jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stephan Seifert
Source: Scientific Reports (article, 2020-03-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Application of random forest based approaches to surface-enhanced Raman scattering data Surface-enhanced Raman scattering (SERS) is a valuable analytical technique for the analysis of biological samples. However, due to the nature of SERS it is often challenging to exploit the generated data to obtain the desired information when no reporter or label molecules are used. Here, the suitability of random forest based approaches is evaluated using SERS data generated by a simulation framework that is also presented. More specifically, it is demonstrated that important SERS signals can be identified, the relevance of predefined spectral groups can be evaluated, and the relations of different SERS signals can be analyzed. It is shown that for the selection of important SERS signals Boruta and surrogate minimal depth (SMD) and for the analysis of spectral groups the competing method Learner of Functional Enrichment (LeFE) should be applied. In general, this investigation demonstrates that the combination of random forest approaches and SERS data is very promising for sophisticated analysis of complex biological samples. Random forest ; Computer science ; Raman scattering ; Exploit ; Relevance (law) ; Biological system ; Artificial intelligence ; Data mining ; Pattern recognition (psychology) ; Raman spectroscopy ; Remote sensing ; Physics ; Optics ; Biology ; Geology Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Spectroscopy and Chemometric Analyses
Authors: Xue‐Cong An | Zhang-Min Li | Yan Zhou | Wenshuai Zhu | Duan‐Jian Tao
Source: Chemical Engineering Journal (article, 2020-03-24)
Field: Materials Science — Materials Chemistry

Rapid capture and efficient removal of low-concentration SO2 in simulated flue gas by hypercrosslinked hollow nanotube ionic polymers Flue gas ; Adsorption ; Desorption ; Diffusion ; Selectivity ; Nanotube ; Chemical engineering ; Polymer ; Ionic bonding ; Chemistry ; Work (physics) ; Materials science ; Carbon nanotube ; Nanotechnology ; Organic chemistry ; Thermodynamics ; Ion ; Catalysis Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Zhiyun Zhang | Guangchen Sun | Wei Chen | Jianhua Su | He Tian
Source: Chemical Science (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

The endeavor of vibration-induced emission (VIE) for dynamic emissions Organic chromophores with large Stokes shifts and dual emissions are fascinating because of their fundamental and applied interest. Vibration-induced emission (VIE) refers to a tunable multiple fluorescence exhibited by saddle-shaped <i>N</i>,<i>N</i>'-disubstituted-dihydribenzo[<i>a</i>,<i>c</i>]phenazines (DHPs), which involves photo-induced configuration vibrations from bent to planar form along the N-N axis. VIE-active molecules show intrinsic long-wavelength emissions in the unconstrained state (planar state) but bright short-wavelength emissions in the constrained state (bent state). The emission response for VIE-active luminogens is highly sensitive to steric hindrance encountered during the planarization process such that a tiny structural variation can induce an evident change in fluorescence. This can often be achieved by tuning the intensity ratio of short- and long-wavelength bands. In some special cases, the alterations in the emission wavelength of VIE fluorophores can be achieved step by step by harnessing the degree of bending angle motion in the excited state. In this perspective, we summarize the latest progress in the field of VIE research. New bent heterocyclic structures, as novel types of VIE molecules, are being developed, and the general features of the chemical structures are also being proposed. Technologically, novel emission color-tuning approaches and VIE-based probes for visualizing biological activity are presented to demonstrate how the dynamic VIE effect can be exploited for cutting-edge applications. Vibration ; Physics ; Acoustics Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Polydiacetylene-based materials and applications
Authors: Jeremy Nugent | Bethany R. Shire | Dimitri F. J. Caputo | Helena D. Pickford | Frank Nightingale | Ian T. T. Houlsby | James J. Mousseau | Edward A. Anderson
Source: Angewandte Chemie International Edition (article, 2020-04-28)
Field: Chemistry — Organic Chemistry

Synthesis of All‐Carbon Disubstituted Bicyclo[1.1.1]pentanes by Iron‐Catalyzed Kumada Cross‐Coupling 1,3-Disubstituted bicyclo[1.1.1]pentanes (BCPs) are important motifs in drug design as surrogates for p-substituted arenes and alkynes. Access to all-carbon disubstituted BCPs via cross-coupling has to date been limited to use of the BCP as the organometallic component, which restricts scope due to the harsh conditions typically required for the synthesis of metallated BCPs. Here we report a general method to access 1,3-C-disubstituted BCPs from 1-iodo-bicyclo[1.1.1]pentanes (iodo-BCPs) by direct iron-catalyzed cross-coupling with aryl and heteroaryl Grignard reagents. This chemistry represents the first general use of iodo-BCPs as electrophiles in cross-coupling, and the first Kumada coupling of tertiary iodides. Benefiting from short reaction times, mild conditions, and broad scope of the coupling partners, it enables the synthesis of a wide range of 1,3-C-disubstituted BCPs including various drug analogues. Aryl ; Electrophile ; Reagent ; Catalysis ; Combinatorial chemistry ; Chemistry ; Organic synthesis ; Coupling reaction ; Coupling (piping) ; Organic chemistry ; Materials science ; Alkyl Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Asymmetric Synthesis and Catalysis
Authors: Xuefang Luo | Rong‐Jun Xie
Source: Journal of Rare Earths (article, 2020-01-25)
Field: Materials Science — Materials Chemistry

Recent progress on discovery of novel phosphors for solid state lighting Phosphor ; Solid-state ; Nanotechnology ; Throughput ; Computer science ; Process engineering ; Luminescence ; Biochemical engineering ; Materials science ; Telecommunications ; Engineering ; Engineering physics ; Optoelectronics Luminescence Properties of Advanced Materials ; Inorganic Chemistry and Materials ; Advanced Photocatalysis Techniques
Authors: Avinash R. Pai | T. Binumol | Deepu A. Gopakumar | Daniel Pasquini | Bastien Seantier | Nandakumar Kalarikkal | Sabu Thomas
Source: Carbohydrate Polymers (article, 2020-06-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultra-fast heat dissipating aerogels derived from polyaniline anchored cellulose nanofibers as sustainable microwave absorbers Materials science ; Polyaniline ; Nanofiber ; Electromagnetic shielding ; Aerogel ; In situ polymerization ; Cellulose ; Polyaniline nanofibers ; Microwave ; Electrical conductor ; EMI ; Absorption (acoustics) ; Porosity ; Conductive polymer ; Composite material ; Electromagnetic interference ; Reflection loss ; Nanotechnology ; Polymerization ; Chemical engineering ; Polymer ; Composite number ; Computer science ; Telecommunications Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Dielectric materials and actuators
Authors: Dmytro Antypov | Aleksander Shkurenko | Prashant M. Bhatt | Youssef Belmabkhout | Karim Adil | Amandine Cadiau | Mikhail Suyetin | Mohamed Eddaoudi | Matthew J. Rosseinsky | Matthew S. Dyer
Source: Nature Communications (article, 2020-11-30)
Field: Chemistry — Inorganic Chemistry

Differential guest location by host dynamics enhances propylene/propane separation in a metal-organic framework Energy-efficient approaches to propylene/propane separation such as molecular sieving are of considerable importance for the petrochemical industry. The metal organic framework NbOFFIVE-1-Ni adsorbs propylene but not propane at room temperature and atmospheric pressure, whereas the isostructural SIFSIX-3-Ni does not exclude propane under the same conditions. The static dimensions of the pore openings of both materials are too small to admit either guest, signalling the importance of host dynamics for guest entrance to and transport through the channels. We use ab initio calculations together with crystallographic and adsorption data to show that the dynamics of the two framework-forming units, polyatomic anions and pyrazines, govern both diffusion and separation. The guest diffusion occurs by opening of the flexible window formed by four pyrazines. In NbOFFIVE-1-Ni, (NbOF<sub>5</sub>)<sup>2-</sup> anion reorientation locates propane away from the window, which enhances propylene/propane separation. Propane ; Isostructural ; Molecular dynamics ; Chemical physics ; Petrochemical ; Materials science ; Adsorption ; Diffusion ; Metal-organic framework ; Chemical engineering ; Chemistry ; Computational chemistry ; Organic chemistry ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Radioactive element chemistry and processing ; Covalent Organic Framework Applications
Authors: Jennifer Schnabel | Romy Ettlinger | Hana Bunzen
Source: ChemNanoMat (article, 2020-05-28)
Field: Chemistry — Inorganic Chemistry

Zn‐MOF‐74 as pH‐Responsive Drug‐Delivery System of Arsenic Trioxide Abstract Although arsenic and its compounds are mainly known as a poison, they also found applications as drugs in medicine. However, their distribution in a therapeutic dose is often problematic due to their high toxicity. One possibility how to address this issue is utilizing drug‐delivery systems. Herein, we report on a metal‐organic framework (MOF) called Zn‐MOF‐74, which comprises Zn(II) ions and 2,5‐dihydroxybenzene‐1,4‐dicarboxylate ligands, as a drug nanocarrier of arsenic trioxide. We synthetized the material in a nanoparticle formulation and showed that due to the open metal sites in Zn‐MOF‐74, it was possible to load the material with a high amount of the As(III)‐drug (in a form of arsenous acid). Moreover, we also investigated the drug release at different pH values. Noticeably, we observed that the As(III)‐release was pH‐triggered and was faster at pH 6.0 than at pH 7.4. Therefore, we suggest Zn‐MOF‐74 as a model example of a MOF containing open metal sites for controlled drug loading and delivery. Arsenic trioxide ; Nanocarriers ; Drug delivery ; Chemistry ; Drug ; Metal-organic framework ; Nanoparticle ; Metal ; Nuclear chemistry ; Arsenic ; Nanotechnology ; Pharmacology ; Materials science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Arsenic contamination and mitigation ; Nanoparticle-Based Drug Delivery
Authors: Julia Nette | Philip D. Howes | Andrew J. deMello
Source: Advanced Materials Technologies (article, 2020-05-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Microfluidic Synthesis of Luminescent and Plasmonic Nanoparticles: Fast, Efficient, and Data‐Rich Abstract Microfluidic approaches to nanomaterial synthesis provide an effective means of making high quality products, with exquisite control over electronic, optical, and structural properties. Furthermore, microfluidic reactors integrating analytics and real‐time reaction control have recently emerged as powerful tools in automating materials exploration and reaction parameter mapping, with a rapidity and efficiency that is inaccessible to traditional flask‐based methods. Herein, the recent innovations in the microfluidic synthesis of photonic nanoparticles, whose varied luminescent and plasmonic properties have found great application in the biomedical and optoelectronic sciences, are presented. Special attention is placed on the achievements and promise of microfluidic approaches in automated multidimensional reaction parameter screening employing in situ optical characterization. In an extended outlook, the future of the field is explored, looking toward the integration of smart control systems and machine learning algorithms, and demonstrating how maximal positive impact can be achieved. Far from degrading or replacing the ingenuity of the experimentalist, these tools will provide new powers of exploration, augmenting the discovery process. Further, through automation and miniaturization, it is expected that such approaches will fulfill the moral imperative of maximizing the benefit derived from the natural resources consumed in conducting research. Microfluidics ; Nanotechnology ; Miniaturization ; Ingenuity ; Computer science ; Plasmonic nanoparticles ; Automation ; Materials science ; Biochemical engineering ; Nanoparticle ; Engineering ; Mechanical engineering Gold and Silver Nanoparticles Synthesis and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced biosensing and bioanalysis techniques
Authors: Ralf Meyer | Andreas Hauser
Source: The Journal of Chemical Physics (article, 2020-02-26)
Field: Materials Science — Materials Chemistry

Geometry optimization using Gaussian process regression in internal coordinate systems Locating the minimum energy structure of molecules, typically referred to as geometry optimization, is one of the first steps of any computational chemistry calculation. Earlier research was mostly dedicated to finding convenient sets of molecule-specific coordinates for a suitable representation of the potential energy surface, where a faster convergence toward the minimum structure can be achieved. More recent approaches, on the other hand, are based on various machine learning techniques and seem to revert to Cartesian coordinates instead for practical reasons. We show that the combination of Gaussian process regression with those coordinate systems employed by state-of-the-art geometry optimizers can significantly improve the performance of this powerful machine learning technique. This is demonstrated on a benchmark set of 30 small covalently bonded molecules. Cartesian coordinate system ; Benchmark (surveying) ; Gaussian ; Computer science ; Convergence (economics) ; Representation (politics) ; Kriging ; Energy minimization ; Coordinate system ; Set (abstract data type) ; Algorithm ; Process (computing) ; Gaussian process ; Surface (topology) ; Geometry ; Mathematical optimization ; Mathematics ; Artificial intelligence ; Machine learning ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Haogang Cai | Srilok Srinivasan | David A. Czaplewski | Alex B. F. Martinson | David J. Gosztola | Liliana Stan | Troy D. Loeffler | Subramanian K. R. S. Sankaranarayanan | Daniel López
Source: npj Computational Materials (article, 2020-08-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse design of metasurfaces with non-local interactions Abstract Conventional metasurfaces have demonstrated efficient wavefront manipulation by using thick and high-aspect-ratio nanostructures in order to eliminate interactions between adjacent phase-shifter elements. Thinner-than-wavelength dielectric metasurfaces are highly desirable because they can facilitate fabrication and integration with both electronics and mechanically tunable platforms. Unfortunately, because their constitutive phase-shifter elements exhibit strong electromagnetic coupling between neighbors, the design requires a global optimization methodology that considers the non-local interactions. Here, we propose a global evolutionary optimization approach to inverse design non-local metasurfaces. The optimal designs are experimentally validated, demonstrating the highest efficiencies for the thinnest transmissive metalenses reported to-date for visible light. In a departure from conventional design methods based on the search of a library of pre-determined and independent meta-atoms, we take full advantage of the strong interactions among nanoresonators to improve the focusing efficiency of metalenses and demonstrate that efficiency improvements can be obtained by lowering the metasurface filling factors. Wavefront ; Inverse ; Coupling (piping) ; Fabrication ; Computer science ; Dielectric ; Materials science ; Phase (matter) ; Nanotechnology ; Optoelectronics ; Optics ; Physics ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Plasmonic and Surface Plasmon Research
Authors: Janine George | David Waroquiers | Davide Di Stefano | Guido Petretto | Gian‐Marco Rignanese | Geoffroy Hautier
Source: Angewandte Chemie International Edition (article, 2020-02-17)
Field: Materials Science — Materials Chemistry

The Limited Predictive Power of the Pauling Rules The Pauling rules have been used for decades to rationalise the crystal structures of ionic compounds. Despite their importance, there has been no statistical assessment of the performances of these five empirical rules so far. Here, we rigorously and automatically test all five Pauling rules for a large data set of around 5000 known oxides. We discuss each Pauling rule separately, stressing their limits and range of application in terms of chemistries and structures. We conclude that only 13 % of the oxides simultaneously satisfy the last four rules, indicating a much lower predictive power than expected. Predictive power ; Power (physics) ; Computer science ; Physics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Nuclear Materials and Properties
Authors: Vinit Sheth | Lin Wang | Resham Bhattacharya | Priyabrata Mukherjee | Stefan Wilhelm
Source: Advanced Functional Materials (article, 2020-11-12)
Field: Materials Science — Biomaterials

Strategies for Delivering Nanoparticles across Tumor Blood Vessels Nanoparticle transport across tumor blood vessels is a key step in nanoparticle delivery to solid tumors. However, the specific pathways and mechanisms of this nanoparticle delivery process are not fully understood. Here, the biological and physical characteristics of the tumor vasculature and the tumor microenvironment are explored and how these features affect nanoparticle transport across tumor blood vessels is discussed. The biological and physical methods to deliver nanoparticles into tumors are reviewed and paracellular and transcellular nanoparticle transport pathways are explored. Understanding the underlying pathways and mechanisms of nanoparticle tumor delivery will inform the engineering of safer and more effective nanomedicines for clinical translation. Materials science ; Nanoparticle ; Nanotechnology ; Biomedical engineering ; Medicine Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Nanoplatforms for cancer theranostics
Authors: Daniil Polykovskiy | Alexander Zhebrak | Benjamín Sánchez-Lengeling | Sergey Golovanov | Oktai Tatanov | Stanislav Belyaev | Rauf Kurbanov | Aleksey Artamonov | Vladimir Aladinskiy | Mark S. Veselov | Artur Kadurin | Simon Johansson | Hongming Chen | Sergey Nikolenko | Alán Aspuru‐Guzik | Alex Zhavoronkov
Source: Frontiers in Pharmacology (preprint, 2020-12-18)
Field: Materials Science — Materials Chemistry

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models Generative models are becoming a tool of choice for exploring the molecular space. These models learn on a large training dataset and produce novel molecular structures with similar properties. Generated structures can be utilized for virtual screening or training semi-supervized predictive models in the downstream tasks. While there are plenty of generative models, it is unclear how to compare and rank them. In this work, we introduce a benchmarking platform called Molecular Sets (MOSES) to standardize training and comparison of molecular generative models. MOSES provides training and testing datasets, and a set of metrics to evaluate the quality and diversity of generated structures. We have implemented and compared several molecular generation models and suggest to use our results as reference points for further advancements in generative chemistry research. The platform and source code are available at https://github.com/molecularsets/moses. Benchmarking ; Generative grammar ; Computer science ; Set (abstract data type) ; Machine learning ; Generative model ; Code (set theory) ; Training set ; Quality (philosophy) ; Chemical space ; Artificial intelligence ; Data mining ; Bioinformatics ; Programming language ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Zhaocheng Liu | Lakshmi Raju | Dayu Zhu | Wenshan Cai
Source: IEEE Journal on Emerging and Selected Topics in Circuits and Systems (article, 2020-01-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Hybrid Strategy for the Discovery and Design of Photonic Structures Metasurfaces and metamaterials are playing a growing role in the control of electromagnetic waves in the application of communication, display, and sensing technologies. However, designing photonic structures as the building blocks of these systems is typically a tedious trial-and-error process that requires extensive simulations with iterative sweeps in a multi-dimensional parameter space. To circumvent this conventional approach and substantially expedite the discovery and development of photonic structures, here we develop a framework leveraging both a deep generative model and a modified evolution strategy to automate the inverse design of engineered photonic materials and devices. The capacity of the proposed methodology is tested through the application to a case study, where metasurfaces in either continuous or discrete topologies are generated in response to customer-defined spectra at the input. Through a variational autoencoder, all potential patterns of unit structures are encoded into a continuous latent space. An evolution strategy is applied to vectors in the latent space to identify an optimized vector whose corresponding metasurface fulfills the design objective. The evaluation shows that over 95% accuracy can be achieved for all the unit patterns of the metasurfaces in the test dataset. Our scheme requires no prior knowledge of the geometry of the photonic structures, and, in principle, allows joint optimization of the dimensional parameters. As such, our work represents an efficient, on-demand, and automated approach for the inverse design of photonic structures with subwavelength features. Photonics ; Computer science ; Metamaterial ; Photonic crystal ; Autoencoder ; Space mapping ; Photonic metamaterial ; Process (computing) ; Electronic engineering ; Artificial intelligence ; Artificial neural network ; Algorithm ; Engineering ; Physics Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Photonic Crystals and Applications