jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Saim Topçu | Tevfik Özen | Aysun Bal | Murat Taş
Source: Pharmacy & Pharmacology International Journal (article, 2021-09-03)
Field: Chemistry — Organic Chemistry

Synthesis, characterization and antioxidant activities of some novel oxime derivatives In this study, nine novel compounds were synthesized in the reaction of ω-chloroisonitrosoacetophenone with 2-chloro-4-methylaniline, 2-chloro-5-methylaniline, 2-chloro-6-methylaniline, 3-chloro-2-methylaniline, 4-chloro-3-methylaniline, 5-chloro2-methylaniline, 2,3-dichloroaniline, 2,4-dichloroaniline and 3,5-dichloroaniline resulting for C1-C9, respectively. Compounds C1, C4-C7 and C9 were handled in single crystalline forms, which the structures were also investigated by X-ray single crystal analysis. Compounds were evaluated for their nonenzymatic lipid peroxidation inhibition, antioxidant and antiradical activities and compared with standard agents at 5, 50, and 100μM concentrations. Compounds are exhibited good antioxidant activities and lipid peroxidation compared to standards (BHA, TBHQ, BHT and trolox). Due to the best of our knowledge for novel oxime compounds, this study is the first report of promising antioxidants to scavenge oxidative stress conditions. Antioxidant ; Community pharmacy ; Oxime ; Chemistry ; Pharmacy ; Pharmacology ; Characterization (materials science) ; Combinatorial chemistry ; Medicine ; Organic chemistry ; Nanotechnology ; Materials science ; Family medicine Free Radicals and Antioxidants ; Synthesis and biological activity ; Synthesis of Organic Compounds
Authors: Zhichu Xiang | Mouquan Liu | Jun Song
Source: Applied Sciences (article, 2021-10-14)
Field: Materials Science — Biomaterials

Stimuli-Responsive Polymeric Nanosystems for Controlled Drug Delivery Biocompatible nanosystems based on polymeric materials are promising drug delivery nanocarrier candidates for antitumor therapy. However, the efficacy is unsatisfying due to nonspecific accumulation and drug release of the nanoparticles in normal tissue. Recently, the nanosystems that can be triggered by tumor-specific stimuli have drawn great interest for drug delivery applications due to their controllable drug release properties. In this review, various polymers and external stimuli that can be employed to develop stimuli-responsive polymeric nanosystems are discussed, and finally, we delineate the challenges in designing this kind of Nanomedicine to improve the therapeutic efficacy. Nanocarriers ; Drug delivery ; Nanomedicine ; Nanotechnology ; Drug ; Biocompatible material ; Materials science ; Nanoparticle ; Biomedical engineering ; Pharmacology ; Medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Peikun Zheng | R.I. Zubatyuk | Wei Wu | Olexandr Isayev | Pavlo O. Dral
Source: ChemRxiv (preprint, 2021-07-22)
Field: Materials Science — Materials Chemistry

