jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marko Milojević | Andraž Stožer | Uroš Maver
Source: Slovenian Medical Journal (article, 2021-02-28)
Field: Materials Science — Biomaterials

Modeli in vitro endokrine trebušne slinavke Želja po razvoju tkivnih nadomestkov, potrebe po preučevanju mehanizmov bolezni v kontroliranih pogojih, pomanjkljivosti živalskih modelov in etične omejitve so bile povod za razvoj področja naprednih modelov in vitro. Te definiramo kot alternativne eksperimentalne sisteme, ki z uporabo modernih tehnik tkivnega inženirstva in aditivne proizvodnje posnemajo strukturo in funkcionalnost tkiv ali organov in vitro. Enostavni modeli in vitro se že uporabljajo v številne namene, vendar zaradi mnogih pomanjkljivosti ne posnemajo dovolj dinamičnih odzivov izvornih tkiv. Za izgradnjo funkcionalno kompleksnejših modelov in vitro je celice potrebno gojiti v tridimenzionalnem (3D) okolju ali bioloških nosilcih. Splošno morajo biološki nosilci posnemati mikroarhitekturo, hierarhično strukturo, fizikalne lastnosti in sestavo izvornega tkiva. Toda napredek k izgradnji kompleksnejših modelov omejuje predvsem difuzija plinov in hranil v notranjost konstrukta. Tehnika 3D tiska je trenutno najobetavnejša rešitev za proizvodnjo naprednih bioloških nosilcev, ki odpravljajo te pomanjkljivosti. 3D tisk poleg zmožnosti sočasne uporabe več biološko kompatibilnih materialov omogoča nalaganje materiala z mikrometrsko prostorsko ločljivostjo pri pogojih, primernih celicam. Vse od zasnove področja modelov in vitro je zaradi želje po preučevanju nastanka sladkorne bolezni razvoj funkcionalnega modela trebušne slinavke eden od osrednjih, a še nedoseženih ciljev. Zlati standard za bazične in translacijske raziskave trebušne slinavke pa je kljub nekaterim pomanjkljivostim metoda tkivne rezine, medtem ko trenutni najnaprednejši 3D zgrajeni modeli trebušne slinavke in vitro posnemajo le osnovne funkcije organa. Namen tega članka je predstavitev modelov in vitro s poudarkom na modelih trebušne slinavke. Predstavil bo tipe modelov in ključne elemente, ki jih je treba pri izgradnji upoštevati. Poudarek bo na kompleksnejših 3D zgrajenih modelih in vitro in tkivnih rezinah, materialnih lastnostih bioloških nosilcev ter tehniki 3D tiska za izgradnjo naprednih bioloških nosilcev. Menimo, da je sočasni razvoj znanosti o materialih, mikroproizvodni tehnologiji in celičnih kulturah izjemno obetavna pot k izgradnji funkcionalnega modela trebušne slinavke in vitro. Physics Collagen: Extraction and Characterization ; Bone Tissue Engineering Materials ; Laser Applications in Dentistry and Medicine
Authors: Lizbeth A. Rodríguez-Cortés | Federico J. Hernández | Mario Rodríguez | Rubén A. Toscano | Arturo Jiménez-Sánchez | Rachel Crespo‐Otero | Braulio Rodríguez‐Molina
Source: ChemRxiv (preprint, 2021-10-22)
Field: Materials Science — Materials Chemistry

Dual-State Emission in Molecular Rotors with Reorientable Benzotriazole Acceptors Dual-State Emission (DSE) of organic molecules in both solution and solid-state is an elusive property that has recently drawn significant interest. In this work, we report two molecular rotors 1 and 2 with exceptional photoluminescence (PL) in solution (ФPL = 0.53 and ФPL = 0.43) and in the solid state (ФPL = 0.92 and ФPL = 0.84). To the best of our knowledge, this is the first report containing Donor-pi-Acceptor-pi-Donor (D-pi-A-pi-D) compounds with high quantum yields in both states that features a rotary acceptor. Furthermore, we describe the marked variations in PL in solution upon polarity and viscosity changes. TD-DFT computations indicate that these changes are facilitated by the fast molecular rotation in these molecules. The symmetry (or lack of it) in 1 and 2 plays a crucial role in producing noticeable differences in the X-ray crystal arrays with variations in the solid-state PL. We attribute this DSE behavior to the favorable combination of the D--A--D architecture and the twisted conformations adopted in the solid state. Acceptor ; Photoluminescence ; Molecule ; Solid-state ; Benzotriazole ; Solvatochromism ; Crystal (programming language) ; Crystallography ; Materials science ; State (computer science) ; Chemistry ; Chemical physics ; Computational chemistry ; Physics ; Physical chemistry ; Optoelectronics ; Condensed matter physics ; Mathematics ; Computer science ; Organic chemistry Luminescence and Fluorescent Materials ; Porphyrin and Phthalocyanine Chemistry ; Lanthanide and Transition Metal Complexes
Authors: Jinjiao Dong | Xinyue Zhu | Siran Feng | Chaochao Zhang | Zhenming Liu | Xiaoqiang Qiao | Yali Song
Source: Chinese Journal of Organic Chemistry (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

Synthesis and Antifungal Activity of 7-Phenyl-6<i>H</i>,7<i>H</i>-1,3,4-thia- diazolo[3,2-<i>a</i>]-thiochromeno[4,3-<i>d</i>]pyrimidine Compounds 3,2-a]-[4,3-d] a a a a b * ,a,c * ,a ( a 071000) ( b 100191) ( c 071000) , 16 7--6H,7H-1,3,4-[3,2-a]- [4,3-d]. ( 1 H NMR)( 13 C NMR)(HRMS) . , , , 5d 4 gmL -1 , ; 5 , 5l , 8 gmL -1 . 1,3,4-[3,2-a]-[4,3-d]; ; Synthesis and Antifungal Activity of 7-Phenyl-6H,7H-1,3,4-thiadiazolo[3, Chemistry ; Antifungal ; Pyrimidine ; Medicinal chemistry ; Stereochemistry ; Microbiology Synthesis and biological activity ; Synthesis and Reactions of Organic Compounds ; Synthesis of Organic Compounds
Authors: Miquel Huix‐Rotllant
Source: Molecules (article, 2021-10-04)
Field: Chemistry — Spectroscopy

Photochemistry of Thymine in Protic Polar Nanomeric Droplets Using Electrostatic Embeding TD-DFT/MM Thymine photochemistry is important for understanding DNA photodamage. In the gas phase, thymine undergoes a fast non-radiative decay from S2 to S1. In the S1 state, it gets trapped for several picoseconds until returning to the ground-state S0. Here, we explore the electrostatic effects of nanomeric droplets of methanol and water on the excited states of thymine. For this purpose, we develop and implement an electrostatic embedding TD-DFT/MM method based on a QM/MM coupling defined through electrostatic potential fitting charges. We show that both in methanol and water, the mechanism is similar to the gas phase. The solvent molecules participate in defining the branching plane of S0/S1 intersection and have a negligible effect on the S1/S2 intersection. Despite the wrong topology of the ground/excited state intersections, electrostatic embedding TD-DFT/MM allows for a fast exploration of the potential energy surfaces and a qualitative picture of the photophysics of thymine in solvent droplets. Thymine ; Excited state ; Chemistry ; Photochemistry ; Electrostatics ; Ground state ; Conical intersection ; Chemical physics ; Solvent ; Radiative transfer ; Atomic physics ; Molecular physics ; Physics ; Physical chemistry ; Optics ; DNA ; Organic chemistry Mass Spectrometry Techniques and Applications ; Advanced Chemical Physics Studies ; Photonic Crystals and Applications
Authors: M. M. Bobrova | L. A. Safonova | А. Е. Efimov | О. И. Агапова | И. И. Агапов
Source: Russian Journal of Transplantology and Artificial Organs (article, 2021-01-27)
Field: Materials Science — Biomaterials

Natural silk fiber microcarriers for cell culture The development of effective and versatile microcarriers is a pressing issue in tissue engineering and regenerative medicine. The objective of this wor k is to create biocompatible fiber microparticles from the cocoons of the Bombyx mori silkworm, and to study their structure and biological properties. Materials and methods . In obtaining microparticles, the Bombyx mori cocoons washed from sericin were cryo-milled in liquid nitrogen. The structure of the resulting microparticles was analyzed via scanning electron microscopy. The cytotoxicity of the obtained fibers was assessed using MTT-cell culture assay of 3T3 mouse fibroblasts. Cell adhesion analysis was performed using the Hep-G 2 human hepatocarcinoma cell line. Cell visualization was performed by staining the nuclei with DAPI fluorescent dye. Results . Natural silk microparticles were obtained in the form of cylindrical fibers with 200–400 μm average length and 15 μm diameter. It was shown that the surface of the resulting microparticles has a rough relief; no pores were found. The microparticles are non-toxic for 3T3 mouse fibroblasts, they maintain a high level of adhesion by human hepatocellular carcinoma HepG 2 cells . Conclusion. The method developed by us for fabrication of biocompatible silk fibroin microparticles in the form of fibers without using toxic reagents and significant time costs is promising for cell cultivation and delivery to the damaged area for tissue and organ regeneration. Fibroin ; Bombyx mori ; Microcarrier ; Sericin ; SILK ; Tissue engineering ; DAPI ; Cell adhesion ; Materials science ; Cell culture ; Adhesion ; Staining ; Chemistry ; Biomedical engineering ; Biophysics ; Cell ; Biochemistry ; Composite material ; Apoptosis ; Biology Silk-based biomaterials and applications ; Viral Infectious Diseases and Gene Expression in Insects ; Silkworms and Sericulture Research
Authors: Matthew Witman | Gustav Ek | Sanliang Ling | Jeffery M. Chames | Sapan Agarwal | Justin Chuan-Chi Wong | Mark D. Allendorf | Martin Sahlberg | Vitalie Stavila
Source: ChemRxiv (preprint, 2021-02-03)
Field: Materials Science — Materials Chemistry

Data-Driven Discovery and Synthesis of High Entropy Alloy Hydrides with Targeted Thermodynamic Stability Solid-state hydrogen storage materials that are optimized for specific use cases could be a crucial facilitator of the hydrogen economy transition. Yet the discovery of novel hydriding materials has historically been a manual process driven by chemical intuition or experimental trial-and-error. Data-driven materials' discovery paradigms provide an alternative to traditional approaches, whereby machine/statistical learning (ML) models are used to efficiently screen materials for desired properties and significantly narrow the scope of expensive/time-consuming first-principles modeling and experimental validation. Here we specifically focus on a relatively new class of hydrogen storage materials, high entropy alloy (HEA) hydrides, whose vast combinatorial composition space and local structural disorder necessitates a data-driven approach that does not rely on exact crystal structures in order to make property predictions. Our ML model quickly screens hydride stability within a large HEA space and permits down selection for laboratory validation based not only on targeted thermodynamic properties, but also secondary criteria such as alloy phase stability and density. To experimentally verify our predictions, we performed targeted synthesis and characterization of several novel hydrides that demonstrate significant destabilization (70x increase in equilibrium pressure, 20 kJ/molH2 decrease in desorption enthalpy) relative to the benchmark HEA hydride, TiVZrNbHfHx. Ultimately, by providing a large composition space in which hydride thermodynamics can be continuously tuned over a wide range, this work will enable efficient materials selection for various applications, especially in areas such as metal hydride based hydrogen compressors, actuators, and heat pumps. Hydrogen storage ; Hydride ; Materials science ; Alloy ; Thermodynamics ; Computer science ; Process engineering ; Physics ; Engineering ; Metal ; Metallurgy Hydrogen Storage and Materials ; Catalysis and Hydrodesulfurization Studies ; Electrocatalysts for Energy Conversion
Authors: Praneeth S Ramesh | Tarak K. Patra
Source: arXiv (Cornell University) (preprint, 2021-11-18)
Field: Materials Science — Materials Chemistry

Polymer Sequence Design via Active Learning Analysis of molecular scale interactions and chemical structure offers an enormous opportunity to tune material properties for targeted applications. However, designing materials from molecular scale is a grand challenge owing to the practical limitations in exploring astronomically large design spaces using traditional experimental or computational methods. Advancements in data sciences and artificial intelligence have produced a host of tools and techniques that can facilitate the efficient exploration of large search spaces. In this work, a blended approach integrating physics-based methods and data science techniques is implemented in order to effectively screen the materials configuration space for accelerating materials design. Here, we survey and assess the efficacy of data-driven methods within the framework of active learning for a challenging design problem viz, sequence optimization of copolymers. We report the impact of surrogate models, kernels, and initial conditions on the convergence of an active learning method for the sequence design problem. This work outlines optimal strategies and parameters for inverse design of polymer sequence via active learning. Sequence (biology) ; Computer science ; Scale (ratio) ; Active learning (machine learning) ; Space (punctuation) ; Convergence (economics) ; Sequence space ; Surrogate model ; Artificial intelligence ; Machine learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Apurba Biswas
Source:  (article, 2021-09-27)
Field: Chemistry — Inorganic Chemistry

Emerging Technologies in Chemistry for the Environmental Sustainability Chemists in all corners of the world have made exceptional contributions to science through innovative technologies and innovations in their research fields. A team of IUPAC experts has already begun to identify emerging technologies in Chemistry that could make our planet more sustainable. MOFs and porous materials for water harvesting, nanopesticides, 3D bioprinting, dual-ion batteries, macromonomers for better plastic recycling, nanosensors, artificial intelligence, high-pressure inorganic chemistry, flow chemistry, and reactive extrusion are among the most important chemical innovations. Water scarcity affects more than 40 percent of the world population, and it is projected to rise. Porous materials, such as metal organic frameworks (MOFs), could provide a solution to this problem, according to the United Nations (UN). Porous materials such as MOFs have a sponge-like chemical structure with microscopic spaces that can selectively trap molecules from gases hydrogen, methane, carbon dioxide, water, to more complex substances such as drugs, and enzymes. Some researchers accidentally discovered that MOFs have an incredible ability to capture water from the atmosphere. Nanopesticides are an alternative to conventional pesticides, as they can be used as smart delivery systems for the release of pesticides in a timely, but controlled fashion, for a desired timeframe. Chemists use radical ring-opening reactions to add heteroatoms and functional groups, such as ester, to structures, which traditionally have an all-carbon backbone, are hydrolysed for better plastic recycling. Nanotechnology ; Chemistry ; Sustainability ; Population ; Materials science Metal-Organic Frameworks: Synthesis and Applications
Authors: Olivia F. Dippo | Kevin Kaufmann | Kenneth S. Vecchio
Source: arXiv (Cornell University) (preprint, 2021-02-11)
Field: Materials Science — Materials Chemistry

High-Throughput Rapid Experimental Alloy Development (HT-READ) The current bulk materials discovery cycle has several inefficiencies from initial computational predictions through fabrication and analyses. Materials are generally evaluated in a singular fashion, relying largely on human-driven compositional choices and analysis of the volumes of generated data, thus also slowing validation of computational models. To overcome these limitations, we developed a high-throughput rapid experimental alloy development (HT-READ) methodology that comprises an integrated, closed-loop material screening process inspired by broad chemical assays and modern innovations in automation. Our method is a general framework unifying computational identification of ideal candidate materials, fabrication of sample libraries in a configuration amenable to multiple tests and processing routes, and analysis of the candidate materials in a high-throughput fashion. An artificial intelligence agent is used to find connections between compositions and material properties. New experimental data can be leveraged in subsequent iterations or new design objectives. The sample libraries are assigned unique identifiers and stored to make data and samples persistent, thus preventing institutional knowledge loss. Computer science ; Throughput ; Identification (biology) ; Sample (material) ; Identifier ; Automation ; Process (computing) ; Fabrication ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Joshua Schrier | Michael Tynes | Lillian Cain
Source: Journal of Chemical Education (article, 2021-03-10)
Field: Chemistry — Physical and Theoretical Chemistry

Determining the Activity Series with the Fewest Experiments Using Sorting Algorithms Determining the activity series of a collection of elements is a classic pedagogical experiment, in which pairs of elements are reacted to determine the relative rank ordering of their reactivity. Determining the optimal sequence of pairwise experiments that minimizes the total number of experiments corresponds to well-known comparison sorting algorithms in computer science. We describe relevant algorithms (insertion sort, binary insertion sort, merge sort, and merge insertion sort) and their application to the activity series problem and discuss ways that this connection can contribute to the introductory chemistry and computer science curricula. In addition to pedagogical interest, this illustrates a simple form of artificial intelligence for chemical experiment planning. sort ; Sorting algorithm ; Merge sort ; Computer science ; Merge (version control) ; Algorithm ; Pairwise comparison ; Sorting ; Merge algorithm ; Theoretical computer science ; Artificial intelligence ; Parallel computing ; Information retrieval Various Chemistry Research Topics ; Genetics, Bioinformatics, and Biomedical Research
Authors: Samuel M. Blau | Hetal Patel | Evan Walter Clark Spotte‐Smith | Xiaowei Xie | Shyam Dwaraknath | Kristin A. Persson
Source:  (preprint, 2021-05-27)
Field: Materials Science — Materials Chemistry

A chemically consistent graph architecture for massive reaction networks applied to solid-electrolyte interphase formation Modeling reactivity with chemical reaction networks could yield fundamental mechanistic understanding that would expedite the development of processes and technologies for energy storage, medicine, catalysis, and more.Thus far, reaction networks have been limited in size by chemically inconsistent graph representations of multi-reactant reactions (e.g.A + B → C) that cannot enforce stoichiometric constraints, precluding the use of optimized shortest-path algorithms.Here, we report a chemically consistent graph architecture that overcomes these limitations using a novel multi-reactant representation and iterative cost-solving procedure.Our approach enables the identification of all low-cost pathways to desired products in massive reaction networks containing reactions of any stoichiometry, allowing for the investigation of vastly more complex systems than previously possible.Leveraging our architecture, we construct the first ever electrochemical reaction network from first-principles thermodynamic calculations to describe the formation of the Li-ion solid electrolyte interphase (SEI), which is critical for passivation of the negative electrode.Using this network comprised of nearly 6,000 species and 4.5 million reactions, we interrogate the formation of a key SEI component, lithium ethylene dicarbonate.We automatically identify previously proposed mechanisms as well as multiple novel pathways containing counter-intuitive reactions that have not, to our knowledge, been reported in the literature.We envision that our framework and data-driven methodology will facilitate efforts to engineer the composition-related properties of the SEI -or of any complex chemical process -through selective control of reactivity. Interphase ; Electrolyte ; Graph ; Architecture ; Computer science ; Chemistry ; Chemical engineering ; Materials science ; Engineering ; Theoretical computer science ; Electrode ; Biology ; Geography ; Physical chemistry ; Cell biology Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Toyin Omojola
Source: ChemRxiv (preprint, 2021-12-22)
Field: Materials Science — Materials Chemistry

Multiscale Models in Heterogeneous Catalysis: Incorporating Dynamics into Reaction Kinetics Modern operando spectroscopy and microscopy, and kinetic investigations have provided qualitative evidence for active site dynamics, catalyst surface dynamics, and charge transport. On the macroscale, intraparticle and interparticle mass and heat transfer can be tuned to optimise selectivity over heterogeneous catalysts. On the microscale, adsorbate-induced restructuring, adsorbate mobility, surface composition, oxidation states, charge transport, bandgap, and the degree of coordination of the active site have been identified for controlling product selectivity. There exist, however, limited physics-based and data-driven multiscale models that can assimilate these qualitative descriptors in an integrated manner to extract quantitative catalyst activity, stability, and product selectivity descriptors. A multiscale model, which describes the evolution of gas species, adspecie accumulation due to reactivity, stability, lifetime, and mobility, charge transport involving electrons and holes, heat transfer for non-isothermal conditions due to reaction exothermicity, and the changing catalyst states is provided. Dynamical effects are included in these models to bridge the gap between laboratory-scale studies and industrial technical reactors. Microscale chemistry ; Catalysis ; Chemical physics ; Selectivity ; Heterogeneous catalysis ; Chemistry ; Materials science ; Diffusion ; Mass transfer ; Isothermal process ; Multiscale modeling ; Thermodynamics ; Computational chemistry ; Physics ; Organic chemistry Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Catalysts for Methane Reforming
Source: Chemistry International (article, 2021-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

IUPAC Blockchain Technology White Paper—Call for input Abstract During the 2019 Council Meeting in Paris, an attendee raised the point that two topics were the subject of high interest in his geographic region, specifically the uses of Blockchain Technology and Artificial Intelligence in scientific research and related activities. To follow-up with these questions, a Task Force was established to develop a White Paper on Blockchain Technology to be published and circulated to all NAO’s prior to the 2021 World Chemistry Congress. Blockchain ; Task force ; White (mutation) ; Task (project management) ; White paper ; Scientific integrity ; Data science ; Computer science ; Library science ; Engineering ethics ; Engineering ; Political science ; Chemistry ; Systems engineering ; Computer security ; Law ; Public administration Various Chemistry Research Topics ; Machine Learning in Materials Science