jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nanxi Li | Yuan Hsing Fu | Yuan Dong | Ting Hu | Zhengji Xu | Qize Zhong | Dongdong Li | Keng Heng Lai | Shiyang Zhu | Qunying Lin | Yuandong Gu | Navab Singh
Source: Nanophotonics (article, 2018-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Large‐area pixelated metasurface beam deflector on a 12‐inch glass wafer for random point generation Abstract Metasurface‐based beam deflector, as an important optical element to bend the light propagation direction, has drawn a lot of interests in research to achieve miniaturization of devices and reduction of system complexity. Based on the 12‐inch immersion lithography technology, in this work, an ultra‐thin and large‐area pixelated metasurface beam deflector with a footprint of 2500 × 2500 μm, formed by nanopillars with diameters from 221 to 396 nm, is demonstrated on a 12‐inch glass wafer. The 21 × 21 array of deflectors is designed to bend the input light in different directions and to generate 441 random points. In addition, the layer transfer on the 12‐inch glass wafer makes the device working in transmission mode at a 940‐nm wavelength. The random point array generated from the experiment shows good match with the design. This pixelated metasurface beam deflector can generate random points simultaneously and has potential to make beam steering by switching each pixel of the beam deflector, which can be applied on motion detection, facial recognition, and light detection and ranging. Optics ; Wafer ; Materials science ; Miniaturization ; Beam (structure) ; Beam steering ; Lithography ; Optoelectronics ; Nanopillar ; Physics ; Nanotechnology ; Nanostructure Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Photonic Crystals and Applications
Authors: Jay K. Bhattarai | Dharmendra Neupane | Bishal Nepal | Vasilii Mikhaylov | Alexei V. Demchenko | Keith J. Stine
Source: Nanomaterials (review, 2018-03-16)
Field: Materials Science — Materials Chemistry

Preparation, Modification, Characterization, and Biosensing Application of Nanoporous Gold Using Electrochemical Techniques Nanoporous gold (np-Au), because of its high surface area-to-volume ratio, excellent conductivity, chemical inertness, physical stability, biocompatibility, easily tunable pores, and plasmonic properties, has attracted much interested in the field of nanotechnology. It has promising applications in the fields of catalysis, bio/chemical sensing, drug delivery, biomolecules separation and purification, fuel cell development, surface-chemistry-driven actuation, and supercapacitor design. Many chemical and electrochemical procedures are known for the preparation of np-Au. Recently, researchers are focusing on easier and controlled ways to tune the pores and ligaments size of np-Au for its use in different applications. Electrochemical methods have good control over fine-tuning pore and ligament sizes. The np-Au electrodes that are prepared using electrochemical techniques are robust and are easier to handle for their use in electrochemical biosensing. Here, we review different electrochemical strategies for the preparation, post-modification, and characterization of np-Au along with the synergistic use of both electrochemistry and np-Au for applications in biosensing. Biosensor ; Nanotechnology ; Nanoporous ; Materials science ; Surface modification ; Biomolecule ; Biocompatibility ; Electrochemistry ; Supercapacitor ; Characterization (materials science) ; Electrode ; Chemical engineering ; Chemistry Nanoporous metals and alloys ; Electrocatalysts for Energy Conversion ; Anodic Oxide Films and Nanostructures
Authors: Tian Chang | Jiaqi Lu | Zhenxing Shen | Yu Huang | Di Lu | Xin Wang | Junji Cao | Rino Morent
Source: Applied Catalysis B: Environmental (article, 2018-11-14)
Field: Materials Science — Materials Chemistry

Simulation and optimization of the post plasma-catalytic system for toluene degradation by a hybrid ANN and NSGA-II method Toluene ; Catalysis ; Yield (engineering) ; Chemistry ; Degradation (telecommunications) ; Multi-objective optimization ; Volumetric flow rate ; Nonthermal plasma ; Plasma ; Materials science ; Chemical engineering ; Biological system ; Analytical Chemistry (journal) ; Chromatography ; Computer science ; Mathematics ; Thermodynamics ; Mathematical optimization ; Organic chemistry ; Composite material ; Engineering Catalytic Processes in Materials Science ; Plasma Applications and Diagnostics ; Gas Sensing Nanomaterials and Sensors
Authors: Xiaoyang Lin | Zhizhong Si | Wenzhi Fu | Jianlei Yang | Side Guo | Yuan Cao | Jin Zhang | Xinhe Wang | Peng Liu | Kaili Jiang | Weisheng Zhao
Source: Nano Research (article, 2018-08-07)
Field: Materials Science — Materials Chemistry

Intelligent identification of two-dimensional nanostructures by machine-learning optical microscopy Two-dimensional (2D) materials and their heterostructures, with wafer-scale synthesis methods and fascinating properties, have attracted significant interest and triggered revolutions in corresponding device applications. However, facile methods to realize accurate, intelligent, and large-area characterizations of these 2D nanostructures are still highly desired. Herein, we report the successful application of machine-learning strategy in the optical identification of 2D nanostructures. The machine-learning optical identification (MOI) method endows optical microscopy with intelligent insight into the characteristic color information of 2D nanostructures in the optical photograph. The experimental results indicate that the MOI method enables accurate, intelligent, and large-area characterizations of graphene, molybdenum disulfide, and their heterostructures, including identifications of the thickness, existence of impurities, and even stacking order. With the convergence of artificial intelligence and nanoscience, this intelligent identification method can certainly promote fundamental research and wafer-scale device applications of 2D nanostructures. Stacking ; Nanostructure ; Identification (biology) ; Optical microscope ; Microscopy 2D Materials and Applications ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Salvador Gutiérrez | Juan Fernández‐Novales | María P. Diago | Javier Tardáguila
Source: Frontiers in Plant Science (article, 2018-07-25)
Field: Chemistry — Analytical Chemistry

On-The-Go Hyperspectral Imaging Under Field Conditions and Machine Learning for the Classification of Grapevine Varieties Grapevine varietal classification is an important plant phenotyping issue for grape growing and wine industry. This task has been achieved from destructive techniques like classic ampelography and DNA analysis under laboratory conditions. This work displays a new approach for the classification of a high number of grapevine (<i>Vitis vinifera</i> L.) varieties under field conditions using on-the-go hyperspectral imaging and different machine learning algorithms. On-the-go imaging was performed under natural illumination using a hyperspectral camera mounted on an all-terrain vehicle at 5 km/h. Spectra were acquired over two different leaf phenological stages on the canopy of 30 different varieties on a commercial vineyard located in La Rioja, Spain. A total of 1,200 spectral samples were generated. Support vector machines (SVM) and artificial neural networks (multilayer perceptrons, MLP) were used for the development of a large number of models, testing different algorithm parameters and spectral pre-processing techniques. Both classifiers yielded notable performance values and were able to train models with recall F1 scores and area under the receiver operating characteristic curve marks up to 0.99 for 5-fold cross validation. Statistical analyses supported that the best SVM kernel was linear and the best activation function for MLP was the hyperbolic tangent function. The prediction performance for individual varieties of MLP ranged from 0.94 to 0.99, displaying low levels of variability. In the case of SVM, slightly higher differences were obtained, ranging from 0.83 to 0.97 for individual varieties. These results support the possibility of deploying an on-the-go hyperspectral imaging system in the field capable of successfully classifying leaves from different grapevine varieties. This technology could thus be considered as a new useful non-destructive tool for plant phenotyping under field conditions. Hyperspectral imaging ; Artificial intelligence ; Support vector machine ; Vineyard ; Pattern recognition (psychology) ; Multilayer perceptron ; Artificial neural network ; Computer science ; Perceptron ; Mathematics ; Machine learning ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Fermentation and Sensory Analysis
Authors: Dana Pascovici | Jemma Wu | Matthew J. McKay | Chitra Joseph | Zainab Noor | Karthik Shantharam Kamath | Yunqi Wu | Shoba Ranganathan | Vivek Gupta | Mehdi Mirzaei
Source: International Journal of Molecular Sciences (review, 2018-12-20)
Field: Chemistry — Spectroscopy

Clinically Relevant Post-Translational Modification Analyses—Maturing Workflows and Bioinformatics Tools Post-translational modifications (PTMs) can occur soon after translation or at any stage in the lifecycle of a given protein, and they may help regulate protein folding, stability, cellular localisation, activity, or the interactions proteins have with other proteins or biomolecular species. PTMs are crucial to our functional understanding of biology, and new quantitative mass spectrometry (MS) and bioinformatics workflows are maturing both in labelled multiplexed and label-free techniques, offering increasing coverage and new opportunities to study human health and disease. Techniques such as Data Independent Acquisition (DIA) are emerging as promising approaches due to their re-mining capability. Many bioinformatics tools have been developed to support the analysis of PTMs by mass spectrometry, from prediction and identifying PTM site assignment, open searches enabling better mining of unassigned mass spectra-many of which likely harbour PTMs-through to understanding PTM associations and interactions. The remaining challenge lies in extracting functional information from clinically relevant PTM studies. This review focuses on canvassing the options and progress of PTM analysis for large quantitative studies, from choosing the platform, through to data analysis, with an emphasis on clinically relevant samples such as plasma and other body fluids, and well-established tools and options for data interpretation. Workflow ; Computational biology ; Posttranslational modification ; Computer science ; Data science ; Bioinformatics ; Translation (biology) ; Proteomics ; Biology ; Genetics Advanced Proteomics Techniques and Applications ; Viral Infectious Diseases and Gene Expression in Insects ; Mass Spectrometry Techniques and Applications
Authors: Seong‐Sun Lee | Keunho Choi | Se‐Hee Kim | Sang‐Young Lee
Source: Advanced Functional Materials (article, 2018-01-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Wearable Supercapacitors Printed on Garments Abstract Electronic garments have garnered considerable attention as a core technology for the upcoming wearable electronics era. To enable ubiquitous operation of electronic garments, they must be monolithically integrated with rechargeable power sources. Here, inspired by printing‐assisted aesthetic clothing designs, a new class of wearable supercapacitors (SCs) is demonstrated that can be directly printed on T‐shirts, which look like letters (or symbols) commonly printed on T‐shirts. The printed SCs consist of activated carbon/multiwalled carbon nanotube/ionic liquid‐based electrodes and ionic liquid/thiol‐ene polymer network skeleton/SiO 2 nanoparticle‐based gel electrolytes. The rheological properties of the electrode/electrolyte pastes are fine‐tuned by varying the colloidal network structure, which affects the printing processability and formation of the nanoscale ion/electron conduction channels. To ensure the seamless unitization and design versatility of the printed SCs, the T‐shirt is sewn with electroconductive stainless steel (SS) threads prior to the printing process. Onto the SS threads acting as shape‐directing current collectors, the electrode/electrolyte pastes are sequentially stencil‐printed and sealed with water‐proof packaging films. The printed SCs exhibit exceptional form factors, flexibility, and thermal stability. Notably, the SC‐printed T‐shirts maintain their electrochemical activity upon exposure to laundering, wringing, ironing, and folding, demonstrating their potential and practical applicability as a promising electronic garment technology. Materials science ; Supercapacitor ; Nanotechnology ; Electrolyte ; Wearable technology ; Wearable computer ; Electrode ; Printed electronics ; Screen printing ; Electronics ; Inkwell ; Electrochemistry ; Composite material ; Computer science ; Electrical engineering ; Embedded system Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Qiang Chen | Luigi Brambilla | Lakshya Daukiya | Kunal S. Mali | Steven De Feyter | Matteo Tommasini | Kläus Müllen | Akimitsu Narita
Source: Angewandte Chemie International Edition (article, 2018-07-09)
Field: Materials Science — Materials Chemistry

Synthesis of Triply Fused Porphyrin‐Nanographene Conjugates Two unprecedented porphyrin fused nanographene molecules, 1 and 2, have been synthesized by the Scholl reaction from tailor-made precursors based on benzo[m]tetraphene-substituted porphyrins. The chemical structures were validated by a combination of high-resolution matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (HR MALDI-TOF MS), IR and Raman spectroscopy, and scanning tunnelling microscopy (STM). The UV-vis-near infrared absorption spectroscopy of 1 and 2 demonstrated broad and largely red-shifted absorption spectra extending up to 1000 and 1400 nm, respectively, marking the significant extension of the π-conjugated systems. Porphyrin ; Raman spectroscopy ; Conjugated system ; Mass spectrometry ; Molecule ; Spectroscopy ; Desorption ; Materials science ; Infrared spectroscopy ; Absorption spectroscopy ; Absorption (acoustics) ; Scanning tunneling microscope ; Photochemistry ; Analytical Chemistry (journal) ; Chemistry ; Adsorption ; Nanotechnology ; Physical chemistry ; Organic chemistry ; Optics ; Polymer ; Chromatography Porphyrin and Phthalocyanine Chemistry ; Surface Chemistry and Catalysis ; Molecular Junctions and Nanostructures
Authors: Ting Zhang | Gang‐Qin Liu | Weng-Hang Leong | Chufeng Liu | M. W. Kwok | To Ngai | Ren‐Bao Liu | Quan Li
Source: Nature Communications (article, 2018-08-03)
Field: Materials Science — Materials Chemistry

Hybrid nanodiamond quantum sensors enabled by volume phase transitions of hydrogels Diamond nitrogen-vacancy (NV) center-based magnetometry provides a unique opportunity for quantum bio-sensing. However, NV centers are not sensitive to parameters such as temperature and pressure, and immune to many biochemical parameters such as pH and non-magnetic biomolecules. Here, we propose a scheme that can potentially enable the measurement of various biochemical parameters using diamond quantum sensing, by employing stimulus-responsive hydrogels as a spacing transducer in-between a nanodiamond (ND, with NV centers) and magnetic nanoparticles (MNPs). The volume phase transition of hydrogel upon stimulation leads to sharp variation in the separation distance between the MNPs and the ND. This in turn changes the magnetic field that the NV centers can detect sensitively. We construct a temperature sensor under this hybrid scheme and show the proof-of-the-principle demonstration of reversible temperature sensing. Applications in the detection of other bio-relevant parameters are envisioned if appropriate types of hydrogels can be engineered. Nanodiamond ; Self-healing hydrogels ; Diamond ; Materials science ; Nanotechnology ; Quantum ; Magnetometer ; Biomolecule ; Quantum sensor ; Magnetic field ; Magnetic nanoparticles ; Nitrogen-vacancy center ; Transducer ; Pressure sensor ; Nanoparticle ; Quantum computer ; Quantum network ; Physics ; Acoustics Diamond and Carbon-based Materials Research ; Advanced Fiber Laser Technologies ; Graphene research and applications
Authors: Vincent Guillerm | Thais Grancha | Inhar Imaz | Judith Juanhuix | Daniel Maspoch
Source: Journal of the American Chemical Society (article, 2018-07-31)
Field: Chemistry — Inorganic Chemistry

Zigzag Ligands for Transversal Design in Reticular Chemistry: Unveiling New Structural Opportunities for Metal–Organic Frameworks Herein we describe the topological influence of zigzag ligands in the assembly of Zr(IV) metal-organic frameworks (MOFs). Through a transversal design strategy using reticular chemistry, we were able to synthesize a family of isoreticular Zr(IV)-based MOFs exhibiting the bcu-rather than the fcu-topology. Our findings underscore the value of the transversal parameter in organic ligands for dictating MOF architectures. Transversal (combinatorics) ; Chemistry ; Zigzag ; Reticular connective tissue ; Metal-organic framework ; Topology (electrical circuits) ; Nanotechnology ; Organic chemistry ; Materials science ; Geometry ; Anatomy Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions ; Magnetism in coordination complexes
Authors: Sreejith Shankar | Morten K. Peters | Kim Steinborn | Bahne Krahwinkel | Frank D. Sönnichsen | Dirk Grote | Wolfram Sander | Thomas Lohmiller | Olaf Rüdiger | Rainer Herges
Source: Nature Communications (article, 2018-11-06)
Field: Materials Science — Materials Chemistry

Light-controlled switching of the spin state of iron(III) Controlled switching of the spin state of transition metal ions, particularly of Fe<sup>II</sup> and Fe<sup>III</sup>, is a prerequisite to achieve selectivity, efficiency, and catalysis in a number of metalloenzymes. Here we report on an iron(III) porphyrin with a photochromic axial ligand which, upon irradiation with two different wavelengths reversibly switches its spin state between low-spin (S = <sup>1</sup>/<sub>2</sub>) and high-spin (S = <sup>5</sup>/<sub>2</sub>) in solution (DMSO-acetone, 2:598). The switching efficiency is 76% at room temperature. The system is neither oxygen nor water sensitive, and no fatigue was observed after more than 1000 switching cycles. Concomitant with the spin-flip is a change in redox potential by ~60 mV. Besides serving as a simple model for the first step of the cytochrome P450 catalytic cycle, the spin switch can be used to switch the spin-lattice relaxation time T<sub>1</sub> of the water protons by a factor of 15. Spin transition ; Spin states ; Molecular switch ; Photochemistry ; Porphyrin ; Materials science ; Catalysis ; Photochromism ; Acetone ; Spin crossover ; Redox ; Oxygen ; Ligand (biochemistry) ; Spin (aerodynamics) ; Chemistry ; Inorganic chemistry ; Crystallography ; Physics ; Molecule Porphyrin and Phthalocyanine Chemistry ; Metal-Catalyzed Oxygenation Mechanisms ; Photosynthetic Processes and Mechanisms
Authors: John E. Herr | Kun Yao | Ryker Mcintyre | David W. Toth | John Parkhill
Source: The Journal of Chemical Physics (article, 2018-03-15)
Field: Materials Science — Materials Chemistry

Metadynamics for training neural network model chemistries: A competitive assessment Neural network model chemistries (NNMCs) promise to facilitate the accurate exploration of chemical space and simulation of large reactive systems. One important path to improving these models is to add layers of physical detail, especially long-range forces. At short range, however, these models are data driven and data limited. Little is systematically known about how data should be sampled, and "test data" chosen randomly from some sampling techniques can provide poor information about generality. If the sampling method is narrow, "test error" can appear encouragingly tiny while the model fails catastrophically elsewhere. In this manuscript, we competitively evaluate two common sampling methods: molecular dynamics (MD), normal-mode sampling, and one uncommon alternative, Metadynamics (MetaMD), for preparing training geometries. We show that MD is an inefficient sampling method in the sense that additional samples do not improve generality. We also show that MetaMD is easily implemented in any NNMC software package with cost that scales linearly with the number of atoms in a sample molecule. MetaMD is a black-box way to ensure samples always reach out to new regions of chemical space, while remaining relevant to chemistry near k<sub>b</sub>T. It is a cheap tool to address the issue of generalization. Metadynamics ; Generality ; Sampling (signal processing) ; Computer science ; Generalization ; Artificial neural network ; Range (aeronautics) ; Chemical space ; Sample (material) ; Data mining ; Training set ; Machine learning ; Artificial intelligence ; Algorithm ; Molecular dynamics ; Mathematics ; Chemistry ; Engineering ; Computational chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Seongok Ryu | Jaechang Lim | Seung Hwan Hong | Woo Youn Kim
Source: arXiv (Cornell University) (preprint, 2018-05-28)
Field: Materials Science — Materials Chemistry

Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network Molecular structure-property relationships are key to molecular engineering for materials and drug discovery. The rise of deep learning offers a new viable solution to elucidate the structure-property relationships directly from chemical data. Here we show that the performance of graph convolutional networks (GCNs) for the prediction of molecular properties can be improved by incorporating attention and gate mechanisms. The attention mechanism enables a GCN to identify atoms in different environments. The gated skip-connection further improves the GCN by updating feature maps at an appropriate rate. We demonstrate that the resulting attention- and gate-augmented GCN could extract better structural features related to a target molecular property such as solubility, polarity, synthetic accessibility and photovoltaic efficiency compared to the vanilla GCN. More interestingly, it identified two distinct parts of molecules as essential structural features for high photovoltaic efficiency, and each of them coincided with the areas of donor and acceptor orbitals for charge-transfer excitations, respectively. As a result, the new model could accurately predict molecular properties and place molecules with similar properties close to each other in a well-trained latent space, which is critical for successful molecular engineering. Chemical space ; Computer science ; Graph ; Molecular graph ; Property (philosophy) ; Molecular engineering ; Materials science ; Biological system ; Artificial intelligence ; Drug discovery ; Chemistry ; Chemical physics ; Theoretical computer science ; Nanotechnology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Christian Brinch Mollerup | Marie Mardal | Petur Weihe Dalsgaard | Kristían Línnet | Leon Barron
Source: Journal of Chromatography A (article, 2018-02-15)
Field: Chemistry — Spectroscopy

Prediction of collision cross section and retention time for broad scope screening in gradient reversed-phase liquid chromatography-ion mobility-high resolution accurate mass spectrometry Chemistry ; Chromatography ; Resolution (logic) ; Mass spectrometry ; Approximation error ; Mean squared prediction error ; Ion-mobility spectrometry ; Artificial neural network ; Identification (biology) ; Analytical Chemistry (journal) ; Biological system ; Artificial intelligence ; Algorithm ; Computer science Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications