jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yunlong Wang | Zhongbing Liu | Ting Li | Lin Chen | Jiayao Lyu | Chunhong Li | Yan Lin | Na Hao | Meiling Zhou | Zhirong Zhong
Source: Theranostics (article, 2019-01-01)
Field: Materials Science — Biomaterials

Enhanced Therapeutic Effect of RGD-Modified Polymeric Micelles Loaded With Low-Dose Methotrexate and Nimesulide on Rheumatoid Arthritis Angiogenesis plays an essential role in the progression of rheumatoid arthritis (RA). RGD peptide shows high affinity and selectivity for integrin α<sub>v</sub>β<sub>3,</sub> which is one of the most extensively examined target of angiogenesis. Nimesulide could improve the anti-rheumatic profile of methotrexate. But the clinical application was limited due to water-insolubility of both methotrexate and nimesulide and lacking targeting ability. Therefore, this study aimed to design a targeted drug delivery system of micelles mediated by RGD plus the passive targeting of micelles to solve the application problems of methotrexate and nimesulide (M/N), and thus enhance their combined therapeutic effect on RA. <b>Methods:</b> RGD was conjugated with NHS-PEG-PLA to form RGD-PEG-PLA for the preparation of RGD-modified drug-loaded micelles (R-M/N-PMs). The size and zeta potential of micelles were measured by dynamic light scattering. Morphology was detected by transmission electron microscopy. The inhibition effect of R-M/N-PMs on angiogenesis was assessed by the chick chorioallantoic membrane assay. The real-time fluorescence imaging analysis was conducted to examine the <i>in vivo</i> distribution of the fluorescence-labeled R-M/N-PMs. Rats arthritis model induced by Freund's adjuvant was used to evaluate the <i>in vivo</i> anti-inflammatory efficacy of R-M/N-PMs. <b>Results:</b> The <i>in vitro</i> study indicated successful development of R-M/N-PMs. R-M/N-PMs could markedly suppress the angiogenesis of chick embryos. The fluorescence-labeled R-M/N-PMs mainly accumulated in arthritic joints. RGD enhanced the targeting ability of micelles and thus promoted retention of micelles in arthritic joints. Moreover, R-M/N-PMs significantly alleviated the joint swelling while reducing bone erosion and serum levels of inflammatory cytokines. It helped to recover the bone microstructure of arthritic rats. <b>Conclusion:</b> Our results confirmed that the targeted delivery of the combination of a low dose of methotrexate and nimesulide mediated by RGD-modified polymeric micelles could enhance the therapeutic effect on rheumatoid arthritis. These findings provide a promising potential for the clinical therapy of rheumatoid arthritis. Nimesulide ; Rheumatoid arthritis ; Methotrexate ; Pharmacology ; Medicine ; Therapeutic effect ; Chemistry ; Internal medicine Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Advancements in Transdermal Drug Delivery
Authors: Samer Gnaim | Anna Scomparin | Anat Eldar‐Boock | Christoph Bauer | Ronit Satchi‐Fainaro | Doron Shabat
Source: Chemical Science (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Light emission enhancement by supramolecular complexation of chemiluminescence probes designed for bioimaging Chemiluminescence offers advantages over fluorescence for bioimaging, since an external light source is unnecessary with chemiluminescent agents. This report demonstrates the first encapsulation of chemiluminescence phenoxy-adamantyl-1,2-dioxetane probes with trimethyl β-cyclodextrin. Clear proof for the formation of a 1 : 1 host-guest complex between the adamantyl-1,2-dioxetane probe and trimethyl β-cyclodextrin was provided by mass spectroscopy and NMR experiments. The calculated association constant of this host-guest system, 253 M<sup>-1</sup>, indicates the formation of a stable inclusion complex. The inclusion complex significantly amplified the light emission intensity relative to the noncomplexed probe under physiological conditions. Complexation of adamantyl-dioxetane with fluorogenic dye-tethered cyclodextrin resulted in light emission through energy transfer to a wavelength that corresponds to the fluorescent emission of the conjugated dye. Remarkably, the light emission intensity of this inclusion complex was approximately 1500-fold higher than that of the non-complexed adamantyl-dioxetane guest. We present the first demonstration of microscopic cell images obtained using a chemiluminescence supramolecular dioxetane probe and demonstrate the utility of these supramolecular complexes by imaging of enzymatic activity and bio-analytes <i>in vitro</i> and <i>in vivo</i>. We anticipate that the described chemiluminescence supramolecular dioxetane probes will find use in various biological applications. Chemiluminescence ; Supramolecular chemistry ; Nanotechnology ; Combinatorial chemistry ; Aggregation-induced emission ; Chemistry ; Photochemistry ; Materials science ; Chromatography ; Fluorescence ; Organic chemistry ; Molecule ; Optics ; Physics Luminescence and Fluorescent Materials ; Supramolecular Self-Assembly in Materials ; Molecular Sensors and Ion Detection
Authors: William A. Ogden | Zhibin Guan
Source: ChemSystemsChem (article, 2019-11-27)
Field: Materials Science — Biomaterials

Redox Chemical‐Fueled Dissipative Self‐Assembly of Active Materials Abstract This work reports a new transient self‐assembly of active materials fueled solely by a mild redox reaction network. Fuel‐driven dissipative, out‐of‐equilibrium assemblies are common in living systems but are challenging to mimic synthetically. Synthetic dissipative assembly systems developed thus far often use relatively toxic fuels and harsh conditions. Herein we report a transient, out‐of‐equilibrium self‐assembly system based on a new chemical reaction network using redox reactions. We use a mild redox chemical reaction network to simultaneously create and destroy a disulfide‐based hydrogelator, leading to transient, dynamic behavior. By closely regulating the reaction kinetics and reagent composition, the macroscopic properties of the active materials are fine‐tuned. The current work represents an important step toward the development of active materials operating under mild conditions. Dissipative system ; Redox ; Reagent ; Work (physics) ; Transient (computer programming) ; Chemical reaction ; Kinetics ; Chemical kinetics ; Chemistry ; Chemical engineering ; Materials science ; Nanotechnology ; Computer science ; Thermodynamics ; Organic chemistry ; Physics ; Engineering Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Micro and Nano Robotics
Authors: Jianling Chen | Xiaojie Chen | Qiuyi Huang | Wenlang Li | Qiaoxi Yu | Longji Zhu | Tianwen Zhu | Siwei Liu | Zhenguo Chi
Source: ACS Applied Materials & Interfaces (article, 2019-08-20)
Field: Materials Science — Materials Chemistry

Amphiphilic Polymer-Mediated Aggregation-Induced Emission Nanoparticles for Highly Sensitive Organophosphorus Pesticide Biosensing Biosensing applications require signal reporters to be sufficiently stable and biosafe as well as highly efficient. Aggregation-induced emission (AIE) nanoparticles have proven to be capable of cell-imaging and cancer therapy; however, realizing sensitive detection of biomolecules remains a great challenge because of their instability, biotoxicity, and lack of modifiable functional groups. Herein, we report a self-assembling strategy to fabricate AIE nanoparticles (PTDNPs) through the dispersion of amphiphilic polymers (PTDs) in phosphate-buffered saline. The PTDs were prepared through radical copolymerization of <i>N</i>-(1,2,2-triphenylvinyl)-4-acetylaniline and dimethyl diallyl ammonium chloride. We found that the particle size, morphology, functional groups, and fluorescence property of PTDNPs can be fine-tuned. Further, PTDNPs-0.10 were chosen as signal reporters to detect organophosphorus pesticides (OPs) with the aid of gold nanoparticles. Their sensing performance on OPs is superior to that using C-dot/quantum dot/rhodamine B as the signal reporter. This study not only provides new possibilities to fabricate novel AIE nanoparticles with exceptional properties, but also facilitates the AIE nanoparticle's application for target analyte biosensing. Materials science ; Biosensor ; Nanoparticle ; Nanotechnology ; Biomolecule ; Amphiphile ; Polymer ; Fluorescence ; Quantum dot ; Copolymer Luminescence and Fluorescent Materials ; Advanced biosensing and bioanalysis techniques ; Molecular Sensors and Ion Detection
Authors: Qian Xiong | Shunxi Dong | Yushuang Chen | Xiaohua Liu | Xiaoming Feng
Source: Nature Communications (article, 2019-05-09)
Field: Chemistry — Organic Chemistry

Asymmetric synthesis of tetrazole and dihydroisoquinoline derivatives by isocyanide-based multicomponent reactions Although isocyanide-based multicomponent reactions were proven to be simple, elegant and facile strategies for the synthesis of highly valuable nitrogen-containing heterocycles, their asymmetric versions accessing to optically active nitrogen heterocyclic compounds are rather limited. Here, we illustrate that, relying on the enantioselective addition of simple isocyanides to C=C bonds, several isocyanide-based multicomponent reactions are realized in the presence of a chiral Mg<sup>II</sup>-N,N'-dioxide catalyst. In the reaction among isocyanide, TMSN<sub>3</sub>, and alkylidene malonate, three- or four-component reactions could be precisely controlled by modulating reaction conditions, supplying two types of enantioenriched tetrazole-derivatives in moderate to high yields. Possible catalytic cycles via a key zwitterionic intermediate, and the vital roles of H<sub>2</sub>O or excess ligand are provided based on control experiments. Moreover, taking advantage of this zwitterionic intermediate as a 1,3-dipole, an enantioselective dearomative [3+2] annulation reaction of nonactivated isoquinolines is achieved, furnishing chiral 1,2-dihydroisoquinolines in good to excellent results. Isocyanide ; Enantioselective synthesis ; Tetrazole ; Combinatorial chemistry ; Chemistry ; Catalysis ; Annulation ; Ligand (biochemistry) ; Malonate ; Organic chemistry Multicomponent Synthesis of Heterocycles ; Synthesis of Tetrazole Derivatives ; Quinazolinone synthesis and applications
Authors: Shashank Balasubramanyam | Mahdi Shirazi | Matthew A. Bloodgood | Longfei Wu | Marcel A. Verheijen | Vincent Vandalon | W. M. M. Kessels | Jan P. Hofmann | Ageeth A. Bol
Source: Chemistry of Materials (article, 2019-06-25)
Field: Materials Science — Materials Chemistry

Edge-Site Nanoengineering of WS<sub>2</sub> by Low-Temperature Plasma-Enhanced Atomic Layer Deposition for Electrocatalytic Hydrogen Evolution Edge-enriched transition metal dichalcogenides, such as WS<sub>2</sub>, are promising electrocatalysts for sustainable production of H<sub>2</sub> through the electrochemical hydrogen evolution reaction (HER). The reliable and controlled growth of such edge-enriched electrocatalysts at low temperatures has, however, remained elusive. In this work, we demonstrate how plasma-enhanced atomic layer deposition (PEALD) can be used as a new approach to nanoengineer and enhance the HER performance of WS<sub>2</sub> by maximizing the density of reactive edge sites at a low temperature of 300 °C. By altering the plasma gas composition from H<sub>2</sub>S to H<sub>2</sub> + H<sub>2</sub>S during PEALD, we could precisely control the morphology and composition and, consequently, the edge-site density as well as chemistry in our WS<sub>2</sub> films. The precise control over edge-site density was verified by evaluating the number of exposed edge sites using electrochemical copper underpotential depositions. Subsequently, we demonstrate the HER performance of the edge-enriched WS<sub>2</sub> electrocatalyst, and a clear correlation among plasma conditions, edge-site density, and the HER performance is obtained. Additionally, using density functional theory calculations we provide insights and explain how the addition of H<sub>2</sub> to the H<sub>2</sub>S plasma impacts the PEALD growth behavior and, consequently, the material properties, when compared to only H<sub>2</sub>S plasma. Atomic layer deposition ; Electrocatalyst ; Materials science ; Plasma ; Enhanced Data Rates for GSM Evolution ; Density functional theory ; Hydrogen ; Deposition (geology) ; Electrochemistry ; Chemical engineering ; Nanotechnology ; Chemical physics ; Layer (electronics) ; Chemistry ; Electrode ; Physical chemistry ; Computational chemistry ; Computer science ; Physics MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Špela Zupančič
Source: Acta Pharmaceutica (review, 2019-03-28)
Field: Materials Science — Biomaterials

Core-shell nanofibers as drug delivery systems Core-shell nanofibers have grown in popularity over the last decade owing to their special features and their many applications in biomedicine. They can be produced by electrospinning of immiscible polymer blends or emulsions through a single nozzle or by electrospinning using a coaxial nozzle. Several of the electrospinning parameters allow great versatility for the compositions and diameters of core-shell nanofibers to be produced. Morphology of core-shell nanofibers can be investigated using transmission electron microscopy and, in some cases, scanning electron microscopy. Several studies have shown that core-shell nanofibers have some advantages over monolithic nanofibers, such as better drug, protein, gene or probiotic incorporation into the nanofibers, greater control over drug release, and maintenance of protein structure and activity during electrospinning. We herein review the production and characterization of core-shell nanofibers, the critical parameters that affect their development, and their advantages as delivery systems. Nanofiber ; Electrospinning ; Materials science ; Nanotechnology ; Drug delivery ; Polyaniline nanofibers ; Scanning electron microscope ; Polymer ; Composite material Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics ; Advanced Sensor and Energy Harvesting Materials
Authors: Rahul Prasanna Misra | J. Pedro de Souza | Daniel Blankschtein | Martin Z. Bazant
Source: Langmuir (article, 2019-07-16)
Field: Chemistry — Physical and Theoretical Chemistry

Theory of Surface Forces in Multivalent Electrolytes Aqueous electrolyte solutions containing multivalent ions exhibit various intriguing properties, including attraction between like-charged colloidal particles, which results from strong ion-ion correlations. In contrast, the classical Derjaguin-Landau-Verwey-Overbeek theory of colloidal stability, based on the Poisson-Boltzmann mean-field theory, always predicts a repulsive electrostatic contribution to the disjoining pressure. Here, we formulate a general theory of surface forces, which predicts that the contribution to the disjoining pressure resulting from ion-ion correlations is always attractive and can readily dominate over entropic-induced repulsions for solutions containing multivalent ions, leading to the phenomenon of like-charge attraction. Ion-specific short-range hydration interactions, as well as surface charge regulation, are shown to play an important role at smaller separation distances but do not fundamentally change these trends. The theory is able to predict the experimentally observed strong cohesive forces reported in cement pastes, which result from strong ion-ion correlations involving the divalent calcium ion. Electrolyte ; Surface force ; Surface (topology) ; Chemistry ; Chemical engineering ; Nanotechnology ; Materials science ; Chemical physics ; Mechanics ; Physics ; Physical chemistry ; Electrode ; Engineering ; Mathematics ; Geometry Electrostatics and Colloid Interactions ; Chemical and Physical Properties in Aqueous Solutions ; Force Microscopy Techniques and Applications
Authors: Florian Bachmann | Hrishikesh Bale | Nicolas Guéninchault | Christian Holzner | E.M. Lauridsen
Source: Journal of Applied Crystallography (article, 2019-05-31)
Field: Materials Science — Materials Chemistry

3D grain reconstruction from laboratory diffraction contrast tomography A method for reconstructing the three-dimensional grain structure from data collected with a recently introduced laboratory-based X-ray diffraction contrast tomography system is presented. Diffraction contrast patterns are recorded in Laue-focusing geometry. The diffraction geometry exposes shape information within recorded diffraction spots. In order to yield the three-dimensional crystallographic microstructure, diffraction spots are extracted and fed into a reconstruction scheme. The scheme successively traverses and refines solution space until a reasonable reconstruction is reached. This unique reconstruction approach produces results efficiently and fast for well suited samples. Diffraction ; Diffraction tomography ; Tomography ; Contrast (vision) ; Optics ; Materials science ; X-ray crystallography ; 3D reconstruction ; Iterative reconstruction ; Geometry ; Physics ; Crystallography ; Computer science ; Mathematics ; Computer vision ; Chemistry Microstructure and mechanical properties ; Advanced X-ray Imaging Techniques ; X-ray Diffraction in Crystallography
Authors: Annika Stuke | Milica Todorović | Matthias Rupp | Christian Kunkel | Kunal Ghosh | Lauri Himanen | Patrick Rinke
Source: The Journal of Chemical Physics (article, 2019-05-28)
Field: Materials Science — Materials Chemistry

Chemical diversity in molecular orbital energy predictions with kernel ridge regression Instant machine learning predictions of molecular properties are desirable for materials design, but the predictive power of the methodology is mainly tested on well-known benchmark datasets. Here, we investigate the performance of machine learning with kernel ridge regression (KRR) for the prediction of molecular orbital energies on three large datasets: the standard QM9 small organic molecules set, amino acid and dipeptide conformers, and organic crystal-forming molecules extracted from the Cambridge Structural Database. We focus on the prediction of highest occupied molecular orbital (HOMO) energies, computed at the density-functional level of theory. Two different representations that encode the molecular structure are compared: the Coulomb matrix (CM) and the many-body tensor representation (MBTR). We find that KRR performance depends significantly on the chemistry of the underlying dataset and that the MBTR is superior to the CM, predicting HOMO energies with a mean absolute error as low as 0.09 eV. To demonstrate the power of our machine learning method, we apply our model to structures of 10k previously unseen molecules. We gain instant energy predictions that allow us to identify interesting molecules for future applications. Benchmark (surveying) ; Kernel (algebra) ; Molecular orbital ; Ridge ; Representation (politics) ; Molecule ; Regression ; Energy (signal processing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Physical and Chemical Molecular Interactions
Authors: Xiangyu Zhang | Kexin Zhang | Yongjin Lee
Source: ACS Applied Materials & Interfaces (article, 2019-12-10)
Field: Chemistry — Inorganic Chemistry

Machine Learning Enabled Tailor-Made Design of Application-Specific Metal–Organic Frameworks In the development of advanced nanoporous materials, one clear and unavoidable challenge in hand is the sheer size (in principle, infinite) of the materials space to be explored. While high-throughput screening techniques allow us to narrow down the enormous-scale database of nanoporous materials, there are still practical limitations stemming from a costly molecular simulation in estimating a material's performance and the necessity of a sophisticated descriptor identifying materials. With an attempt to transition away from the screening-based approaches, this paper presents a computational approach combining the Monte Carlo tree search and recurrent neural networks for the tailor-made design of metal-organic frameworks toward the desired target applications. In the demonstration cases for methane-storage and carbon-capture applications, our approach showed significant efficiency in designing promising and novel metal-organic frameworks. We expect that this approach would easily be extended to other applications by simply adjusting the reward function according to the target performance property. Materials science ; Metal-organic framework ; Nanotechnology ; Systems engineering ; Engineering ; Adsorption ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Hao Wang | Xun Guo | Linfeng Zhang | Handong Wang | Jianming Xue
Source: Applied Physics Letters (article, 2019-06-17)
Field: Materials Science — Materials Chemistry

Deep learning inter-atomic potential model for accurate irradiation damage simulations We propose a hybrid scheme that smoothly interpolates the Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential with a deep learning potential energy model. The resulting deep potential-ZBL model can not only provide overall good performance on the predictions of near-equilibrium material properties but also capture the right physics when atoms are extremely close to each other, an event that frequently happens in computational simulations of irradiation damage events. We applied this scheme to the simulation of the irradiation damage processes in the face-centered-cubic aluminum system and found better descriptions in terms of the defect formation energy, evolution of collision cascades, displacement threshold energy, and residual point defects than the widely adopted ZBL modified embedded atom method potentials and their variants. Our work provides a reliable and feasible scheme to accurately simulate the irradiation damage processes and opens up extra opportunities to solve the predicament of lacking accurate potentials for enormous recently discovered materials in the irradiation effect field. Irradiation ; Displacement (psychology) ; Molecular dynamics ; Deep learning ; Atom (system on chip) ; Materials science ; Work (physics) ; Crystallographic defect ; Statistical physics ; Residual ; Computer science ; Physics ; Algorithm ; Artificial intelligence ; Nuclear physics ; Condensed matter physics ; Thermodynamics ; Parallel computing ; Quantum mechanics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear reactor physics and engineering