jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexandre Fong | George Shu | Barry McDonogh | Bosoon Park
Source:  (article, 2020-10-04)
Field: Chemistry — Analytical Chemistry

Detecting foodborne pathogens with darkfield hyperspectral microscopy The presence of pathogenic microorganisms such as salmonella, listeria and E. coli in foods is a major threat to consumer safety. The failure to detect these pathogens can result in severe outbreaks of foodborne illness. There are several technologies utilized in food pathogen detection today including plating, nucleic acid-based polymerase chain reaction techniques and immunoassays. While these technologies have their merits, each approach requires significant sample incubation and total time to answer of 18 – 72hrs. HinaLea is working in collaboration with the USDA to develop a system which will significantly accelerate the identification of foodborne pathogens. The system combines darkfield microscopy, hyperspectral imaging, machine learning and automation in a standalone unit. Our vision is to move towards real-time identification of pathogens in the food production environment. Hyperspectral imaging ; Food safety ; Biological warfare ; Listeria monocytogenes ; Identification (biology) ; Foodborne pathogen ; Salmonella ; Listeria ; Biotechnology ; Computer science ; Biology ; Artificial intelligence ; Food science ; Bacteria ; Toxicology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Aimen El Orche | Mohamed Mbarki | Amine Cheikh | Mustapha Bouatia
Source: E3S Web of Conferences (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Assessment of non-destructive spectroscopy and chemometrics tools for the development of green analytical methods to determine the shelf-life of olive oils The development of sustainable and environmentally friendly analytical methods for agri-food products and the modification of reference methods is an essential issue to be treated in green analytical chemistry. The potential application of non-destructive spectroscopic techniques with chemometrics tools to achieve these principles are examined in this work. In this study a new sustainable analytical approach based on the use of fluorescence spectroscopy and multivariate analysis methods of Machine-Learning(Support Vector Machine regression) and chemometrics (Partial Least Square regression) have been developed to control the quality of virgin olive oils in Morocco according to their shelf life. The spectral data of 45 samples were first analyzed by principal component analysis method (PCA), the PCA method shows an important classification of the three groups of olive oil according to their shelf life. The use of the regression methods SVM and PLS shows a high ability to predict the quality of olive oils, this ability is shown by the high value of R-square and the low value of root mean square error of calibration and crossvalidation (RMSEC, RMSECV), the validation of these models by cross-validation shows the potential of this sustainable analytical approach in the determination of the quality of virgin olive oils. Chemometrics ; Principal component analysis ; Partial least squares regression ; Multivariate statistics ; Principal component regression ; Support vector machine ; Mathematics ; Mean squared error ; Cross-validation ; Statistics ; Olive oil ; Biological system ; Machine learning ; Artificial intelligence ; Computer science ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Balaranjan Selvaratnam | Ranjit T. Koodali | Pere Miró
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Prediction of optoelectronic properties of Cu<sub>2</sub>O using neural network potential Neural network potentials (NNPs) trained against density functional theory (DFT) are capable of reproducing the potential energy surface at a fraction of the computational cost. However, most NNP implementations focus on energy and forces. In this work, we modified the NNP model introduced by Behler and Parrinello to predict Fermi energy, band edges, and partial density of states of Cu2O. Our NNP can reproduce the DFT potential energy surface and properties at a fraction of the computational cost. We used our NNP to perform molecular dynamics (MD) simulations and validated the predicted properties against DFT calculations. Our model achieved a root mean squared error of 16 meV for the energy prediction. Furthermore, we show that the standard deviation of the energies predicted by the ensemble of training snapshots can be used to estimate the uncertainty in the predictions. This allows us to switch from the NNP to DFT on-the-fly during the MD simulation to evaluate the forces when the uncertainty is high. Density functional theory ; Artificial neural network ; Work (physics) ; Molecular dynamics ; Mean squared error ; Statistical physics ; Potential energy surface ; Energy (signal processing) ; Standard deviation ; Computational physics ; Materials science ; Computer science ; Physics ; Chemistry ; Computational chemistry ; Mathematics ; Thermodynamics ; Molecule ; Quantum mechanics ; Machine learning ; Statistics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Eric S. Harper | Matthew S. Mills
Source:  (article, 2020-08-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Bayesian optimization of neural networks for the inverse design of all-dielectric metasurfaces The next generation of multi-functional optical materials will customize electric field responses via a careful arrangement of micro- and nano- scale scatterers to achieve targeted optical performance otherwise unattainable in traditional bulk media. Macroscopically, such designed materials collectively respond to radiation based on the geometric shape, distribution, and inherent material properties of these sub-wavelength structures. The core challenge is in prescribing a configuration which results in a desired property. It becomes immediately clear these metamaterial systems pose significant challenges because of the near-infinite design space one needs to consider. Recently, artificial neural networks (ANNs) have been used to successfully approach this intractable problem. In the specific context of designing an all-dielectric metasurface reflector, we showed that joining two properly trained ANNs can both emulate Maxwell equations as well as inversely correlate reflection and transmission spectra.<sup>1</sup> Though highly accurate ANNs were trained, the ANNs employed were never optimized in terms of architecture (e.g. number of layers, number of nodes, shape of the network) or hyperparameters (e.g. batch sizes, activation functions, loss functions). In this manuscript, we apply Bayesian optimization with Gaussian processes to first optimize the architecture and then the hyperparameters of the spectra predicting networks described in Ref. 1. The goal is not only to improve upon the previously implemented ANNs but to analyze the effect of different ANN architectures and convergence settings on overall spectra predictive performance. Hyperparameter ; Artificial neural network ; Computer science ; Context (archaeology) ; Bayesian optimization ; Metamaterial ; Convergence (economics) ; Gaussian ; Inverse problem ; Mathematical optimization ; Artificial intelligence ; Optics ; Physics ; Mathematics Metamaterials and Metasurfaces Applications ; Millimeter-Wave Propagation and Modeling ; Optical and Acousto-Optic Technologies
Authors: T. L. Mitran | George Alexandru Nemneş
Source: Carbon (article, 2020-12-18)
Field: Materials Science — Materials Chemistry

Ground state charge density prediction in C-BN nanoflakes using rotation equivariant feature-free artificial neural networks Artificial neural network ; Rotation (mathematics) ; Feature (linguistics) ; Charge (physics) ; Materials science ; Equivariant map ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Graphene research and applications
Authors: Vigneshwari Subramanian | Ekaterina L. Ratkova | David Scott Palmer | Ola Engkvist | Maxim V. Fedorov | Antonio Llinàs
Source: ChemRxiv (preprint, 2020-02-06)
Field: Materials Science — Materials Chemistry

Multi-Solvent Models for Solvation Free Energy Predictions Using 3D-RISM Hydration Thermodynamic Descriptors The potential to predict Solvation Free Energies (SFEs) in any solvent using a machine learning (ML) model based on thermodynamic output, extracted from 3D-RISM simulations in water is investigated. The models on multiple solvents take into account both the solute and solvent description and offer the possibility to predict SFEs of any solute in any solvent with root mean squared errors less than 1 kcal/mol. Validations that involve exclusion of fractions or clusters of the solutes or solvents exemplify the model’s capability to predict SFEs of novel solutes and solvents with diverse chemical profiles. In addition to being predictive, our models can identify the solute and solvent features that influence SFE predictions. Furthermore, using 3D-RISM hydration thermodynamic output to predict SFEs in any organic solvent reduces the need to run 3D-RISM simulations in all these solvents. Altogether, our multi-solvent models for SFE predictions that take advantage of the solvation effects are expected to have an impact in the property prediction space. Solvation ; Solvent ; Chemistry ; Thermodynamics ; Solvent effects ; Organic chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Emmanuelle Ferreira Requião Silva | Bruna Rosa da Silva Santos | Geovani C. Brandão | Mariângela Vieira Lopes Silva | Erik Galvão Paranhos da Silva | Wagna Piler Carvalho dos Santos | Walter Nei Lopes dos Santos | Ana Maria Pinto dos Santos
Source: Brazilian Journal of Development (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Screening of minerals, proximate composition and physico-chemical characteristics in the discrimination of Oiti (Licania tomentosa (Benth.) Fritsch.) using Kohonen self-organizing maps, PCA and HCA Oiti ( Licania tomentosa (Benth.) Fritsch.) is a native fruit of Brazil that is not vastly utilized in the human diet due to the scarcity of information about its nutritional characterization. In this paper, the proximate composition, physicochemical characterization and mineral content of oiti pulp were investigated. Multivariate analysis techniques (Kohonen Neural Network, PCA and HCA) were used to evaluate whether there were significant differences among the samples collected in the city of Salvador, Bahia, Brazil, regarding to nutritional composition. The samples were digested in microwave ovens using the HNO 3 and H 2 O 2 oxidative mixture for multi-element determination of Ca, Cu, K, Mg, Mn, P and Zn by inductively coupled plasma optical emission spectrometry (ICP OES). Residual carbon content was determined to evaluate the efficiency of the acid digestion procedure. The method accuracy was confirmed by the analysis of the certified reference material of spinach leaves (1570a) and apple leaves (1515) from the National Institute of Standards and Technology (NIST). The results obtained for proximate composition and physical-chemical characterization analysis were in ranges of: 33.2 to 42.2% for moisture; 0.73 to 1.63% for ashes; 1.66 to 4.59% for total lipids; 1.89 to 3.07% for total proteins; 50.6 to 60.6% for carbohydrates; 0.04 to 0.06% for titratable acidity; 6.14 to 6.76 for pH; 0.88 to 5.69 mg 100g -1 for vitamin C; 10 to 14% for soluble solids content (ºBrix); 179 to 323 for ratio; 3.93 to 12.4% for reducing sugars; 0.89 to 4.52% for non-reducing sugars; 5.99 to 15.1% for total sugars and 248 to 279 Kcal in 100g for total calories value. The concentration values obtained for the elements in oiti samples in mg 100g -1 were in ranges of: 121 to 259 for Ca; 10.0 to 12.6 for Cu; 394 to 775 for K; 32.9 to 62.7 for Mg; 10.5 to 12.3 for Mn; 48.6 to 70.4 for P and 0.335 to 1.66 for Zn. The results indicate that oiti fruits have a nutritional value and can be used as a potential alternative source of nutrients for human diet and for the development of new food products. Proximate ; Chemistry ; Total dissolved solids ; Food science ; Botany ; Biology ; Environmental science Heavy Metals in Plants ; Agricultural and Food Sciences ; Phytochemicals and Antioxidant Activities
Authors: Sabrya E. van Heijst | Masaki Mukai | Eiji Okunishi | Hiroki Hashiguchi | Laurien I. Roest | Louis Maduro | Juan Rojo | Sonia Conesa‐Boj
Source: arXiv (Cornell University) (preprint, 2020-09-17)
Field: Materials Science — Materials Chemistry

Illuminating the electronic properties of WS$_2$ polytypism with\n electron microscopy Tailoring the specific stacking sequences (polytypes) of layered materials\nrepresents a powerful strategy to identify and design novel physical\nproperties. While nanostructures built upon transition-metal dichalcogenides\n(TMDs) with either the 2H or 3R crystalline phases have been routinely studied,\nour knowledge of those based on mixed 2H/3R polytypes is far more limited. Here\nwe report on the characterization of mixed 2H/3R free-standing WS$_2$\nnanostructures displaying a flower-like configuration by means of advanced\ntransmission electron microscopy. We correlate their rich variety of\nshape-morphology combinations with relevant local electronic properties such as\ntheir edge, surface, and bulk plasmons. Electron energy-loss spectroscopy\ncombined with machine learning reveals that the 2H/3R polytype displays an\nindirect band gap with $E_{\\rm BG}=1.6^{+0.3}_{-0.2}~{\\rm eV}$. Further, we\nidentify the presence of energy-gain peaks in the EEL spectra characterized by\na gain-to-loss ratio $I_G/I_L&gt;1$. Such property could be exploited to develop\nnovel cooling strategies for atomically thin TMD nanostructures and devices\nbuilt upon them. Our results represent a stepping stone towards an improved\nunderstanding of TMDs nanostructures based on mixed crystalline phases.\n Stacking ; Materials science ; Nanostructure ; Characterization (materials science) ; Transmission electron microscopy ; Electron energy loss spectroscopy ; Plasmon ; Scanning transmission electron microscopy ; Nanotechnology ; Spectroscopy ; Electron ; Band gap ; Crystallography ; Optoelectronics ; Chemistry ; Physics ; Nuclear magnetic resonance 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science
Authors: S. Karthik | V. P. Arunachalam | M Prem Sundhar | R. Raghul | C Sukumar
Source: IOP Conference Series Materials Science and Engineering (article, 2020-12-01)
Field: Materials Science — Polymers and Plastics

Optimization on the Dry Sliding Wear Behaviour of Mercerized Coconut Inflorescence Fibril Fortified Unsaturated Polyester Composites Abstract The contemporary work emphasizes the importance of using coconut inflorescence fibril as a viable alternate to manmade fibril in polymer matrix composites for application components undergoing wear. The extracted inflorescence fibril from coconut tree is exposed to mercerization treatment with 5% wt/vol of aqueous solution. The composite samples were fabricated by varying the fibril content (10, 20, 30 and 40 wt %) of fibril/polyester matrix. The composite specimens were subjected to wear test employing a pin on disc tribometric setup under dry sliding state. Design of experiments inspired taguchi analysis is employed for wear analysis. The experiments were performed for 16 trial runs for the load (5, 10, 15, 20 N), sliding distance of (250, 500, 750 and 100 m) and sliding velocity of (6.3, 12.5, 19 and 25.3 m/s) as the operating parameters. The parametric analysis revealed fibril content is the more inducing parameter which have a notable impact on the wear behaviour of the composites ensued by sliding distance. Artificial Neural Networks (ANN) is adopted to forecast the dry sliding wear behaviour of the composites over a broad scope of test conditions. The extensive experimentation leads to the conclusion that fortification of inflorescence fibril to the polyester resin resulted in improvement of wear resistance up to optimum fibril content and then decrease in wear resistance was observed when the fibril content increases. Finally SEM analysis was performed on the wornout surfaces to study the governing mechanism such as ploughing, wedge formation and fibril pullouts. Fibril ; Materials science ; Composite material ; Polyester ; Composite number ; Chemistry Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Polymer Nanocomposites and Properties
Authors: Binjie Ren | Chun Jiang
Source: Applied Optics (article, 2020-01-02)
Field: Materials Science — Materials Chemistry

Designing a multi-quantum-dot-doped wavelength down-shifting model of c-Si solar cells by using machine learning The efficiency of solar cell energy conversion may be significantly improved by better exploitation of the original solar spectrum. In this paper, we propose a multi-quantum-dot-doped wavelength down-shifting (WDS) model that can reduce the energy loss resulting from the mismatch between the solar spectrum and the solar cell material's responsivity. The WDS layer can nearly perfectly absorb the photons in the range of 300 to 700 nm and then re-emit them at a longer wavelength. The distribution of quantum dots in the WDS layer is provided based on analysis of its spectral properties with a machine learning regression algorithm. Finally, the relationship between the optimizing ability of the model and the Stokes shift of the quantum dots is also discussed. Ideally, our model can provide up to 29% improvement to the external quantum efficiency of c-Si solar cells, which fully demonstrates the potential of our model. Quantum dot ; Solar cell ; Optoelectronics ; Responsivity ; Optics ; Physics ; Wavelength ; Photon ; Quantum efficiency ; Solar energy ; Doping ; Materials science ; Photodetector Silicon Nanostructures and Photoluminescence ; Quantum Dots Synthesis And Properties ; Thin-Film Transistor Technologies
Authors: Nasser Jalili‐Jahani | Azadeh Fatehi
Source: Journal of Separation Science (article, 2020-02-13)
Field: Chemistry — Spectroscopy

Multivariate image analysis–quantitative structure‐retention relationship study of polychlorinated biphenyls using partial least squares and radial basis function neural networks Polychlorinated biphenyls belong to a class of hazardous and environmental pollutants. Gas chromatography separation and experimental relative retention time evaluation of these compounds on a poly (94% methyl/5% phenyl) silicone-based capillary non-bonded and cross-linked column are time consuming and expensive. In this study, relative retention times were estimated using two-dimensional images of molecules based on a newly implemented rapid and simple quantitative structure retention relationship methodology. The resulting descriptors were subjected to partial least square and principal component-radial basis function neural networks as linear and nonlinear models, respectively, to attain a statistical explanation of the retention behavior of the molecules. The high numerical values of correlation coefficients and low root mean square errors in the case of the partial least square model, confirm the supremacy of this model as well as the linear dependency of images of molecules to their relative retention times. Evaluation of the best correlation model performed using internal and external tests and its good applicability domain was checked using a distance to the model in the X-Space plot. This study provides a practical and effective method for analytical chemists working with chromatographic platforms to improve predictive confidence of studies that seek to identify unknown molecules or impurities. Principal component analysis ; Partial least squares regression ; Radial basis function ; Applicability domain ; Chemistry ; Artificial neural network ; Biological system ; Multivariate statistics ; Mean squared error ; Chromatography ; Analytical Chemistry (journal) ; Statistics ; Mathematics ; Quantitative structure–activity relationship ; Artificial intelligence ; Computer science Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods
Authors: Florian Speck
Source: OPUS Repository (Kooperativer Bibliotheksverbund Berlin-Brandenburg) (article, 2020-01-01)
Field: Chemistry — Electrochemistry

Degradation Mechanisms of Electrocatalysts in Alkaline Media The scientific part of this dissertation was made possible by Prof Electrocatalyst ; Commercialization ; Nanotechnology ; Alkaline earth metal ; Biochemical engineering ; Chemistry ; Environmental science ; Electrochemistry ; Materials science ; Business ; Engineering ; Alkali metal ; Electrode Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Extraction and Separation Processes
Authors: Sirong Li | Zijun Zhou | Zuoxiu Tie | Bing Wang | Meng Ye | Lei Du | Ran Cui | Wei Liu | Cuihong Wan | Quanyi Liu | Sheng Zhao | Quan Wang | Yihong Zhang | Shuo Zhang | Huigang Zhang | Yan Du | Hui Wei
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-12-09)
Field: Materials Science — Materials Chemistry

Data-informed discovery of hydrolytic nanozymes Abstract Nanozyme is a collection of nanomaterials with enzyme-like activity but exhibits higher environmental tolerance and long-term stability than their natural counterparts. Improving the catalytic activity and expanding the category of nanozymes are prerequisites to complement or even supersede natural enzymes. Specifically, a powerful hydrolytic nanozyme is demanded to degrade the unsustainable substance which natural enzymes hardly achieve. However, the development of hydrolytic nanozymes is still hindered by diverse hydrolytic substrates and following complicated mechanisms. Here, we apply two strategies which are informed by data to screen and predict catalytic active sites of MOF (metal–organic framework) based hydrolytic nanozymes. One is to increase the intrinsic activity by finely tuned Lewis acidity of the metal clusters. The other is to adjust the volume density of the active sites by shortening the length of ligands. Finally, we construct a Ce-FMA-MOF-based hydrolytic nanozyme with robust cleavage ability towards phosphate bonds, amide bonds, glycosidic bonds whose energy ascend in order; and even their mixture, biofilms. This work provides a rational methodology to design hydrolytic nanozyme, enriches the diversity of nanozymes, and potentially sheds a light on the evolution of enzyme engineering in the future. Chemistry ; Hydrolysis ; Catalysis ; Combinatorial chemistry ; Peptide bond ; Nanotechnology ; Biochemical engineering ; Enzyme ; Organic chemistry ; Materials science Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques