jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aleksandra Mitrović | Stefan Wild | Vicent Lloret | Michael Fickert | Mhamed Assebban | Bence G. Márkus | Ferenc Simon | Frank Hauke | Gonzalo Abellán | Andreas Hirsch
Source: Chemistry - A European Journal (article, 2020-10-13)
Field: Materials Science — Materials Chemistry

Interface Amorphization of Two‐Dimensional Black Phosphorus upon Treatment with Diazonium Salts Two-dimensional (2D) black phosphorus (BP) represents one of the most appealing 2D materials due to its electronic, optical, and chemical properties. Many strategies have been pursued to face its environmental instability, covalent functionalization being one of the most promising. However, the extremely low functionalization degrees and the limitations in proving the nature of the covalent functionalization still represent challenges in many of these sheet architectures reported to date. Here we shine light on the structural evolution of 2D-BP upon the addition of electrophilic diazonium salts. We demonstrated the absence of covalent functionalization in both the neutral and the reductive routes, observing in the latter case an unexpected interface conversion of BP to red phosphorus (RP), as characterized by Raman, <sup>31</sup> P-MAS NMR, and X-ray photoelectron spectroscopies (XPS). Furthermore, thermogravimetric analysis coupled to gas chromatography and mass spectrometry (TG-GC-MS), as well as electron paramagnetic resonance (EPR) gave insights into the potential underlying radical mechanism, suggesting a Sandmeyer-like reaction. Surface modification ; Covalent bond ; X-ray photoelectron spectroscopy ; Raman spectroscopy ; Chemistry ; Black phosphorus ; Electron paramagnetic resonance ; Thermogravimetric analysis ; Mass spectrometry ; Combinatorial chemistry ; Photochemistry ; Organic chemistry ; Chemical engineering ; Materials science ; Physical chemistry ; Nuclear magnetic resonance 2D Materials and Applications ; Advanced Photocatalysis Techniques ; Perovskite Materials and Applications
Authors: Jiabi Li | Xi Wu | Shengwei Liu
Source: Acta Physico-Chimica Sinica (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Fluorinated TiO&lt;sub&gt;2&lt;/sub&gt; Hollow Photocatalysts for Photocatalytic Applications Photocatalysis ; Chemical engineering ; Materials science ; Nanotechnology ; Chemistry ; Nuclear chemistry ; Catalysis ; Organic chemistry ; Engineering Polyoxometalates: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Thiruvilla S. Mahadevan | Jincheng Du
Source: Journal of the American Ceramic Society (article, 2020-09-19)
Field: Materials Science — Ceramics and Composites

Atomic and micro‐structure features of nanoporous aluminosilicate glasses from reactive molecular dynamics simulations Abstract Long‐term chemical durability of borosilicate glasses that makes them a widely accepted form of nuclear waste disposal is achieved through the formation of a porous aluminosilicate gel layer that provides passivity and limits the transport of water to the reaction front. Detailed understanding of the porous silicate gel layer is thus critical in elucidating the corrosion mechanism of these glasses and to design of new glass composition for waste immobilization and other applications. In this paper, we use the diffuse charge reactive potential to generate porous aluminosilicate glass structures with compositions equivalent to the gel layers formed at the glass‐water interface with an aim to understand the processing condition on the microstructure and atomic structure of these systems. We demonstrate the use of the charge scaling techniques is an effective approach to generate these porous structures with controllable pore mophologies. After initial validation of the potentials and calcium aluminosilicate glass structures using neutron diffraction, we created gel structures with compositions similar to well‐known model nuclear waste borosilicate glasses. The porosities and the pore size distribution bear a strong correlation to the processing temperature, as well as to the local atomic structure. Thus, by controlling the processing parameters, the generated porous structures can be customized to closely resemble gel structures due to borosilicate glass corrosion. These results provide insights of the micro‐ and atomic structure features of the porous aluminosilicate glasses and on the optimal procedure to generate porous structures that can be comparable to experimentally observed gel layer structures thus to elaborate on the correlations between the structure and phenomena in glass‐water interactions. Borosilicate glass ; Aluminosilicate ; Materials science ; Nanoporous ; Porosity ; Corrosion ; Microstructure ; Chemical engineering ; Porous glass ; Silicate ; Mineralogy ; Nanotechnology ; Composite material ; Chemistry Glass properties and applications ; Clay minerals and soil interactions ; Building materials and conservation
Authors: Diana Díaz‐García | Karla Montalbán‐Hernández | Irene Mena-Palomo | Patriciu Achimaș-Cădariu | Antonio Rodrı́guez-Diéguez | Eduardo López‐Collazo | Sanjiv Prashar | Karina Ovejero Paredes | Marco Filice | Eva Fischer‐Fodor | Santiago Gómez‐Ruiz
Source: Pharmaceutics (article, 2020-06-03)
Field: Chemistry — Organic Chemistry

Role of Folic Acid in the Therapeutic Action of Nanostructured Porous Silica Functionalized with Organotin(IV) Compounds against Different Cancer Cell Lines The synthesis, characterization and cytotoxic activity against different cancer cell lines of various mesoporous silica-based materials containing folate targeting moieties and a cytotoxic fragment based on a triphenyltin(IV) derivative have been studied. Two different mesoporous nanostructured silica systems have been used: firstly, micronic silica particles of the MSU-2 type and, secondly, mesoporous silica nanoparticles (MSNs) of about 80 nm. Both series of materials have been characterized by different methods, such as powder X-ray diffraction, X-ray fluorescence, absorption spectroscopy and microscopy. In addition, these systems have been tested against four different cancer cell lines, namely, OVCAR-3, DLD-1, A2780 and A431, in order to observe if the size of the silica-based systems and the quantity of incorporated folic acid influence their cytotoxic action. The results show that the materials are more active when the quantity of folic acid is higher, especially in those cells that overexpress folate receptors such as OVCAR-3 and DLD-1. In addition, the study of the potential modulation of the soluble folate receptor alpha (FOLR1) by treatment with the synthesized materials has been carried out using OVCAR-3, DLD-1, A2780 and A431 tumour cell lines. The results show that a relatively high concentration of folic acid functionalization of the nanostructured silica together with the incorporation of the cytotoxic tin fragment leads to an increase in the quantity of the soluble FOLR1 secreted by the tumour cells. In addition, the studies reported here show that this increase of the soluble FOLR1 occurs presumably by cutting the glycosyl-phosphatidylinositol anchor of membrane FR-α and by the release of intracellular FR-α. This study validates the potential use of a combination of mesoporous silica materials co-functionalized with folate targeting molecules and an organotin(IV) drug as a strategy for the therapeutic treatment of several cancer cells overexpressing folate receptors. Folate receptor ; Cytotoxic T cell ; Chemistry ; Mesoporous silica ; Cell culture ; Cancer cell ; Folic acid ; Mesoporous material ; Combinatorial chemistry ; Biochemistry ; Cancer ; In vitro Ferrocene Chemistry and Applications ; Bone health and treatments ; Organometallic Compounds Synthesis and Characterization
Authors: Kathrin Brömmel | Sarah Maskri | Iván Maisuls | Christian Paul Konken | Marius Rieke | Zoltán Pethő | Cristian A. Strassert | Oliver Koch | Albrecht Schwab | Bernhard Wünsch
Source: Angewandte Chemie International Edition (article, 2020-02-25)
Field: Materials Science — Materials Chemistry

Synthesis of Small‐Molecule Fluorescent Probes for the In Vitro Imaging of Calcium‐Activated Potassium Channel K<sub>Ca</sub>3.1 Small-molecule probes for the in vitro imaging of K<sub>Ca</sub> 3.1 channel-expressing cells were developed. Senicapoc, showing high affinity and selectivity for the K<sub>Ca</sub> 3.1 channels, was chosen as the targeting component. BODIPY dyes 15-20 were synthesized and connected by a Cu<sup>I</sup> -catalyzed azide-alkyne [3+2]cycloaddition with propargyl ether senicapoc derivative 8, yielding fluorescently labeled ligands 21-26. The dimethylpyrrole-based imaging probes 25 and 26 allow staining of K<sub>Ca</sub> 3.1 channels in NSCLC cells. The specificity was shown by removing the punctate staining pattern by pre-incubation with senicapoc. The density of K<sub>Ca</sub> 3.1 channels detected with 25 and by immunostaining was identical. The punctate structure of the labeled channels could also be observed in living cells. Molecular modeling showed binding of the senicapoc-targeting component towards the binding site within the ion channel and orientation of the linker with the dye along the inner surface of the ion channel. Chemistry ; Fluorescence ; In vitro ; Biophysics ; Linker ; Potassium channel ; Molecule ; Small molecule ; Staining ; Immunostaining ; Biochemistry Luminescence and Fluorescent Materials ; Advanced biosensing and bioanalysis techniques ; Nicotinic Acetylcholine Receptors Study
Authors: Gayathri Rajeev | Elizabeth Melville | Allison J. Cowin | Beatriz Prieto‐Simón | Nicolas H. Voelcker
Source: Frontiers in Chemistry (article, 2020-03-06)
Field: Materials Science — Materials Chemistry

Porous Alumina Membrane-Based Electrochemical Biosensor for Protein Biomarker Detection in Chronic Wounds A label-free electrochemical detection platform for the sensitive and rapid detection of Flightless I (Flii) protein, a biomarker of wound chronicity, has been developed using nanoporous anodic alumina (NAA) membranes modified with Flii antibody recognition sites. The electrochemical detection is based on the nanochannel blockage experienced upon Flii capture by immobilized antibodies within the nanochannels. This capture impedes the diffusion of redox species [[Fe(CN)<sub>6</sub>]<sup>4-/3-</sup>] toward a gold electrode attached at the backside of the modified NAA membrane. Partial blockage causes a decrease in the oxidation current of the redox species at the electrode surface which is used as an analytical signal by the reported biosensor. The resulting biosensing system allows detection of Flii at the levels found in wounds. Two types of assays were tested, sandwich and direct, showing <3 and 2 h analysis time, respectively, a significant reduction in time from the nearly 48 h required for the conventional Western blot assay. Slightly higher sensitivity values were observed for the sandwich-based strategy. With faster analysis, lack of matrix effects, robustness, ease of use and cost-effectiveness, the developed sensing platform has the potential to be translated into a point-of-care (POC) device for chronic wound management and as a simple alternative characterization tool in Flii research. Biosensor ; Nanoporous ; Membrane ; Detection limit ; Electrochemistry ; Electrode ; Redox ; Materials science ; Biomarker ; Nanotechnology ; Chemistry ; Chromatography ; Inorganic chemistry ; Biochemistry Anodic Oxide Films and Nanostructures ; Smart Materials for Construction ; Corrosion Behavior and Inhibition
Authors: Éric Bousquet | H. Hamdi | Pablo Aguado‐Puente | Ekhard K. H. Salje | Emilio Artacho | Philippe Ghosez
Source: Physical Review Research (article, 2020-03-04)
Field: Materials Science — Polymers and Plastics

First-principles characterization of single-electron polaron in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>WO</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math> Electronic transport properties in WO3 relate to polarons, which are hard to capture from first-principles simulations. The authors stabilize and characterize a self-trapped single polaron from density functional calculations with an hybrid functional confirming a 2D disk shape, which was deduced from experimental observations Polaron ; Characterization (materials science) ; Hybrid functional ; Electron ; Density functional theory ; Physics ; Quantum mechanics ; Optics Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors ; ZnO doping and properties
Authors: Quentin Wilmart | Mohamed Boukhicha | H. Graef | D. Mele | José M. Palomo | Michaël Rosticher | Takashi Taniguchi | Kenji Watanabe | Vincent Bouchiat | Emmanuel Baudin | Jean‐Marc Berroir | Erwann Bocquillon | Gwendal Fève | Emiliano Pallecchi | Bernard Plaçais
Source: Applied Sciences (article, 2020-01-08)
Field: Materials Science — Materials Chemistry

High-Frequency Limits of Graphene Field-Effect Transistors with Velocity Saturation The current understanding of physical principles governing electronic transport in graphene field effect transistors (GFETs) has reached a level where we can model quite accurately device operation and predict intrinsic frequency limits of performance. In this work, we use this knowledge to analyze DC and RF transport properties of bottom-gated graphene on boron nitride field effect transistors exhibiting pronounced velocity saturation by substrate hyperbolic phonon polariton scattering, including Dirac pinch-off effect. We predict and demonstrate a maximum oscillation frequency exceeding 20 GHz . We discuss the intrinsic 0.1 THz limit of GFETs and envision plasma resonance transistors as an alternative for sub-THz narrow-band detection. Graphene ; Transistor ; Saturation velocity ; Materials science ; Saturation (graph theory) ; Terahertz radiation ; Optoelectronics ; Field-effect transistor ; Phonon ; Condensed matter physics ; Physics ; Nanotechnology ; Quantum mechanics ; Voltage Graphene research and applications ; Carbon Nanotubes in Composites ; Molecular Junctions and Nanostructures
Authors: Meng Huang | Yupeng Ying | Baixin Liu | Yuzhen Lv | Bo Qi | Chengrong Li
Source: High Voltage (article, 2020-02-03)
Field: Materials Science — Materials Chemistry

Enhanced voltage distribution and breakdown strength performances of oil‐paper composite insulation by adding TiO <sub>2</sub> nanoparticles Abtsract Oil–paper composite insulation (OPCI) is used as the main insulation material in the converter transformer. However, the dielectric properties of insulating oil and oil‐impregnated paper are markedly mismatched under direct current (DC) voltage, resulting in an unbalanced distribution of voltage, which is considered to be a major cause of large insulation margin but frequent failures. Therefore, it is necessary to adjust the voltage distribution in OPCI to enhance its breakdown strength. In this work, surface‐treated TiO 2 nanoparticles were employed to modify OPCI. The polarisation current tests revealed that by changing the additive amount of nanoparticles, the polarisation intensities of both oil and oil‐impregnated paper could be adjusted. Based on this, a model taking relaxation polarisations into full consideration was established to analyse the voltage distribution in OPCI and predict the optimal concentration. The prepared OPCI with the concentration of 0.02 g/L has desired balanced voltage distribution due to the influence of nanoparticles on interface charge, and therefore high breakdown strengths under both DC voltage and polarity reversal voltage, which can be increased by 22.6% at most. This work puts forward a novel idea to improve insulation property of OPCI and provides a reference for broader application of nanomaterial in power equipment. Materials science ; Composite number ; Composite material ; Nanoparticle ; Voltage ; Breakdown voltage ; Nanotechnology ; Electrical engineering ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Islanding Detection in Power Systems
Authors: K. Mikami
Source: Polymer (article, 2020-07-13)
Field: Materials Science — Materials Chemistry

Interactive-quantum-chemical-descriptors enabling accurate prediction of an activation energy through machine learning Quantum chemical ; Molecule ; Supramolecular chemistry ; Intermolecular force ; Steric effects ; Quantum chemistry ; Polymer ; Chemistry ; Chemical reaction ; Computer science ; Computational chemistry ; Materials science ; Chemical physics ; Nanotechnology ; Biological system ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Arshad Ali | Jawad Ahmad | Ahsen Tahir | Brian Stewart | Azam Nekahi
Source: Energies (article, 2020-07-30)
Field: Materials Science — Materials Chemistry

Forecasting Flashover Parameters of Polymeric Insulators under Contaminated Conditions Using the Machine Learning Technique There is a vital need to understand the flashover process of polymeric insulators for safe and reliable power system operation. This paper provides a rigorous investigation of forecasting the flashover parameters of High Temperature Vulcanized (HTV) silicone rubber based on environmental and polluted conditions using machine learning. The modified solid layer method based on the IEC 60507 standard was utilised to prepare samples in the laboratory. The effect of various factors including Equivalent Salt Deposit Density (ESDD), Non-soluble Salt Deposit Density (NSDD), relative humidity and ambient temperature, were investigated on arc inception voltage, flashover voltage and surface resistance. The experimental results were utilised to engineer a machine learning based intelligent system for predicting the aforementioned flashover parameters. A number of machine learning algorithms such as Artificial Neural Network (ANN), Polynomial Support Vector Machine (PSVM), Gaussian SVM (GSVM), Decision Tree (DT) and Least-Squares Boosting Ensemble (LSBE) were explored in forecasting of the flashover parameters. The prediction accuracy of the model was validated with a number of error cost functions, such as Root Mean Squared Error (RMSE), Normalized RMSE (NRMSE), Mean Absolute Percentage Error (MAPE) and R. For improved prediction accuracy, bootstrapping was used to increase the sample space. The proposed PSVM technique demonstrated the best performance accuracy compared to other machine learning models. The presented machine learning model provides promising results and demonstrates highly accurate prediction of the arc inception voltage, flashover voltage and surface resistance of silicone rubber insulators in various contaminated and humid conditions. Mean squared error ; Machine learning ; Support vector machine ; Extreme learning machine ; Arc flash ; Artificial intelligence ; Mean absolute percentage error ; Engineering ; Algorithm ; Artificial neural network ; Voltage ; Mathematics ; Computer science ; Statistics ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Jaqueline A. Picache | Jody C. May | John A. McLean
Source: Analytical Chemistry (article, 2020-07-06)
Field: Chemistry — Spectroscopy

Chemical Class Prediction of Unknown Biomolecules Using Ion Mobility-Mass Spectrometry and Machine Learning: Supervised Inference of Feature Taxonomy from Ensemble Randomization This work presents a machine learning algorithm referred to as the supervised inference of feature taxonomy from ensemble randomization (SIFTER), which supports the identification of features derived from untargeted ion mobility-mass spectrometry (IM-MS) experiments. SIFTER utilizes random forest machine learning on three analytical measurements derived from IM-MS (collision cross section, CCS), mass-to-charge (<i>m</i>/<i>z</i>), and mass defect (Δ<i>m</i>) to classify unknown features into a taxonomy of chemical kingdom, super class, class, and subclass. Each of these classifications is assigned a calculated probability as well as alternate classifications with associated probabilities. After optimization, SIFTER was tested against a set of molecules not used in the training set. The average success rate in classifying all four taxonomy categories correctly was found to be >99%. Analysis of molecular features detected from a complex biological matrix and not used in the training set yielded a lower success rate where all four categories were correctly predicted for ∼80% of the compounds. This decline in performance is in part due to incompleteness of the training set across all potential taxonomic categories, but also resulting from a nearest-neighbor bias in the random forest algorithm. Ongoing efforts are focused on improving the class prediction accuracy of SIFTER through expansion of empirical data sets used for training as well as improvements to the core algorithm. Random forest ; Artificial intelligence ; Inference ; Machine learning ; Taxonomy (biology) ; Chemistry ; Pattern recognition (psychology) ; Set (abstract data type) ; Ensemble learning ; Class (philosophy) ; Test set ; Computer science Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Sunkyu Yu | Xianji Piao | Namkyoo Park
Source: Nature Communications (article, 2020-09-24)
Field: Materials Science — Materials Chemistry

Machine learning identifies scale-free properties in disordered materials The vast amount of design freedom in disordered systems expands the parameter space for signal processing. However, this large degree of freedom has hindered the deterministic design of disordered systems for target functionalities. Here, we employ a machine learning approach for predicting and designing wave-matter interactions in disordered structures, thereby identifying scale-free properties for waves. To abstract and map the features of wave behaviors and disordered structures, we develop disorder-to-localization and localization-to-disorder convolutional neural networks, each of which enables the instantaneous prediction of wave localization in disordered structures and the instantaneous generation of disordered structures from given localizations. We demonstrate that the structural properties of the network architectures lead to the identification of scale-free disordered structures having heavy-tailed distributions, thus achieving multiple orders of magnitude improvement in robustness to accidental defects. Our results verify the critical role of neural network structures in determining machine-learning-generated real-space structures and their defect immunity. Robustness (evolution) ; Convolutional neural network ; Artificial neural network ; Identification (biology) ; Degrees of freedom (physics and chemistry) ; Parameter space ; Deep learning Machine Learning in Materials Science ; Topological Materials and Phenomena ; Quantum many-body systems