are disallowed for periodic crystals in most cases. To date, more than one hundred stable quasicrystals have been reported, leading to the discovery of many new and exciting phenomena. However, the pace of the discovery of new quasicrystals has lowered in recent years, largely owing to the lack of clear guiding principles for the synthesis of new quasicrystals. Here, it is shown that the discovery of new quasicrystals can be accelerated with a simple machine-learning workflow. With a list of the chemical compositions of known stable quasicrystals, approximant crystals, and ordinary crystals, a prediction model is trained to solve the three-class classification task and its predictability compared to the observed phase diagrams of ternary aluminum systems is evaluated. The validation experiments strongly support the superior predictive power of machine learning, with the overall prediction accuracy of the phase prediction task reaching ≈0.728. Furthermore, analyzing the input-output relationships black-boxed into the model, nontrivial empirical equations interpretable by humans that describe conditions necessary for stable quasicrystal formation are identified. Quasicrystal ; Materials science ; Amorphous solid ; Ternary operation ; Predictability ; Phase (matter) ; Machine learning ; Statistical physics ; Artificial intelligence ; Computer science ; Condensed matter physics ; Physics ; Crystallography ; Chemistry ; Quantum mechanics Quasicrystal Structures and Properties ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Chengpeng Zhu | Chao Li | Di Wu | Wan Ye | Shuangxi Shi | Hui Ming | Xiaoyong Zhang | Kechao Zhou
Source: Journal of Materials Research and Technology (article, 2021-02-21)
Field: Materials Science — Materials Chemistry

A titanium alloys design method based on high-throughput experiments and machine learning In this work, the effect of Mo and Cr on microstructure and mechanical properties of newly titanium alloys (Ti–3Al–2Nb-1.2V–1Zr–1Sn-xCr-yMo) was investigated, and a composition-microstructure-properties relationship was established by diffusion multiple. The microstructure characterization (volume fraction, size of α phases) for alloys with different molybdenum equivalent (Mo[q]) was predicted by machine learning (BP neural network), and the result shows a good agreement between the predicted results and experimental values. Combining diffusion multiple and BP neural network, a Ti alloy (Ti–3Al–2Nb-1.2V–1Zr–1Sn–4Cr–4Mo) with outstanding mechanical properties was successfully designed. The mechanical test result shows that excellent balance of strength (YS~1200 MPa) and plasticity (El~12%) can be achieved after the solution treatment at 750 °C and aging at 550 °C for 6 h. During deformation, Primary globular primary α phases were elongated, and secondary acicular α phases resisted the dislocation slipping, which provides good plasticity and strength for the alloys, respectively. Materials science ; Microstructure ; Titanium alloy ; Alloy ; Deformation (meteorology) ; Titanium ; Plasticity ; Molybdenum ; Dislocation ; Volume fraction ; Composite material ; Metallurgy Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties
Authors: Jaak Simm | Lina Humbeck | Adam Zalewski | Noé Sturm | Wouter Heyndrickx | Yves Moreau | Bernd Beck | Ansgar Schuffenhauer
Source: Journal of Cheminformatics (article, 2021-12-01)
Field: Chemistry — Analytical Chemistry

Splitting chemical structure data sets for federated privacy-preserving machine learning With the increase in applications of machine learning methods in drug design and related fields, the challenge of designing sound test sets becomes more and more prominent. The goal of this challenge is to have a realistic split of chemical structures (compounds) between training, validation and test set such that the performance on the test set is meaningful to infer the performance in a prospective application. This challenge is by its own very interesting and relevant, but is even more complex in a federated machine learning approach where multiple partners jointly train a model under privacy-preserving conditions where chemical structures must not be shared between the different participating parties. In this work we discuss three methods which provide a splitting of a data set and are applicable in a federated privacy-preserving setting, namely: a. locality-sensitive hashing (LSH), b. sphere exclusion clustering, c. scaffold-based binning (scaffold network). For evaluation of these splitting methods we consider the following quality criteria (compared to random splitting): bias in prediction performance, classification label and data imbalance, similarity distance between the test and training set compounds. The main findings of the paper are a. both sphere exclusion clustering and scaffold-based binning result in high quality splitting of the data sets, b. in terms of compute costs sphere exclusion clustering is very expensive in the case of federated privacy-preserving setting. Computer science ; Data mining ; Data science Analytical chemistry methods development ; Machine Learning in Materials Science ; Computational Drug Discovery Methods