Artificial Intelligence-Enhanced Quantum Chemical Method with Broad Applicability High-level quantum mechanical (QM) calculations are indispensable for accurate explanation of natural phenomena on the atomistic level. Their staggering computational cost, however, poses great limitations, which luckily can be lifted to a great extent by exploiting advances in artificial intelligence (AI). Here we introduce the general-purpose, highly transferable artificial intelligence–quantum mechanical method 1 (AIQM1). It approaches the accuracy of the ‘gold-standard’ coupled cluster QM method with low computational speed of the approximate low-level semiempirical QM methods. AIQM1 can provide accurate ground-state energies for diverse organic compounds as well as geometries for even challenging systems such as large conjugated compounds (fullerene C60) close to experiment. Noteworthy, our method’s accuracy is also good for ions and excited-state properties, although the neural network part of AIQM1 was never fitted to these properties. Quantum ; Excited state ; Computer science ; Artificial neural network ; Ground state ; Quantum chemistry ; Ion ; Quantum computer ; Artificial intelligence ; Statistical physics ; Physics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Li Yang | Lei Sun | Yanliang Zhao | Jikai Sun | Qiwen Deng | Honglei Wang | Wei Deng
Source: Physical Chemistry Chemical Physics (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Digital-intellectual design of microporous organic polymers Microporous organic polymers (MOPs) are a new class of microporous materials. Due to their high porosity, large pore volume, and large surface area, MOPs exhibit excellent performance in gas adsorption and storage, membrane separation, ion capture, heterogeneous catalysis, light energy conversion and storage, capacitance, and other fields. However, selecting high-performance materials for specific applications from thousands of candidate MOPs is a key problem. Traditional design strategies for new materials with targeted properties, including trial-and-error and relying on the experiences of domain experts, are time- and cost-consuming. With the rapid development of computation technology and theoretical chemistry, the discovery of new materials is no longer a purely experimental subject. Breaking away from the traditional trial-and-error strategy for materials discovery, materials design is emerging and gaining increasing attention. In addition, the ability to collect "big data" has greatly improved and has further stimulated the development of new methods for materials design and discovery. In this perspective, we examine how data-driven techniques combine artificial intelligence (AI) and human expertise, playing a significant role in the design of MOPs. Such analytics can significantly reduce time-to-insight and accelerate the cost-effective materials discovery, which is the goal for designing future MOPs. Microporous material ; Computer science ; Nanotechnology ; Analytics ; Process engineering ; Materials science ; Data science ; Engineering Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Christine Zardecki | Chenghua Shao | Maria Voigt | S.K. Burley
Source: The FASEB Journal (article, 2021-05-01)
Field: Materials Science — Materials Chemistry

Protein Data Bank: 50 Years of Macromolecular Structures Enabling Research and Education The Protein Data Bank (PDB) was established in 1971 as the first open‐access digital data resource in biology. Beginning with only seven protein structures, the PDB archive has ballooned to &gt;170,000 structures of proteins, DNA, and RNA (totaling &gt;1 billion atoms). Today, the PDB is universally regarded as a core data science resource of fundamental importance to the wider life‐science community and long‐term preservation of machine‐readable biological data. PDB structures are the molecules of life. Knowledge of 3D structures (shapes) of biomolecules, how they evolve with time, and how they function in nature is essential for understanding critical areas of science. PDB data impacts basic and applied research on health and disease of humans, animals, and plants; production of food and energy; and other research pertaining to global prosperity and environmental sustainability. Structure data are also important to biopharmaceutical and biotechnology companies, accelerating data‐driven discovery of new drugs, materials, and devices. Today, powerful pulsed X‐ray facilities, cryogenic electron microscopes, and new integrative/hybrid (I/H) methods for structure determination are accelerating biomedical research with functional insights into ever more complex biological systems at the atomic level. Cryo‐electron tomography even allows study of molecular machines “caught in the act” inside frozen cells. The PDB is managed by the Worldwide Protein Data Bank partnership (wwPDB; wwpdb.org ). RCSB PDB ( RCSB.org ) operates the US wwPDB data center, and makes PDB data available at no charge and without limitations. The 50th anniversary of the PDB archive is being celebrated with special symposia and related resources, including a May 4‐5 virtual meeting hosted by the ASBMB. These celebrations will include first hand accounts traversing five decades from the beginnings of structural biology all the way to ongoing efforts aimed at combatting the COVID‐19 pandemic. Protein Data Bank (RCSB PDB) ; Protein Data Bank ; Structural bioinformatics ; Resource (disambiguation) ; Computer science ; Data science ; Biology ; Protein structure ; Biochemistry Enzyme Structure and Function ; Genetics, Bioinformatics, and Biomedical Research ; Advanced Proteomics Techniques and Applications
Authors: Aravind Krishnamoorthy | Ankit Mishra | Deepak Kamal | Sungwook Hong | Ken-ichi Nomura | Subodh Tiwari | Aiichiro Nakano | Rajiv Kalia | Rampi Ramprasad | Priya Vashishta
Source: SoftwareX (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

EZFF: Python library for multi-objective parameterization and uncertainty quantification of interatomic forcefields for molecular dynamics Parameterization of interatomic forcefields is a necessary first step in performing molecular dynamics simulations. This is a non-trivial global optimization problem involving quantification of multiple empirical variables against one or more properties. We present EZFF, a lightweight Python library for parameterization of several types of interatomic forcefields implemented in several molecular dynamics engines against multiple objectives using genetic-algorithm-based global optimization methods. The EZFF scheme provides unique functionality such as the parameterization of hybrid forcefields composed of multiple forcefield interactions as well as built-in quantification of uncertainty in forcefield parameters and can be easily extended to other forcefield functional forms as well as MD engines. Python (programming language) ; Molecular dynamics ; Uncertainty quantification ; Interatomic potential ; Scheme (mathematics) ; Global optimization Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Protein Structure and Dynamics
Authors: Edoardo Fertitta | Sujit Das | Debalina Banerjee | Farbod Ebrahimi | Clément Barraud | Kai Du | He Tian | Chris J. Pickard | Cédric Weber | R. Ramesh | P. B. Littlewood | David Dubbink
Source: npj Computational Materials (article, 2021-06-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Study of disorder in pulsed laser deposited double perovskite oxides by first-principle structure prediction Abstract Double perovskite oxides, with generalized formula A 2 BB $$^{\prime}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mrow> <mml:mo>′</mml:mo> </mml:mrow> </mml:msup> </mml:math> O 6 , attract wide interest due to their multiferroic and charge transfer properties. They offer a wide range of potential applications such as spintronics and electrically tunable devices. However, great practical limitations are encountered, since a spontaneous order of the B-site cations is notoriously hard to achieve. In this joint experimental-theoretical work, we focused on the characterization of double perovskites La 2 TiFeO 6 and La 2 VCuO 6 films grown by pulsed laser deposition and interpretation of the observed B-site disorder and partial charge transfer between the B-site ions. A random structure sampling method was used to show that several phases compete due to their corresponding configurational entropy. In order to capture a representative picture of the most relevant competing microstates in realistic experimental conditions, this search included the potential formation of non-stoichiometric phases as well, which could also be directly related to the observed partial charge transfer. We optimized the information encapsulated in the potential energy landscape, captured via structure sampling, by evaluating both enthalpic and entropic terms. These terms were employed as a metric for the competition of different phases. This approach, applied herein specifically to La 2 TiFeO 6 , highlights the presence of highly entropic phases above the ground state which can explain the disorder observed frequently in the broader class of double perovskite oxides. Perovskite (structure) ; Materials science ; Charge (physics) ; Stoichiometry ; Physics ; Chemistry ; Physical chemistry ; Crystallography ; Quantum mechanics Magnetic and transport properties of perovskites and related materials ; Multiferroics and related materials ; Advanced Condensed Matter Physics
Authors: Yifei Xu | Marco Bonizzoni
Source: Sensors (article, 2021-05-24)
Field: Chemistry — Spectroscopy

Discrimination and Quantitation of Biologically Relevant Carboxylate Anions Using A [Dye•PAMAM] Complex Carboxylate anions are analytical targets with environmental and biological relevance, whose detection is often challenging in aqueous solutions. We describe a method for discrimination and quantitation of carboxylates in water buffered to pH 7.4 based on their differential interaction with a supramolecular fluorescent sensor, self-assembled from readily available building blocks. A fifth-generation poly(amidoamine) dendrimer (PAMAM G5), bound to organic fluorophores (calcein or pyranine) through noncovalent interactions, forms a [dye•PAMAM] complex responsive to interaction with carboxylates. The observed changes in absorbance, and in fluorescence emission and anisotropy, were interpreted through linear discriminant analysis (LDA) and principal component analysis (PCA) to differentiate 10 structurally similar carboxylates with a limit of discrimination around 100 μM. The relationship between the analytes' chemical structures and the system's response was also elucidated. This insight allowed us to extend the system's capabilities to the simultaneous identification of the nature and concentration of unknown analytes, with excellent structural identification results and good concentration recovery, an uncommon feat for a pattern-based sensing system. Carboxylate ; Chemistry ; Analyte ; Pyranine ; Fluorescence ; Absorbance ; Detection limit ; Dendrimer ; Aqueous solution ; Supramolecular chemistry ; Combinatorial chemistry ; Chromatography ; Molecule ; Organic chemistry Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Advanced Chemical Sensor Technologies
Authors: Horadugoda Gamage Sujatha Pushpakanthi Hewageegana | Ayuma Uththami Hewageegana | L.D.A.M. Arawwawala
Source: Evidence-based Complementary and Alternative Medicine (review, 2021-01-15)
Field: Chemistry — Analytical Chemistry

