jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Du Li | Shihui Wen | Mengya Kong | Yongtao Liu | Wei Hu | Bingyang Shi | Xiangyang Shi | Dayong Jin
Source: Analytical Chemistry (article, 2020-07-29)
Field: Materials Science — Materials Chemistry

Highly Doped Upconversion Nanoparticles for <i>In Vivo</i> Applications Under Mild Excitation Power One of the major challenges in using upconversion nanoparticles (UCNPs) is to improve their brightness. This is particularly true for <i>in vivo</i> studies, as the low power excitation is required to prevent the potential photo toxicity to live cells and tissues. Here, we report that the typical NaYF<sub>4</sub>:Yb<sub>0.2</sub>,Er<sub>0.02</sub> nanoparticles can be highly doped, and the formula of NaYF<sub>4</sub>:Yb<sub>0.8</sub>,Er<sub>0.06</sub> can gain orders of magnitude more brightness, which is applicable to a range of mild 980 nm excitation power densities, from 0.005 W/cm<sup>2</sup> to 0.5 W/cm<sup>2</sup>. Our results reveal that the concentration of Yb<sup>3+</sup> sensitizer ions plays an essential role, while increasing the doping concentration of Er<sup>3+</sup> activator ions to 6 mol % only has incremental effect. We further demonstrated a type of bright UCNPs 12 nm in total diameter for <i>in vivo</i> tumor imaging at a power density as low as 0.0027 W/cm<sup>2</sup>, bringing down the excitation power requirement by 42 times. This work redefines the doping concentrations to fight for the issue of concentration quenching, so that ultrasmall and bright nanoparticles can be used to further improve the performance of upconversion nanotechnology in photodynamic therapy, light-triggered drug release, optogenetics, and night vision enhancement. Photon upconversion ; Excitation ; Brightness ; Doping ; Chemistry ; In vivo ; Nanoparticle ; Ion ; Activator (genetics) ; Fluorescence ; Nanotechnology ; Optoelectronics ; Optics ; Materials science ; Physics ; Organic chemistry Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Luminescence and Fluorescent Materials
Authors: Nyasha Allen | Lisa J. White | Jessica E. Boles | George T. Williams | Dominique Chu | Rebecca J. Ellaby | Helena J. Shepherd | Kendrick K. L. Ng | Laura R. Blackholly | Ben Wilson | Daniel P. Mulvihill | Jennifer R. Hiscock
Source: ChemMedChem (article, 2020-09-17)
Field: Chemistry — Spectroscopy

Towards the Prediction of Antimicrobial Efficacy for Hydrogen Bonded, Self‐Associating Amphiphiles Herein we report 50 structurally related supramolecular self-associating amphiphilic (SSA) salts and related compounds. These SSAs are shown to act as antimicrobial agents, active against model Gram-positive (methicillin-resistant Staphylococcus aureus) and/or Gram-negative (Escherichia coli) bacteria of clinical interest. Through a combination of solution-state, gas-phase, solid-state and in silico measurements, we determine 14 different physicochemical parameters for each of these 50 structurally related compounds. These parameter sets are then used to identify molecular structure-physicochemical property-antimicrobial activity relationships for our model Gram-negative and Gram-positive bacteria, while simultaneously providing insight towards the elucidation of SSA mode of antimicrobial action. Amphiphile ; Antimicrobial ; Hydrogen bond ; Chemistry ; Combinatorial chemistry ; Nanotechnology ; Computational biology ; Materials science ; Organic chemistry ; Molecule ; Biology Molecular Sensors and Ion Detection ; Antimicrobial agents and applications ; Antimicrobial Peptides and Activities
Authors: Cuilian Liu | Eduard O. Bobylev | Yang Fu | David A. Poole | Koen Robeyns | Charles‐André Fustin | Yann Garcia | Joost N. H. Reek | Michael L. Singleton
Source: Chemistry - A European Journal (article, 2020-05-07)
Field: Chemistry — Organic Chemistry

Balancing Ligand Flexibility versus Rigidity for the Stepwise Self‐Assembly of M<sub>12</sub>L<sub>24</sub> via M<sub>6</sub>L<sub>12</sub> Metal–Organic Cages Non-covalent interactions are important for directing protein folding across multiple intermediates and can even provide access to multiple stable structures with different properties and functions. Herein, we describe an approach for mimicking this behavior in the self-assembly of metal-organic cages. Two ligands, the bend angles of which are controlled by non-covalent interactions and one ligand lacking the above-mentioned interactions, were synthesized and used for self-assembly with Pd<sup>2+</sup> . As these weak interactions are easily broken, the bend angles have a controlled flexibility giving access to M<sub>2</sub> (L1)<sub>4</sub> , M<sub>6</sub> (L2)<sub>12</sub> , and M<sub>12</sub> (L2)<sub>24</sub> cages. By controlling the self-assembly conditions this process can be directed in a stepwise fashion. Additionally, the multiple endohedral hydrogen-bonding sites on the ligand were found to play a role in the binding and discrimination of neutral guests. Covalent bond ; Ligand (biochemistry) ; Hydrogen bond ; Rigidity (electromagnetism) ; Folding (DSP implementation) ; Crystallography ; Metal ; Self-assembly ; Flexibility (engineering) ; Chemistry ; Materials science ; Nanotechnology ; Molecule ; Mathematics ; Receptor ; Engineering ; Organic chemistry Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Kengo Inoue | Kentaro Okano
Source: Asian Journal of Organic Chemistry (article, 2020-07-21)
Field: Chemistry — Organic Chemistry

Trapping of Transient Organolithium Compounds Abstract Organolithium chemistry has been widely utilized in organic synthesis as a reliable tool for introducing various functional groups. The intrinsic high reactivity of organolithiums allows rapid chemical transformation; however, the transient organolithiums bearing an electrophilic moiety often cause the undesired reactions such as self‐dimerization, isomerization, and decomposition, which prevent further synthetic application. In this minireview, we classify the reactions involving the short‐lived organolithiums and focus on the recent progress in flow chemistry, which allows to trap the highly reactive organolithium species. In addition, this review includes other approaches using the related organometallic species that can be performed in a conventional batch reactor. To place the recent development of this field in perspective, the established strategies controlling the reactivities of the short‐lived organolithiums provide a short‐step, efficient, and protective group‐free synthesis of functionalized organic molecules in medicinal, agrochemical, and material chemistry. Chemistry ; Isomerization ; Moiety ; Electrophile ; Reactivity (psychology) ; Combinatorial chemistry ; Organic chemistry ; Organolithium compounds ; Ion ; Catalysis Coordination Chemistry and Organometallics ; Catalytic Cross-Coupling Reactions ; Asymmetric Synthesis and Catalysis
Authors: Mihajlo Novakovic | Ēriks Kupče | Andreas Oxenfarth | Marcos D. Battistel | Darón I. Freedberg | Harald Schwalbe | Lucio Frydman
Source: Nature Communications (article, 2020-10-21)
Field: Chemistry — Spectroscopy

Sensitivity enhancement of homonuclear multidimensional NMR correlations for labile sites in proteins, polysaccharides, and nucleic acids Multidimensional TOCSY and NOESY are central experiments in chemical and biophysical NMR. Limited efficiencies are an intrinsic downside of these methods, particularly when targeting labile sites. This study demonstrates that the decoherence imparted on these protons through solvent exchanges can, when suitably manipulated, lead to dramatic sensitivity gains per unit time in the acquisition of these experiments. To achieve this, a priori selected frequencies are encoded according to Hadamard recipes, while concurrently subject to looped selective inversion or selective saturation procedures. Suitable processing then leads to protein, oligosaccharide and nucleic acid cross-peak enhancements of ≈200-1000% per scan, in measurements that are ≈10-fold faster than conventional counterparts. The extent of these gains will depend on the solvent exchange and relaxation rates of the targeted sites; these gains also benefit considerably from the spectral resolution provided by ultrahigh fields, as corroborated by NMR experiments at 600 MHz and 1 GHz. The mechanisms underlying these experiments' enhanced efficiencies are analyzed on the basis of three-way polarization transfer interplays between the water, labile and non-labile protons, and the experimental results are rationalized using both analytical and numerical derivations. Limitations as well as further extensions of the proposed methods, are also discussed. Homonuclear molecule ; Nucleic acid ; Nuclear magnetic resonance spectroscopy ; Two-dimensional nuclear magnetic resonance spectroscopy ; Polarization (electrochemistry) ; Sensitivity (control systems) ; Stacking ; Hadamard transform ; Analytical Chemistry (journal) Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Protein Structure and Dynamics
Authors: Philippe Schwaller | Alain C. Vaucher | Teodoro Laino | Jean‐Louis Reymond
Source: ChemRxiv (preprint, 2020-11-26)
Field: Materials Science — Materials Chemistry

Data augmentation strategies to improve reaction yield predictions and estimate uncertainty Chemical reactions describe how precursor molecules react together and transform into products. The reaction yield describes the percentage of the precursors successfully transformed into products relative to the theoretical maximum. The prediction of reaction yields can help chemists navigate reaction space and accelerate the design of more effective routes. Here, we investigate the best-studied high-throughput experiment data set and show how data augmentation on chemical reactions can improve yield predictions' accuracy, even when only small data sets are available. Previous work used molecular fingerprints, physics-based or categorical descriptors of the precursors. In this manuscript, we fine-tune natural language processing-inspired reaction transformer models on different augmented data sets to predict yields solely using a text-based representation of chemical reactions. When the random training sets contain 2.5% or more of the data, our models outperform previous models, including those using physics-based descriptors as inputs. Moreover, we demonstrate the use of test-time augmentation to generate uncertainty estimates, which correlate with the prediction errors. Categorical variable ; Computer science ; Chemical space ; Yield (engineering) ; Data set ; Set (abstract data type) ; Representation (politics) ; Experimental data ; Algorithm ; Artificial intelligence ; Machine learning ; Chemistry ; Mathematics ; Statistics ; Drug discovery ; Physics ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Guanghui Ge | Yongzhe Tang | Yuxia Li | Liangsong Huang
Source: Applied Sciences (article, 2020-10-09)
Field: Materials Science — Materials Chemistry

Effect of Environmental Temperature on the Insulating Performance of Epoxy/MgO Nanocomposites This article reports on the development of nano-MgO/epoxy resin composites with various mass ratios via a solution blending method. The influence of MgO nanofillers on the thermal properties and the effect of environmental temperature on the insulating properties of the composite material were analyzed. The results show that the thermal conductivity of the composites increased with an increasing MgO nanofiller content. Compared with the pure epoxy resin, the thermal conductivity increased by 75% when the content of MgO nanoparticles was 7%. The volume resistivity first increased and then decreased with an increasing doping concentration. The volume resistivity increased by 26.8% in comparison with the pure epoxy resin when the content of MgO nanoparticles was 1%, while its dielectric constant and dielectric loss increased with temperature. In addition, the dielectric constant increased and the dielectric loss first decreased and then increased with an increasing MgO nanoparticle content. Moreover, the MgO composites changed from a glassy to a rubbery state, and the breakdown strength was significantly reduced with an increased temperature. When the temperature was higher than the glass transition temperature, the breakdown strength decreased by 51.3% compared with the maximum breakdown strength at 20 °C. As the content of MgO nanoparticles increased, the breakdown strength of the composite first increased and then decreased. The highest breakdown strength was achieved when the content of MgO nanoparticles was 1%, which was 11.1% higher than that of the pure epoxy resin. It was concluded that the MgO nanofillers can significantly improve the thermal properties of epoxy composites and their insulation performance at high temperatures. Materials science ; Epoxy ; Dielectric ; Composite material ; Nanocomposite ; Dielectric strength ; Glass transition ; Electrical resistivity and conductivity ; Dielectric loss ; Thermal conductivity ; Composite number ; Nanoparticle ; Doping ; Polymer ; Nanotechnology High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Electromagnetic wave absorption materials
Authors: Apostolos K. Kiritsakis | Konstantinos Kiritsakis | Chrysostomos K. Tsitsipas
Source: Journal of Food Bioactives (review, 2020-09-01)
Field: Chemistry — Organic Chemistry

A review of the evolution in the research of antioxidants in olives and olive oil during the last four decades Olives and olive oil have established, through centuries, strong bonds with the prosperity and well being of the Mediterranean people. Numerous studies have featured them as main antioxidant sources, based on their phenolic profile, which includes phenolic acids and alcohols, polyphenols, lignans, secoiridoids, oleacein and oleocanthal. Several factors, such as cultivar, fruit maturation, processing methods, storage conditions etc. affect the presence of these significant constituents in the products. Re-use of olive mill waste water has been studied to enrich the phenolic profile of the final product. Olive oil is considered a functional food with extensive use in the food industry. The biological effects of olives and virgin olive oil components include protection against cardiovascular diseases, anti-inflammation action, neuro and endothelial protection etc. Research studies, in vitro and in vivo, in humans and in animals have been performed to better understand the metabolism and bioactivity of olives and olive oil phenolics. Olives and olive oil antioxidants are not only absorbed by the body, but can also attach to the lining of the digestive tract, contributing to the health impact afforded by these products. Laboratory methods and techniques used so far for the qualitative and quantitative identification of these compounds are cited. Polyphenol ; Olive oil ; Food science ; Antioxidant ; Mediterranean diet ; Nutraceutical ; Human health ; Chemistry ; Biology ; Biotechnology ; Biochemistry ; Medicine Edible Oils Quality and Analysis ; Phytochemicals and Antioxidant Activities ; Antioxidant Activity and Oxidative Stress
Authors: Shihang Wang | Shihu Yu | Jianying Li | Shengtao Li
Source: Materials (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Effects of Functionalized Nano-TiO2 on the Molecular Motion in Epoxy Resin-Based Nanocomposites Epoxy resin-based nanocomposites have been widely researched for being potential insulating materials in high voltage power equipment. In this paper, nano-TiO<sub>2</sub> particles were chosen and surface-modified by a silane coupling agent containing an epoxy group. The effect of functionalized nano-TiO<sub>2</sub> doping on the physical properties of epoxy resin was studied. The results of differential scanning calorimetry show that <i>T</i><sub>g</sub> increased significantly and can be increased by up to 35 °C. Therefore, it is believed that the suppression of molecular motion by the addition of nanofillers works effectively in the case of this functionalized nano-TiO<sub>2</sub> and a strong interaction between the epoxy resin and the nano-TiO<sub>2</sub> was formed after surface modification. Consequently, dynamic mechanical properties, thermal conductivity, electrical conductivity, and trap characteristics of epoxy resin are all adjusted after introducing functionalized nano-TiO<sub>2</sub>. All of these physical properties were analyzed from the perspective of suppression of molecular motion, and it is of significance to establish the theory of a nanocomposite dielectric. Besides, the results show that the epoxy/TiO<sub>2</sub> nanocomposite is expected to be applied in the insulation system of electrical equipment. Epoxy ; Materials science ; Nanocomposite ; Composite material ; Nano- ; Dielectric ; Silane ; Differential scanning calorimetry ; Doping ; Surface modification ; Chemical engineering High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Polymer Nanocomposites and Properties
Authors: Hiroyuki Hosokawa | Emma Calvert | Koji Shimojima
Source: Journal of Magnetism and Magnetic Materials (article, 2020-12-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning prediction for magnetic properties of Sm-Fe-N based alloys produced by melt spinning Materials science ; Hyperparameter ; Spinning ; Melt spinning ; Artificial neural network ; Process (computing) ; Machine learning ; Computer science ; Composite material Magnetic Properties of Alloys ; Powder Metallurgy Techniques and Materials ; Magnetic Properties and Applications
Authors: Amit Kaushal | Rajath Alexander | P.T. Rao | Jyoti Prakash | Kinshuk Dasgupta
Source: Carbon Trends (article, 2020-12-18)
Field: Materials Science — Materials Chemistry

