jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: K. Szwedziak | E. Polańczyk | Żaneta Grzywacz | Gniewko Niedbała | Wiktoria Wojtkiewicz
Source: Sustainability (article, 2020-06-20)
Field: Chemistry — Analytical Chemistry

Neural Modeling of the Distribution of Protein, Water and Gluten in Wheat Grains during Storage An important requirement in the grain industry is to obtain fast information on the quality of purchased and stored grain. Therefore, it is of great importance to search for innovative solutions aimed at the monitoring and fast assessment of quality parameters of stored wheat The results of the evaluation of total protein, water and gluten content by means of near infrared spectrometry are presented in the paper. Multiple linear regression analysis (MLR) and neural modeling were used to analyze the obtained results. The results obtained show no significant changes in total protein (13.13 ± 0.15), water (10.63 ± 0.16) or gluten (30.56 ± 0.54) content during storage. On the basis of the collected data, a model artificial neural network (ANN) MLP 52-6-3 was created, which, with the use of four independent features, allows us to determine changes in the content of water, protein and gluten in stored wheat. The chosen network returned good error values: learning, below 0.001; testing, 0.015; and validation, 0.008. The obtained results and their interpretation are an important element in the warehouse industry. The information obtained in this way about the state of the quality of stored grain will allow for a fast reaction in case of the threat of lowering the quality parameters of the stored grain. Gluten ; Artificial neural network ; Warehouse ; Grain quality ; Wheat gluten ; Quality (philosophy) ; Environmental science ; Agricultural engineering ; Computer science ; Process engineering ; Mathematics ; Biological system ; Food science ; Machine learning ; Chemistry ; Agronomy ; Engineering ; Geography Spectroscopy and Chemometric Analyses ; Food Industry and Aquatic Biology ; Animal Nutrition and Health
Authors: Huazhou Chen | Bin Lin | Ken Cai | An Chen | Shaoyong Hong
Source: Infrared Physics & Technology (article, 2020-12-02)
Field: Chemistry — Analytical Chemistry

Quantitative analysis of organic acids in pomelo fruit using FT-NIR spectroscopy coupled with network kernel PLS regression Kernel (algebra) ; Mean squared error ; Pattern recognition (psychology) ; Partial least squares regression ; Biological system ; Feature selection ; Artificial intelligence ; Feature (linguistics) ; Computer science ; Near-infrared spectroscopy ; Artificial neural network ; Mathematics ; Correlation coefficient ; Machine learning ; Statistics ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Maryam Salehi | Fatemeh Hakkak | S.M. Sadati Tilebon | Maryam Ataeefard | Mehdi Rafizadeh
Source: eXPRESS Polymer Letters (article, 2020-01-01)
Field: Materials Science — Biomaterials

Intelligently optimized electrospun polyacrylonitrile/poly(vinylidene fluoride) nanofiber: Using artificial neural networks In this work, an intelligent approach is applied for the first time in the modelling and optimization of electrospun Polyacrylonitrile/Poly(Vinylidene Fluoride) (PAN/PVdF) nanofiber properties. A genetic algorithm-based computer code was developed to optimize the architecture of an artificial neural network, by which an accurate model was developed for the prediction of nanofiber diameter, the standard deviation of nanofiber diameter and porosity of electrospun membrane. Electrospinning of polyacrylonitrile/poly(vinylidene fluoride) (PAN/PVdF) was applied to obtain a quantitative relationship between selected electrospinning parameters (namely applied voltage, solution concentration, and PVdF composition) and nanofiber diameter, the standard deviation of nanofiber diameter and porosity of electrospun membrane. The morphology and nanofiber diameter were investigated by field emission scanning electron microscopy (FESM). The range of produced nanofiber diameters was from 116 to 379 nm. It seemed that the nanofiber diameter and standard deviation of nanofiber diameter decrease with PVdF composition and increase with solution concentration. The applied voltage had no important effect on the nanofiber diameters. The porosity of the electrospun membrane decreases with solution concentration and increases with PVdF composition. Polyacrylonitrile ; Nanofiber ; Electrospinning ; Materials science ; Porosity ; Membrane ; Composite material ; Fluoride ; Chemical engineering ; Polymer chemistry ; Polymer Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Yunxuan Ding | Yarong Xu | Yu Mao | Ziyun Wang | P. Hu
Source: Chemical Communications (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Achieving rational design of alloy catalysts using a descriptor based on a quantitative structure–energy equation An approach to rationally design optimal alloy catalysts is established using nitric oxide (NO) oxidation as an example. We introduce a quantitative structure-energy equation to predict the chemisorption energies of adsorbates on alloy catalysts. The structure-energy descriptor is used to rationally design Pt-based and Ni-based alloy catalysts for NO oxidation. Full first principles calculations with kinetic simulations demonstrate that these designed catalysts possess much better catalytic performances than the traditional catalysts. Rational design ; Alloy ; Catalysis ; Energy (signal processing) ; Chemistry ; Materials science ; Computational chemistry ; Computer science ; Chemical engineering ; Mathematics ; Biological system ; Nanotechnology ; Metallurgy ; Engineering ; Organic chemistry ; Statistics ; Biology Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Aravind Krishnamoorthy | Ankit Mishra | Nicholas Grabar | Nitish Baradwaj | Rajiv K. Kalia | Aiichiro Nakano | Priya Vashishta
Source: Computer Physics Communications (article, 2020-04-28)
Field: Materials Science — Materials Chemistry

Evolutionary multi-objective optimization and Pareto-frontal uncertainty quantification of interatomic forcefields for thermal conductivity simulations Thermal conductivity ; Multi-objective optimization ; Computer science ; Mathematical optimization ; Parameterized complexity ; Weighting ; Statistical physics ; Interatomic potential ; Molecular dynamics ; Algorithm ; Mathematics ; Physics ; Chemistry ; Thermodynamics ; Computational chemistry Thermal properties of materials ; Machine Learning in Materials Science ; Heat Transfer and Optimization
Authors: Marta R. Alexandre | Alexandra I. Costa | Mário N. Berberan‐Santos | José V. Prata
Source: Molecules (article, 2020-05-15)
Field: Materials Science — Materials Chemistry

Finding Value in Wastewaters from the Cork Industry: Carbon Dots Synthesis and Fluorescence for Hemeprotein Detection Valorisation of industrial low-value waste residues was preconized. Hence, carbon dots (C-dots) were synthesized from wastewaters of the cork industry-an abundant and affordable, but environmentally-problematic industrial effluent. The carbon nanomaterials were structurally and morphologically characterised, and their photophysical properties were analysed by an ensemble of spectroscopy techniques. Afterwards, they were successfully applied as highly-sensitive fluorescence probes for the direct detection of haemproteins. Haemoglobin, cytochrome <i>c</i> and myoglobin were selected as specific targets owing to their relevant roles in living organisms, wherein their deficiencies or surpluses are associated with several medical conditions. For all of them, remarkable responses were achieved, allowing their detection at nanomolar levels. Steady-state and time-resolved fluorescence, ground-state UV-Vis absorption and electronic circular dichroism techniques were used to investigate the probable mechanisms behind the fluorescence turn-off of C-dots. Extensive experimental evidence points to a static quenching mechanism. Likewise, resonance energy transfer and collisional quenching have been discarded as excited-state deactivating mechanisms. It was additionally found that an oxidative, photoinduced electron transfer occurs for cytochrome <i>c</i>, the most electron-deficient protein. Besides, C-dots prepared from citric acid/ethylenediamine were comparatively assayed for protein detection and the differences between the two types of nanomaterials highlighted. Fluorescence ; Quenching (fluorescence) ; Photochemistry ; Chemistry ; Nanomaterials ; Circular dichroism ; Excited state ; Electron transfer ; Ethylenediamine ; Analytical Chemistry (journal) ; Nanotechnology ; Materials science ; Organic chemistry ; Stereochemistry Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Heavy Metal Exposure and Toxicity
Authors: Ngoc-Diem Tran Thi | The-Duong Do | Jae-Ryong Jung | Hyang-Eun Jo | Yong‐Hwa Kim
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Anomaly Detection for Partial Discharge in Gas-Insulated Switchgears Using Autoencoder In this article, we propose a new anomaly detection method to detect the partial discharge in a gas-insulated switchgear. An autoencoder was used for anomaly detection and was modeled on the one-class classification problem. Based on the one-class classification scenario, in which the training data exploited the noise data only, the proposed autoencoder learned the low-dimensional latent information from the high-dimensional space of the input signal. Then, the reconstruction error was used as a fault indicator, and the threshold was determined using the partial discharge data. The performance of the proposed AE was verified by on-site noise and PRPD experiments, using an online UHF PD monitoring system in the real-world environment. The results showed that the proposed autoencoder not only achieved 86.75% detection performance for the on-site noise and partial discharge data in gas-insulated switchgears but also allowed better detection performance than the one-class support vector machine learning procedure by 40.5%. Switchgear ; Partial discharge ; Autoencoder ; Anomaly detection ; Pattern recognition (psychology) ; Noise (video) ; Computer science ; Artificial intelligence ; Anomaly (physics) ; Support vector machine ; Fault (geology) ; Fault detection and isolation ; Speech recognition ; Engineering ; Deep learning ; Physics ; Voltage ; Electrical engineering ; Geology ; Actuator High voltage insulation and dielectric phenomena ; Anomaly Detection Techniques and Applications ; Elevator Systems and Control
Authors: Yang Hou | Thunyaluk Pojtanabuntoeng | Mariano Iannuzzi
Source: npj Materials Degradation (article, 2020-12-03)
Field: Materials Science — Materials Chemistry

Use of electrochemical current noise method to monitor carbon steel corrosion under mineral wool insulation Abstract Corrosion of carbon steel under mineral wool insulation was studied using the electrochemical current noise (ECN) method. Intensities of corrosion were validated using gravimetry, and the form of corrosion confirmed using optical microscopy. The standard deviation of the current noise signal agreed with weight loss results and was demonstrated as a reliable indicator of the degree of corrosion under mineral wool insulation. Recurrence quantification analysis was used to extract feature variables from ECN signals, which were later used to develop a random forest model to identify the type of corrosion, i.e., uniform or localised corrosion. The trained model was successfully applied to predict the extent of localised corrosion associated with mineral wool insulation. Electrochemical noise ; Corrosion ; Materials science ; Carbon steel ; Mineral wool ; Metallurgy ; Composite material ; Current (fluid) ; Noise (video) ; Electrochemistry ; Electrode ; Engineering ; Chemistry ; Electrical engineering Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques
Authors: Stefanie Schmier | Maximilian Jentzsch | Thomas Speck | Marc Thielen
Source: Materials & Design (article, 2020-07-13)
Field: Materials Science — Biomaterials

Fracture mechanics of the endocarp of Cocos nucifera Fracture mechanics and form-structure-function relationships were studied in the hierarchically structured endocarp of the coconut (Cocos nucifera). We mechanically tested various endocarp sample types: (1) whole endocarp; (2) arch-shaped samples prepared from the endocarp in either an equatorial or a meridional orientation; (3) small endocarp samples. The last-mentioned were tested either in their naturally grown state or with both surfaces ground coplanarly. Samples were either compression- or impact-loaded in various orientations and/or diverse setup configurations (constrained vs. un-constrained). The results establish (1) that the orientation of the whole endocarp has no significant effect on the fracture force, (2) that the orientation of arch-shaped samples affects fracture behaviour, whereas (3) test velocity does not and (4) that the compressive strength of rectangular samples is highest when applied normal to the endocarp surface. Thus, on a comparatively small scale, the endocarp is a transversely isotropic material, whereas on a larger scale, the ellipsoidal shape of the coconut compensates for weaknesses introduced by the main vascular bundles. Coconut shells are therefore good examples of the way in which potential disadvantages that concern stability and that arise from biological necessities and evolutionary constraints can be counteracted by clever material arrangements and designs. Cocos nucifera ; Fracture (geology) ; Materials science ; Compression (physics) ; Orientation (vector space) ; Isotropy ; Texture (cosmology) ; Composite material ; Fracture mechanics ; Mechanics ; Mathematics ; Geometry ; Biology ; Botany ; Physics ; Computer science ; Optics Calcium Carbonate Crystallization and Inhibition ; Magnetic and Electromagnetic Effects ; Agricultural Engineering and Mechanization
Authors: Rageia Elfageih | Alexandros Karyolaimos | Grant Kemp | Jan‐Willem De Gier | Gunnar von Heijne | Renuka Kudva
Source: Protein Science (article, 2020-08-13)
Field: Materials Science — Materials Chemistry

Cotranslational folding of alkaline phosphatase in the periplasm of <scp> <i>Escherichia coli</i> </scp> Cotranslational protein folding studies using Force Profile Analysis, a method where the SecM translational arrest peptide is used to detect folding-induced forces acting on the nascent polypeptide, have so far been limited mainly to small domains of cytosolic proteins that fold in close proximity to the translating ribosome. In this study, we investigate the cotranslational folding of the periplasmic, disulfide bond-containing Escherichia coli protein alkaline phosphatase (PhoA) in a wild-type strain background and a strain background devoid of the periplasmic thiol: disulfide interchange protein DsbA. We find that folding-induced forces can be transmitted via the nascent chain from the periplasm to the polypeptide transferase center in the ribosome, a distance of ~160 Å, and that PhoA appears to fold cotranslationally via at least two disulfide-stabilized folding intermediates. Thus, Force Profile Analysis can be used to study cotranslational folding of proteins in an extra-cytosolic compartment, like the periplasm. Periplasmic space ; DsbA ; Protein folding ; Biochemistry ; Chemistry ; Escherichia coli ; Protein disulfide-isomerase ; Folding (DSP implementation) ; Ribosome ; Cytosol ; Biophysics ; Biology ; Enzyme ; RNA Enzyme Structure and Function ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Petar Žuvela | Jay Liu | Ming Wah Wong | Tomasz Bączek
Source: Molecules (article, 2020-07-06)
Field: Chemistry — Spectroscopy

Prediction of Chromatographic Elution Order of Analytical Mixtures Based on Quantitative Structure-Retention Relationships and Multi-Objective Optimization Prediction of the retention time from the molecular structure using quantitative structure-retention relationships is a powerful tool for the development of methods in reversed-phase HPLC. However, its fundamental limitation lies in the fact that low error in the prediction of the retention time does not necessarily guarantee a prediction of the elution order. Here, we propose a new method for the prediction of the elution order from quantitative structure-retention relationships using multi-objective optimization. Two case studies were evaluated: (i) separation of organic molecules in a Supelcosil LC-18 column, and (ii) separation of peptides in seven columns under varying conditions. Results have shown that, when compared to predictions based on the conventional model, the relative root mean square error of the elution order decreases by 48.84%, while the relative root mean square error of the retention time increases by 4.22% on average across both case studies. The predictive ability in terms of both retention time and elution order and the corresponding applicability domains were defined. The models were deemed stable and robust with few to no structural outliers. Elution ; Outlier ; Chemistry ; Retention time ; Chromatography ; Mean squared error ; High-performance liquid chromatography ; Biological system ; Mathematics ; Statistics Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Computational Drug Discovery Methods
Authors: Jordan Vincent | Hui Wang | Omar Nibouche | Paul Maguire
Source: Plasma Sources Science and Technology (article, 2020-07-09)
Field: Chemistry — Spectroscopy

Detecting trace methane levels with plasma optical emission spectroscopy and supervised machine learning Abstract Trace methane detection in the parts per million range is reported using a novel detection scheme based on optical emission spectra from low temperature atmospheric pressure microplasmas. These bright low-cost plasma sources were operated under non-equilibrium conditions, producing spectra with a complex and variable sensitivity to trace levels of added gases. A data-driven machine learning approach based on partial least squares discriminant analysis was implemented for CH 4 concentrations up to 100 ppm in He, to provide binary classification of samples above or below a threshold of 2 ppm. With a low-resolution spectrometer and a custom spectral alignment procedure, a prediction accuracy of 98% was achieved, demonstrating the power of machine learning with otherwise prohibitively complex spectral analysis. This work establishes proof of principle for low cost and high-resolution trace gas detection with the potential for field deployment and autonomous remote monitoring. Trace gas ; Methane ; Spectroscopy ; TRACE (psycholinguistics) ; Spectral line ; Analytical Chemistry (journal) ; Chemistry ; Linear discriminant analysis ; Spectrometer ; Emission spectrum ; Artificial intelligence ; Optics ; Computer science ; Physics ; Environmental chemistry Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Air Quality Monitoring and Forecasting
Authors: Marlies Lauwers | Benny De Cauwer | David Nuyttens | Simon Cool | Jan Pieters
Source: Sensors (article, 2020-04-28)
Field: Chemistry — Analytical Chemistry

Hyperspectral Classification of Cyperus esculentus Clones and Morphologically Similar Weeds <i>Cyperus esculentus</i> (yellow nutsedge) is one of the world's worst weeds as it can cause great damage to crops and crop production. To eradicate <i>C. esculentus,</i> early detection is key-a challenging task as <i>it</i> is often confused with other <i>Cyperaceae</i> and displays wide genetic variability. In this study, the objective was to classify <i>C. esculentus</i> clones and morphologically similar weeds. Hyperspectral reflectance between 500 and 800 nm was tested as a measure to discriminate between (I) <i>C. esculentus</i> and morphologically similar <i>Cyperaceae</i> weeds, and between (II) different clonal populations of <i>C. esculentus</i> using three classification models: random forest (RF), regularized logistic regression (RLR) and partial least squares-discriminant analysis (PLS-DA). RLR performed better than RF and PLS-DA, and was able to adequately classify the samples. The possibility of creating an affordable multispectral sensing tool, for precise in-field recognition of <i>C. esculentus</i> plants based on fewer spectral bands, was tested. Results of this study were compared against simulated results from a commercially available multispectral camera with four spectral bands. The model created with customized bands performed almost equally well as the original PLS-DA or RLR model, and much better than the model describing multispectral image data from a commercially available camera. These results open up the opportunity to develop a dedicated robust tool for <i>C. esculentus</i> recognition based on four spectral bands and an appropriate classification model. Cyperus ; Hyperspectral imaging ; Multispectral image ; Partial least squares regression ; Artificial intelligence ; Noxious weed ; Pattern recognition (psychology) ; Linear discriminant analysis ; Spectral signature ; Remote sensing ; Computer science ; Mathematics ; Biology ; Agronomy ; Weed ; Statistics ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Isotope Analysis in Ecology
Authors: Zhong Ren | Cong Wang | Heewhan Shin | Sepalika Bandara | Indika Kumarapperuma | Michael Y. Ren | Weijia Kang | Xiaojing Yang
Source: IUCrJ (article, 2020-09-18)
Field: Materials Science — Materials Chemistry

An automated platform for <i>in situ</i> serial crystallography at room temperature Direct observation of functional motions in protein structures is highly desirable for understanding how these nanomachineries of life operate at the molecular level. Because cryogenic temperatures are non-physiological and may prohibit or even alter protein structural dynamics, it is necessary to develop robust X-ray diffraction methods that enable routine data collection at room temperature. We recently reported a crystal-on-crystal device to facilitate <i>in situ</i> diffraction of protein crystals at room temperature devoid of any sample manipulation. Here an automated serial crystallography platform based on this crystal-on-crystal technology is presented. A hardware and software prototype has been implemented, and protocols have been established that allow users to image, recognize and rank hundreds to thousands of protein crystals grown on a chip in optical scanning mode prior to serial introduction of these crystals to an X-ray beam in a programmable and high-throughput manner. This platform has been tested extensively using fragile protein crystals. We demonstrate that with affordable sample consumption, this <i>in situ</i> serial crystallography technology could give rise to room-temperature protein structures of higher resolution and superior map quality for those protein crystals that encounter difficulties during freezing. This serial data collection platform is compatible with both monochromatic oscillation and Laue methods for X-ray diffraction and presents a widely applicable approach for static and dynamic crystallographic studies at room temperature. Protein crystallization ; Diffraction ; Software ; Computer science ; In situ ; Crystal (programming language) ; Crystallography ; Monochromatic color ; X-ray crystallography ; Sample (material) ; Materials science ; Crystallization ; Chemistry ; Optics ; Physics Enzyme Structure and Function ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications