jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Delphine Parrot | Stefano Papazian | Daniel H. Foil | Deniz Taşdemir
Source: Planta Medica (review, 2018-01-31)
Field: Chemistry — Spectroscopy

Imaging the Unimaginable: Desorption Electrospray Ionization – Imaging Mass Spectrometry (DESI-IMS) in Natural Product Research Imaging mass spectrometry (IMS) has recently established itself in the field of "spatial metabolomics." Merging the sensitivity and fast screening of high-throughput mass spectrometry with spatial and temporal chemical information, IMS visualizes the production, location, and distribution of metabolites in intact biological models. Since metabolite profiling and morphological features are combined in single images, IMS offers an unmatched chemical detail on complex biological and microbiological systems. Thus, IMS-type "spatial metabolomics" emerges as a powerful and complementary approach to genomics, transcriptomics, and classical metabolomics studies. In this review, we summarize the current state-of-the-art IMS methods with a strong focus on desorption electrospray ionization (DESI)-IMS. DESI-IMS utilizes the original principle of electrospray ionization, but in this case solvent droplets are rastered and desorbed directly on the sample surface. The rapid and minimally destructive DESI-IMS chemical screening is achieved at ambient conditions and enables the accurate view of molecules in tissues at the µm-scale resolution. DESI-IMS analysis does not require complex sample preparation and allows repeated measurements on samples from different biological sources, including microorganisms, plants, and animals. Thanks to its easy workflow and versatility, DESI-IMS has successfully been applied to many different research fields, such as clinical analysis, cancer research, environmental sciences, microbiology, chemical ecology, and drug discovery. Herein we discuss the present applications of DESI-IMS in natural product research. Desorption electrospray ionization ; Mass spectrometry imaging ; Metabolomics ; Mass spectrometry ; Extractive electrospray ionization ; Chemical imaging ; Nanotechnology ; Chemistry ; Ambient ionization ; Computer science ; Electrospray ionization ; Analytical Chemistry (journal) ; Environmental chemistry ; Ionization ; Chromatography ; Materials science ; Chemical ionization ; Artificial intelligence ; Protein mass spectrometry ; Hyperspectral imaging Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Menglei Wang | Xuan Lai | Longquan Shao | Li Li
Source: International Journal of Nanomedicine (review, 2018-07-31)
Field: Materials Science — Materials Chemistry

Evaluation of immunoresponses and cytotoxicity from skin exposure to metallic nanoparticles Nanotechnology is an interdisciplinary science that has developed rapidly in recent years. Metallic nanoparticles (NPs) are increasingly utilized in dermatology and cosmetology, because of their unique properties. However, skin exposure to NPs raises concerns regarding their transdermal toxicity. The tight junctions of epithelial cells form the skin barrier, which protects the host against external substances. Recent studies have found that NPs can pass through the skin barrier into deeper layers, indicating that skin exposure is a means for NPs to enter the body. The distribution and interaction of NPs with skin cells may cause toxic side effects. In this review, possible penetration pathways and related toxicity mechanisms are discussed. The limitations of current experimental methods on the penetration and toxic effects of metallic NPs are also described. This review contributes to a better understanding of the risks of topically applied metallic NPs and provides a foundation for future studies. Nanotechnology ; Nanotoxicology ; Penetration (warfare) ; Transdermal ; Cytotoxicity ; Skin barrier ; Materials science ; Nanoparticle ; Chemistry ; Medicine ; Dermatology ; Pharmacology ; In vitro Nanoparticles: synthesis and applications ; Immunotoxicology and immune responses ; Advancements in Transdermal Drug Delivery
Authors: Stefano Dissegna | Pia Vervoorts | Claire L. Hobday | Tina Düren | Dominik Daisenberger | Andrew J. Smith | Roland A. Fischer | Gregor Kieslich
Source: Journal of the American Chemical Society (article, 2018-08-31)
Field: Chemistry — Inorganic Chemistry

Tuning the Mechanical Response of Metal–Organic Frameworks by Defect Engineering The incorporation of defects into crystalline materials provides an important tool to fine-tune properties throughout various fields of materials science. We performed high-pressure powder X-ray diffraction experiments, varying pressures from ambient to 0.4 GPa in 0.025 GPa increments to probe the response of defective UiO-66 to hydrostatic pressure for the first time. We observe an onset of amorphization in defective UiO-66 samples around 0.2 GPa and decreasing bulk modulus as a function of defects. Intriguingly, the observed bulk moduli of defective UiO-66(Zr) samples do not correlate with defect concentration, highlighting the complexity of how defects are spatially incorporated into the framework. Our results demonstrate the large impact of point defects on the structural stability of metal-organic frameworks (MOFs) and pave the way for experiment-guided computational studies on defect engineered MOFs. Chemistry ; Bulk modulus ; Hydrostatic pressure ; Metal-organic framework ; Diffraction ; Melting point ; Hydrostatic equilibrium ; Moduli ; Crystallographic defect ; Metal ; High pressure ; Modulus ; Nanotechnology ; Chemical physics ; Chemical engineering ; Crystallography ; Composite material ; Thermodynamics ; Physical chemistry ; Materials science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Stefan van der Vorm | Jacob M. A. van Hengst | Marloes Bakker | Herman S. Overkleeft | Gijsbert A. van der Marel | Jeroen D. C. Codée
Source: Angewandte Chemie International Edition (article, 2018-03-31)
Field: Chemistry — Organic Chemistry

Mapping the Relationship between Glycosyl Acceptor Reactivity and Glycosylation Stereoselectivity The reactivity of both coupling partners-the glycosyl donor and acceptor-is decisive for the outcome of a glycosylation reaction, in terms of both yield and stereoselectivity. Where the reactivity of glycosyl donors is well understood and can be controlled through manipulation of the functional/protecting-group pattern, the reactivity of glycosyl acceptor alcohols is poorly understood. We here present an operationally simple system to gauge glycosyl acceptor reactivity, which employs two conformationally locked donors with stereoselectivity that critically depends on the reactivity of the nucleophile. A wide array of acceptors was screened and their structure-reactivity/stereoselectivity relationships established. By systematically varying the protecting groups, the reactivity of glycosyl acceptors can be adjusted to attain stereoselective cis-glucosylations. Stereoselectivity ; Reactivity (psychology) ; Glycosyl ; Chemistry ; Glycosylation ; Acceptor ; Nucleophile ; Glycosyl donor ; Stereochemistry ; Organic chemistry ; Catalysis ; Biochemistry Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Legume Nitrogen Fixing Symbiosis
Authors: Noel M. O’Boyle | Andrew Dalke
Source: ChemRxiv (preprint, 2018-09-19)
Field: Materials Science — Materials Chemistry

DeepSMILES: An Adaptation of SMILES for Use in Machine-Learning of Chemical Structures &lt;div&gt;Background&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;There has been increasing interest in the use of deep neural networks for de novo design of molecules with desired properties. A common approach is to train a generative model on SMILES strings and then use this to generate SMILES strings for molecules with a desired property. Unfortunately, these SMILES strings are often not syntactically valid due to elements of SMILES syntax that must occur in pairs.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;Results&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;We describe a SMILES-like syntax called DeepSMILES that addresses two of the main reasons for invalid syntax when using a probabilistic model to generate SMILES strings. The DeepSMILES syntax avoids the problem of unbalanced parentheses by only using close parentheses, where the number of parentheses indicates the branch length. In addition, DeepSMILES avoids the problem of pairing ring closure symbols by using only a single symbol at the ring closing location, where the symbol indicates the ring size. We show that this syntax can be interconverted to/from SMILES with string processing without any loss of information, including stereo configuration.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;Conclusion&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;We believe that DeepSMILES will be useful, not just for those using SMILES in deep neural networks, but also for other computational methods that use SMILES as the basis for generating molecular structures such as genetic algorithms.&lt;/div&gt; Syntax ; Symbol (formal) ; Ring (chemistry) ; Computer science ; Mathematics ; Artificial intelligence ; Chemistry ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Janis Timoshenko | Cody J. Wrasman | Mathilde Luneau | Tanya Shirman | Matteo Cargnello | Simon R. Bare | Joanna Aizenberg | C. M. Friend | Anatoly I. Frenkel
Source: Nano Letters (article, 2018-12-03)
Field: Materials Science — Materials Chemistry

Probing Atomic Distributions in Mono- and Bimetallic Nanoparticles by Supervised Machine Learning Properties of mono- and bimetallic metal nanoparticles (NPs) may depend strongly on their compositional, structural (or geometrical) attributes, and their atomic dynamics, all of which can be efficiently described by a partial radial distribution function (PRDF) of metal atoms. For NPs that are several nanometers in size, finite size effects may play a role in determining crystalline order, interatomic distances, and particle shape. Bimetallic NPs may also have different compositional distributions than bulk materials. These factors all render the determination of PRDFs challenging. Here extended X-ray absorption fine structure (EXAFS) spectroscopy, molecular dynamics simulations, and supervised machine learning (artificial neural-network) method are combined to extract PRDFs directly from experimental data. By applying this method to several systems of Pt and PdAu NPs, we demonstrate the finite size effects on the nearest neighbor distributions, bond dynamics, and alloying motifs in mono- and bimetallic particles and establish the generality of this approach. Bimetallic strip ; Nanoparticle ; Materials science ; Extended X-ray absorption fine structure ; Molecular dynamics ; Chemical physics ; Atom (system on chip) ; Radial distribution function ; Artificial neural network ; Particle (ecology) ; Nanometre ; Coordination number ; Nanotechnology ; Metal ; Computational chemistry ; Chemistry ; Absorption spectroscopy ; Machine learning ; Computer science ; Physics ; Metallurgy ; Optics ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electrocatalysts for Energy Conversion
Authors: Zheng Li | Siwen Wang | Hongliang Xin
Source: Nature Catalysis (article, 2018-09-10)
Field: Materials Science — Materials Chemistry

Toward artificial intelligence in catalysis Intuition ; Intermetallic ; Catalysis ; Artificial intelligence ; Computer science ; Materials science ; Chemistry ; Cognitive science ; Psychology ; Metallurgy ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Stefan Thomas | Jan Behmann | Angelina Steier | Thorsten Kraska | Onno Muller | Uwe Rascher | Anne‐Katrin Mahlein
Source: Plant Methods (article, 2018-06-08)
Field: Chemistry — Analytical Chemistry

Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform The hyperspectral phenotyping system combines the advantages of field based canopy level measurement systems (high throughput, automatization, low manual workload) with those of laboratory based leaf level measurement systems (high spatial resolution, controlled environment, stable conditions for time series measurements). This allows an accurate and objective disease severity assessment without the need for trained experts, who perform visual rating, as well as detection of disease symptoms in early stages. Therefore, it is a promising tool for plant resistance breeding. Powdery mildew ; Hyperspectral imaging ; Cultivar ; Canopy ; Remote sensing ; Computer science ; Image resolution ; Bottleneck ; Artificial intelligence ; Agronomy ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Osman Konuray | Xavier Fernández‐Francos | Xavier Ramis | Àngels Serra
Source: Polymers (review, 2018-02-12)
Field: Chemistry — Organic Chemistry

State of the Art in Dual-Curing Acrylate Systems Acrylate chemistry has found widespread use in dual-curing systems over the years. Acrylates are cheap, easily handled and versatile monomers that can undergo facile chain-wise or step-wise polymerization reactions that are mostly of the "click" nature. Their dual-curing processes yield two distinct and temporally stable sets of material properties at each curing stage, thereby allowing process flexibility. The review begins with an introduction to acrylate-based click chemistries behind dual-curing systems and relevant reaction mechanisms. It then provides an overview of reaction combinations that can be encountered in these systems. It finishes with a survey of recent and breakthrough research in acrylate dual-curing materials for shape memory polymers, optical materials, photolithography, protective coatings, structured surface topologies, and holographic materials. Acrylate ; Curing (chemistry) ; Materials science ; Dual (grammatical number) ; Polymer chemistry ; Polymer science ; Composite material ; Art ; Polymer ; Copolymer Photopolymerization techniques and applications ; Advanced Polymer Synthesis and Characterization ; Silicone and Siloxane Chemistry
Authors: Jack Yang | Sandip De | Josh E. Campbell | Sean Li | Michele Ceriotti | Graeme M. Day
Source: Chemistry of Materials (article, 2018-06-18)
Field: Materials Science — Materials Chemistry

Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction Predictive computational methods have the potential to significantly accelerate the discovery of new materials with targeted properties by guiding the choice of candidate materials for synthesis. Recently, a planar pyrrole-based azaphenacene molecule (pyrido[2,3-b]pyrido[3′,2′:4,5]pyrrolo[3,2-g]indole, 1) was synthesized and shown to have promising properties for charge transport, which relate to stacking of molecules in its crystal structure. Building on our methods for evaluating small-molecule organic semiconductors using crystal structure prediction, we have screened a set of 27 structural isomers of 1 to assess charge mobility in their predicted crystal structures. Machine-learning techniques are used to identify structural classes across the landscapes of all molecules and we find that, despite differences in the arrangement of hydrogen bond functionality, the predicted crystal structures of the molecules studied here can be classified into a small number of packing types. We analyze the predicted property landscapes of the series of molecules and discuss several metrics that can be used to rank the molecules as promising semiconductors. The results suggest several isomers with superior predicted electron mobilities to 1 and suggest two molecules in particular that represent attractive synthetic targets. Crystal structure prediction ; Molecule ; Stacking ; Organic semiconductor ; Crystal (programming language) ; Chemical physics ; Crystal structure ; Materials science ; Hydrogen bond ; Crystal engineering ; Pyrrole ; Computational chemistry ; Small molecule ; Semiconductor ; Chemistry ; Crystallography ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Francesco Maresca | Daniele Dragoni | Gábor Cśanyi | Nicola Marzari | W.A. Curtin
Source: npj Computational Materials (article, 2018-11-28)
Field: Materials Science — Materials Chemistry

Screw dislocation structure and mobility in body centered cubic Fe predicted by a Gaussian Approximation Potential Abstract The plastic flow behavior of bcc transition metals up to moderate temperatures is dominated by the thermally activated glide of screw dislocations, which in turn is determined by the atomic-scale screw dislocation core structure and the associated kink-pair nucleation mechanism for glide. Modeling complex plasticity phenomena requires the simulation of many atoms and interacting dislocations and defects. These sizes are beyond the scope of first-principles methods and thus require empirical interatomic potentials. Especially for the technological important case of bcc Fe, existing empirical interatomic potentials yield spurious behavior. Here, the structure and motion of the screw dislocations in Fe are studied using a new Gaussian Approximation Potential (GAP) for bcc Fe, which has been shown to reproduce the potential energy surface predicted by density-functional theory (DFT) and many associated properties. The Fe GAP predicts a compact, non-degenerate core structure, a single-hump Peierls potential, and glide on {110}, consistent with DFT results. The thermally activated motion at finite temperatures occurs by the expected kink-pair nucleation and propagation mechanism. The stress-dependent enthalpy barrier for screw motion, computed using the nudged-elastic-band method, follows closely a form predicted by standard theories with a zero-stress barrier of ~1 eV, close to the experimental value of 0.84 eV, and a Peierls stress of ~2 GPa consistent with DFT predictions of the Peierls potential. Dislocation ; Nucleation ; Interatomic potential ; Materials science ; Peierls stress ; Condensed matter physics ; Plasticity ; Density functional theory ; Glide plane ; Molecular dynamics ; Thermodynamics ; Physics ; Dislocation creep ; Chemistry ; Computational chemistry Microstructure and mechanical properties ; High Temperature Alloys and Creep ; Nuclear Materials and Properties
Authors: Matti S. Toivonen | Olimpia D. Onelli | Gianni Jacucci | Ville A. Lovikka | Orlando J. Rojas | Olli Ikkala | Silvia Vignolini
Source: Advanced Materials (article, 2018-03-13)
Field: Materials Science — Biomaterials

Anomalous‐Diffusion‐Assisted Brightness in White Cellulose Nanofibril Membranes The understanding of the interaction between light and complex, random structures is the key for designing and tailoring the optical appearance and performance of many materials that surround us, ranging from everyday consumer products, such as those for personal care, paints, and paper, to light diffusers used in the LED-lamps and solar cells. Here, it is demonstrated that the light transport in membranes of pure cellulose nanofibrils (CNFs) can be controlled to achieve bright whiteness in structures only a few micrometers thick. This is in contrast to other materials, such as paper, which require hundreds of micrometers to achieve a comparable appearance. The diffusion of light in the CNF membranes is shown to become anomalous by tuning the porosity and morphological features. Considering also their strong mechanical properties and biocompatibility, such white coatings are proposed as a new application for cellulose nanofibrils. Materials science ; Membrane ; Cellulose ; Diffusion ; Brightness ; Chemical engineering ; Nanotechnology ; Optics ; Thermodynamics Advanced Cellulose Research Studies ; Electrohydrodynamics and Fluid Dynamics ; Liquid Crystal Research Advancements
Authors: Juhwan Noh | Seoin Back | Jaehoon Kim | Yousung Jung
Source: Chemical Science (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Active learning with non-<i>ab initio</i>input features toward efficient CO<sub>2</sub>reduction catalysts In a conventional chemisorption model, the d-band center theory (augmented sometimes with the upper edge of the d-band for improved accuracy) plays a central role in predicting adsorption energies and catalytic activity as a function of the d-band center of solid surfaces, but it requires density functional calculations that can be quite costly for the purposes of large scale screening of materials. In this work, we propose to use the d-band width of the muffin-tin orbital theory (to account for the local coordination environment) plus electronegativity (to account for adsorbate renormalization) as a simple set of alternative descriptors for chemisorption which do not require <i>ab initio</i> calculations for large-scale first-hand screening. This pair of descriptors is then combined with machine learning methods, namely, neural network (NN) and kernel ridge regression (KRR). We show, for a toy set of 263 alloy systems, that the CO adsorption energy on the (100) facet can be predicted with a mean absolute deviation error of 0.05 eV. We achieved this high accuracy by utilizing an active learning algorithm, without which the accuracy was 0.18 eV. In addition, the results of testing the method with other facets such as (111) terrace and (211) step sites suggest that the present model is also capable of handling different coordination environments effectively. As an example of the practical application of this machine, we identified Cu<sub>3</sub>Y@Cu* as an active and cost-effective electrochemical CO<sub>2</sub> reduction catalyst to produce CO with an overpotential ∼1 V lower than a Au catalyst. Reduction (mathematics) ; Ab initio ; Catalysis ; Chemistry ; Computational chemistry ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalytic Processes in Materials Science