Artificial neural network, Pareto optimization, and Taguchi analysis for the synthesis of single-walled carbon nanotubes Floating-catalyst chemical vapour deposition (FCCVD) is a promising method for large-scale synthesis of single-walled carbon nanotube (SWCNT). The current work focuses on the comprehensive optimization studies with the objective of maximizing the yield and minimizing the diameter of SWCNTs considering the complex dependency and interaction effects between the process parameters. Data-analytics driven process parameter selection criterion was utilized for heat map generation of the eight process parameters. Orthogonal design of experiments encompassing Taguchi L-18 array and subsequent analysis of variance reveals strong interaction effects between the process parameters and demonstrates conflicting nature between the two objectives, yield and diameter of SWCNTs. Pareto optimization was used to capture the conflicting behaviour of the dual objectives in true sense. Artificial neural network (ANN) coupled with Taguchi analysis brings out the interaction effects of process parameters that could predict the output with more than 90% accuracy. The rigorous analysis concludes that the furnace temperature mostly affects the diameter, whereas, methane flow-rate determines the SWCNT yield. The optimum condition for higher SWCNT yield with minimum diameter was obtained at a lower methane and hydrogen flow while keeping furnace temperature and argon flow at the highest level and thiophene at the lowest level. Taguchi methods ; Carbon nanotube ; Yield (engineering) ; Pareto principle ; Orthogonal array ; Materials science ; Artificial neural network ; Volumetric flow rate ; Design of experiments ; Process (computing) ; Process engineering ; Computer science ; Mathematical optimization ; Nanotechnology ; Mathematics ; Mechanics ; Engineering ; Statistics ; Composite material ; Machine learning ; Physics Carbon Nanotubes in Composites ; Thermal properties of materials ; Nanowire Synthesis and Applications
Authors: Martin Čalkovský | Erich Müller | Matthias Meffert | Nadejda Firman | Frederik Mayer | Martin Wegener | Dagmar Gerthsen
Source: Materials Characterization (article, 2020-12-03)
Field: Materials Science — Surfaces, Coatings and Films

Comparison of segmentation algorithms for FIB-SEM tomography of porous polymers: Importance of image contrast for machine learning segmentation Materials science ; Segmentation ; Focused ion beam ; Scanning electron microscope ; Artificial intelligence ; Image segmentation ; Porosity ; Electron tomography ; Computer science ; Computer vision ; Biomedical engineering ; Ion ; Composite material ; Physics ; Scanning transmission electron microscopy Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Ion-surface interactions and analysis
Authors: Noha E. El-Attar | Mohamed K. Hassan | Othman A. Alghamdi | Wael A. Awad
Source: Scientific Reports (article, 2020-12-07)
Field: Chemistry — Analytical Chemistry

Deep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt Reliance on deep learning techniques has become an important trend in several science domains including biological science, due to its proven efficiency in manipulating big data that are often characterized by their non-linear processes and complicated relationships. In this study, Convolutional Neural Networks (CNN) has been recruited, as one of the deep learning techniques, to be used in classifying and predicting the biological activities of the essential oil-producing plant/s through their chemical compositions. The model is established based on the available chemical composition's information of a set of endemic Egyptian plants and their biological activities. Another type of machine learning algorithms, Multiclass Neural Network (MNN), has been applied on the same Essential Oils (EO) dataset. This aims to fairly evaluate the performance of the proposed CNN model. The recorded accuracy in the testing process for both CNN and MNN is 98.13% and 81.88%, respectively. Finally, the CNN technique has been adopted as a reliable model for classifying and predicting the bioactivities of the Egyptian EO-containing plants. The overall accuracy for the final prediction process is reported as approximately 97%. Hereby, the proposed deep learning model could be utilized as an efficient model in predicting the bioactivities of, at least Egyptian, EOs-producing plants. Artificial intelligence ; Deep learning ; Convolutional neural network ; Computer science ; Machine learning ; Process (computing) ; Artificial neural network ; Biological data ; Set (abstract data type) ; Bioinformatics ; Biology Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Computational Drug Discovery Methods
Authors: Colin A. Grambow | Lagnajit Pattanaik | William H. Green
Source: ChemRxiv (preprint, 2020-02-14)
Field: Materials Science — Materials Chemistry

Deep Learning of Activation Energies Quantitative prediction of reaction properties, such as activation energy, have been limited due to a lack of available training data. Such predictions would be useful for computer-assisted reaction mechanism generation and organic synthesis planning. We develop a template-free deep learning model to predict activation energy given reactant and product graphs and train the model on a new, diverse data set of gas-phase quantum chemistry reactions. We demonstrate that our model achieves accurate predictions and agrees with an intuitive understanding of chemical reactivity. With the continued generation of quantitative chemical reaction data and development of methods that leverage such data, we expect many more methods for reactivity prediction to become available in the near future. Leverage (statistics) ; Computer science ; Reactivity (psychology) ; Reaction mechanism ; Deep learning ; Set (abstract data type) ; Data set ; Training set ; Artificial intelligence ; Chemistry ; Biochemical engineering ; Catalysis ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques