jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ravikumar B. Shinde | Murugan Veerapandian | Ajeet Kaushik | Pandiaraj Manickam
Source: Frontiers in Bioengineering and Biotechnology (review, 2020-04-28)
Field: Materials Science — Materials Chemistry

State-of-Art Bio-Assay Systems and Electrochemical Approaches for Nanotoxicity Assessment Innovations in the field of nanotechnology, material science and engineering has rendered fruitful utilities in energy, environment and healthcare. Particularly, emergence of surface engineered nanomaterials offered novel varieties in the daily consumables and healthcare products including therapeutics and diagnostics. However, the nanotoxicity and bioactivity of the nanomaterials upon interaction with biological system has raised critical concerns to individual as well as to the environment. Several biological models including plant and animal sources have been identified to study the toxicity of novel nanomaterials, correlating the physio-chemical properties. Biological interaction of nanomaterials and its mediated physiological functions are studied using conventional cell/molecular biological assays to understand the expression levels of genetic information specific to intra/extra cellular enzymes, cell viability, proliferation and function. However, modern research still demands advanced bioassay methods to screen the acute and chronic effects of nanomaterials at the real-time. In this regard, bioelectrochemical techniques, with the recent advancements in the microelectronics, proved to be capable of providing non-invasive measurement of the nanotoxicity effects (<i>in vivo</i> and <i>in vitro</i>) both at single cellular and multicellular levels. This review attempted to provide a detailed information on the recent advancements made in development of bioassay models and systems for assessing the nanotoxicology. With a short background information on engineered nanomaterials and physiochemical properties specific to consumer application, present review highlights the multiple bioassay models evolved for toxicological studies. Emphasize on multiple mechanisms involved in the cell toxicity and electrochemical probing of the biological interactions, revealing the cytotoxicity were also provided. Limitations in the existing electrochemical techniques and opportunities for the future research focusing the advancement in single molecular and whole cell bioassay has been discussed. Nanotoxicology ; Nanotechnology ; Biochemical engineering ; Multicellular organism ; Nanomedicine ; Computational biology ; Computer science ; Chemistry ; Biology ; Engineering ; Materials science ; Cell ; Nanoparticle Nanoparticles: synthesis and applications ; Electrochemical Analysis and Applications ; Neuroscience and Neural Engineering
Authors: Soogeun Park | Eva Ceulemans | Katrijn Van Deun
Source: Journal of Chemometrics (article, 2020-07-20)
Field: Chemistry — Analytical Chemistry

Sparse common and distinctive covariates regression Abstract Having large sets of predictors from multiple sources concerning the same observation units and the same criterion is becoming increasingly common in chemometrics. When analyzing such data, chemometricians often have multiple objectives: prediction of the criterion, variable selection, and identification of underlying processes associated to individual predictor sources or to several sources jointly. Existing methods offer solutions regarding the first two aims of uncovering the predictive mechanisms and relevant variables therein for a single block of predictor variables, but the challenge of uncovering joint and distinctive predictive mechanisms and the relevant variables therein in the multisource setting still needs to be addressed. To this end, we present a multiblock extension of principal covariates regression that aims to find the complex mechanisms in which several or single sources may be involved; taken together, these mechanisms predict an outcome of interest. We call this method sparse common and distinctive covariates regression (SCD‐CovR). Through a simulation study, we demonstrate that SCD‐CovR provides competitive solutions when compared with related methods. The method is also illustrated via an application to a publicly available dataset. Covariate ; Computer science ; Identification (biology) ; Regression ; Feature selection ; Predictive modelling ; Regression analysis ; Outcome (game theory) ; Selection (genetic algorithm) ; Machine learning ; Artificial intelligence ; Data mining ; Statistics ; Econometrics ; Mathematics ; Biology Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Advanced Statistical Methods and Models
Authors: Nobuya Sato | Tomoki Yamashita | Tamio Oguchi | Koji Hukushima | Takashi Miyake
Source: Physical Review Materials (article, 2020-03-19)
Field: Materials Science — Materials Chemistry

Adjusting the descriptor for a crystal structure search using Bayesian optimization We perform a crystal structure search using Bayesian optimization and evaluate its efficiency with a varying parameter value in the descriptor. Applying the crystal structure search to crystalline silicon shows that the efficiency of the search depends heavily on the parameter value. We find that the efficiency is linked to the distribution of the descriptor. Therefore, we introduce an information measure of the distribution to estimate an appropriate parameter value for performing the crystal structure search efficiently. The measure can also be used to predetermine an appropriate parameter value. The validity of the measure is confirmed with its applications to silicon oxide and yttrium-cobalt alloy. Measure (data warehouse) ; Materials science ; Bayesian optimization ; Value (mathematics) ; Crystal (programming language) ; Silicon ; Yttrium ; Bayesian probability ; Distribution (mathematics) ; Crystal structure ; Biological system ; Data mining ; Computer science ; Oxide ; Artificial intelligence ; Mathematics ; Crystallography ; Machine learning ; Optoelectronics ; Metallurgy ; Mathematical analysis Machine Learning in Materials Science ; Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography
Authors: Sonia M. Underwood | Alex T. Kararo | Gabriela Gadia
Source: Chemistry Education Research and Practice (article, 2020-11-27)
Field: Chemistry — Physical and Theoretical Chemistry

Investigating the impact of three-dimensional learning interventions on student understanding of structure–property relationships The ability to predict macroscopic properties using a compound's chemical structure is an essential idea for chemistry as well as other disciplines such as biology. In this study we investigate how different levels of interventions impact the components of students’ explanations (claims, evidence, and reasoning) of structure–property relationships, particularly related to boiling point trends. These interventions, aligned with Three-Dimensional Learning (3DL), were investigated with four different cohorts of students: Cohort 1 – a control group of students enrolled in an active learning general chemistry course; Cohort 2 – students enrolled in the same active learning general chemistry course but given Intervention 1 (a 3DL worksheet administered during class time); Cohort 3 – students enrolled in the same active learning general chemistry course but given Intervention 1 and Intervention 2 (a 3DL course exam question administered after instruction); and Cohort 4 – a reference group of students enrolled in a transformed active learning general chemistry curriculum in which 3DL is an essential feature and includes Intervention 1 and Intervention 2 as part of the curriculum. We found that Cohort 2 students (with the 3DL worksheet intervention) were more likely than the control group (Cohort 1) to correctly predict the compound with a higher boiling point as well as incorporate ideas of strength of intermolecular forces into their explanations of boiling point differences. When a 3DL exam question was given as a follow up to the 3DL worksheet, students in Cohort 3 were more likely than Cohorts 1 and 2 to correctly identify the claim. Further comparison showed that Cohort 4 (transformed general chemistry curriculum) were more likely than Cohorts 1–3 to also include the ideas of energy needed to overcome stronger forces for a more sophisticated explanation (50% of Cohort 4 students compared to 17–33% for Cohorts 1–3). In addition, 80% of Cohort 4 students were able to construct a correct representation of hydrogen bonding as a non-covalent interaction compared to 13–57% for the other three cohorts. Worksheet ; Cohort ; Psychological intervention ; Mathematics education ; Curriculum ; Psychology ; Intervention (counseling) ; Medical education ; Chemistry ; Medicine ; Pedagogy ; Internal medicine Various Chemistry Research Topics ; Problem and Project Based Learning ; Innovative Teaching Methods
Authors: Bruno Pedro Lazzaretti | Leandro Souza da Silva | Gerson Laerson Drescher | André Carnieletto Dotto | Darinês Britzke | José Laerte Nörnberg
Source: Ciência Rural (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Prediction of soil organic matter and clay contents by near-infrared spectroscopy - NIRS ABSTRACT: Among the soil constituents, special attention is given to soil organic matter (SOM) and clay contents, since, among other aspects, they are key factors to nutrient retention and soil aggregates formation, which directly affect the crop production potential. The methods commonly used for the quantification of these constituents have some disadvantages, such as the use of chemical reactants and waste generation. An alternative to these methods is the near-infrared spectroscopy (NIRS) technique. The aim of this research is to evaluate models for SOM and clay quantification in soil samples using spectral data by NIRS. A set (n = 400) of soil samples previously analyzed by traditional methods were used to generate a NIRS calibration curve. The clay content was determined by the hydrometer method while SOM content was determined by sulfochromic solution. For calibration, we used the original spectra (absorbance) and spectral pretreatment (Savitzky-Golay smoothing derivative) in the following models: multiple linear regression (MLR), partial last squares regression (PLSR), support vector machine (SVM) and Gaussian process regression (GPR). The curve validation was performed with the SVM model (best performance in the calibration based on R² and RMSE) in two ways: with 40 random samples from the calibration set and another set with 200 new unknown samples. The soil clay content affects the predictive ability of the calibration curve to estimate SOM content by NIRS. Validation curves showed poorer performance (lower R² and higher RMSE) when generated from unknown samples, where the model tends to overestimate the lower levels and to underestimate the higher levels of clay and SOM. Despite the potential of NIRS technique to predict these attributes, further calibration studies are still needed to use this technique in soil analysis laboratories. Partial least squares regression ; Calibration ; Soil organic matter ; Soil test ; Soil science ; Mathematics ; Environmental science ; Chemistry ; Soil water ; Statistics Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Mineral Processing and Grinding
Authors: Augusto Della Torre | Gianluca Montenegro | Angelo Onorati | Tarcisio Cerri | Enrico Tronconi | Isabella Nova
Source: SAE technical papers on CD-ROM/SAE technical paper series (article, 2020-04-14)
Field: Materials Science — Materials Chemistry

