jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stuart Kyle | Zita M. Jessop | Ayesha Al‐Sabah | Karl Hawkins | Aled R. Lewis | Thierry G.G. Maffeïs | Cécile Charbonneau | Andrea Gazze | Lewis W. Francis | Mikhail Iakovlev | Kim Nelson | Stephen J. Eichhorn | Iain S. Whitaker
Source: Carbohydrate Polymers (article, 2018-06-22)
Field: Materials Science — Biomaterials

Characterization of pulp derived nanocellulose hydrogels using AVAP® technology Nanocellulose ; Materials science ; Porosity ; Nanocrystal ; Nanorod ; Nanomaterials ; Chemical engineering ; Composite material ; Pulp (tooth) ; Nanotechnology ; Transmission electron microscopy ; Cellulose Advanced Cellulose Research Studies ; Polysaccharides and Plant Cell Walls ; Lignin and Wood Chemistry
Authors: Daniel S. Müller | Olivier Baslé | Marc Mauduit
Source: Beilstein Journal of Organic Chemistry (review, 2018-12-07)
Field: Chemistry — Organic Chemistry

A tutorial review of stereoretentive olefin metathesis based on ruthenium dithiolate catalysts Stereoretentive olefin metathesis based on ruthenium dithiolate complexes has become a very active field of research within the past years. This unique catalyst class is able to kinetically produce both <i>Z</i>- and <i>E</i>-alkenes in high stereochemical purity (typically >95:5) starting from stereochemically pure <i>Z</i>- or <i>E</i>-alkenes. The aim of this tutorial review is to organize the reported information concerning ruthenium dithiolate catalysts in a logic manner, thus providing an "operators handbook" for chemists who wish to apply this methodology in synthesis. Ruthenium ; Olefin metathesis ; Chemistry ; Catalysis ; Metathesis ; Salt metathesis reaction ; Combinatorial chemistry ; Olefin fiber ; Organic chemistry Synthetic Organic Chemistry Methods ; Synthesis and Catalytic Reactions ; Sulfur-Based Synthesis Techniques
Authors: Sivamaran Venkatesan | V. Balasubramanian | Mannathusamy Gopalakrishnan | Viswabaskaran Viswanathan | A. Gourav Rao
Source: International nano letters. (article, 2018-11-01)
Field: Materials Science — Materials Chemistry

Effect of chemical vapor deposition parameters on the diameter of multi-walled carbon nanotubes The multi-walled carbon nanotubes (MWCNTs) with controlled diameter distribution are useful in the fabrication of composite materials as reinforcement, due to their superior strength and toughness. Chemical vapor deposition (CVD) is a viable process to synthesize MWCNTs. In this investigation, an attempt has been made to study the effect of CVD process parameters (reaction temperature, flow rate of precursor gas, process time) on the mean diameter of MWCNTs. The MWCNTs of controlled diameter distribution was selectively grown on NiO catalyst supported by Al2O3 nano particles. The argon and acetylene were used as carrier and carbon precursor gas, respectively. The catalyst and carbon samples were characterized using field emission scanning electron microscopy, high-resolution transmission electron microscopy, Raman spectroscopy and thermogravimetric/differential thermal analysis. From this investigation, it is understood that the increase in reaction temperature and flow rate of precursor gas increased the mean diameter of MWCNTs but increase in process time decreased the diameter. The diameter distribution and quality of MWCNTs are strongly influenced by the diameter of the catalyst particles. Materials science ; Carbon nanotube ; Chemical vapor deposition ; Raman spectroscopy ; Acetylene ; Scanning electron microscope ; Thermogravimetric analysis ; Chemical engineering ; Carbon fibers ; Transmission electron microscopy ; Nanochemistry ; Catalysis ; Composite material ; Nanotechnology ; Composite number ; Organic chemistry ; Chemistry Carbon Nanotubes in Composites ; Graphene research and applications ; Nanotechnology research and applications
Authors: Jike Lyu | Ignasi Fina | Raul Solanas | Josep Fontcuberta | Florencio Sánchez
Source: Scientific Reports (article, 2018-01-05)
Field: Materials Science — Materials Chemistry

Tailoring Lattice Strain and Ferroelectric Polarization of Epitaxial BaTiO3 Thin Films on Si(001) Ferroelectric BaTiO<sub>3</sub> films with large polarization have been integrated with Si(001) by pulsed laser deposition. High quality c-oriented epitaxial films are obtained in a substrate temperature range of about 300 °C wide. The deposition temperature critically affects the growth kinetics and thermodynamics balance, resulting on a high impact in the strain of the BaTiO<sub>3</sub> polar axis, which can exceed 2% in films thicker than 100 nm. The ferroelectric polarization scales with the strain and therefore deposition temperature can be used as an efficient tool to tailor ferroelectric polarization. The developed strategy overcomes the main limitations of the conventional strain engineering methodologies based on substrate selection: it can be applied to films on specific substrates including Si(001) and perovskites, and it is not restricted to ultrathin films. Ferroelectricity ; Pulsed laser deposition ; Epitaxy ; Thin film ; Polarization (electrochemistry) ; Strain engineering ; Atmospheric temperature range Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Antonino Proto | Daniele Bibbo | Martin Černý | David Vala | Vladimír Kašík | Lukáš Peter | Silvia Conforto | Maurizio Schmid | Marek Penhaker
Source: Sensors (article, 2018-06-13)
Field: Materials Science — Materials Chemistry

Thermal Energy Harvesting on the Bodily Surfaces of Arms and Legs through a Wearable Thermo-Electric Generator This work analyzes the results of measurements on thermal energy harvesting through a wearable Thermo-electric Generator (TEG) placed on the arms and legs. Four large skin areas were chosen as locations for the placement of the TEGs. In order to place the generator on the body, a special manufactured band guaranteed the proper contact between the skin and TEG. Preliminary measurements were performed to find out the value of the resistor load which maximizes the power output. Then, an experimental investigation was conducted for the measurement of harvested energy while users were performing daily activities, such as sitting, walking, jogging, and riding a bike. The generated power values were in the range from 5 to 50 &mu;W. Moreover, a preliminary hypothesis based on the obtained results indicates the possibility to use TEGs on leg for the recognition of locomotion activities. It is due to the rather high and different biomechanical work, produced by the gastrocnemius muscle, while the user is walking rather than jogging or riding a bike. This result reflects a difference between temperatures associated with the performance of different activities. Wearable computer ; Generator (circuit theory) ; Energy harvesting ; Thermal ; Thermal energy ; Electric generator ; Electrical engineering ; Energy (signal processing) ; Engineering ; Computer science ; Automotive engineering ; Physics ; Embedded system ; Power (physics) Advanced Thermoelectric Materials and Devices ; Innovative Energy Harvesting Technologies ; Energy Harvesting in Wireless Networks
Authors: Sayan Banerjee | A. Sreenithya | Raghavan B. Sunoj
Source: Physical Chemistry Chemical Physics (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Machine learning for predicting product distributions in catalytic regioselective reactions Gaining predictable control over various forms of selectivities, such as enantio- and/or regio-selectivities, has been a long-standing goal in chemical catalysis. Although a number of factors such as the molecular features of the reactants and catalysts, as well as the reaction conditions, can influence the outcome of a reaction, it is not quite conspicuous as to what combinations of these parameters would offer a desired form of selectivity. We use machine learning tools, such as the neural network (NN), decision tree (DT), logistic regression (LR) and Random forest algorithms, to (a) analyze the outcome of an important catalytic regio-selective difluorination reaction of alkenes, and (b) decipher the complex interplay of various molecular parameters and their non-linear dependencies. The connection between what features of alkenes will yield 1,1-difluorination and how subtle changes would steer the reaction to 1,2-difluorination under identical conditions is enunciated. The NN was able to accurately predict whether a given alkene would yield a 1,1- or 1,2-difluorinated product. A combination of DT and the random forest classifier offered important chemical insights, which could be used in making a more rational choice of the reactant alkene for the desired regioisomeric product. The results could have far reaching implications in predicting which regioisomer is likely to be formed under a given set of conditions, and thus this technique is capable of expediting the development of catalytic transformations. Regioselectivity ; Catalysis ; Product (mathematics) ; Product distribution ; Computer science ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Web Data Mining and Analysis
Authors: Lorena Simón-Vidal | Oihane García‐Calvo | Uxue Oteo | Sonia Arrasate | Esther Lete | Nuria Sotomayor | Humberto González‐Díaz
Source: Journal of Chemical Information and Modeling (article, 2018-06-13)
Field: Chemistry — Inorganic Chemistry

Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies Machine learning (ML) algorithms are gaining importance in the processing of chemical information and modeling of chemical reactivity problems. In this work, we have developed a perturbation-theory and machine learning (PTML) model combining perturbation theory (PT) and ML algorithms for predicting the yield of a given reaction. For this purpose, we have selected Parham cyclization, which is a general and powerful tool for the synthesis of heterocyclic and carbocyclic compounds. This reaction has both structural (substitution pattern on the substrate, internal electrophile, ring size, etc.) and operational variables (organolithium reagent, solvent, temperature, time, etc.), so predicting the effect of changes on substrate design (internal elelctrophile, halide, etc.) or reaction conditions on the yield is an important task that could help to optimize the reaction design. The PTML model developed uses PT operators to account for perturbations under experimental conditions and/or structural variables of all the molecules involved in a query reaction, compared to a reaction of reference. Thus, a dataset of >100 reactions has been collected for different substrates and internal electrophiles, under different reaction conditions, with a wide range of yields (0-98%). The best PTML model found using General Linear Regression (GLR) has R = 0.88 in training and R = 0.83 in external validation series for 10 000 pairs of query and reference reactions. The PTML model has a final R = 0.95 for all reactions using multiple reactions of reference. We also report a comparative study of linear versus nonlinear PTML models based on artificial neural network (ANN) algorithms. PTML-ANN models (LNN, MLP, RBF) with R ≈ 0.1-0.8 do not outperform the first PMTL model. This result confirms the validity of the linearity of the model. Next, we carried out an experimental and theoretical study of nonreported Parham reactions to illustrate the practical use of the PTML model. A 500 000-point simulation and a Hammett analysis of the reactivity space of Parham reactions are also reported. Electrophile ; Reagent ; Computer science ; Linear model ; Algorithm ; Machine learning ; Chemistry ; Artificial neural network ; Chemical reaction ; Artificial intelligence ; Biological system ; Organic chemistry ; Catalysis Asymmetric Hydrogenation and Catalysis ; Asymmetric Synthesis and Catalysis ; Chemical synthesis and alkaloids
Authors: Kevin Ryczko | Kyle Mills | Iryna Luchak | Christa M. Homenick | Isaac Tamblyn
Source: Computational Materials Science (article, 2018-03-16)
Field: Materials Science — Materials Chemistry

Convolutional neural networks for atomistic systems Convolutional neural network ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Jessica Roberts | Aoife Power | James Chapman | Shaneel Chandra | Daniel Cozzolino
Source: Fermentation (article, 2018-03-13)
Field: Chemistry — Analytical Chemistry

The Use of UV-Vis Spectroscopy in Bioprocess and Fermentation Monitoring Real-time analytical tools to monitor bioprocess and fermentation in biological and food applications are becoming increasingly important. Traditional laboratory-based analyses need to be adapted to comply with new safety and environmental guidelines and reduce costs. Many methods for bioprocess fermentation monitoring are spectroscopy-based and include visible (Vis), infrared (IR) and Raman. This paper describes the main principles and recent developments in UV-Vis spectroscopy to monitor bioprocess and fermentation in different food production applications. Bioprocess ; Biochemical engineering ; Fermentation ; Process engineering ; Process analytical technology ; Biotechnology ; Environmental science ; Computer science ; Engineering ; Chemistry ; Food science ; Biology ; Chemical engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Listeria monocytogenes in Food Safety
Authors: Ling Yang | Ting Wu | Yun Liu | Juan Zou | Yunmao Huang | V. Sarath Babu | Li Lin
Source: Journal of Spectroscopy (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared Spectroscopy Consumers concern about food adulteration. Pork meat is the principal adulterated species of beef and mutton. The conventional detection methods have their own limitations; therefore, we sought to develop an efficient and economical identification method using an infrared spectroscopy technique for meat. The Mahalanobis distance method was used to remove outliers in spectrum data. Interferences were eliminated using multiple scatter correction, standard normal variate, Savitzky-Golay smoothing, and normalization. The partial least square discriminant analysis (PLS-DA) and support vector machine (SVM) were used to establish identification models. In the Mahalanobis distance method, the coefficient of test sets was increased from 0.93 to 0.99; the RMSEC and RMSECV were decreased from 0.17 to 0.09 and 0.21 to 0.11 accordingly. The coefficient of determination in-between the calibration and testing sets in PLS-DA reached 0.99 and 0.99, RMSEC was 0.06, and both the RMSECV and RMSEP were 0.08. In contrast, in SVM, methods were 0.97 and 0.96. The RMSEC, RMSECV, and RMSEP were 0.15, 0.17, and 0.24, respectively. In summary, using a combination of infrared spectroscopy technology with PLS-DA was a better identification method than the SVM method that can be used as an effective method to identify pork, beef, and mutton meat samples. Mahalanobis distance ; Linear discriminant analysis ; Normalization (sociology) ; Mathematics ; Food science ; Pattern recognition (psychology) ; Artificial intelligence ; Statistics ; Chemistry ; Computer science Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Meat and Animal Product Quality
Authors: Anagnostis Toulfatzis | George Pantazopoulos | Constantine David | Dimitrios Sagris | Alkiviadis S. Paipetis
Source: Metals (article, 2018-04-08)
Field: Materials Science — Materials Chemistry

Machinability of Eco-Friendly Lead-Free Brass Alloys: Cutting-Force and Surface-Roughness Optimization The machinability in turning mode of three lead-free brass alloys, CuZn42 (CW510L), CuZn38As (CW511L) and CuZn36 (C27450) was evaluated in comparison with a reference free-cutting leaded brass CuZn39Pb3 (CW614N), as far as the quality characteristics, i.e., cutting force and surface roughness, were concerned. A design of experiments (DOE) technique, according to the Taguchi L16 orthogonal array (OA) methodology, as well as analysis of variance (ANOVA) were employed in order to identify the critical-to-machinability parameters and to obtain their optimum values for high-performance machining. The experimental design consisted of four factors (cutting speed, depth of cut, feed rate and alloy) with four levels for each factor using the “smaller-the-better” criterion for quality characteristics’ optimization. The data means and signal-to-noise (S/N) responses indicated that the depth of cut and the feed rate were the most influential factors for the cutting force and surface roughness, respectively. The optimized machining parameters for cutting force (34.59 N) and surface roughness (1.22 μm) minimization were determined. Confirmation experiments (cutting force: 39.37 N and surface roughness: 1.71 μm) seem to show that they are in close agreement to the main conclusions, thereby validating the findings of the statistical evaluation performed. Machinability ; Brass ; Taguchi methods ; Surface roughness ; Machining ; Materials science ; Orthogonal array ; Surface finish ; Metallurgy ; Design of experiments ; Composite material ; Mechanical engineering ; Engineering drawing ; Mathematics ; Engineering ; Statistics ; Copper Metallurgy and Material Science ; Aluminum Alloy Microstructure Properties ; Nanoporous metals and alloys
Authors: Claudio Zeni | Kevin Rossi | Aldo Glielmo | Ádám Fekete | Nicola Gaston | Francesca Baletto | Alessandro De Vita
Source: The Journal of Chemical Physics (article, 2018-06-11)
Field: Materials Science — Materials Chemistry

Building machine learning force fields for nanoclusters We assess Gaussian process (GP) regression as a technique to model interatomic forces in metal nanoclusters by analyzing the performance of 2-body, 3-body, and many-body kernel functions on a set of 19-atom Ni cluster structures. We find that 2-body GP kernels fail to provide faithful force estimates, despite succeeding in bulk Ni systems. However, both 3- and many-body kernels predict forces within an ∼0.1 eV/Å average error even for small training datasets and achieve high accuracy even on out-of-sample, high temperature structures. While training and testing on the same structure always provide satisfactory accuracy, cross-testing on dissimilar structures leads to higher prediction errors, posing an extrapolation problem. This can be cured using heterogeneous training on databases that contain more than one structure, which results in a good trade-off between versatility and overall accuracy. Starting from a 3-body kernel trained this way, we build an efficient non-parametric 3-body force field that allows accurate prediction of structural properties at finite temperatures, following a newly developed scheme [A. Glielmo et al., Phys. Rev. B 95, 214302 (2017)]. We use this to assess the thermal stability of Ni<sub>19</sub> nanoclusters at a fractional cost of full ab initio calculations. Extrapolation ; Nanoclusters ; Kernel (algebra) ; Stability (learning theory) ; Gaussian process ; Kriging ; Force field (fiction) ; Field (mathematics) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Pere Martí-Puig | Alejandro Blanco-M. | J. J. Cárdenas | J. Cusidó | Jordi Solé‐Casals
Source: Environmental Modelling & Software (article, 2018-05-21)
Field: Chemistry — Analytical Chemistry

Effects of the pre-processing algorithms in fault diagnosis of wind turbines Outlier ; SCADA ; Wind power ; Quantile ; Turbine ; Computer science ; Fault (geology) ; Reliability engineering ; Missing data ; Noise (video) ; Downtime ; Data mining ; Real-time computing ; Algorithm ; Engineering ; Machine learning ; Artificial intelligence ; Statistics ; Mathematics Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Industrial Vision Systems and Defect Detection
Authors: Juntao Xiong | Zhen Liu | Rui Lin | Rongbin Bu | Zhiliang He | Zhengang Yang | Cuixiao Liang
Source: Sensors (article, 2018-03-25)
Field: Chemistry — Analytical Chemistry

Green Grape Detection and Picking-Point Calculation in a Night-Time Natural Environment Using a Charge-Coupled Device (CCD) Vision Sensor with Artificial Illumination Night-time fruit-picking technology is important to picking robots. This paper proposes a method of night-time detection and picking-point positioning for green grape-picking robots to solve the difficult problem of green grape detection and picking in night-time conditions with artificial lighting systems. Taking a representative green grape named <i>Centennial Seedless</i> as the research object, daytime and night-time grape images were captured by a custom-designed visual system. Detection was conducted employing the following steps: (1) The RGB (red, green and blue). Color model was determined for night-time green grape detection through analysis of color features of grape images under daytime natural light and night-time artificial lighting. The R component of the RGB color model was rotated and the image resolution was compressed; (2) The improved Chan-Vese (C-V) level set model and morphological processing method were used to remove the background of the image, leaving out the grape fruit; (3) Based on the character of grape vertical suspension, combining the principle of the minimum circumscribed rectangle of fruit and the Hough straight line detection method, straight-line fitting for the fruit stem was conducted and the picking point was calculated using the stem with an angle of fitting line and vertical line less than 15°. The visual detection experiment results showed that the accuracy of grape fruit detection was 91.67% and the average running time of the proposed algorithm was 0.46 s. The picking-point calculation experiment results showed that the highest accuracy for the picking-point calculation was 92.5%, while the lowest was 80%. The results demonstrate that the proposed method of night-time green grape detection and picking-point calculation can provide technical support to the grape-picking robots. Charge-coupled device ; Point (geometry) ; Computer vision ; Artificial intelligence ; Charge (physics) ; Computer science ; Natural (archaeology) ; Optics ; Physics ; Mathematics ; Geography Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Advanced Chemical Sensor Technologies
Authors: Adrian Szewczyk | Magdalena Prokopowicz | Wiesław Sawicki | Dorota Majda | Gavin Walker
Source: Microporous and Mesoporous Materials (article, 2018-07-29)
Field: Chemistry — Organic Chemistry

Aminopropyl-functionalized mesoporous silica SBA-15 as drug carrier for cefazolin: adsorption profiles, release studies, and mineralization potential Cefazolin ; Adsorption ; Chemistry ; Langmuir adsorption model ; Mineralization (soil science) ; Freundlich equation ; Mesoporous silica ; Kinetics ; Langmuir ; Mesoporous material ; Surface modification ; Chemical engineering ; Chromatography ; Organic chemistry ; Catalysis ; Biochemistry ; Physical chemistry ; Antibiotics Nanomaterials for catalytic reactions ; Mesoporous Materials and Catalysis ; Nanoparticle-Based Drug Delivery