jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sander Korver | Eva Schouten | Othonas A. Moultos | Peter Vergeer | M.M.P. Grutters | L.J.C. Peschier | Thijs J. H. Vlugt | Mahinder Ramdin
Source: Scientific Reports (article, 2020-11-25)
Field: Materials Science — Materials Chemistry

Artificial intelligence and thermodynamics help solving arson cases In arson cases, evidence such as DNA or fingerprints is often destroyed. One of the most important evidence modalities left is relating fire accelerants to a suspect. When gasoline is used as accelerant, the aim is to find a strong indication that a gasoline sample from a fire scene is related to a sample of a suspect. Gasoline samples from a fire scene are weathered, which prohibits a straightforward comparison. We combine machine learning, thermodynamic modeling, and quantum mechanics to predict the composition of unweathered gasoline samples starting from weathered ones. Our approach predicts the initial (unweathered) composition of the sixty main components in a weathered gasoline sample, with error bars of ca. 4% when weathered up to 80% w/w. This shows that machine learning is a valuable tool for predicting the initial composition of a weathered gasoline, and thereby relating samples to suspects. Arson ; Computer science ; Thermodynamics ; Psychology ; Physics ; Criminology Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: S. Stepanović | Rui Lai | Marcus Elstner | Maja Gruden | Pablo García‐Fernández | Qiang Cui
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Improvement of d–d interactions in density functional tight binding for transition metal ions with a ligand field model: assessment of a DFTB3+<i>U</i> model on nickel coordination compounds To improve the description of interactions among the localized d, f electrons in transition metals, we have introduced a ligand-field motivated contribution into the Density Functional Tight Binding (DFTB) model. Referred to as DFTB3+U, the approach treats the d, f electron repulsions with rotationally invariant orbital-orbital interactions and a Hartree-Fock model; this represents a major conceptual improvement over the original DFTB3 approach, which treats the d, f-shell interactions in a highly averaged fashion without orbital level of description. The DFTB3+U approach is tested using a series of nickel compounds that feature Ni(ii) and Ni(iii) oxidation states. By using parameters developed with the original DFTB3 Hamiltonian and empirical +U parameters (F0/2/4 Slater integrals), we observe that the DFTB3+U model indeed provides substantial improvements over the original DFTB3 model for a number of properties of the nickel compounds, including the population and spin polarization of the d-shell, nature of the frontier orbitals, ligand field splitting and the energy different between low and high spin states at OPBE optimized structures. This proof-of-concept study suggests that with self-consistent parameterization of the electronic and +U parameters, the DFTB3+U model can develop into a promising model that can be used to efficiently study reactive events involving transition metals ion condensed phase systems. The methodology can be integrated with other approximate QM methods as well, such as the extended tight binding (xTB) approach. Atomic orbital ; Tight binding ; Ligand field theory ; Nickel ; Hamiltonian (control theory) ; Transition metal ; Binding energy ; Population ; Chemistry ; Ion ; Physics ; Electron ; Computational chemistry ; Electronic structure ; Atomic physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electrocatalysts for Energy Conversion
Authors: Kyung-Do Kwon | Ahyeong Lee | Jongkuk Lim | Soo‐Hyun Cho | Wanghee Lee | Byoung–Kwan Cho | Youngwook Seo
Source: Korean Journal of Agricultural Science (article, 2020-12-01)
Field: Chemistry — Analytical Chemistry

Quality grading of Hanwoo (Korean native cattle breed) sub-images using convolutional neural network The aim of this study was to develop a marbling classification and prediction model using small parts of sirloin images based on a deep learning algorithm, namely, a convolutional neural network (CNN). Samples were purchased from a commercial slaughterhouse in Korea, images for each grade were acquired, and the total images (n = 500) were assigned according to their grade number: 1++, 1+, 1, and both 2 & 3. The image acquisition system consists of a DSLR camera with a polarization filter to remove diffusive reflectance and two light sources (55 W). To correct the distorted original images, a radial correction algorithm was implemented. Color images of sirloins of Hanwoo (mixed with feeder cattle, steer, and calf) were divided and sub-images with image sizes of 161 &#215; 161 were made to train the marbling prediction model. In this study, the convolutional neural network (CNN) has four convolution layers and yields prediction results in accordance with marbling grades (1++, 1+, 1, and 2&3). Every single layer uses a rectified linear unit (ReLU) function as an activation function and max-pooling is used for extracting the edge between fat and muscle and reducing the variance of the data. Prediction accuracy was measured using an accuracy and kappa coefficient from a confusion matrix. We summed the prediction of sub-images and determined the total average prediction accuracy. Training accuracy was 100% and the test accuracy was 86%, indicating comparably good performance using the CNN. This study provides classification potential for predicting the marbling grade using color images and a convolutional neural network algorithm. Hanwoo ; Korean Native ; Convolutional neural network ; Breed ; Grading (engineering) ; Geography ; Cartography ; Artificial intelligence ; Computer science ; Biology ; Animal science ; Ecology Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Food Quality and Safety Studies
Authors: Jamal Belhadi | Florent Ravaux | H. Bouyanfif | Mustapha Jouiad | M. El Marssi
Source: Applied Surface Science (article, 2020-02-13)
Field: Materials Science — Materials Chemistry

Quantification and mapping of elastic strains in ferroelectric [BaZrO3]xᴧ/[BaTiO3](1-x)ᴧ superlattices Superlattice ; Materials science ; Epitaxy ; Pulsed laser deposition ; Diffraction ; Transmission electron microscopy ; Diffusion ; Substrate (aquarium) ; Ferroelectricity ; Scanning transmission electron microscopy ; Thin film ; High-resolution transmission electron microscopy ; Crystallography ; Crystal (programming language) ; Optics ; Condensed matter physics ; Optoelectronics ; Nanotechnology ; Layer (electronics) ; Chemistry ; Physics Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials ; Magnetic and transport properties of perovskites and related materials
Authors: Tae Jun Yoon | Matthew Vigil | Eric Y Raby | Rajinder Singh | Katie A. Maerzke | Robert P. Currier | Alp T. Findikoğlu
Source: Journal of Molecular Liquids (article, 2020-04-03)
Field: Materials Science — Materials Chemistry

Dielectric relaxation of neodymium chloride in water and in methanol Dielectric ; Relaxation (psychology) ; Aqueous solution ; Solvent ; Methanol ; Neodymium ; Dilution ; Chemistry ; Analytical Chemistry (journal) ; Chloride ; Materials science ; Inorganic chemistry ; Thermodynamics ; Physical chemistry ; Optics ; Organic chemistry ; Optoelectronics Solid-state spectroscopy and crystallography ; Spectroscopy and Quantum Chemical Studies ; Chemical and Physical Properties in Aqueous Solutions
Authors: Johan Hellsvik | Roberto Díaz Pérez | R. Matthias Geilhufe | Martin Månsson | Alexander V. Balatsky
Source: Physical Review Materials (article, 2020-02-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spin wave excitations of magnetic metalorganic materials The Organic Materials Database (OMDB) is an open database hosting about 22 000 electronic band structures, density of states, and other properties for stable and previously synthesized three-dimensional organic crystals. The web interface of the OMDB offers various search tools for the identification of novel functional materials such as band structure pattern matching and density of states similarity search. In this work, the OMDB is extended to include magnetic excitation properties. For inelastic neutron scattering, we focus on the dynamic structure factor $S(\mathbf{q},\ensuremath{\omega})$ which contains information on the excitation modes of the material. We introduce a new dataset containing atomic magnetic moments and Heisenberg exchange parameters for which we calculate the spin wave spectra and dynamic structure factor with linear spin wave theory and atomistic spin dynamics. We thus develop the materials informatics tools to identify novel functional organic and metalorganic magnets. Spin wave ; Inelastic neutron scattering ; Density functional theory ; Spin (aerodynamics) ; Excitation ; Electronic structure ; Electronic band structure ; Magnetic moment ; Focus (optics) ; Antiferromagnetism Magnetism in coordination complexes ; Machine Learning in Materials Science ; Advanced NMR Techniques and Applications
Authors: Riley D. Logan | Bryan Joseph Scherrer | Jacob Senecal | Neil S. Walton | Amy Peerlinck | John W. Sheppard | Joseph A. Shaw
Source:  (article, 2020-04-22)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging and machine learning for monitoring produce ripeness Hyperspectral imaging is a powerful remote sensing tool capable of capturing rich spectral and spatial information. Although the origins of hyperspectral imaging are in terrestrial remote sensing, new applications are emerging rapidly. Owing to its non-destructive nature, hyperspectral imaging has become a useful tool for monitoring produce ripeness. This paper describes the process that uses a visible near-infrared (VNIR) hyperspectral imager from Resonon, Inc., coupled with machine learning algorithms to assess the ripeness of various pieces of produce. The images were converted to reflectance across a spectral range of 387.12 nm to 1023.5 nm, with a spectral resolution of 2.12 nm. A convolutional neural network was used to perform age classification for potatoes, bananas, and green peppers. Additionally, a genetic algorithm was used to determine the wavelengths carrying the most useful information for age classification. Experiments were run using RGB images, full spectrum hyperspectral images, and the genetic algorithm feature selection method. Results showed that the genetic algorithm-based feature selection method outperforms RGB images for all tested produce, outperforms hyperspectral imagery for bananas, and matches hyperspectral imagery performance for green peppers. This feature selection method is being used to develop a low-cost multi-spectral imager for use in monitoring produce in grocery stores. Hyperspectral imaging ; VNIR ; Remote sensing ; Artificial intelligence ; Computer science ; RGB color model ; Ripeness ; Feature selection ; Feature (linguistics) ; Pattern recognition (psychology) ; Computer vision ; Convolutional neural network ; Remote sensing application ; Genetic algorithm ; Geography ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Remote-Sensing Image Classification
Authors: Andrea Sekulović | R. Verrijk | Thomas Rades | Adam Grabarek | Wim Jiskoot | Andrea Hawe | Jukka Rantanen
Source: Journal of Pharmaceutical and Biomedical Analysis (article, 2020-11-05)
Field: Chemistry — Analytical Chemistry

Simultaneous automated image analysis and Raman spectroscopy of powders at an individual particle level Chemistry ; Raman spectroscopy ; Particle (ecology) ; Analytical Chemistry (journal) ; Spectroscopy ; Chromatography ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; X-ray Diffraction in Crystallography
Authors: Pasquale Arpaïa | Marco Buzio | Alessandro Parrella | Mariano Pentella | Pedro M. Ramos
Source: IEEE Transactions on Instrumentation and Measurement (article, 2020-12-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Static-Sample Magnetometer for Characterizing Weak Magnetic Materials In this article, a static-sample magnetometer is presented to measure the relative permeability of weakly magnetic materials. The method consists of scanning the magnetic field inside a dipole magnet by using an NMR teslameter to measure the perturbation of a test specimen on the externally applied field. Then, an inverse problem is used to compute the specimen's relative permeability. As a case study, the measurement of three different materials with different shapes and dimensions is carried out. The method was validated by measuring the same material by vibrating sample magnetometry as proposed by the standard ASTM A342/A342M-14. The expanded measurement uncertainty of the relative permeability, evaluated by Monte Carlo simulations, is about 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> for all the cases, with a level of confidence of 95%. Magnetometer ; Relative permeability ; Magnet ; Magnetic field ; Monte Carlo method ; Permeability (electromagnetism) ; Measure (data warehouse) ; Inverse ; Nuclear magnetic resonance ; Magnetostatics ; Materials science ; Physics ; Computational physics ; Analytical Chemistry (journal) ; Mathematics ; Statistics ; Computer science ; Chemistry ; Composite material ; Geometry ; Chromatography ; Data mining ; Quantum mechanics Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Nuclear Physics and Applications
Authors: Government Rabboni Mike
Source: Indian Journal of Science and Technology (article, 2020-01-24)
Field: Materials Science — Polymers and Plastics

Optimizing the Cellulose Content of Shea Butter Bark Wood Fibre through Alkali Pretreatment for Composite Application Objectives: This study was aimed to optimize the cellulose content (CC) increase with the percentage removal of hemicellulose (HC) and lignin content (LC) of shell butter bark wood fiber (SBBWF) by alkalization process. The truth remains that natural fibers have many uses to be employed in all human activities. The minimization of plant waste is a secret for close economy by effectively converting it to useful ventures. Methods/findings: The SBBWF was inserted in variant solutions for the concentration of sodium hydroxide (CSH) at 3–9 wt% and the soaking time (ST) for 6–18 h. The SBBWF before and after pretreatment were analyzed through gravimetric method and Fourier transform infra-red (FTIR) to determine its composition and organic substance with corresponding functionality, respectively. The optimization was examined using the central composite design (CCD), an option in response surface methodology (RSM). The results at optimal conditions indicated that the increment in CC with the reduction of HC and LC was 97.9988%, 44.5245%, and 45.716%, respectively. The factors at this junction were CSH and ST at 3 wt% and 18 h, respectively. The R2 values of CC, HC and LC had an approximation nearer to 1. The errors approximated by RSM and experimental readings were <0.43%. Application: Therefore, the modified SBBWF at this state is potential material in the component of composite manufacturing to be applied in domestic utilization. Keywords: Shell Butter Bark Wood Fiber, SBBWF, Wealth from Waste, Natural Source, Wood Fibre. Hemicellulose ; Sodium hydroxide ; Lignin ; Response surface methodology ; Central composite design ; Fourier transform infrared spectroscopy ; Composite number ; Cellulose ; Pulp and paper industry ; Bark (sound) ; Materials science ; Fiber ; Composite material ; Chemistry ; Chemical engineering ; Chromatography ; Organic chemistry Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies
Authors: Anton Matasov | Varvara Krasavina
Source: SN Applied Sciences (article, 2020-07-31)
Field: Materials Science — Materials Chemistry

Visualization of superconducting materials Superconductivity ; Visualization ; Valence (chemistry) ; Ionization energy ; Thermal conductivity ; Pairwise comparison ; Materials science ; RADIUS ; Specific heat ; Valence electron ; Ionization ; Atomic radius ; High-temperature superconductivity ; Conductivity ; Electron ; Condensed matter physics ; Database ; Physics ; Computer science ; Data mining ; Ion ; Nuclear physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Superconducting Materials and Applications
Authors: Miranda Holmes-Cerfon
Source: The Journal of Chemical Physics (article, 2020-10-26)
Field: Materials Science — Materials Chemistry

Simulating sticky particles: A Monte Carlo method to sample a stratification Many problems in materials science and biology involve particles interacting with strong, short-ranged bonds that can break and form on experimental timescales. Treating such bonds as constraints can significantly speed up sampling their equilibrium distribution, and there are several methods to sample probability distributions subject to fixed constraints. We introduce a Monte Carlo method to handle the case when constraints can break and form. More generally, the method samples a probability distribution on a stratification: a collection of manifolds of different dimensions, where the lower-dimensional manifolds lie on the boundaries of the higher-dimensional manifolds. We show several applications of the method in polymer physics, self-assembly of colloids, and volume calculation in high dimensions. Monte Carlo method ; Probability distribution ; Stratification (seeds) ; Sample (material) ; Sampling (signal processing) ; Particle filter ; Distribution (mathematics) ; Rare events Pickering emulsions and particle stabilization ; Material Dynamics and Properties ; Block Copolymer Self-Assembly
Authors: Nur Azizah Bachik | Norhashila Hashim | Aimrun Wayayok | A. Salmiaton | Mohd Shukuri Mohamad Ali
Source: Journal of Agricultural and Food Engineering (review, 2020-03-12)
Field: Chemistry — Analytical Chemistry

Optical imaging techniques for rice diseases detection: A review Rice diseases have caused great economic losses to farmers in rice cultivation. The current assessment of rice disease evaluation still relies on manual, subjective, and laborious techniques. The manual and subjective evaluations lead to uncertainties since some diseases have almost similar characterisation. The applications of immunological, molecular, and microscope techniques are time-consuming, costly, and skills dependent. Thus, optical techniques are recommended to facilitate the control of diseases through their feasibility, rapidity, and accuracy, which can lead to better management strategies, besides improving production activity. These techniques for detecting and monitoring the diseases are important for precaution and prevention action. The present review discusses the existing and potential optical techniques for the detection of rice diseases. The techniques include optical imaging that consists of computer vision, spectroscopy, multispectral imaging, hyperspectral imaging (HSI), and remote sensing. Thus, this work presents in-depth information related to the nondestructive and potential applications of optical imaging techniques for rice disease detection. Hyperspectral imaging ; Multispectral image ; Computer science ; Optical imaging ; Risk analysis (engineering) ; Remote sensing ; Biotechnology ; Artificial intelligence ; Medicine ; Biology ; Optics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Oliver M. Crook | Aikaterini Geladaki | Daniel J. H. Nightingale | Owen L. Vennard | Kathryn S. Lilley | Laurent Gatto | Paul Kirk
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-05-05)
Field: Chemistry — Spectroscopy

A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection Abstract The cell is compartmentalised into complex micro-environments allowing an array of specialised biological processes to be carried out in synchrony. Determining a protein’s sub-cellular localisation to one or more of these compartments can therefore be a first step in determining its function. High-throughput and high-accuracy mass spectrometry-based sub-cellular proteomic methods can now shed light on the localisation of thousands of proteins at once. Machine learning algorithms are then typically employed to make protein-organelle assignments. However, these algorithms are limited by insufficient and incomplete annotation. We propose a semi-supervised Bayesian approach to novelty detection, allowing the discovery of additional, previously unannotated sub-cellular niches. Inference in our model is performed in a Bayesian framework, allowing us to quantify uncertainty in the allocation of proteins to new sub-cellular niches, as well as in the number of newly discovered compartments. We apply our approach across 10 mass spectrometry based spatial proteomic datasets, representing a diverse range of experimental protocols. Application of our approach to hyper LOPIT datasets validates its utility by recovering enrichment with chromatin-associated proteins without annotation and uncovers sub-nuclear compartmentalisation which was not identified in the original analysis. Moreover, using sub-cellular proteomics data from Saccharomyces cerevisiae , we uncover a novel group of proteins trafficking from the ER to the early Golgi apparatus. Overall, we demonstrate the potential for novelty detection to yield biologically relevant niches that are missed by current approaches. Computer science ; Proteomics ; Novelty detection ; Computational biology ; Novelty ; Proteome ; Annotation ; Inference ; Bayesian probability ; Artificial intelligence ; Machine learning ; Data mining ; Biology ; Bioinformatics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Bacterial Genetics and Biotechnology
Authors: Javier Brugés Martelo | Jan Lundgren | Mattias Andersson
Source: Sensors (article, 2020-12-31)
Field: Chemistry — Analytical Chemistry

Paperboard Coating Detection Based on Full-Stokes Imaging Polarimetry The manufacturing of high-quality extruded low-density polyethylene (PE) paperboard intended for the food packaging industry relies on manual, intrusive, and destructive off-line inspection by the process operators to assess the overall quality and functionality of the product. Defects such as cracks, pinholes, and local thickness variations in the coating can occur at any location in the reel, affecting the sealable property of the product. To detect these defects locally, imaging systems must discriminate between the substrate and the coating. We propose an active full-Stokes imaging polarimetry for the classification of the PE-coated paperboard and its substrate (before applying the PE coating) from industrially manufactured samples. The optical system is based on vertically polarized illumination and a novel full-Stokes imaging polarimetry camera system. From the various parameters obtained by polarimetry measurements, we propose implementing feature selection based on the distance correlation statistical method and, subsequently, the implementation of a support vector machine algorithm that uses a nonlinear Gaussian kernel function. Our implementation achieves 99.74% classification accuracy. An imaging polarimetry system with high spatial resolution and pixel-wise metrological characteristics to provide polarization information, capable of material classification, can be used for in-process control of manufacturing coated paperboard. Polarimetry ; Paperboard ; Coating ; Metrology ; Computer science ; Optics ; Materials science ; Grinding ; Support vector machine ; Computer vision ; Artificial intelligence ; Scattering ; Composite material ; Physics Spectroscopy and Chemometric Analyses ; Optical Polarization and Ellipsometry ; Leaf Properties and Growth Measurement