Purification, Detoxification, and Incineration Methods of Minerals and Metals in Traditional Medicine Formulations of Sri Lanka The review has demonstrated different types of purification/detoxification or incineration techniques of minerals used in herbo-mineral preparations. In addition, there is an urgent need for comprehensive survey or evaluation to check whether purification/detoxification or incineration techniques of metals/minerals are practiced properly in the country. Realgar ; Incineration ; Chemistry ; Environmental chemistry ; Detoxification (alternative medicine) ; Environmental science ; Waste management ; Mineralogy ; Engineering ; Medicine Heavy Metals in Plants ; Phytochemicals and Medicinal Plants ; Pharmacological Effects of Medicinal Plants
Authors: Jonas Beyer | Kenichi Kato | Bo B. Iversen
Source: IUCrJ (article, 2021-03-05)
Field: Materials Science — Materials Chemistry

Synchrotron total-scattering data applicable to dual-space structural analysis Synchrotron powder X-ray diffraction (PXRD) is a well established technique for investigating the atomic arrangement of crystalline materials. At modern beamlines, X-ray scattering data can be collected in a total-scattering setting, which additionally opens up the opportunity for direct-space structural analysis through the atomic pair distribution function (PDF). Modelling of PXRD and PDF data is typically carried out separately, but employing a concurrent structural model to both direct- and reciprocal-space data has the possibility to enhance total-scattering data analysis. However, total-scattering measurements applicable to such dual-space analyses are technically demanding. Recently, the technical demands have been fulfilled by a MYTHEN microstrip detector system (OHGI), which meets the stringent requirements for both techniques with respect to <i>Q</i> range, <i>Q</i> resolution and dynamic range. In the present study, we evaluate the quality of total-scattering data obtained with OHGI by separate direct- and reciprocal-space analysis of Si. Excellent agreement between structural parameters in both spaces is found, demonstrating that the total-scattering data from OHGI can be utilized in dual-space structural analysis <i>e.g.</i> for <i>in situ</i> and <i>operando</i> measurements. Reciprocal lattice ; Scattering ; Powder diffraction ; Synchrotron ; Anomalous scattering ; Synchrotron radiation ; Diffraction ; Pair distribution function ; Resolution (logic) ; Crystallography ; Computational physics ; Materials science ; Optics ; Physics ; Computer science ; Chemistry X-ray Diffraction in Crystallography ; Advanced ceramic materials synthesis ; Chemical Synthesis and Characterization
Authors: Liam Yourston | Polikron Dhoqina | Nolan Marshall | Rujani Mahmud | Ethen Kuether | Alexey V. Krasnoslobodtsev
Source: Processes (article, 2021-09-23)
Field: Materials Science — Materials Chemistry

Hg2+ Detection with Rational Design of DNA-Templated Fluorescent Silver Nanoclusters Atomically precise silver nanoclusters (AgNCs) are small nanostructures consisting of only a few atoms of silver. The combination of AgNCs with cytosine-rich single-stranded oligonucleotides results in DNA-templated silver nanoclusters (DNA-AgNCs). DNA-AgNCs are highly luminescent and can be engineered with reproducible and unique fluorescent properties. Furthermore, using nucleic acids as templates for the synthesis of AgNCs provides additional practical benefits by expanding optical activity beyond the visible spectral range and creating the possibility for color tunability. In this study, we explore DNA oligonucleotides designed to fold into hairpin-loop (HL) structures which modulate optical properties of AgNCs based on the size of the loop containing different number of cytosines (HL-CN). Depending on the size of the loop, AgNCs can be manufactured to have either single or multiple emissive states. Such hairpin-loop structures provide an additional stability for AgNCs and further control over the base composition of the loop, allowing for the rational design of AgNCs’ optical properties. We demonstrate the potential of AgNCs in detecting Hg2+ by utilizing the HL-C13 design and its variants HL-T2C11, HL-T4C9, and HL-T6C7. The replacement of cytosines with thymines in the loop was intended to serve as an additional sink for mercury ions extending the detectable range of Hg2+. While AgNC@HL-T0C13 exhibits an interpretable quenching curve, AgNC@HL-T6C7 provides the largest detectable range of Hg2+. The results presented herein suggest that it is possible to use a rational design of DNA-AgNCs based on the composition of loop sequence in HL structures for creating biosensors to detect heavy metals, particularly Hg2+. Nanoclusters ; Oligonucleotide ; Rational design ; Materials science ; DNA ; Fluorescence ; Cytosine ; Nucleic acid ; Template ; Guanine ; Nanotechnology ; Chemistry ; Nucleotide ; Physics ; Biochemistry Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Carbon and Quantum Dots Applications
Authors: Matthias Smuda | Kati Finzel | Martin Hantusch | Jonas Ströh | Nicole Pienack | Azat Khadiev | Huayna Terraschke | Michael Ruck | Thomas Doert
Source: Dalton Transactions (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Formation of Bi<sub>2</sub>Ir nanoparticles in a microwave-assisted polyol process revealing the suboxide Bi<sub>4</sub>Ir<sub>2</sub>O Intermetallic phases are usually obtained by crystallization from the melt. However, phases containing elements with widely different melting and boiling points, as well as nanoparticles, which provide a high specific surface area, are hardly accessible <i>via</i> such a high-temperature process. The polyol process is one option to circumvent these obstacles by using a solution-based approach at moderate temperatures. In this study, the formation of Bi<sub>2</sub>Ir nanoparticles in a microwave-assisted polyol process was investigated. Solutions were analyzed using UV-Vis spectroscopy and the reaction was tracked with synchrotron-based <i>in situ</i> powder X-ray diffraction (PXRD). The products were characterized by PXRD and high-resolution transmission electron microscopy. Starting from Bi(NO<sub>3</sub>)<sub>3</sub> and Ir(OAc)<sub>3</sub>, the new suboxide Bi<sub>4</sub>Ir<sub>2</sub>O forms as an intermediate phase at about 160 °C. Its structure was determined by a combination of PXRD and quantum-chemical calculations. Bi<sub>4</sub>Ir<sub>2</sub>O decomposes in vacuum at about 250 °C and is reduced to Bi<sub>2</sub>Ir by hydrogen at 150 °C. At about 240 °C, the polyol process leads to the immediate reduction of the two metal-containing precursors and crystallization of Bi<sub>2</sub>Ir nanoparticles. Powder diffraction ; Suboxide ; Nanoparticle ; Materials science ; Polyol ; Crystallization ; Chemical engineering ; Transmission electron microscopy ; Crystallography ; Physical chemistry ; Chemistry ; Nanotechnology ; Oxide ; Metallurgy ; Polyurethane ; Composite material Nuclear Materials and Properties ; Advanced Chemical Physics Studies ; Hydrogen Storage and Materials
Authors: Yuan Tian | Turab Lookman | Dezhen Xue
Source: Chinese Physics B (article, 2021-03-24)
Field: Materials Science — Materials Chemistry

Efficient sampling for decision making in materials discovery* Accelerating materials discovery crucially relies on strategies that efficiently sample the search space to label a pool of unlabeled data. This is important if the available labeled data sets are relatively small compared to the unlabeled data pool. Active learning with efficient sampling methods provides the means to guide the decision making to minimize the number of experiments or iterations required to find targeted properties. We review here different sampling strategies and show how they are utilized within an active learning loop in materials science. Computer science ; Sampling (signal processing) ; Sample (material) ; Machine learning ; Space (punctuation) ; Sequential sampling ; Data mining ; Active learning (machine learning) ; Artificial intelligence ; Statistics ; Mathematics Machine Learning in Materials Science ; Machine Learning and Algorithms ; Electronic and Structural Properties of Oxides