jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fabienne Speck | David Rombach | Hans‐Achim Wagenknecht
Source: Beilstein Journal of Organic Chemistry (article, 2019-01-04)
Field: Chemistry — Organic Chemistry

<i>N</i>-Arylphenothiazines as strong donors for photoredox catalysis – pushing the frontiers of nucleophilic addition of alcohols to alkenes A new range of <i>N</i>-phenylphenothiazine derivatives was synthesized as potential photoredox catalysts to broaden the substrate scope for the nucleophilic addition of methanol to styrenes through photoredox catalysis. These <i>N</i>-phenylphenothiazines differ by their electron-donating and electron-withdrawing substituents at the phenyl group, covering both, σ and π-type groups, in order to modulate their absorbance and electrochemical characteristics. Among the synthesized compounds, alkylaminylated <i>N</i>-phenylphenothiazines were identified to be highly suitable for photoredox catalysis. The dialkylamino substituents of these <i>N</i>-phenylphenothiazines shift the estimated excited state reduction potential up to -3.0 V (vs SCE). These highly reducing properties allow the addition of methanol to α-methylstyrene as less-activated substrate for this type of reaction. Without the help of an additive, the reaction conditions were optimized to achieve a quantitative yield for the Markovnivkov-type addition product after 20 h of irradiation. Chemistry ; Photoredox catalysis ; Catalysis ; Nucleophile ; Methanol ; Nucleophilic addition ; Substrate (aquarium) ; Photochemistry ; Yield (engineering) ; Medicinal chemistry ; Combinatorial chemistry ; Organic chemistry ; Photocatalysis Radical Photochemical Reactions ; Phenothiazines and Benzothiazines Synthesis and Activities ; Sulfur-Based Synthesis Techniques
Authors: Piotr Król | Bożena Król
Source: Journal of Materials Science (article, 2019-09-03)
Field: Materials Science — Polymers and Plastics

Structures, properties and applications of the polyurethane ionomers On the basis of the reports from 2010 to 2018, the chemical structure, production methods and applications of polyurethane ionomers were reviewed. The paper presents ionogenic reagents and counterions responsible for the incorporation of anionic and cationic groups into polyurethane chains and the resulting physicochemical properties of these polymers. The most important applications of synthesized ionomers as a waterborne polyurethane, elastomer materials, biomaterials and materials for special applications (electronics, nanocomposites) were presented. Polyurethane ; Materials science ; Elastomer ; Polymer science ; Polymer ; Cationic polymerization ; Ionomer ; Counterion ; Nanocomposite ; Polyurea ; Polymer chemistry ; Composite material ; Copolymer ; Organic chemistry ; Chemistry ; Ion Polymer composites and self-healing ; Carbon dioxide utilization in catalysis ; Environmental Engineering and Cultural Studies
Authors: Gabriele Magna | Donato Monti | Corrado Di Natale | Roberto Paolesse | Manuela Stefanelli
Source: Molecules (review, 2019-11-26)
Field: Materials Science — Materials Chemistry

The Assembly of Porphyrin Systems in Well-Defined Nanostructures: An Update The interest in assembling porphyrin derivatives is widespread and is accounted by the impressive impact of these suprastructures of controlled size and shapes in many applications from nanomedicine and sensors to photocatalysis and optoelectronics. The massive use of porphyrin dyes as molecular building blocks of functional materials at different length scales relies on the interdependent pair properties, consisting of their chemical stability/synthetic versatility and their quite unique physicochemical properties. Remarkably, the driven spatial arrangement of these platforms in well-defined suprastructures can synergically amplify the already excellent properties of the individual monomers, improving conjugation and enlarging the intensity of the absorption range of visible light, or forming an internal electric field exploitable in light-harvesting and charge-and energy-transport processes. The countless potentialities offered by these systems means that self-assembly concepts and tools are constantly explored, as confirmed by the significant number of published articles related to porphyrin assemblies in the 2015-2019 period, which is the focus of this review. Porphyrin ; Nanotechnology ; Nanomedicine ; Nanostructure ; Materials science ; Photocatalysis ; Absorption (acoustics) ; Chemistry ; Photochemistry ; Nanoparticle ; Catalysis ; Organic chemistry Porphyrin and Phthalocyanine Chemistry ; Surface Chemistry and Catalysis ; Photosynthetic Processes and Mechanisms
Authors: Marina Radulaski | Jingyuan Linda Zhang | Yan‐Kai Tzeng | Konstantinos G. Lagoudakis | Hitoshi Ishiwata | Constantin Dory | Kevin A. Fischer | Yousif Kelaita | Shuo Sun | Peter C. Maurer | Kassem Alassaad | Gabriel Ferro | Zhi‐Xun Shen | Nicholas A. Melosh | Steven Chu | Jelena Vučković
Source: Laser & Photonics Review (article, 2019-07-22)
Field: Materials Science — Materials Chemistry

Nanodiamond Integration with Photonic Devices Abstract The progress in integration of nanodiamond with photonic devices is analyzed in the light of quantum optical applications. Nanodiamonds host a variety of optically active defects, called color centers, which provide rich ground for photonic engineering. Theoretical introduction describing light and matter interaction between optical modes and a quantum emitter is presented, including the role of the Debye–Waller factor typical of color center emission. The synthesis of diamond nanoparticles is discussed in an overview of methods leading to experimentally realized hybrid platforms of nanodiamond with gallium phosphide, silicon dioxide, and silicon carbide. The trade‐offs in the substrate index of refraction values are reviewed in the context of the achieved strength of light and matter interaction. Thereby, the recent results on the growth of color center‐rich nanodiamond on prefabricated silicon carbide microdisk resonators are presented. These hybrid devices achieve up to fivefold enhancement of the diamond color‐center light emission and can be employed in integrated quantum photonics. Nanodiamond ; Photonics ; Diamond ; Materials science ; Optoelectronics ; Silicon carbide ; Light emission ; Context (archaeology) ; Nanotechnology ; Optics ; Physics Diamond and Carbon-based Materials Research ; Advanced Fiber Laser Technologies ; Nonlinear Optical Materials Studies
Authors: Caroline West | Elise Lemasson
Source: Journal of Chromatography A (article, 2019-02-04)
Field: Chemistry — Spectroscopy

Unravelling the effects of mobile phase additives in supercritical fluid chromatography—Part II: Adsorption on the stationary phase Chemistry ; Phase (matter) ; Ammonium acetate ; Solubility ; Methanol ; Chromatography ; Adsorption ; Solvent ; Supercritical fluid chromatography ; Solvation ; Supercritical fluid ; Salt (chemistry) ; Ammonium ; High-performance liquid chromatography ; Organic chemistry Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Microfluidic and Capillary Electrophoresis Applications
Authors: Subhasish Mandal | Kristjan Haule | Karin M. Rabe | David Vanderbilt
Source: npj Computational Materials (article, 2019-11-29)
Field: Materials Science — Materials Chemistry

Systematic beyond-DFT study of binary transition metal oxides Abstract Various methods going beyond density functional theory (DFT), such as DFT+U, hybrid functionals, meta-GGAs, GW, and DFT-embedded dynamical mean field theory (eDMFT), have been developed to describe the electronic structure of correlated materials, but it is unclear how accurate these methods can be expected to be when applied to a given strongly correlated solid. It is thus of pressing interest to compare their accuracy as they apply to different categories of materials. Here we introduce a novel paradigm in which a chosen set of beyond-DFT methods is systematically and uniformly tested on a chosen class of materials. For a first application, we choose the target materials to be the binary transition metal oxides FeO, CoO, MnO, and NiO in their antiferromagnetic phase and present a head-to-head comparison of spectral properties as computed using the various methods. We also compare with available experimental angle-resolved photoemission spectroscopy (ARPES), inverse-photoemission spectroscopy, and with optical absorption. For the class of compounds studied here, we find that both B3LYP and eDMFT reproduce the experiments quite well, with eDMFT doing best, in particular when comparing with the ARPES data. Density functional theory ; Binary number ; Antiferromagnetism ; Transition metal ; Electronic structure ; Field (mathematics) ; Set (abstract data type) ; Angle-resolved photoemission spectroscopy ; Phase transition Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography ; Multiferroics and related materials
Authors: Yuhuan Li | Nan Wang | Xumin Huang | Fangyuan Li | Thomas P. Davis | Ruirui Qiao | Daishun Ling
Source: ACS Applied Bio Materials (article, 2019-11-08)
Field: Materials Science — Biomaterials

Polymer-Assisted Magnetic Nanoparticle Assemblies for Biomedical Applications Magnetic nanoparticle (MNP) assemblies have demonstrated great potential in biomedical applications due to their controllable magnetic properties and collective functions. Among the versatile approaches to obtaining MNP assemblies, polymer-assisted assembling methods offer unique advances, by which the assembled nanostructures could exert the merits of both the inorganic magnetic nanoparticles and organic polymeric materials to realize combined advantages for medical diagnosis and treatment. Precise control over the interactions among different building moieties and spatial organization of magnetic nanoparticles with the aid of polymers provides promising strategies in manipulating the physical, chemical, and biological properties of nanoassemblies for biomedical applications. In this review, we summarize the recent progress of polymer-assisted MNP assemblies, which include the mutual interactions between building blocks, architectural diversity of the assemblies, and their synthetic strategies along with biomedical applications. The current review provides a comprehensive insight into controlled and intelligent nanomedicines, which shall facilitate the development of next-generation high-performance theranostic agents based on MNP assemblies. Nanotechnology ; Magnetic nanoparticles ; Materials science ; Nanoparticle ; Polymer Nanoparticle-Based Drug Delivery ; Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Aini Palizhati | Wen Zhong | Kevin Tran | Seoin Back | Zachary W. Ulissi
Source: Journal of Chemical Information and Modeling (article, 2019-10-23)
Field: Materials Science — Materials Chemistry

Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks The surface energy of inorganic crystals is important in understanding experimentally relevant surface properties and designing materials for many applications. Predictive methods and data sets exist for surface energies of monometallic crystals. However, predicting these properties for bimetallic or more complicated surfaces is an open challenge. Computing cleavage energy is the first step in calculating surface energy across a large space. Here, we present a workflow to predict cleavage energies <i>ab initio</i> using high-throughput DFT and a machine learning framework. We calculated the cleavage energy of 3033 intermetallic alloys with combinations of 36 elements and 47 space groups. This high-throughput workflow was used to seed a database of cleavage energies. The database was used to train a crystal graph convolutional neural network (CGCNN). The CGCNN model provides an accurate prediction of cleavage energy with a mean absolute test error of 0.0071 eV/Å<sup>2</sup>. It can also qualitatively reproduce nanoparticle surface distributions (Wulff constructions). Our workflow provides quantitative insights into unexplored chemical space by predicting which surfaces are relatively stable and therefore more realistic. The insights allow us to down-select interesting candidates that we can study with robust theoretical and experimental methods for applications such as catalyst screening and nanomaterials synthesis. Bimetallic strip ; Convolutional neural network ; Cleavage (geology) ; Workflow ; Intermetallic ; Chemical space ; Materials science ; Computer science ; Nanomaterials ; Nanoparticle ; Throughput ; Artificial neural network ; Computational science ; Nanotechnology ; Catalysis ; Artificial intelligence ; Chemistry ; Database Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Electrocatalysts for Energy Conversion
Authors: Shuai Liu | Jie Li | Kochise Bennett | Brad Ganoe | Tim Stauch | Martin Head‐Gordon | Alexander Hexemer | Daniela Ushizima | Teresa Head‐Gordon
Source: The Journal of Physical Chemistry Letters (article, 2019-07-15)
Field: Chemistry — Spectroscopy

Multiresolution 3D-DenseNet for Chemical Shift Prediction in NMR Crystallography We have developed a deep learning algorithm for chemical shift prediction for atoms in molecular crystals that utilizes an atom-centered Gaussian density model for the 3D data representation of a molecule. We define multiple channels that describe different spatial resolutions for each atom type that utilizes cropping, pooling, and concatenation to create a multiresolution 3D-DenseNet architecture (MR-3D-DenseNet). Because the training and testing time scale linearly with the number of samples, the MR-3D-DenseNet can exploit data augmentation that takes into account the property of rotational invariance of the chemical shifts, thereby also increasing the size of the training data set by an order of magnitude without additional cost. We obtain very good agreement for <sup>13</sup>C, <sup>15</sup>N, and <sup>17</sup>O chemical shifts when compared to <i>ab initio</i> quantum chemistry methods, with the highest accuracy found for <sup>1</sup>H chemical shifts that is comparable to the error between the <i>ab initio</i> results and experimental measurements. Principal component analysis (PCA) is used to both understand these greatly improved predictions for <sup>1</sup>H , as well as indicating that chemical shift prediction for <sup>13</sup>C, <sup>15</sup>N, and <sup>17</sup>O, which have far fewer training environments than the <sup>1</sup>H atom type, will improve once more unique training samples are made available to exploit the deep network architecture. Chemical shift ; Concatenation (mathematics) ; Pooling ; Computer science ; Ab initio ; Gaussian ; Atom (system on chip) ; Algorithm ; Artificial intelligence ; Chemistry ; Mathematics ; Computational chemistry ; Physical chemistry Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality ; X-ray Diffraction in Crystallography
Authors: Audrius Doblies | Benjamin Boll | Bodo Fiedler
Source: Polymers (article, 2019-02-19)
Field: Materials Science — Materials Chemistry

Prediction of Thermal Exposure and Mechanical Behavior of Epoxy Resin Using Artificial Neural Networks and Fourier Transform Infrared Spectroscopy Thermal degradation detection of cured epoxy resins and composites is currently limited to severe thermal damage in practice. Evaluating the change in mechanical properties after a short-time thermal exposure, as well as estimating the history of thermally degraded polymers, has remained a challenge until now. An approach to accurately predict the mechanical properties, as well as the thermal exposure time and temperature of epoxy resin, using Fourier-transform infrared spectroscopy (FTIR)-spectroscopy, data processing, and artificial neural networks, is presented here. Therefore, an epoxy resin has been fully cured and exposed to elevated temperatures for different time periods. A FTIR-spectrometer was used to measure molecular changes, using mid-IR (MIR)-FTIR for film samples and near-IR (NIR)-FTIR for bulk samples. A quantitative analysis of the thermally degraded film samples shows oxidation, chain-scission, and dehydration in the FTIR spectra in the MIR-range. Using NIR spectroscopy for the bulk samples, only minor changes in the FTIR spectra could be detected. However, using data processing, molecular information was extracted from the NIR range and a degradation model, using an artificial neural network, has been trained. Even though the changes due to thermal exposure were small, the presented model is capable of accurately predicting the time, temperature, and residual strength of the polymer. Fourier transform infrared spectroscopy ; Epoxy ; Materials science ; Spectroscopy ; Polymer ; Infrared spectroscopy ; Composite material ; Fourier transform spectroscopy ; Analytical Chemistry (journal) ; Chemical engineering ; Chemistry ; Organic chemistry Thermal and Kinetic Analysis ; Cultural Heritage Materials Analysis ; Thermography and Photoacoustic Techniques
Authors: Hai-Jin Lu | Nan Zou | Ryan Jacobs | Ben Afflerbach | Xiao‐Gang Lu | Dane Morgan
Source: Computational Materials Science (article, 2019-07-17)
Field: Materials Science — Materials Chemistry

Error assessment and optimal cross-validation approaches in machine learning applied to impurity diffusion Kriging ; Mean squared error ; Hyperparameter ; Feature selection ; Machine learning ; Diffusion ; Computer science ; Gaussian process ; Model selection ; Regression ; Gaussian ; Artificial intelligence ; Feature (linguistics) ; Statistics ; Mathematics ; Chemistry ; Physics ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; X-ray Diffraction in Crystallography
Authors: Yasemin Basdogan | Mitchell C. Groenenboom | Ethan Henderson | Sandip De | Susan B. Rempe | John A. Keith
Source: Journal of Chemical Theory and Computation (article, 2019-12-06)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Approach for Studying Solvation Environments Molecular-level understanding and characterization of solvation environments are often needed across chemistry, biology, and engineering. Toward practical modeling of local solvation effects of any solute in any solvent, we report a static and all-quantum mechanics-based cluster-continuum approach for calculating single-ion solvation free energies. This approach uses a global optimization procedure to identify low-energy molecular clusters with different numbers of explicit solvent molecules and then employs the smooth overlap for atomic positions learning kernel to quantify the similarity between different low-energy solute environments. From these data, we use sketch maps, a nonlinear dimensionality reduction algorithm, to obtain a two-dimensional visual representation of the similarity between solute environments in differently sized microsolvated clusters. After testing this approach on different ions having charges 2+, 1+, 1-, and 2-, we find that the solvation environment around each ion can be seen to usually become more similar in hand with its calculated single-ion solvation free energy. Without needing either dynamics simulations or an a priori knowledge of local solvation structure of the ions, this approach can be used to calculate solvation free energies within 5% of experimental measurements for most cases, and it should be transferable for the study of other systems where dynamics simulations are not easily carried out. Solvation ; Computer science ; Data science ; Artificial intelligence ; Human–computer interaction ; Machine learning ; Chemistry ; Molecule Machine Learning in Materials Science ; Advanced Data Processing Techniques ; Neural Networks and Applications
Authors: Yu Wang | Xinxing Peng | Alex Abelson | Penghao Xiao | Caroline Qian | Lei Yu | Colin Ophus | Peter Ercius | Lin‐Wang Wang | Matt Law | Haimei Zheng
Source: Science Advances (article, 2019-06-01)
Field: Materials Science — Materials Chemistry

Dynamic deformability of individual PbSe nanocrystals during superlattice phase transitions The behavior of individual nanocrystals during superlattice phase transitions can profoundly affect the structural perfection and electronic properties of the resulting superlattices. However, details of nanocrystal morphological changes during superlattice phase transitions are largely unknown due to the lack of direct observation. Here, we report the dynamic deformability of PbSe semiconductor nanocrystals during superlattice phase transitions that are driven by ligand displacement. Real-time high-resolution imaging with liquid-phase transmission electron microscopy reveals that following ligand removal, the individual PbSe nanocrystals experience drastic directional shape deformation when the spacing between nanocrystals reaches 2 to 4 nm. The deformation can be completely recovered when two nanocrystals move apart or it can be retained when they attach. The large deformation, which is responsible for the structural defects in the epitaxially fused nanocrystal superlattice, may arise from internanocrystal dipole-dipole interactions. Superlattice ; Nanocrystal ; Materials science ; Phase transition ; Nanotechnology ; Phase (matter) ; Optoelectronics ; Chemistry ; Condensed matter physics ; Physics Quantum Dots Synthesis And Properties ; nanoparticles nucleation surface interactions ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Muhammad Zulfajri | Sandhiya Dayalan | W.J. Li | Chia-Jung Chang | Yuan‐Pin Chang | Genin Gary Huang
Source: Sensors (article, 2019-11-16)
Field: Materials Science — Materials Chemistry

Nitrogen-Doped Carbon Dots from Averrhoa carambola Fruit Extract as a Fluorescent Probe for Methyl Orange In this study, a simple and green hydrothermal treatment was performed to prepare nitrogen-doped carbon dots (NCDs) from Averrhoa carambola (AC) fruit extract as a carbon precursor and L-arginine (Arg) as a nitrogen dopant. The AC-NCDs were characterized by UV light, fluorescence spectroscopy, transmission electron microscopy, FTIR spectroscopy, Raman spectroscopy, UV-vis spectroscopy, and zeta potential analyzer. The AC-NCDs were spherical and the average diameter was estimated to be 6.67 nm. The AC-NCDs exhibited the maximum emission intensity at 446 nm with 360 nm excitation wavelength. The fluorescence quenching behavior of AC-NCDs after interacting with methyl orange (MO) dye was studied. The interaction of AC-NCDs and MO was achieved within 3 min and the fluorescence quenching was maintained to a fixed value even after 30 min. The linearity was obtained in the range of 1 to 25 μM MO with a 0.30 μM detection limit. Furthermore, the pH values affected the quenching behavior of the AC-NCDs/MO system where the interaction mechanisms were driven by the electrostatic interaction, π-π interaction, inner filter effect, and energy transfer. The pH 5 maintained higher quenching efficiency while other pH values slightly decreased the quenching efficiency. Incoming applications, the AC-NCDs can be used in various important fields, especially for environmental protection. Averrhoa carambola ; Analytical Chemistry (journal) ; Raman spectroscopy ; Quenching (fluorescence) ; Fluorescence ; Spectroscopy ; Methyl orange ; Materials science ; Fluorescence spectroscopy ; Dopant ; Fourier transform infrared spectroscopy ; Chemistry ; Nuclear chemistry ; Doping ; Photocatalysis ; Chemical engineering ; Optoelectronics ; Chromatography ; Optics ; Organic chemistry Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Quantum Dots Synthesis And Properties