jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Priscilla Mendonça Matos | Robert A. Stockman
Source: Organic & Biomolecular Chemistry (review, 2020-01-01)
Field: Chemistry — Organic Chemistry

Synthetic approaches and applications of sulfonimidates This review article explores the synthesis of the organosulfur(vi) species named sulfonimidates, focusing on their synthesis from sulfur(ii), sulfur(iv) and sulfur(vi) reagents, and investigates their recent resurgeance in interest as intermediates to access other important organosulfur compounds. Sulfonimidates have been utilized as precursors for polymers, sulfoximine and sulfonimidamide drug candidates and as alkyl transfer reagents. Organosulfur compounds ; Chemistry ; Sulfur ; Reagent ; Alkyl ; Combinatorial chemistry ; Organic chemistry Synthesis and Catalytic Reactions ; Sulfur-Based Synthesis Techniques ; Catalytic C–H Functionalization Methods
Authors: Giordano Mancini | Marco Fusè | Federico Lazzari | Balasubramanian Chandramouli | Vincenzo Barone
Source: The Journal of Chemical Physics (article, 2020-09-24)
Field: Chemistry — Spectroscopy

Unsupervised search of low-lying conformers with spectroscopic accuracy: A two-step algorithm rooted into the island model evolutionary algorithm The fruitful interplay of high-resolution spectroscopy and quantum chemistry has a long history, especially in the field of small, semi-rigid molecules. However, in recent years, the targets of spectroscopic studies are shifting toward flexible molecules, characterized by a large number of closely spaced energy minima, all contributing to the overall spectrum. Here, artificial intelligence comes into play since it is at the basis of powerful unsupervised techniques for the exploration of soft degrees of freedom. Integration of such algorithms with a two-stage QM/QM' (Quantum Mechanical) exploration/refinement strategy driven by a user-friendly graphical interface is the topic of the present paper. We will address in particular: (i) the performances of different semi-empirical methods for the exploration step and (ii) the comparison between stochastic and meta-heuristic algorithms in achieving a cheap yet complete exploration of the conformational space for medium sized chromophores. As test cases, we choose three amino acids of increasing complexity, whose full conformer enumeration has been reached only very recently. Next, we show that systems in condensed phases can be treated at the same level and with the same efficiency when employing a polarizable continuum description of the solvent. Finally, the challenging issue represented by the vibrational circular dichroism spectra of some rhodium complexes with flexible ligands has been addressed, showing that our fully unsupervised approach leads to remarkable agreement with the experiment. Algorithm ; Maxima and minima ; Conformational isomerism ; Computer science ; Chromophore ; Quantum ; Quantum machine learning ; Molecule ; Physics ; Quantum algorithm ; Mathematics ; Quantum mechanics Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Marissa A. Jones | Sung Hoon Cho | Nathan Heath Patterson | Raf Van de Plas | Jeffrey M. Spraggins | Mark Boothby | Richard M. Caprioli
Source: Analytical Chemistry (article, 2020-04-16)
Field: Chemistry — Spectroscopy

Discovering New Lipidomic Features Using Cell Type Specific Fluorophore Expression to Provide Spatial and Biological Specificity in a Multimodal Workflow with MALDI Imaging Mass Spectrometry Identifying the spatial distributions of biomolecules in tissue is crucial for understanding integrated function. Imaging mass spectrometry (IMS) allows simultaneous mapping of thousands of biosynthetic products such as lipids but has needed a means of identifying specific cell-types or functional states to correlate with molecular localization. We report, here, advances starting from identity marking with a genetically encoded fluorophore. The fluorescence emission data were integrated with IMS data through multimodal image processing with advanced registration techniques and data-driven image fusion. In an unbiased analysis of spleens, this integrated technology enabled identification of ether lipid species preferentially enriched in germinal centers. We propose that this use of genetic marking for microanatomical regions of interest can be paired with molecular information from IMS for any tissue, cell-type, or activity state for which fluorescence is driven by a gene-tracking allele and ultimately with outputs of other means of spatial mapping. Mass spectrometry imaging ; Chemistry ; Computational biology ; Fluorophore ; Molecular imaging ; Mass spectrometry ; Biomolecule ; Fluorescence ; Biochemistry ; Genetics ; Biology Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Mohammad Akbar Faqeerzada | Santosh Lohumi | Geonwoo Kim | Rahul Joshi | Hoonsoo Lee | Moon S. Kim | Byoung–Kwan Cho
Source: Sensors (article, 2020-10-16)
Field: Chemistry — Analytical Chemistry

Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method The widely used techniques for analyzing the quality of powdered food products focus on targeted detection with a low-throughput screening of samples. Owing to potentially significant health threats and large-scale adulterations, food regulatory agencies and industries require rapid and non-destructive analytical techniques for the detection of unexpected compounds present in products. Accordingly, shortwave-infrared hyperspectral imaging (SWIR-HSI) for high throughput authenticity analysis of almond powder was investigated in this study. Two different varieties of almond powder, adulterated with apricot and peanut powder at different concentrations, were imaged using the SWIR-HSI system. A one-class classifier technique, known as data-driven soft independent modeling of class analogy (DD-SIMCA), was used on collected data sets of pure and adulterated samples. A partial least square regression (PLSR) model was further developed to predict adulterant concentrations in almond powder. Classification results from DD-SIMCA yielded 100% sensitivity and 89-100% specificity for different validation sets of adulterated samples. The results obtained from the PLSR analysis yielded a high determination coefficient (R<sup>2</sup>) and low error values (<1%) for each variety of almond powder adulterated with apricot; however, a relatively higher error rates of 2.5% and 4.4% for the two varieties of almond powder adulterated with peanut powder, which indicates the performance of quantitative analysis model could vary with sample condition, such as variety, originality, etc. PLSR-based concentration mapped images visually characterized the adulterant (apricot) concentration in the almond powder. These results demonstrate that the SWIR-HSI technique combined with the one-class classifier DD-SIMCA can be used effectively for a high-throughput quality screening of almond powder regarding potential adulteration. Adulterant ; Hyperspectral imaging ; Food products ; Mathematics ; Food science ; Partial least squares regression ; Environmental science ; Chemistry ; Artificial intelligence ; Chromatography ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Muhammad Ashraf Sabri | Mohammad H. Al‐Sayah | Susan Sen | Taleb Ibrahim | Oussama M. El‐Kadri
Source: Scientific Reports (article, 2020-09-29)
Field: Materials Science — Materials Chemistry

Fluorescent aminal linked porous organic polymer for reversible iodine capture and sensing A novel triazene-anthracene-based fluorescent aminal linked porous organic polymer (TALPOP) was prepared via metal free-Schiff base polycondensation reaction of 9,10-bis-(4,6-diamino-S-triazin-2-yl)anthracene and 2-furaldehyde. The polymer has exceptional chemical and thermal stabilities and exhibit good porosity with Brunauer-Emmett-Teller surface area of 401 m<sup>2</sup>g<sup>-1</sup>. The combination of such porosity along with the highly conjugated heteroatom-rich framework enabled the polymer to exhibit exceptional iodine vapor uptake of up to 314 wt % and reversible iodine adsorption in solution. Because of the inclusion of the anthracene moieties, the TALPOP exhibited excellent detection sensitivity towards iodine via florescence quenching with K<sub>sv</sub> value of 2.9 × 10<sup>3</sup> L mol<sup>-1</sup>. The cost effective TALPOP along with its high uptake and sensing of iodine, make it an ideal material for environmental remediation. Aminal ; Anthracene ; Iodine ; Polymer ; Fluorescence ; Chemistry ; Porosity ; Adsorption ; Photochemistry ; Polymer chemistry ; Inorganic chemistry ; Materials science ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Yuanhong Min | Yi Wang
Source: Frontiers in Chemistry (review, 2020-05-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Manipulating Bimetallic Nanostructures With Tunable Localized Surface Plasmon Resonance and Their Applications for Sensing Metal nanocrystals with well-controlled shape and unique localized surface plasmon resonance (LSPR) properties have attracted tremendous attention in both fundamental studies and applications. Compared with monometallic counterparts, bimetallic nanocrystals endow scientists with more opportunities to precisely tailor their LSPR and thus achieve excellent performances for various purposes. The aim of this mini review is to present the recent process in manipulating bimetallic nanostructures with tunable LSPR and their applications for sensing. We first highlight several significant strategies in controlling the elemental ratio and spatial arrangement of bimetallic nanocrystals, followed by discussing on the relationship between their composition/morphology and LSPR properties. We then focus on the plasmonic sensors based on the LSPR peak shift, which can be well-controlled by seed-mediated growth and selective etching. This review provides insights of understanding the "rules" involving in the formation of bimetallic nanocrystals with different structures and desired LSPR properties, and also forecasts the development directions of plasmonic sensors in the future. Bimetallic strip ; Surface plasmon resonance ; Nanostructure ; Nanotechnology ; Materials science ; Plasmon ; Resonance (particle physics) ; Surface plasmon ; Localized surface plasmon ; Surface (topology) ; Nanoparticle ; Optoelectronics ; Physics ; Atomic physics ; Metal Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Nanofabrication and Lithography Techniques
Authors: Sha Luo | Chun‐Wei Liu | Sui Zhou | Wei Li | Chunhui Ma | Shouxin Liu | Yin Wang | Hero J. Heeres | Weiqing Zheng | K. Seshan | Songbo He
Source: Chemosphere (article, 2020-07-23)
Field: Materials Science — Materials Chemistry

ZnO nanorod arrays assembled on activated carbon fibers for photocatalytic degradation: Characteristics and synergistic effects Well-aligned ZnO nanorod arrays were assembled on activated carbon fibers by a stepwise sequence of sol-gel and hydrothermal synthesis methods. These ZnO nanorod arrays on activated carbon fibers having different characteristics such as surface area, rod concentration, aspect ratio and defect level, were applied as catalysts for the photodegradation of an aqueous methylene blue solution. They showed very promising methylene blue adsorbility in the dark (ca. 0.025-0.031 mg methylene blue m<sup>-2</sup> catalyst, vs. 0.072 mg methylene blue m<sup>-2</sup> activated carbon fibers). Significantly, the defect level of ZnO nanorod arrays has a major effect on the turnover frequency compared to other characteristics. A synergistic effect between activated carbon fibers and ZnO nanocrystals on enhancing turnover frequency was more significant for the well-assembled ZnO nanorod arrays on activated carbon fibers catalysts compared to the mechanically mixed ZnO powder with activated carbon fibers catalyst. Further, turnover frequency for the ZnO nanorod arrays on activated carbon fibers (0.00312 mol<sub>methylene blue</sub> mol<sub>ZnO</sub><sup>-1</sup> h<sup>-1</sup>) was twice higher than that for the corresponding bare ZnO nanorod arrays, and 3 times higher than that for a commercial ZnO powder. In addition, ZnO nanorod arrays on activated carbon fibers show high degradation (77.5%) and mineralization (55.0%) levels for methylene blue, and also good reusability (or stability) as demonstrated by a sequential 5-time recycle routine. These outstanding features indicate that activated carbon fibers supported ZnO nanorod arrays have significant potential to be used as catalysts for photodegradation. Photocatalysis ; Degradation (telecommunications) ; Nanorod ; Activated carbon ; Chemical engineering ; Materials science ; Nanotechnology ; Chemistry ; Environmental chemistry ; Adsorption ; Catalysis ; Computer science ; Telecommunications ; Organic chemistry ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Advanced Photocatalysis Techniques
Authors: Riccardo Marin | Daniel Jaque | Antonio Benayas
Source: Nanoscale Horizons (article, 2020-12-23)
Field: Materials Science — Materials Chemistry

Switching to the brighter lane: pathways to boost the absorption of lanthanide-doped nanoparticles Lanthanide-doped nanoparticles (LNPs) are speedily colonizing several research fields, such as biological (multimodal) imaging, photodynamic therapy, volumetric encoding displays, and photovoltaics. Yet, the electronic transitions of lanthanide ions obey the Laporte rule, which dramatically hampers their light absorption capabilities. As a result, the brightness of these species is severely restricted. This intrinsic poor absorption capability is the fundamental obstacle for untapping the full potential of LNPs in several of the aforementioned fields. Among others, three of the most promising physicochemical approaches that have arisen during last two decades to face the challenges of increasing LNP absorption are plasmonic enhancement, organic-dye sensitization, and coupling with semiconductors. The fundamental basis, remarkable highlights, and comparative achievements of each of these pathways for absorption enhancement are critically discussed in this minireview, which also includes a detailed discussion of the exciting perspectives ahead. Lanthanide ; Doping ; Nanoparticle ; Absorption (acoustics) ; Materials science ; Nanotechnology ; Chemistry ; Optoelectronics ; Organic chemistry ; Ion Luminescence Properties of Advanced Materials ; Lanthanide and Transition Metal Complexes ; Quantum Dots Synthesis And Properties
Authors: Hui Sun | Benedetto Marelli
Source: Nature Communications (article, 2020-01-17)
Field: Materials Science — Biomaterials

Polypeptide templating for designer hierarchical materials Despite advances in directing the assembly of biomacromolecules into well-defined nanostructures, leveraging pathway complexity of molecular disorder to order transition while bridging materials fabrication from nano- to macroscale remains a challenge. Here, we present templated crystallization of structural proteins to nanofabricate hierarchically structured materials up to centimeter scale, using silk fibroin as an example. The process involves the use of ordered peptide supramolecular assemblies as templates to direct the folding and assembly of silk fibroin into nanofibrillar structures. Silk polymorphs can be engineered by varying the peptide seeds used. Modulation of the relative concentration between silk fibroin and peptide seeds, silk fibroin molecular weight and pH allows control over nanofibrils morphologies and mechanical properties. Finally, facile integration of the bottom-up templated crystallization with emerging top-down techniques enables the generation of macroscopic nanostructured materials with potential applications in information storage/encryption, surface functionalization, and printable three-dimensional constructs of customized architecture and controlled anisotropy. Fibroin ; Nanotechnology ; Template ; Materials science ; SILK ; Nanoscopic scale ; Crystallization ; Nanostructure ; Self-assembly ; Nanobiotechnology ; Supramolecular chemistry ; Fabrication ; Chemistry ; Nanoparticle ; Crystallography ; Crystal structure Silk-based biomaterials and applications ; Supramolecular Self-Assembly in Materials ; Biochemical and Structural Characterization
Authors: Mujin Yang | D.J.M. King | Ivan Povstugar | Y. R. Wen | Junhua Luan | Bernd Kuhn | Zengbao Jiao | Cuiping Wang | M.R. Wenman | Xingjun Liu
Source: Acta Materialia (article, 2020-12-08)
Field: Materials Science — Metals and Alloys

Precipitation behavior in G-phase strengthened ferritic stainless steels Materials science ; Precipitation ; Metallurgy ; Phase (matter) ; Meteorology Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; High Temperature Alloys and Creep
Authors: Xiao Fan | Navneet Soin | Haitao Li | Hua Li | Xiaohong Xia | Junfeng Geng
Source: Energy & environment materials (article, 2020-02-17)
Field: Chemistry — Organic Chemistry

Fullerene (C<sub>60</sub>) Nanowires: The Preparation, Characterization, and Potential Applications Fullerene (C 60 ) nanowires have attracted significant attention in the past two decades due to their outstanding chemical and physical properties, which render the material a wide range of potential applications. Much effort has been devoted to exploring the growth methods, structural and compositional characterizations, and application‐related investigations of this novel carbon nanomaterial. Here, we present a review of C 60 nanowires in which we will first describe the recent development in the material preparations, analytical techniques, crystal structures, chemical compositions, and the investigations of polymerization processes. Afterward, we will discuss the mechanistic studies on the nanowires’ growth as the mechanism research is of great importance for their size control, large‐scale preparation, and for the exploration of applications in a wide range of fields. Finally, we will discuss the potential applications in several directions, including optical, electrical, mechanical, and biological fields, as well as our perspectives to future developments. Nanotechnology ; Nanowire ; Nanomaterials ; Characterization (materials science) ; Materials science ; Fullerene ; Chemistry Fullerene Chemistry and Applications ; Graphene research and applications ; Diamond and Carbon-based Materials Research
Authors: Obaidur Rahaman | Alessio Gagliardi
Source: Journal of Chemical Information and Modeling (article, 2020-10-29)
Field: Materials Science — Materials Chemistry

Deep Learning Total Energies and Orbital Energies of Large Organic Molecules Using Hybridization of Molecular Fingerprints The ability to predict material properties without the need for resource-consuming experimental efforts can immensely accelerate material and drug discovery. Although ab initio methods can be reliable and accurate in making such predictions, they are computationally too expensive on a large scale. The recent advancements in artificial intelligence and machine learning as well as the availability of large quantum mechanics derived datasets enable us to train models on these datasets as a benchmark and to make fast predictions on much larger datasets. The success of these machine learning models highly depends on the machine-readable fingerprints of the molecules that capture their chemical properties as well as topological information. In this work, we propose a common deep learning-based framework to combine different types of molecular fingerprints to enhance prediction accuracy. A graph neural network (GNN), many-body tensor representation (MBTR), and a set of simple molecular descriptors (MD) were used to predict the total energies, highest occupied molecular orbital (HOMO) energies, and lowest unoccupied molecular orbital (LUMO) energies of a dataset containing ∼62k large organic molecules with complex aromatic rings and remarkably diverse functional groups. The results demonstrate that a combination of best performing molecular fingerprints can produce better results than the individual ones. The simple and flexible deep learning framework developed in this work can be easily adapted to incorporate other types of molecular fingerprints. Computer science ; HOMO/LUMO ; Molecular descriptor ; Benchmark (surveying) ; Artificial intelligence ; Molecular graph ; Ab initio ; Molecular orbital ; Representation (politics) ; Machine learning ; Deep learning ; Artificial neural network ; Graph ; Molecule ; Physics ; Quantitative structure–activity relationship ; Theoretical computer science ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Claudia Mangold | Shunda Chen | Giuseppe Barbalinardo | Jörg Behler | Pascal Pochet | Konstantinos Termentzidis | Yang Han | Laurent Chaput | David Lacroix | Davide Donadio
Source: Journal of Applied Physics (article, 2020-06-22)
Field: Materials Science — Materials Chemistry

Transferability of neural network potentials for varying stoichiometry: Phonons and thermal conductivity of Mn<i>x</i>Ge<i>y</i> compounds Germanium manganese compounds exhibit a variety of stable and metastable phases with different stoichiometries. These materials entail interesting electronic, magnetic, and thermal properties both in their bulk form and as heterostructures. Here, we develop and validate a transferable machine learning potential, based on the high-dimensional neural network formalism, to enable the study of MnxGey materials over a wide range of compositions. We show that a neural network potential fitted on a minimal training set reproduces successfully the structural and vibrational properties and the thermal conductivity of systems with different local chemical environments, and it can be used to predict phononic effects in nanoscale heterostructures. Thermal conductivity ; Transferability ; Artificial neural network ; Metastability ; Phonon ; Nanoscopic scale ; Thermal ; Conductivity Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices