jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matthias Alfeld | Hanns‐Simon Eckhardt | Jörg Kraft | Michael Maiwald | Björn Meermann | Klaus Merz | Claudia Pacholski | Simon Prikler | Joachim Richert | Gerald Steiner | Wolf von Tümpling
Source: Nachrichten aus der Chemie (article, 2020-03-01)
Field: Chemistry — Spectroscopy

Trendbericht Analytische Chemie Abstract Zusätzlich zu Methodenentwicklung, Miniaturisierung und Kopplungsverfahren zeigen sich die Hyperspektroskopie zusammen mit Imaging‐Verfahren, der Einzelmolekülnachweis und der Einsatz von 3‐D‐Druckern als neue Schwerpunkte. Hinzu kommen künstliche Intelligenz bei Sensoren, Bildgebungsverfahren und Prozesssteuerung sowie die Vernetzung von Analyse‐ und Laborgeräten. Trends und Forschungsthemen aus der analytischen Chemie, zusammengestellt von elf Autoren, koordiniert von Günter Gauglitz. Humanities ; Chemistry ; Philosophy Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Igor Smirnov | Vladimir V. Krasnoborodko | Dmitry A. Syrovatsky | A. Yu. Spiridonov
Source: MATEC Web of Conferences (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Research of algorithms for recognizing and determining the coordinates of apple fruits on the crown of a tree This paper presents the results of a study of the Apple fruit recognition system on the crown of a tree based on the use of an artificial neural network (ANN). The article describes the process of conducting a multi-factor experiment to determine the relationship between the operating conditions of ANN: illumination, shooting distance, photo resolution, and determining their optimal parameters that allow obtaining the highest quality results. The obtained mathematical model reflects the relationship of such factors as illumination, distance to the object, shooting resolution and their influence on the reliability (accuracy) of object recognition in the photo. The optimal parameters of these factors are determined, at which the maximum value of recognition reliability of the desired objects is reached. Reliability (semiconductor) ; Crown (dentistry) ; Artificial neural network ; Tree (set theory) ; Object (grammar) ; Process (computing) ; Artificial intelligence ; Computer science ; Value (mathematics) ; Pattern recognition (psychology) ; Algorithm ; Cognitive neuroscience of visual object recognition ; Mathematics ; Computer vision ; Machine learning Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Toni Monleón-Getino | AC Marca Home Care
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-11-19)
Field: Chemistry — Organic Chemistry

Statistical calibration of microbial suspensions in carrier controls during a textile disinfection ring trial Abstract Introduction The high number of uncontrollable variables in microbiological systems increases experimental complexity and reduces accuracy, potentially leading to data misinterpretation or uncorrectable errors. During an interlaboratory calibration analysis it was observed that the microbial logarithmic reduction (LR) caused by disinfectants depends not only on the type of disinfectant but also on the initial microbial load in the fabric carriers, which can produce a misinterpretation of the results. Fabric carriers are commonly used in standard tests such as EN16616 and ASTM2274. Objective A method based on statistical calibration is proposed using a regression line between N 0 (initial microbial load in the carrier) and LR to eliminate the influence of one on the other. Results An example with Candida albicans is presented. Once the method was applied, the influence of N 0 on LR was eliminated and the new LR values can be used for factorial experiments, for example, to check the efficacy of disinfectants or detergents without depending on the microbial load placed in the carrier. Factorial experiment ; Calibration ; Disinfectant ; Logarithm ; Textile ; Factorial ; Biological system ; Statistics ; Mathematics ; Process engineering ; Pulp and paper industry ; Materials science ; Chemistry ; Composite material ; Engineering ; Biology Antimicrobial agents and applications ; Medical Device Sterilization and Disinfection ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: Ruchika Zalpouri | Rohit Sharma
Source: International Journal of Chemical Studies (article, 2020-03-01)
Field: Chemistry — Analytical Chemistry

Development of simple, rapid and accurate method for identification of head rice and brokens using image processing The quality analysis of rice is done regularly during storage period at Food Corporation of India (FCI) and other godowns. This quality evaluation is done manually by trained person. This process is tedious and reliable results dependent on the skill of the person. Machine vision is an emerging technology for rapid identification of grain quality. It provides alternative for an automated, non-destructive and cost-effective technique. Therefore, present study was undertaken to develop and validate a simple, rapid and accurate method for identification of head rice and brokens of three varieties i.e. Punjab Pusa Basmati 1509, PR121 and PR124 using digital image processing method. The images of randomly selected 40 grains each of head rice and brokens for each variety were captured through flatbed scanner followed by processing of images using open source Image J software. The values of various morphological parameters (viz. length, width, thickness, sphericity, geometric mean diameter and volume) of sample grain extracted were compared by the values calculated using digital vernier caliper. Statistical analysis showed that the results obtained by digital vernier caliper can be linearly correlated to the results obtained by image processing. For varieties Punjab Pusa Basmati 1509, PR121 and PR124, variation in manually calculated length and predicted length was 98.5%, 98.3% and 93.8% and the variation in manually calculated width and predicted width was 97.6%, 96.9% and 97.9%. All the results were found significant at 5% level of significance (p Calipers ; Vernier scale ; Sphericity ; Artificial intelligence ; Image processing ; Digital image ; Mathematics ; Digital image processing ; Computer science ; Computer vision ; Statistics ; Image (mathematics) ; Geography Spectroscopy and Chemometric Analyses ; GABA and Rice Research
Authors: Omid Hemmatyar | Mohammad Ali Abbassi | Babak Rahmani | Mohammad Memarian | Khashayar Mehrany
Source: IEEE Transactions on Antennas and Propagation (preprint, 2020-06-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Wide-Band/Angle Blazed Dual-Mode Metallic Groove Gratings A simple approach to acquire wide-band/angle blazing operation in 1-D metallic gratings is investigated by means of mode-matching and equivalent circuit analysis. The gratings are single-groove perfectly conducting gratings that support two propagating guided modes (opposed to typical single-mode cases). It is shown under what conditions one can achieve blazing over a wide range of frequencies and angles. Most importantly, it is identified what governs blazing, in particular the true matching condition that needs to be satisfied, and how the structures' multiple resonances play role in achieving such matching and tailoring the bandwidth. Parameters of the proposed equivalent circuit model for the case of the transverse magnetic (TM) polarization are analytically derived. The accuracy of the models is verified through a comparison against the results of full-wave simulations. The procedure to achieve wide-band/angle blazing performance is delineated, and the design parameters are explicitly given. It is shown that this structure can strongly transfer the power of the TM polarized incident wave to the -1st diffraction order with a fractional bandwidth of ≈50% at working frequency $f_{0}=10$ GHz and for -10 dB specular reflection loss, thus realizing a broadband structure. From the circuit model, an insightful discussion is presented at various mechanisms at play when Bragg and off-Bragg blazing occurs and fully justifies the behavior of the device. The proposed technique opens up new vistas in a wide range of applications, such as spectroscopy, Littrow cavities, beam splitters, refractive index biosensors, and frequency scanned antenna reflectors. Optics ; Specular reflection ; Broadband ; Equivalent circuit ; Beam splitter ; Transverse plane ; Grating ; Polarization (electrochemistry) ; Materials science ; Physics ; Optoelectronics ; Engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Plasmonic and Surface Plasmon Research
Authors: Yuhao Wu | Sawyer D. Campbell | Eric B. Whiting | Lei Kang | Pingjuan L. Werner | Douglas H. Werner
Source:  (article, 2020-07-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Phase-change Materials in Multifunctional Reconfigurable Metasurfaces Metasurfaces, 2D periodic subwavelength electromagnetic resonant structures, enable diverse optical responses in ultrathin geometries. By integrating phase-change materials (PCMs) with optically or thermally tunable material properties into the unit cell geometries, multiple functionalities can be achieved in a single reconfigurable device. However, the extra degrees of freedom offered by PCMs also presents a difficulty, especially since each state of the PCM requires a unique combination of multiple resonances to achieve a desired functionality. To this end, multi-objective optimization algorithms offer the possibility to directly inverse design reconfigurable devices by enabling multiple functionalities to be simultaneously optimized. These devices usually consist of counterintuitively complicated structures that provide novel resonance mechanisms. Degrees of freedom (physics and chemistry) ; Materials science ; Computer science ; Electronic engineering ; Resonance (particle physics) ; Optoelectronics ; Nanotechnology ; Physics ; Engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Plasmonic and Surface Plasmon Research
Authors: Sawyer D. Campbell | Yuhao Wu | Eric B. Whiting | Lei Kang | Pingjuan L. Werner | Douglas H. Werner
Source: 2020 International Applied Computational Electromagnetics Society Symposium (ACES) (article, 2020-07-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synthesizing High-performance Reconfigurable Meta-devices through Multi-objective Optimization Metasurfaces offer the potential to realize large SWaP (size, weight, and power) reduction over conventional optical elements for their ability to achieve comparable functionalities in ultrathin geometries. Moreover, metasurfaces designed with phase change materials offer the potential to go beyond what is achievable by conventional optics by enabling multiple functionalities in a single reconfigurable meta-device. However, designing a single metasurface geometry that simultaneously achieves multiple desired functionalities while meeting all bandwidth requirements and fabrication constraints is a very challenging problem. Fortunately, this challenge can be overcome by the use of state-of-the-art multi-objective optimization algorithms which are well-suited for the inverse-design of multi-functional meta-devices. Computer science ; Bandwidth (computing) ; Electronic engineering ; Fabrication ; Swap (finance) ; Computer architecture ; Computer engineering ; Engineering ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Materials and Mechanics
Authors: Kristoffer H. Hellton
Source: arXiv (Cornell University) (preprint, 2020-01-27)
Field: Chemistry — Analytical Chemistry

Penalized angular regression for personalized predictions Personalization is becoming an important feature in many predictive applications. We introduce a penalized regression method implementing personalization inherently in the penalty. Personalized angle (PAN) regression constructs regression coefficients that are specific to the covariate vector for which one is producing a prediction, thus personalizing the regression model itself. This is achieved by penalizing the angles in a hyperspherical parametrization of the regression coefficients. For an orthogonal design matrix, it is shown that the PAN estimate is the solution to a low-dimensional eigenvector equation. Using a parametric bootstrap procedure to select the tuning parameter, simulations show that PAN regression can outperform ordinary least squares and ridge regression in terms of prediction error. We further prove that by combining the PAN penalty with an $L_{2}$ penalty the resulting method will have uniformly smaller mean squared prediction error than ridge regression, asymptotically. Finally, we demonstrate the method in a medical application. Regression ; Covariate ; Ordinary least squares ; Regression analysis ; Parametrization (atmospheric modeling) ; Nonparametric regression ; Polynomial regression ; Mathematics ; Design matrix ; Computer science ; Principal component regression ; Parametric statistics ; Applied mathematics ; Statistics ; Algorithm Spectroscopy and Chemometric Analyses ; Genetic and phenotypic traits in livestock
Authors: Peize Lin | Xinguo Ren | Lixin He
Source: arXiv (Cornell University) (preprint, 2020-09-26)
Field: Chemistry — Spectroscopy

Efficient Hybrid Density Functional Calculations for Large Periodic Systems Using Numerical Atomic Orbitals We present an efficient, linear-scaling implementation for building the (screened) Hartree-Fock exchange (HFX) matrix for periodic systems within the framework of numerical atomic orbital (NAO) basis functions. Our implementation is based on the localized resolution of the identity approximation by which two-electron Coulomb repulsion integrals can be obtained by only computing two-center quantities -- a feature that is highly beneficial to NAOs. By exploiting the locality of basis functions and efficient prescreening of the intermediate three- and two-index tensors, one can achieve a linear scaling of the computational cost for building the HFX matrix with respect to the system size. Our implementation is massively parallel, thanks to a MPI/OpenMP hybrid parallelization strategy for distributing the computational load and memory storage. All these factors add together to enable highly efficient hybrid functional calculations for large-scale periodic systems. In this work we describe the key algorithms and implementation details for the HFX build as implemented in the ABACUS code package. The performance and scalability of our implementation with respect to the system size and the number of CPU cores are demonstrated for selected benchmark systems up to 4096 atoms. Computer science ; Linear scale ; Massively parallel ; Benchmark (surveying) ; Scalability ; Parallel computing ; Computational science ; Scaling ; Supercomputer ; Mathematics Advanced NMR Techniques and Applications ; Advanced Chemical Physics Studies ; Physics of Superconductivity and Magnetism
Authors: Ben A. Johnson
Source: KTH Publication Database DiVA (KTH Royal Institute of Technology) (article, 2020-01-01)
Field: Chemistry — Electrochemistry

Interrogating Diffusional Mass and Charge Transport in Catalytic Metal-Organic Frameworks Molecular catalysts are efficient and selective for the electrochemical conversion of small molecules for energy conversion. Application of molecular species in a large-scale industrial setting requires stabilization in a heterogeneous support material. Metal-organic frameworks (MOFs), having high surface areas for increased active site density, have shown promise as potential platforms in which to incorporate molecular catalysts. However, moving from a homogenous environment to catalysis in porous media, necessitates efficient mass and charge transport to the imbedded catalysts. Either diffusional charge transport or diffusion of substrate have the potential to limit the overall observed rate of product formation, if they are slower than the intrinsic rate of the catalytic reaction. This thesis seeks to examine the effect of diffusional mass and charge transport on molecular catalysis in MOFs. First, chemically driven water oxidation is examined using a molecular ruthenium catalyst covalently grafted in MIL(Cr)-101 (MIL = Materials Institute Lavoisier) (Chapter 3). A formal kinetic analysis using a steady-state reaction-diffusion model revealed the limitations incurred by mass transport of the chemical oxidant through the pores of the framework. Importantly, it was shown that interference from mass transport obscures turn-over frequencies, and intrinsic reaction kinetics are only measured under certain conditions. The following chapter entails a modified electrode with a UiO MOF film (UiO = University of Oslo) containing a molecular catalyst, which is used for electrochemically mediated water oxidation (Chapter 4). The diffusional electron-hopping process is examined and discussed in the context of optimizing overall catalytic current densities. In Chapter 5, a new UiO-type MOF thin film is developed containing exclusively molecularly discrete naphthalene diimide linkers, which are redox-active. This can potentially provide charge transport pathways to imbedded catalysts in a two-component system. In addition, the electron-hopping diffusion coefficient was characterized in both non-aqueous and aqueous electrolytes. Lastly, the capacity of the charge-hopping process occurring in these redox-active MOF films to drive a model catalytic reaction is quantified (Chapter 6). Analysis by cyclic voltammetry is utilized to gain insight into the contributions to the current from the catalytic reaction, electron-hopping, substrate diffusion in the film, as well as mass transport in solution. Charge (physics) ; Catalysis ; Metal-organic framework ; Mass transport ; Chemistry ; Chemical physics ; Nanotechnology ; Materials science ; Physics ; Engineering physics ; Physical chemistry ; Organic chemistry ; Adsorption Electrochemical Analysis and Applications ; Surface Chemistry and Catalysis
Authors: Pankaj Rajak | Aravind Krishnamoorthy | Ankit Mishra | Rajiv K. Kalia | Aiichiro Nakano | Priya Vashishta
Source: arXiv (Cornell University) (preprint, 2020-09-14)
Field: Materials Science — Materials Chemistry

Predictive Synthesis of Quantum Materials by Probabilistic Reinforcement\n Learning Predictive materials synthesis is the primary bottleneck in realizing new\nfunctional and quantum materials. Strategies for synthesis of promising\nmaterials are currently identified by time-consuming trial and error approaches\nand there are no known predictive schemes to design synthesis parameters for\nnew materials. We use reinforcement learning to predict optimal synthesis\nschedules, i.e. a time-sequence of reaction conditions like temperatures and\nreactant concentrations, for the synthesis of a prototypical quantum material,\nsemiconducting monolayer MoS$_{2}$, using chemical vapor deposition. The\npredictive reinforcement leaning agent is coupled to a deep generative model to\ncapture the crystallinity and phase-composition of synthesized MoS$_{2}$ during\nCVD synthesis as a function of time-dependent synthesis conditions. This model,\ntrained on 10000 computational synthesis simulations, successfully learned\nthreshold temperatures and chemical potentials for the onset of chemical\nreactions and predicted new synthesis schedules for producing well-sulfidized\ncrystalline and phase-pure MoS$_{2}$, which were validated by computational\nsynthesis simulations. The model can be extended to predict profiles for\nsynthesis of complex structures including multi-phase heterostructures and can\nalso predict long-time behavior of reacting systems, far beyond the domain of\nthe MD simulations used to train the model, making these predictions directly\nrelevant to experimental synthesis.\n Reinforcement learning ; Bottleneck ; Computer science ; Probabilistic logic ; Materials science ; Nanotechnology ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; Software Engineering Research ; Software Engineering Techniques and Practices
Authors: André-Pierre Blanchard-Dionne | Olivier J. F. Martin
Source: OSA Continuum (preprint, 2020-12-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Successive training of a generative adversarial network for the design of an optical cloak At the nanoscale level, optical properties of materials depend greatly on their shape. Finding the right geometry for a specific property remains a fastidious and long task, even with the help of modelling tools. In this work, we overcome this challenge by using artificial intelligence to guide a reverse engineering method. We present an optimization algorithm based on a deep convolution generative adversarial network for the design a 2-dimensional optical cloak. The optical cloak consists in a shell of uniform and isotropical dielectric material, and the cloaking is achieved via the geometry of this shell. We use a feedback loop from the solutions of this generative network to successively retrain it and improve its ability to predict and find optimal geometries. This generative method allows to find a global solution to the optimization problem without any prior knowledge of good cloaking geometries. Cloak ; Cloaking ; Shell (structure) ; Generative grammar ; Generative adversarial network ; Convolution (computer science) ; Computer science ; Algorithm ; Artificial intelligence ; Metamaterial ; Deep learning ; Engineering ; Optics ; Mechanical engineering ; Physics ; Artificial neural network Metamaterials and Metasurfaces Applications ; Advanced Optical Imaging Technologies
Authors: Seo-Hyun Pak | Hwan Kim | Chan-gyu Park
Source: Desalination and Water Treatment (article, 2020-08-01)
Field: Chemistry — Organic Chemistry

Study of removal characteristics of anionic and cationic metals using poly(methacrylic acid) grafted mesoporous natural zeolite Cationic polymerization ; Zeolite ; Methacrylic acid ; Mesoporous material ; Chemistry ; Materials science ; Chemical engineering ; Polymer chemistry ; Catalysis ; Organic chemistry ; Copolymer ; Polymer Inorganic and Organometallic Chemistry ; Polymer Synthesis and Characterization ; Material Properties and Applications