jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kurt, A.
Source: Global NEST Journal (article, 2020-09-22)
Field: Chemistry — Electrochemistry

ANODIC OXIDATION OF CEFACLOR ANTIBIOTIC IN AQUEOUS SOLUTION CONTAINING POTASSIUM CHLORIDE &lt;p&gt;At this study, electrochemical oxidation of cefaclor antibiotic was investigated with new generation Sn/Sb/Ni-Ti anodes. Platinized titanium was used as cathode material. Chemical oxygen demand (COD), total organic carbon (TOC) and cefaclor (CEF) active substance parameters were used to evaluate the process efficiency. Salt (KCl) addition (mg L-1), pH value, current density (mA cm-2) and reaction time (minute) were the factors controlling the reactions. Kinetic evaluations were performed during the application of electrochemical oxidation processes to obtain pseudo-first degree kinetics. 750 mg L-1 KCl, pH 7 and 50 mA cm-2 current density were found as the optimum conditions at room temperature (25 °C). Thus, ≤ % 99 removal efficiencies were found for COD and TOC parameters after 60 min reaction and complete mineralization of CEF was occured in just 30 min at the optimum conditions. Consequently, Sn/Sb/Ni-Ti anodes were found very useful and successful for cefaclor oxidation and mineralization. The advantages of the processes are, complete mineralization at shorter reaction time with low current densities and there is no need to pH arrangement.&lt;/p&gt;&#x0D; Mineralization (soil science) ; Chemistry ; Electrochemistry ; Cefaclor ; Aqueous solution ; Inorganic chemistry ; Potassium ; Chloride ; Anode ; Nuclear chemistry ; Nitrogen ; Electrode ; Organic chemistry ; Cephalosporin Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: Maowen Song | Di Wang | Zhaxylyk A. Kudyshev | Yi Xuan | Zhuoxian Wang | Alexandra Boltasseva | Vladimir M. Shalaev | Alexander V. Kildishev
Source: arXiv (Cornell University) (preprint, 2020-09-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enabling optical steganography, data storage, and encryption with plasmonic colors Plasmonic color generation utilizing ultra-thin metasurfaces as well as metallic nanoparticles hold a great promise for a wide range of applications, including color displays, data storage, and information encryption due to its high spatial resolution and mechanical/chemical stability. Most of the recently demonstrated systems generate static colors; however, more advanced applications such as data storage require fast and flexible means to tune the plasmonic colors, while keeping them vibrant and stable. Here, a surface-relief aluminum metasurface that reflects polarization-tunable plasmonic colors is designed and experimentally demonstrated. Excitation of localized surface plasmons encodes discrete combinations of the incident and reflected polarized light into diverse colors. A single storage unit - a nanopixel - stores a multiple-bit piece of information in the orientation of its constituent nanoantennae. This information is then reliably retrieved by inspecting the reflected color sequence with two linear polarizers. It is the broad color variability and high spatial resolution of the proposed encoding approach that supports a strong promise for rapid parallel read-out and encryption of high-density optical data. Our method also enables the robust generation of dynamic kaleidoscopic images with no detrimental "cross-talk" effect. The approach opens up a new route for advanced dynamic steganography, high-density parallel-access optical data storage, and optical information encryption. Encryption ; Polarizer ; Computer science ; Computer data storage ; Plasmon ; Optical storage ; Polarization (electrochemistry) ; 3D optical data storage ; Materials science ; Optics ; Optoelectronics ; Computer hardware ; Physics ; Chemistry Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Optical Wireless Communication Technologies
Authors: Mahdi Rashvand | Mahmoud Soltani Firouz
Source: Research in Agricultural Engineering (article, 2020-03-27)
Field: Chemistry — Analytical Chemistry

Dielectric technique combined with artificial neural network and support vector regression in moisture content prediction of olive Olives are one of the most important agriculture crops in the world, which are harvested in different stages of growth for various uses. One of the ways to detect the adequate time to process the olives is to determine their moisture content. In this study, to determine the moisture content of olives, a dielectric technique was used in seven periods of harvesting and three different varieties of olive including Oily, Mary and Fishemi. The dielectric properties of the olive fruits were measured using an electronic device in the range of 0.1-30 MHz. Artificial Neural Network (ANN) and Support Vector Regression (SVR) methods were applied to develop the prediction models by using the obtained data acquired by the system. The best results (R = 0.999 and MSE = 0.014) were obtained by the ANN model with a topology of 384-12-1 (384 features in the input vector, 12 neurons in the hidden layer and 1 output). The results obtained indicated the acceptable accuracy of the dielectric technique combined with the ANN model. Artificial neural network ; Support vector machine ; Dielectric ; Water content ; Artificial intelligence ; Biological system ; Range (aeronautics) ; Process (computing) ; Regression ; Mathematics ; Regression analysis ; Machine learning ; Computer science ; Materials science ; Engineering ; Statistics ; Composite material ; Biology ; Geotechnical engineering ; Electrical engineering Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Postharvest Quality and Shelf Life Management
Authors: Matthias Lerbinger
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2020-11-20)
Field: Materials Science — Materials Chemistry

Local shear rearrangements in glassy systems : from micromechanics to structural relaxation in supercooled liquids In this thesis, the local yield stress method is applied and extended to study single, irreversible atomistic rearrangementsas well as structural relaxation in a model glass-forming liquid, a two dimensional binary Lennard-Jones mixture. Themethod gives access to the local mechanical response of an inherent configuration in a direct and non-perturbativemanner while controlling the length scales and loading directions. In the first part, the focus is on a small inclusion ofthe glass. Its micromechanical response is probed in the athermal quasi-static limit. The influence of the length scale,at which the mechanical response is probed, is discussed. The variation of the threshold statistics with the size of theprobing zone can be understood on the basis of a simple geometric argument and a weakest link assumption. Then, upondetermining the dependence of the local critical yield stress on the shear loading direction with a high angular resolution,it is observed that only a finite and discrete number of shear rearrangements is accessible, each of them having a distinctweak plane. Furthermore, the critical yield stress shows a high sensitivity towards the pressure in the simulation box. It isfound that for the length scale studied, a Mohr-Coulomb yield criterion describes piecewise accurately the critical shearstress. In the second part, a connection between structure and dynamics of model supercooled liquids is established. Thenovelty in the present work is the characterization of the structure through local slip thresholds. A strong correlation isfound between the stress barriers in the softest direction calculated in the as-quenched state and observables associatedto the relaxation of the liquid structure at parent temperature. As expected, a higher correlation coefficient is detected forliquids equilibrated at lower temperatures, as the potential energy landscape increasingly influences the dynamics. Supercooling ; Length scale ; Materials science ; Simple shear ; Statistical physics ; Nucleation ; Observable ; Micromechanics ; Dilatant ; Mechanics ; Shear stress ; Thermodynamics ; Physics Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: Bruna Gava Floriam | Fabíola Manhas Verbi Pereira | Érica Regina Filletti
Source: Eclética Química (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Estimating bulk density in leguminous grains with different traits using color parameters from digital images combined with artificial neural networks Dry grains from leguminous species, such as soybeans (Glycine max L.), common beans (Phaseolus vulgaris L.), chickpeas (Cicer arietinum L.) and corn (Zea mays L.), are regularly consumed for human nutrition. This paper showed the possibility of estimating bulk density as quality parameter of 4 different dry grains (soybeans, common beans, chickpeas and corn) in a same model using the average values of color descriptors from digital images combined with an artificial neural network, with low computational costs. These food products are good sources of carbohydrates, protein and dietary fiber, and they possess significant amounts of vitamins and minerals and a high energetic value. Estimation of the physicochemical properties of grains is challenging due to variations in shape, texture, and size and because the grain colors appear similar to the naked eye. In this work, an analytical method was developed based on digital images converted into ten color scale descriptors combined with a neural model to provide an accurate parameter for grain quality control with a low computational cost. The bulk densities of four type of grains, i.e., soybeans, beans, chickpeas and corn, were predicted using numerical data represented by the average values of color histograms of a ten color scale (red - R, green - G, blue - B, hue - H, saturation - S, value - V, relative RGB and luminosity - L) from digital images combined with artificial neural networks (ANNs). The reference bulk densities were empirically measured. A very good correlation between the reference values and values predicted by the ANN was achieved, and with a single ANN developed for the four grains, a correlation coefficient of 0.98 was observed for the test set. Moreover, the relative errors were between 0.01 and 5.6% for the test set. Hue ; RGB color model ; Artificial intelligence ; Artificial neural network ; Mathematics ; Biological system ; Phaseolus ; Correlation coefficient ; Histogram ; Botany ; Computer science ; Biology ; Statistics ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Food composition and properties ; Leaf Properties and Growth Measurement
Authors: Lars Carlsen | Rainer Brüggemann
Source: Informatica (article, 2020-03-15)
Field: Chemistry — Spectroscopy

The Iris dataset revisited. A partial ordering study The well-known Iris data set has been studied applying partial ordering methodology. Previous studies, e.g., applying supervision learning such as neural networks (NN) and support-vector machines (SVM) perfectly distinguish between the three Iris subgroups, i.e., Iris Setosa, Iris Versicolour and Iris Virginica, respectively, in contrast to, e.g., K-means clustering that only separates the full Iris data set in two clusters. In the present study applying partial ordering methodology further discloses the difference between the different classification methods. The partial ordering data appears to be in perfect agreement with the results of the K-means clustering, which means that the clear separation in the three Iris subsets applying NN and SVM is neither recognized by clustering nor by partial ordering methodology. IRIS (biosensor) ; Cluster analysis ; Pattern recognition (psychology) ; Support vector machine ; Artificial intelligence ; Data set ; Computer science ; Set (abstract data type) ; Iris recognition ; Data mining ; Mathematics ; Biometrics Molecular spectroscopy and chirality ; Algebraic structures and combinatorial models ; Advanced Combinatorial Mathematics
Authors: Fatin Najihah Mat Ali | Siti Ujila Masuri | Lingeswaran Kaniappan | Radhiyatul Hikmah binti Abu | Nur Ismarrubie Zahari
Source: Journal of Advanced Research in Fluid Mechanics and Thermal Sciences (article, 2020-03-25)
Field: Materials Science — Biomaterials

Adsorption Morphology of Nano Scale Inhibitors for Oilfield Mineral Scale Mitigation Formation of mineral scales is a major issue in oil and gas industry which requires the use of scale inhibitors via squeeze treatment to mitigate the problem. Unfortunately, the retention ability of the existing scale inhibitor is low due to low adsorption of scale inhibitors onto the mineral surface, resulting in less efficient performance of scale inhibition. Recent advancement shows that nano scale inhibitors are believed to be able to migrate deep into reservoirs and adsorb onto the mineral surface during the shut-in period. Since the adsorption ability of scale inhibitors onto the mineral surface is an important characteristic, in this work adsorption morphology of several nano scale inhibitors onto mineral surface was investigated. For this purpose, this paper first describes the synthesis of these nano scale inhibitors which are nano Ca-DTPMP, nano Si-Zn-DTPMP, and nano Si-Ca-DTPMP. Results from Transmission Electron Microscopy analysis indicate the particle size of these nano scale inhbibitors in comparison to the commercial DTPMP and Ca-DTPMP. To investigate the adsorption morphology of these nano scale inhibitors onto mineral surface, static adsorption tests were done using Kaolinite as the selected mineral. Results from Field Emission Electron Microscopy analysis show the morphological changes of the Kaolinite surface after application of the scale inhibitors. Nano Si-Zn-DTPMP scale inhibitor exhibits smallest particle size of 31 nm to 37 nm based on the TEM image. As supported by the FESEM image on the adsorption morphology, this small particle size contributes to better adsorption of this scale inhibitor than its counterparts. Adsorption ; Scale (ratio) ; Nano- ; Morphology (biology) ; Mineral ; Chemical engineering ; Nanoscopic scale ; Materials science ; Nanotechnology ; Geology ; Chemistry ; Metallurgy ; Engineering ; Organic chemistry ; Geography ; Cartography Calcium Carbonate Crystallization and Inhibition ; Minerals Flotation and Separation Techniques ; Magnetic and Electromagnetic Effects
Authors: Luca Di Liello | Pierfrancesco Ardino | Jacopo Gobbi | Paolo Morettin | Stefano Teso | Andrea Passerini
Source: Lirias (KU Leuven) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Efficient Generation of Structured Objects with Constrained Adversarial Networks Generative Adversarial Networks (GANs) struggle to generate structured objects like molecules and game maps. The issue is that structured objects must satisfy hard requirements (e.g., molecules must be chemically valid) that are difficult to acquire from examples alone. As a remedy, we propose Constrained Adversarial Networks (CANs), an extension of GANs in which the constraints are embedded into the model during training. This is achieved by penalizing the generator proportionally to the mass it allocates to invalid structures. In contrast to other generative models, CANs support efficient inference of valid structures (with high probability) and allows to turn on and off the learned constraints at inference time. CANs handle arbitrary logical constraints and leverage knowledge compilation techniques to efficiently evaluate the disagreement between the model and the constraints. Our setup is further extended to hybrid logical-neural constraints for capturing very complex constraints, like graph reachability. An extensive empirical analysis shows that CANs efficiently generate valid structures that are both high-quality and novel. Computer science ; Reachability ; Adversarial system ; Leverage (statistics) ; Inference ; Generator (circuit theory) ; Generative grammar ; Theoretical computer science ; Artificial intelligence ; Graph ; Machine learning Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Machine Learning and Data Classification
Authors: Chengyun Zhang | Ling Wang | Yejian Wu | Yun Zhang | An Su | Hongliang Duan
Source: ChemRxiv (preprint, 2020-11-02)
Field: Materials Science — Materials Chemistry

Transformer: Linking Atom Mapping and Neural Machine Translation Atom mapping reveals the corresponding relationship between reactant and product atoms in chemical reactions, which is important for drug design, exploration for underlying chemical mechanism, reaction classification and so on. Here, we present a new method that links atom mapping and neural machine translation using the transformer model. In contrast to the previous algorithms, our method runs reaction prediction and captures the information of corresponding atoms in parallel. Meanwhile, we use a set of approximately 360K reactions without atom mapping information for obtaining general chemical knowledge and transfer it to atom mapping task on another dataset which contains 50K atom-mapped reactions. With manual evaluation, the top-1 accuracy of the transformer model in atom mapping reaches 91.4%. we hope our work can provide an important step toward solving the challenge problem of atom mapping in a linguistic perspective. Atom (system on chip) ; Computer science ; Transformer ; Machine translation ; Biological system ; Artificial intelligence ; Chemistry ; Algorithm ; Natural language processing ; Data mining ; Physics ; Voltage ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: F. Abdullah | M. A. M. Piah | Nordiana Azlin Othman | Abdul Talib Din
Source: Bulletin of Electrical Engineering and Informatics (article, 2020-08-25)
Field: Materials Science — Materials Chemistry

Prediction of surface leakage current of overhead insulators under environmental and electrical stresses Leakage current is one of the critical aspects to consider for overhead transmission line insulator’s condition and performance assessment. As the leakage current increase, the size of the dry band also will increase leads to the dry band arcing, deteriorate the insulator performance and contribute to the development of insulator flashover. Based on the literature study, other than the existence of contaminations on the surface of insulator combined with moisture, the variation in leakage current is also affected by the environmental and electrical stresses. Previous researches have shown the effect of environmental and electrical stresses on surface leakage current based on experimental and simulation results. This paper outlines an analytical approach based on dimensional analysis to propose a new mathematical model of leakage current under environmental and electrical stresses. To justify the applicability of the derived dimensional model, the new model has been validated using previous researcher’s experimental results. The validation indicated that the proposed model had shown a good agreement with the previous experimental results. The proposed dimensional equation for this research work can be potentially used as a predictive performance model to evaluate and monitor the leakage current and insulator’s performance Insulator (electricity) ; Arc flash ; Leakage (economics) ; Transmission line ; Electronic engineering ; Materials science ; Moisture ; Mechanics ; Electrical engineering ; Engineering ; Optoelectronics ; Composite material ; Physics High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Electrical Fault Detection and Protection
Authors: Ali Ganjloo | Mohsen Zandi | Mandana Bimakr | S Monajem
Source: Food Science and Technology (article, 2020-10-01)
Field: Chemistry — Analytical Chemistry

Ripening Stages Control of Cherry Tomato Coated with Aloe Vera Gel using Artificial Vision System Aloe vera ; Ripening ; Horticulture ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Smart Agriculture and AI
Authors: Benjamin A. Neely | Paul M. Stemmer | Brian C. Searle | Laura E. Herring | LeRoy Martin | Mukul K. Midha | Brett S. Phinney | Baozhen Shan | Magnus Palmblad | Yan Wang | Pratik Jagtap | Joanna Kirkpatrick
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-11-20)
Field: Chemistry — Spectroscopy

2019 Association of Biomolecular Resource Facilities Multi-Laboratory Data-Independent Acquisition Study Abstract Despite the advantages of fewer missing values by collecting fragment ion data on all analytes in the sample, as well as the potential for deeper coverage, the adoption of data-independent acquisition (DIA) in core facility settings has been slow. The Association of Biomolecular Resource Facilities conducted a large interlaboratory study to evaluate DIA performance in laboratories with various instrumentation. Participants were supplied with generic methods and a uniform set of test samples. The resulting 49 DIA datasets act as benchmarks and have utility in education and tool development. The sample set consisted of a tryptic HeLa digest spiked with high or low levels of four exogenous proteins. Data are available in MassIVE MSV000086479. Additionally, we demonstrate how the data can be analysed by focusing on two datasets using different library approaches and show the utility of select summary statistics. These data can be used by DIA newcomers, software developers, or DIA experts evaluating performance with different platforms, acquisition settings and skill levels. Computer science ; Sample (material) ; Data acquisition ; Software ; Set (abstract data type) ; Data set ; Resource (disambiguation) ; Data mining ; Instrumentation (computer programming) ; Data science ; Database ; Information retrieval ; Operating system ; Chemistry ; Artificial intelligence ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Protein purification and stability
Authors: Anthony Trubiano | Miranda Holmes‐Cerfon
Source: Physical review. E (article, 2020-04-27)
Field: Materials Science — Materials Chemistry

From canyons to valleys: Numerically continuing sticky-hard-sphere clusters to the landscapes of smoother potentials We study the energy landscapes of particles with short-range attractive interactions as the range of the interactions increases. Starting with the set of local minima for 6≤N≤12 hard spheres that are "sticky," i.e., they interact only when their surfaces are exactly in contact, we use numerical continuation to evolve the local minima (clusters) as the range of the potential increases, using both the Lennard-Jones and Morse families of interaction potentials. As the range increases, clusters merge, until at long ranges only one or two clusters are left. We compare clusters obtained by continuation with different potentials and find that for short and medium ranges, up to about 30% of particle diameter, the continued clusters are nearly identical, both within and across families of potentials. For longer ranges, the clusters vary significantly, with more variation between families of potentials than within a family. We analyze the mechanisms behind the merge events and find that most rearrangements occur when a pair of nonbonded particles comes within the range of the potential. An exception occurs for nonharmonic clusters, i.e., those that have a zero eigenvalue in their Hessian, which undergo a more global rearrangement. Maxima and minima ; Hessian matrix ; Merge (version control) ; Range (aeronautics) ; Physics ; SPHERES ; Statistical physics ; Cluster (spacecraft) ; Mathematics ; Materials science ; Mathematical analysis Material Dynamics and Properties ; Theoretical and Computational Physics ; Stochastic processes and statistical mechanics