Numerical Optimization of a SCR System Based on the Injection of Pure Gaseous Ammonia for the NOx Reduction in Light-Duty Diesel Engines &lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Selective Catalytic Reduction (SCR) systems are nowadays widely applied for the reduction of NOx emitted from Diesel engines. The typical process is based on the injection of aqueous urea in the exhaust gases before the SCR catalyst, which determines the production of the ammonia needed for the catalytic reduction of NOx. However, this technology is affected by two main limitations: a) the evaporation of the urea water solution (UWS) requires a sufficiently high temperature of the exhaust gases and b) the formation of solid deposits during the UWS evaporation is a frequent phenomenon which compromise the correct operation of the system. In this context, to overcome these issues, a technology based on the injection of gaseous ammonia has been recently proposed: in this case, ammonia is stored at the solid state in a cartridge containing a Strontium Chloride salt and it is desorbed by means of electrical heating.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;In this work, an after-treatment system based on the injection of gaseous ammonia in the SCR system is considered. Numerical 1D and 3D CFD simulations are applied in order to optimize the NOx reduction process. In particular, CFD methodology is applied to study in details the process of injection of the gaseous ammonia in the main exhaust gas stream and the effectiveness of the mixing process. Different geometrical layouts are compared to evaluate their performances in terms of uniformity of the NH3 distribution across the inlet section of the catalyst and pressure drop introduced in the exhaust line. Moreover, a 1D simulation tool is applied to evaluate the performances of the entire exhaust after-treatment system. The 1D model is calibrated on the basis of the information coming from detailed CFD simulations, in particular for what concerns the modeling of the effects of the different mixer geometries in terms on NH3 distribution. In this case, a simplified 3D catalyst is simulated, to take into account the maldistribution of ammonia at the inlet cross section and to evaluate its impact on the global deNOx performance of the system for different dosing strategies and for different levels of the ammonia maldistribution.&lt;/div&gt;&lt;/div&gt; NOx ; Reduction (mathematics) ; Diesel fuel ; Ammonia ; Automotive engineering ; Selective catalytic reduction ; Diesel engine ; Environmental science ; Waste management ; Chemistry ; Engineering ; Catalysis ; Combustion Catalytic Processes in Materials Science ; Advanced Combustion Engine Technologies ; Vehicle emissions and performance
Authors: Müller, Boris | Berner, Johannes | Bechinger, Clemens | Krüger, Matthias
Source: KOPS (University of Konstanz) (article, 2020-01-14)
Field: Materials Science — Materials Chemistry

Properties of a nonlinear bath : experiments, theory, and a stochastic Prandtl-Tomlinson model A colloidal particle is a prominent example of a stochastic system, and, if suspended in a simple viscous liquid, very closely resembles the case of an ideal random walker. A variety of new phenomena have been observed when such colloid is suspended in a viscoelastic fluid instead, for example pronounced nonlinear responses when the viscoelastic bath is driven out of equilibrium. Here, using a micron-sized particle in a micellar solution, we investigate in detail, how these nonlinear bath properties leave their fingerprints already in equilibrium measurements, for the cases where the particle is unconfined or trapped in a harmonic potential. We find that the coefficients in an effective linear (generalized) Langevin equation show intriguing inter-dependencies, which can be shown to arise only in nonlinear baths: for example, the friction memory can depend on the external potential that acts only on the colloidal particle (as recently noted in simulations of molecular tracers in water in (2017 Phys. Rev. X 7 041065)), it can depend on the mass of the colloid, or, in an overdamped setting, on its bare diffusivity. These inter-dependencies, caused by so-called fluctuation renormalizations, are seen in an exact small time expansion of the friction memory based on microscopic starting points. Using linear response theory, they can be interpreted in terms of microrheological modes of force-controlled or velocity-controlled driving. The mentioned nonlinear markers are observed in our experiments, which are astonishingly well reproduced by a stochastic Prandtl–Tomlinson model mimicking the nonlinear viscoelastic bath. The pronounced nonlinearities seen in our experiments together with the good understanding in a simple theoretical model make this system a promising candidate for exploration of colloidal motion in nonlinear stochastic environments. Physics ; Nonlinear system ; Langevin equation ; Viscoelasticity ; Prandtl number ; Statistical physics ; Particle (ecology) ; Classical mechanics ; Mechanics ; Thermodynamics ; Convection ; Quantum mechanics Material Dynamics and Properties ; Advanced Thermodynamics and Statistical Mechanics ; Lipid Membrane Structure and Behavior
Authors: Daniel Zahn | Tobias Frömel
Source: Current Opinion in Environmental Science & Health (article, 2020-10-03)
Field: Chemistry — Spectroscopy

Finding a needle in a haystack—analyte-driven tools and techniques for information extraction and prioritization of chemicals from environmental (chromatography-)HRMS nontarget screening data Haystack ; Flagging ; Analyte ; Context (archaeology) ; Prioritization ; Computer science ; Data science ; Chemistry ; Chromatography ; Artificial intelligence ; Biology ; Engineering ; Management science ; Geography Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Zhijun Wang | Sara W. Erasmus | Xiaotong Liu | Saskia M. van Ruth
Source: Sensors (article, 2020-10-13)
Field: Chemistry — Analytical Chemistry

Study on the Relations between Hyperspectral Images of Bananas (Musa spp.) from Different Countries, Their Compositional Traits and Growing Conditions Bananas are some of the most popular fruits around the world. However, there is limited research that explores hyperspectral imaging of bananas and its relationship with the chemical composition and growing conditions. In the study, the relations that exist between the visible near-infrared hyperspectral reflectance imaging data in the 400-1000 nm range of the bananas collected from different countries, the compositional traits and local growing conditions (altitude, temperature and rainfall) and production management (organic/conventional) were explored. The main compositional traits included moisture, starch, dietary fibre, protein, carotene content and the CIE L*a*b* colour values were also determined. The principal component analysis showed the preliminary separation of bananas from different geographical origins and production systems. The compositional and spectral data revealed positively and negatively moderate correlations (r around ±0.50, <i>p</i> < 0.05) between the carotene, starch content, and colour values (a*, b*) on the one hand and the wavelength ranges 405-525 nm, 615-645 nm, 885-985 nm on the other hand. Since the variation in composition and colour values were related to rainfall and temperature, the spectral information is likely also influenced by the growing conditions. The results could be useful to the industry for the improvement of banana quality and traceability. Hyperspectral imaging ; Starch ; Principal component analysis ; Horticulture ; Composition (language) ; Mathematics ; Environmental science ; Food science ; Agronomy ; Remote sensing ; Chemistry ; Biology ; Geography ; Statistics Spectroscopy and Chemometric Analyses ; Sensory Analysis and Statistical Methods ; Phytochemicals and Antioxidant Activities
Authors: Xianghui Zhang | Cody B. Cockreham | Zhiyang Huang | Hui Sun | Chen Yang | Oscar Marin‐Flores | Baodong Wang | Xiaofeng Guo | Su Ha | Hongwu Xu | Di Wu
Source: The Journal of Physical Chemistry Letters (article, 2020-05-26)
Field: Materials Science — Materials Chemistry

Thermodynamics of Water–Cationic Species–Framework Guest–Host Interactions within Transition Metal Ion-Exchanged Mordenite Relevant to Selective Anaerobic Oxidation of Methane to Methanol Low-temperature anaerobic methane conversion to methanol (MTM) using copper ion-exchanged mordenite (Cu-MOR) as the catalyst and water as the sole source of oxygen is promising for sustainable utilization of methane. Integrating <i>in situ</i> calorimetric, spectroscopic, and structural methodologies, we report a systematic study on energetics of water-cationic species-framework guest-host interactions as a function of water loading for several mordenites relevant to low-temperature MTM. Notably, the near-zero coverage hydration enthalpy on Cu-MOR is -133.1 ± 6.0 kJ/mol water, which is related to Cu-MOR regeneration using water as oxidant. The copper oxo sites are thermally stable up to 915 °C and remain chemically intact as an oxygen source after complete hydration and dehydration. This study underscores the importance of manipulating the oxidation state and coordination chemistry of transition metal guest species in zeolites by fine-tuning the partial pressure of water as a strategy for rational design, synthesis, and modification of catalysts. Mordenite ; Chemistry ; Catalysis ; Methane ; Inorganic chemistry ; Transition metal ; Cationic polymerization ; Methanol ; Anaerobic oxidation of methane ; Copper ; Oxygen ; Enthalpy ; Zeolite ; Organic chemistry ; Thermodynamics Catalytic Processes in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Catalysis and Oxidation Reactions
Authors: Yo Inoue | Shunta Sasaki | Hiroshi Moritake
Source: Journal of Applied Physics (article, 2020-02-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-quality tuning of cholesteric liquid crystal lasers based on polymer composite system Electrically fine tuning is demonstrated in a cholesteric liquid crystal (ChLC) laser based on the polymer composite system. Photo-polymerization in the mixture of LC monomer and LC hardly causes phase separation due to miscibility, forming a microscopically well-mixed polymer-LC composite structure like gel. Light scattering in the polymer-LC composite system decreases with increasing the polymer component, and the composite of equal parts of the polymer and LC exhibits high transparency in electric modulation of the selective reflection band, resulting in high-quality tuning of lasing wavelength in ChLC laser. In tuning operation, the ChLC laser retains a high slope efficiency greater than 19%, a low threshold smaller than 3 nJ/pulse, and a spectral linewidth narrower than 0.13 nm, while the change in lasing wavelength is fully continuous and reaches 13 nm. Materials science ; Lasing threshold ; Composite number ; Liquid crystal ; Laser ; Polymer ; Optoelectronics ; Polymerization ; Cholesteric liquid crystal ; Laser linewidth ; Optics ; Wavelength ; Composite material Liquid Crystal Research Advancements ; Photonic and Optical Devices ; Photonic Crystals and Applications
Authors: Albert Cabré | Martí Garçon | Albert Gallén | Lorenzo Grisoni | Arnald Grabulosa | Xavier Verdaguer | Antoni Riéra
Source: ChemCatChem (article, 2020-05-18)
Field: Chemistry — Inorganic Chemistry

Iridium‐Catalyzed Asymmetric Isomerization of Primary Allylic Alcohols Using MaxPHOX Ligands: Experimental and Theoretical Study Abstract The asymmetric isomerization of primary allylic alcohols to chiral aldehydes using iridium‐catalysts bearing P,N ‐MaxPHOX ligands has been studied. These catalysts can be fine‐tuned as they present three different stereogenic centers to modulate both the reactivity and enantioselectivity of a family of different substrates. The experimental part is supported by a DFT study of the reaction mechanism, which provides new insights into the key steps of this transformation. Isomerization ; Stereocenter ; Allylic rearrangement ; Iridium ; Catalysis ; Chemistry ; Reactivity (psychology) ; Primary (astronomy) ; Combinatorial chemistry ; Enantioselective synthesis ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Synthetic Organic Chemistry Methods ; Asymmetric Synthesis and Catalysis