jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xingyuan Zhang | Zhao Guo | Xiao Zhang | Linji Gong | Xinghua Dong | Yanyan Fu | Qing Wang | Zhanjun Gu
Source: Scientific Reports (article, 2019-03-26)
Field: Materials Science — Materials Chemistry

Mass production of poly(ethylene glycol) monooleate-modified core-shell structured upconversion nanoparticles for bio-imaging and photodynamic therapy Developing robust and high-efficient synthesis approaches has significant importance for the expanded applications of upconversion nanoparticles (UCNPs). Here, we report a high-throughput synthesis strategy to fabricate water-dispersible core-shell structured UCNPs. Firstly, we successfully obtain more than 10 grams core UCNPs with high quality from one-pot reaction using liquid rare-earth precursors. Afterwards, different core-shell structured UCNPs are fabricated by successive layer-by-layer strategy to get enhanced fluorescence property. Finally, the hydrophobic UCNPs are modified with poly(ethylene glycol) monooleate (PEG-OA) though a novel physical grinding method. On the basis of mass-production, we use the as-prepared PEG-UCNPs to construct an 808-nm stimuli photodynamic therapy agent, and apply them in cancer therapy and bio-imaging. Ethylene glycol ; Materials science ; Photodynamic therapy ; PEG ratio ; Nanoparticle ; Photon upconversion ; Core (optical fiber) ; Nanotechnology ; Chemical engineering ; Luminescence ; Chemistry ; Organic chemistry ; Optoelectronics ; Composite material Luminescence Properties of Advanced Materials ; Nanoplatforms for cancer theranostics ; Luminescence and Fluorescent Materials
Authors: Ali Ebrahimi‐Mamaghani | Seyed Hamed Mirtalebi | Mohammad Taghi Ahmadian
Source: Materials Research Express (article, 2019-10-14)
Field: Materials Science — Materials Chemistry

Magneto-mechanical stability of axially functionally graded supported nanotubes In this paper, size-dependent vibration analysis of axially functionally graded (AFG) supported nanotubes conveying nanoflow under longitudinal magnetic fields are performed, aiming at performance improvement of fluid-interaction nanosystems. Either the density or the elastic modulus of the AFG nanotube varies linearly or exponentially along the axial direction. Based on the nonlocal continuum theory, the higher-order dynamical equation of motion of the system is derived considering no-slip boundary condition. Galerkin discretization technique and eigenvalue analysis are implemented to solve the modeled equation. The validity of the simplified model is justified by comparing the results with findings currently available in the literature. Influence of material gradient, magnetic strength, and nonlocal parameter on the system's stability is illustrated. The results indicated that the elastic modulus gradient parameter can profoundly displace the instability threshold of the system and the effect of the density profile is negligible. Stability analysis showed that by fine-tuning of material gradation, the nonlocal effect can be significantly alleviated. Furthermore, it was shown that at high or low values of elastic gradient parameters, the stability borders are highly sensitive to magnetic field strength. Results of this paper can be applied as a benchmark in the optimal design of nanofluidic systems. Axial symmetry ; Galerkin method ; Mechanics ; Boundary value problem ; Materials science ; Magnetic field ; Vibration ; Instability ; Magnetic potential ; Modulus ; Classical mechanics ; Physics ; Mathematical analysis ; Mathematics ; Finite element method ; Composite material ; Thermodynamics ; Acoustics Nonlocal and gradient elasticity in micro/nano structures ; Numerical methods in engineering ; Composite Structure Analysis and Optimization
Authors: Gabriele Di Carlo | Alessio Orbelli Biroli | Maddalena Pizzotti | Francesca Tessore
Source: Frontiers in Chemistry (review, 2019-04-11)
Field: Materials Science — Materials Chemistry

Efficient Sunlight Harvesting by A4 β-Pyrrolic Substituted ZnII Porphyrins: A Mini-Review Dye-Sensitized Solar Cells (DSSCs) are a highly promising alternative to conventional photovoltaic silicon-based devices, due to the potential low cost and the interesting conversion efficiencies. A key-role is played by the dye, and porphyrin sensitizers have drawn great interest because of their excellent light harvesting properties mimicking photosynthesis. Indeed, porphyrins are characterized by strong electronic absorption bands in the visible region up to the near infrared and by long-lived π<sup>*</sup> singlet excited states. Moreover, the presence of four <i>meso</i> and eight β-pyrrolic positions allows a fine tuning of their photoelectrochemical properties through structural modification. <i>Trans</i>-A<sub>2</sub>BC push-pull Zn<sup>II</sup> porphyrins, characterized by a strong and directional electron excitation process along the push-pull system, have been extensively investigated. On the other hand, A<sub>4</sub> β-pyrrolic substituted tetraaryl Zn<sup>II</sup> porphyrins, which incorporate a tetraaryl porphyrinic core as a starting material, have received lower attention, even if they are synthetically more attractive and show several advantages such as a more sterically hindered architecture and enhanced solubility in most common organic solvents. The present contribution intends to review the most prominent A<sub>4</sub> β-substituted Zn<sup>II</sup> porphyrins reported in the literature so far for application in DSSCs, focusing on the strategies employed to enhance the light harvesting capability of the dye and on a comparison with <i>meso</i>-substituted analogs. Porphyrin ; Photochemistry ; Dye-sensitized solar cell ; Steric effects ; Absorption (acoustics) ; Materials science ; Chemistry ; Fluorescence ; Nanotechnology ; Organic chemistry Porphyrin and Phthalocyanine Chemistry ; TiO2 Photocatalysis and Solar Cells ; Advanced Photocatalysis Techniques
Authors: Francesca Oltolina | Donato Colangelo | Ivana Miletto | Nausicaa Clemente | Marta Miola | Enrica Verné | María Prat | Antonia Follenzi
Source: Nanomaterials (article, 2019-11-06)
Field: Materials Science — Biomaterials

Tumor Targeting by Monoclonal Antibody Functionalized Magnetic Nanoparticles Tumor-targeted drug-loaded nanocarriers represent innovative and attractive tools for cancer therapy. Several magnetic nanoparticles (MNPs) were analyzed as potential tumor-targeted drug-loaded nanocarriers after functionalization with anti-Met oncogene (anti-Met/HGFR) monoclonal antibody (mAb) and doxorubicin (DOXO). Their cytocompatibility, stability, immunocompetence (immunoprecipitation), and their interactions with cancer cells in vitro (Perl's staining, confocal microscopy, cytotoxic assays: MTT, real time toxicity) and with tumors in vivo (Perl's staining) were evaluated. The simplest silica- and calcium-free mAb-loaded MNPs were the most cytocompatible, the most stable, and showed the best immunocompetence and specificity. These mAb-functionalized MNPs specifically interacted with the surface of Met/HGFR-positive cells, and not with Met/HGFR-negative cells; they were not internalized, but they discharged in the targeted cells DOXO, which reached the nucleus, exerting cytotoxicity. The presence of mAbs on DOXO-MNPs significantly increased their cytotoxicity on Met/HGFR-positive cells, while no such effect was detectable on Met/HGFR-negative cells. Bare MNPs were biocompatible in vivo; mAb presence on MNPs induced a better dispersion within the tumor mass when injected in situ in Met/HGFR-positive xenotumors in NOD/SCID-γ<sup>null</sup> mice. These MNPs may represent a new and promising carrier for in vivo targeted drug delivery, in which applied gradient and alternating magnetic fields can enhance targeting and induce hyperthermia respectively. Monoclonal antibody ; Nanoparticle ; Magnetic nanoparticles ; Antibody ; Nanotechnology ; Materials science ; Virology ; Cancer research ; Medicine ; Immunology Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; RNA Interference and Gene Delivery
Authors: Praful R. Nair
Source: Polymers (review, 2019-04-05)
Field: Materials Science — Biomaterials

Delivering Combination Chemotherapies and Targeting Oncogenic Pathways via Polymeric Drug Delivery Systems The side-effects associated with chemotherapy necessitates better delivery of chemotherapeutics to the tumor. Nanoparticles can load higher amounts of drug and improve delivery to tumors, increasing the efficacy of treatment. Polymeric nanoparticles, in particular, have been used extensively for chemotherapeutic delivery. This review describes the efforts made to deliver combination chemotherapies and inhibit oncogenic pathways using polymeric drug delivery systems. Combinations of chemotherapeutics with other drugs or small interfering RNA (siRNA) combinations have been summarized. Special attention is given to the delivery of drug combinations that involve either paclitaxel or doxorubicin, two popular chemotherapeutics in clinic. Attempts to inhibit specific pathways for oncotherapy have also been described. These include inhibition of oncogenic pathways (including those involving HER2, EGFR, MAPK, PI3K/Akt, STAT3, and HIF-1α), augmentation of apoptosis by inhibiting anti-apoptosis proteins (Bcl-2, Bcl-xL, and survivin), and targeting dysregulated pathways such as Wnt/β-catenin and Hedgehog. Survivin ; Paclitaxel ; Drug delivery ; Doxorubicin ; PI3K/AKT/mTOR pathway ; Wnt signaling pathway ; Drug ; Small interfering RNA ; Pharmacology ; Apoptosis ; Cancer research ; Chemotherapy ; Signal transduction ; Chemistry ; Medicine ; RNA ; Internal medicine Nanoparticle-Based Drug Delivery ; HER2/EGFR in Cancer Research ; Cancer Research and Treatments
Authors: Paul Mathieu | Yannick Coppel | Marc Respaud | Quyen T. Nguyen | Sébastien Boutry | Sophie Laurent | Dimitri Stanicki | Céline Henoumont | Fernando Novio | Júlia Lorenzo | David Montpeyó | Catherine Amiens
Source: Molecules (article, 2019-12-17)
Field: Materials Science — Biomaterials

Silica Coated Iron/Iron Oxide Nanoparticles as a Nano-Platform for T2 Weighted Magnetic Resonance Imaging The growing concern over the toxicity of Gd-based contrast agents used in magnetic resonance imaging (MRI) motivates the search for less toxic and more effective alternatives. Among these alternatives, iron-iron oxide (Fe@FeOx) core-shell architectures have been long recognized as promising MRI contrast agents while limited information on their engineering is available. Here we report the synthesis of 10 nm large Fe@FeOx nanoparticles, their coating with a 11 nm thick layer of dense silica and functionalization by 5 kDa PEG chains to improve their biocompatibility. The nanomaterials obtained have been characterized by a set of complementary techniques such as infra-red and nuclear magnetic resonance spectroscopies, transmission electron microscopy, dynamic light scattering and zetametry, and magnetometry. They display hydrodynamic diameters in the 100 nm range, zetapotential values around -30 mV, and magnetization values higher than the reference contrast agent RESOVIST<sup>®</sup>. They display no cytotoxicity against 1BR3G and HCT116 cell lines and no hemolytic activity against human red blood cells. Their nuclear magnetic relaxation dispersion (NMRD) profiles are typical for nanomaterials of this size and magnetization. They display high r<sub>2</sub> relaxivity values and low r<sub>1</sub> leading to enhanced r<sub>2</sub>/r<sub>1</sub> ratios in comparison with RESOVIST<sup>®</sup>. All these data make them promising contrast agents to detect early stage tumors. Nanomaterials ; Iron oxide ; Biocompatibility ; MRI contrast agent ; Materials science ; Nanoparticle ; Iron oxide nanoparticles ; Magnetization ; Nuclear magnetic resonance ; Transmission electron microscopy ; Dynamic light scattering ; Nanotechnology ; Magnetic nanoparticles ; Magnetic resonance imaging ; Nanomedicine ; Magnetic field ; Metallurgy Nanoparticle-Based Drug Delivery ; Iron oxide chemistry and applications ; Characterization and Applications of Magnetic Nanoparticles
Authors: Qiushan Yu | Su Yuchang | Rabigul Tursun | Jing Zhang
Source: RSC Advances (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Synthesis and characterization of low density porous nickel zinc ferrites Ni-Zn ferrite has important applications in the field of soft magnetic materials due to its excellent magnetic properties, but its high bulk density hinders its promotion. Herein, an oxalate precursor was prepared by a coprecipitation method with metal sulfate and oxalic acid as raw materials. The low density porous Ni-Zn ferrite powder was prepared by thermal decomposition in an aerobic environment with the oxalate precursor. The microstructure, morphology, and dielectric and magnetic properties of Ni-Zn ferrite were studied by thermogravimetric and differential scanning calorimetry, X-ray powder diffraction, X-ray photoelectron spectroscopy, Fourier transform-infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, tap density testing for powder, vibrating sample magnetometry, specific surface and aperture analysis and vector network analysis. The results showed that the purity, morphology, grain size and saturation magnetization of Ni-Zn ferrite were controlled by many factors such as synthetic temperature, retaining time and environmental conditions. Under an oxygen atmosphere, pure Ni-Zn ferrite can be prepared from an oxalate precursor by a thermal process. The ferrite has a wood-splitting appearance and a multi-layered internal cavity structure, and the bulk density is only 1/3 of the general ferrite. It has good soft magnetic and microwave absorbing properties, which makes it a potential excellent material for microwave absorbers. Characterization (materials science) ; Zinc ; Nickel ; Porosity ; Materials science ; Chemical engineering ; Nanotechnology ; Metallurgy ; Composite material Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Copper-based nanomaterials and applications
Authors: Philippe Schwaller | Daniel Probst | Alain C. Vaucher | Vishnu H Nair | David Kreutter | Teodoro Laino | Jean‐Louis Reymond
Source: ChemRxiv (preprint, 2019-09-27)
Field: Materials Science — Materials Chemistry

Mapping the Space of Chemical Reactions using Attention-Based Neural Networks &lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;Organic reactions are usually assigned to classes grouping reactions with similar reagents and mechanisms. Reaction classes facilitate communication of complex concepts and efficient navigation through chemical reaction space. However, the classification process is a tedious task, requiring the identification of the corresponding reaction class template via annotation of the number of molecules in the reactions, the reaction center and the distinction between reactants and reagents. In this work, we show that transformer-based models can infer reaction classes from non-annotated, simple text-based representations of chemical reactions. Our best model reaches a classification accuracy of 98.2%. We also show that the learned representations can be used as reaction fingerprints which capture fine-grained differences between reaction classes better than traditional reaction fingerprints. The unprecedented insights into chemical reaction space enabled by our learned fingerprints is illustrated by an interactive reaction atlas providing visual clustering and similarity searching. &lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Code: https://github.com/rxn4chemistry/rxnfp&lt;/p&gt;&lt;p&gt;Tutorials: https://rxn4chemistry.github.io/rxnfp/&lt;/p&gt;&lt;p&gt;Interactive reaction atlas: https://rxn4chemistry.github.io/rxnfp//tmaps/tmap_ft_10k.html&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt; Computer science ; Reagent ; Chemical space ; Cluster analysis ; Artificial intelligence ; Computational biology ; Chemistry ; Biology ; Biochemistry Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Advanced Text Analysis Techniques
Authors: Orlando Trejo | Anup L. Dadlani | Francisco De La Paz | Shinjita Acharya | Rob Kravec | Dennis Nordlund | Ritimukta Sarangi | Fritz B. Prinz | Jan Torgersen | Neil P. Dasgupta
Source: Chemistry of Materials (article, 2019-11-04)
Field: Materials Science — Materials Chemistry

Elucidating the Evolving Atomic Structure in Atomic Layer Deposition Reactions with in Situ XANES and Machine Learning Precision synthesis of thin films requires an improved mechanistic understanding of the structural evolution of materials at the atomic scale. Atomic layer deposition (ALD) is a critical nanofabrication technique that enables fine-tuning of atomic structure and thickness as a result of its layer-by-layer growth behavior. In this study, in situ X-ray absorption spectra of the S K-edge during ALD growth of ZnS thin films on TiO2 nanoparticles were collected and analyzed. The experimental results show that both sulfide and sulfate species form during the nucleation phase of ZnS on TiO2. As film growth proceeds, the S K-edge of the in situ ZnS film converges to that of a representative ex situ ALD ZnS film. By building representative atomistic models, a high-throughput screening method was developed to determine the most probable atomic configurations as the film structure evolves. The screening method consisted of a supervised machine learning analysis of thousands of simulated X-ray absorption near edge structure (XANES) spectra. Atomic-level insight was gained into changes in the coordination environment of surface species as they transitioned from the nucleation phase toward the crystalline ZnS phase. The experimental and computational strategies presented herein provide an example of how in situ synchrotron-based characterization can be leveraged using robust modeling approaches to elucidate the ordering of atoms during thin-film growth. XANES ; Atomic layer deposition ; Nucleation ; Thin film ; Atomic units ; Materials science ; In situ ; Deposition (geology) ; Nanoparticle ; Nanotechnology ; Characterization (materials science) ; Layer (electronics) ; Absorption (acoustics) ; Synchrotron ; Phase (matter) ; Spectral line ; Chemistry ; Optics ; Physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Nicholas E. Jackson | Alec Bowen | Juan Pablo
Source: Macromolecules (article, 2019-12-19)
Field: Materials Science — Materials Chemistry

Efficient Multiscale Optoelectronic Prediction for Conjugated Polymers Conjugated polymers represent a high-potential material class due to the tunability of their optoelectronic properties via straightforward processing protocols. However, to in silico tailor these optoelectronic properties, one must compute the conformationally dependent electronic structure over mesoscopic length and time scales. This task represents a challenging multiscale computational problem in which quantum chemistry and atomistic or coarse-grained molecular dynamics must be integrated. Recently, we introduced a methodology, called electronic coarse graining (ECG), that utilizes artificial neural networks to compute ab initio quality electronic structures using only the reduced degrees of freedom of coarse-grained models. Here, we adapt ECG to variable-molecular weight conjugated oligomers by casting the problem in the framework of sequence detection. A machine-learning (ML) architecture employing one-dimensional convolutional neural networks and long short-term memory networks is utilized to compute ground-state orbital energies, charge density distributions, and optical spectra solely from the coarse-grained model’s configurational degrees of freedom. Robust molecular weight transferability for ECG is established via a Δ-ML approach that leverages model electronic Hamiltonians for ground and excited-state property determination. The accuracy and transferability of the ECG methodology opens the door for scalable optoelectronic property prediction in conjugated polymers directly from coarse-grained degrees of freedom. Mesoscopic physics ; Computer science ; Degrees of freedom (physics and chemistry) ; Convolutional neural network ; Conjugated system ; Electronic structure ; Granularity ; Artificial neural network ; Materials science ; Statistical physics ; Polymer ; Artificial intelligence ; Chemistry ; Physics ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: Sebastian Dick | Mariví Fernández-Serra
Source: The Journal of Chemical Physics (article, 2019-10-08)
Field: Materials Science — Materials Chemistry

Learning from the density to correct total energy and forces in first principle simulations We propose a new molecular simulation framework that combines the transferability, robustness, and chemical flexibility of an ab initio method with the accuracy and efficiency of a machine learning model. The key to achieve this mix is to use a standard density functional theory (DFT) simulation as a preprocessor for the atomic and molecular information, obtaining a good quality electronic density. General, symmetry preserving, atom-centered electronic descriptors are then built from this density to train a neural network to correct the baseline DFT energies and forces. These electronic descriptors encode much more information than local atomic environments, allowing a simple neural network to reach the accuracy required for the problem of study at a negligible additional cost. The balance between accuracy and efficiency is determined by the baseline simulation. This is shown in results where high level quantum chemical accuracy is obtained for simulations of liquid water at standard DFT cost or where high level DFT-accuracy is achieved in simulations with a low-level baseline DFT calculation at a significantly reduced cost. Density functional theory ; Computer science ; Robustness (evolution) ; Artificial neural network ; Ab initio ; Electronic density ; Molecular dynamics ; Artificial intelligence ; Algorithm ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Teruyasu Mizoguchi | Shin Kiyohara
Source: Microscopy (article, 2019-11-11)
Field: Materials Science — Materials Chemistry

Machine learning approaches for ELNES/XANES Materials characterization is indispensable for materials development. In particular, spectroscopy provides atomic configuration, chemical bonding and vibrational information, which are crucial for understanding the mechanism underlying the functions of a material. Despite its importance, the interpretation of spectra using human-driven methods, such as manual comparison of experimental spectra with reference/simulated spectra, is becoming difficult owing to the rapid increase in experimental spectral data. To overcome the limitations of such methods, we develop new data-driven approaches based on machine learning. Specifically, we use hierarchical clustering, a decision tree and a feedforward neural network to investigate the electron energy loss near edge structures (ELNES) spectrum, which is identical to the X-ray absorption near edge structure (XANES) spectrum. Hierarchical clustering and the decision tree are used to interpret and predict ELNES/XANES, while the feedforward neural network is used to obtain hidden information about the material structure and properties from the spectra. Further, we construct a prediction model that is robust against noise by data augmentation. Finally, we apply our method to noisy spectra and predict six properties accurately. In summary, the proposed approaches can pave the way for fast and accurate spectrum interpretation/prediction as well as local measurement of material functions. XANES ; Computer science ; Artificial neural network ; Spectral line ; Cluster analysis ; Artificial intelligence ; Biological system ; Pattern recognition (psychology) ; Machine learning ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography