jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yeliz İpek
Source: Journal of Boron (article, 2021-10-29)
Field: Materials Science — Polymers and Plastics

Influence of Particle Size, Additive Ratio and Chemical Structure of Zinc Borate on Thermal and Mechanical Properties of Polyvinyl Chloride Zinc borate (ZB) is a flame retardant and smoke supressant compound of boron. It is widely used to achieve flame resistant polymer composites. Polyvinyl chloride (PVC) is one of the commercially important polymers mostly used for cable insulating, pen and door, pipes, floorings, toys etc.. On the other hand, PVC relases toxic gases during fire as decomposition products. Zinc borate doping is thought to be a solution for this problem. However, this solution leads a new problem. Zinc borate effects the mechanical properties of PVC. Mechanical properties of composites were determined by tensile test, impact test, hardness test and thermomechanical behaviours were detected with dynamic mechanical analysis (DMA). In the present study, to optimize the contribution of zinc borate, the powder with different formulations (2ZnO·3B2O3·3.5H2O and 4ZnO·B2O3·H2O), with different additive ratios (1% and 5% by weight) and different particle sizes (nano‒sized and micro‒sized) were added to the PVC structure and the mechanical and thermomechanical properties of each composite were investigated. Zinc borate ; Polyvinyl chloride ; Zinc ; Materials science ; Boron ; Thermal decomposition ; Fire retardant ; Ultimate tensile strength ; Composite material ; Particle size ; Polymer ; Particle (ecology) ; Chemical engineering ; Metallurgy ; Chemistry ; Organic chemistry Polymer Science and PVC ; Flame retardant materials and properties ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Aditya R. Pachghare Lakhan D. Baheti
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2021-11-01)
Field: Chemistry — Analytical Chemistry

COMPUTER AIDED DRUG DEVELOPMENT (CADD) PROCESS AND SCREENING: HIGH THROUGHPUT SCREENING (HTS) Computer aided drug development (CADD) comprises of a broad range of theoretical and computational approaches, CADD method have a major role in modern drug discovery, design and development that plays a major part in clinical use and clinical trials .it also highlights the current trends and methods used in development of novel methods i.e. computer aided repurposing and emerging concepts and technologies in molecular modeling and chemo informatics. In this once the drug molecule is identified/selected it is processed for development. In which different parameters of lead compound are assessed on basis of vitro assay (experimental analysis), vivo model (animal studies) and silicon approach (software based). From the last decade incremental developments in R&amp;D processes, the selection of tools for optimizing the functionality and viability has become the important factor in early drug development and is said to be the key for development of effective therapeutic agents. Drug design is important part of drug development, identification of active components in natural products gives lead compounds for development into drug. The current process of High Throughput Screening (HTS) through wet chemistry or in silicon is reviewed. The development of drug through protein engineering is used for antibody drug conjugates to improve specificity. HTS has the capacity to profile thousands of chemical concentrations response formats with reduce rate of false positive and false negative, therefore from last decade it used for compound libraries screening. Titration based quantitative screening is used for toxicology. Throughput ; Process (computing) ; Computer science ; High-throughput screening ; Computer-aided ; Drug ; Computer Aided Design ; Process engineering ; Engineering ; Medicine ; Pharmacology ; Bioinformatics ; Biology Analytical Methods in Pharmaceuticals
Authors: Subita Bhagat | N. Jaya prakash
Source: YMER Digital (article, 2021-12-25)
Field: Chemistry — Organic Chemistry

Comparative study of Metallocene catalyst propylene polymerization with different iteration rates This paper proposed a mathematical model corresponding to metallocene catalyzed propylene polymerization that uses the Me2Si [Ind]2ZrCL2 and Et [Ind]2ZrCL2. Comprehensive kinetic models consisting of mass and population balance equations, are developed based on elementary reactions proposed in the reaction mechanism. The result from the above indicates that metallocene catalysts in the presence of ethylene zirconium dichloride and methylene zirconium dichloride shows modularity and new peaks are obtained. The temperature variation from 25 to 75 also increase the rate and reason for the same could be chain shuttling polymerization. The model is presented through simulative study. Initially genetic approach is used but convergence rate is poor. To achieve best possible result, particle swarm optimization is used. The optimization approach with particle swarm optimization is implemented. The local and global solutions are comparable entities and replace each other in case value of local variable is not optimized. From the simulative study it is discovered that Et [Ind]2ZrCL2 produce best possible polymers both at 25 and 750C. Metallocene ; Polymerization ; Materials science ; Polymer ; Catalysis ; Particle swarm optimization ; Zirconium ; Polymer chemistry ; Chemistry ; Mathematics ; Mathematical optimization ; Organic chemistry ; Composite material Organometallic Complex Synthesis and Catalysis ; Carbon dioxide utilization in catalysis ; biodegradable polymer synthesis and properties
Authors: Yahya Saleh | Vishnu Sanjay | Armin Iske | Andrey Yachmenev | Jochen Küpper
Source: The Journal of Chemical Physics (preprint, 2021-10-13)
Field: Materials Science — Materials Chemistry

Active learning of potential-energy surfaces of weakly bound complexes with regression-tree ensembles Several pool-based active learning (AL) algorithms were employed to model potential-energy surfaces (PESs) with a minimum number of electronic structure calculations. Theoretical and empirical results suggest that superior strategies can be obtained by sampling molecular structures corresponding to large uncertainties in their predictions while at the same time not deviating much from the true distribution of the data. To model PESs in an AL framework, we propose to use a regression version of stochastic query by forest, a hybrid method that samples points corresponding to large uncertainties while avoiding collecting too many points from sparse regions of space. The algorithm is implemented with decision trees that come with relatively small computational costs. We empirically show that this algorithm requires around half the data to converge to the same accuracy in comparison to the uncertainty-based query-by-committee algorithm. Moreover, the algorithm is fully automatic and does not require any prior knowledge of the PES. Simulations on a 6D PES of pyrrole(H<sub>2</sub>O) show that <15 000 configurations are enough to build a PES with a generalization error of 16 cm<sup>-1</sup>, whereas the final model with around 50 000 configurations has a generalization error of 11 cm<sup>-1</sup>. Generalization ; Computer science ; Regression ; Decision tree ; Tree (set theory) ; Sampling (signal processing) ; Algorithm ; Space (punctuation) ; Energy (signal processing) ; Distribution (mathematics) ; Mathematical optimization ; Artificial intelligence ; Mathematics ; Statistics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Kaushik Sengupta | G.D. Hopkins | Danilo Manstretta | Mona M. Hella | Patrick Roblin
Source: IEEE Microwave Magazine (article, 2021-04-01)
Field: Chemistry — Organic Chemistry

2021 Microwave Week Workshops Describes the above-named upcoming workshop event. May include topics to be covered or calls for papers. Event (particle physics) ; Telecommunications ; Microwave ; Computer science ; Engineering ; Physics Microwave-Assisted Synthesis and Applications ; COVID-19 Digital Contact Tracing ; Advanced Computational Techniques in Science and Engineering
Authors: Esra Ceylan | Gülyaz Al | Ayben KILIÇ PEKGÖZLÜ | Deniz Aydemir
Source: Bartın Orman Fakültesi Dergisi (article, 2021-12-08)
Field: Materials Science — Materials Chemistry

Bio-foam from Kraft Black Liquor Bio-based materials have superior properties in terms of health and environmental friendly as compared with synthetic materials. This makes bio-based materials a good alternative for the future. The sensitivity on this issue has increased even more with the natural disasters that have occurred in recent years and the depletion of resources. In this study, it was aimed to develop alternative bio-based material foams from wastes of paper mills to petroleum-based foams. The mixtures of black liquor and pine bark as the paper mill wastes were selected as the main materials in the production of bio-foams. The changes in the foam structure were determined by adding with different rates of Tween 80 and protein foaming agents (2 g and 4.4 g). The structural and chemical properties of the foams were determined using scanning electron microscopy (SEM) and Fourier Transform Infrared spectroscopy (FTIR), respectively. In SEM analysis, the maximum cell size was found as 55.24 µm for the F-3 (2 g protein foaming agent) sample, while the minimum cell size was 15 µm for F-1 (4.4g protein foaming agent) sample. The maximum and minimum cell size and wall thickness were determined respectively as 5.93 µm for F-2 (4.4g Tween 80) sample and 3.66 µm for F-4 (2 g Tween 80) sample. Bio-foam from kraft liquor has shown a promising success with its foaming features and adequate porous structure for using as an insulation material. By replacing petroleum-based foams with bio-based foams, a new value-added alternative material can be produced from paper mill wastes. Fourier transform infrared spectroscopy ; Kraft paper ; Scanning electron microscope ; Materials science ; Foaming agent ; Porosity ; Pulp and paper industry ; Chemical engineering ; Composite material Pickering emulsions and particle stabilization ; Lignin and Wood Chemistry ; biodegradable polymer synthesis and properties
Authors: Daniel S. King | Laura Gagliardi | Donald G. Truhlar
Source: ChemRxiv (preprint, 2021-06-28)
Field: Materials Science — Materials Chemistry

Machine-Learned Energy Functionals for Strongly Correlated Systems We introduce multiconfiguration data-driven functional theory (MC-DDFT) as a new approach to multiconfiguration nonclassical functional theory (MC-NCFT), in which the classical energy of a multiconfigurational wave function is combined with a machine-learned functional for the nonclassical exchange-correlation energy. We also present results obtained by a related approach, multiconfiguration energy-correcting functional theory (MC-ECFT), in which the total energy of a wave function method (e.g. CASSCF or NEVPT2) is corrected with a machine-learned functional. On a dataset of carbene singlet-triplet energy splittings, we demonstrate that these new multiconfiguration data-driven functional methods (MC-DDFMs) are able to achieve near-benchmark performance on systems not used for training while being less active-space dependent than multiconfiguration pair-density functional theory using currently available translated functionals. Density functional theory ; Wave function ; Singlet state ; Orbital-free density functional theory ; Energy (signal processing) ; Hybrid functional ; Energy functional ; Function (biology) ; Physics ; Space (punctuation) ; Time-dependent density functional theory ; Benchmark (surveying) ; Statistical physics ; Quantum mechanics ; Computer science ; Excited state Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Advanced NMR Techniques and Applications
Authors: Janamkumar Pandya
Source: Scholarworks (University of Massachusetts Amherst) (article, 2021-01-07)
Field: Chemistry — Analytical Chemistry

ANALYSIS OF TITANIUM DIOXIDE NANOPARTICLES IN FOODS USING RAMAN SPECTROSCOPIC TECHNIQUES Titanium dioxide (TiO2) and its nanoparticles (NPs) are widely used in various applications. Recently, the presence of TiO2 NPs in food and consumer products raised safety concerns to human health and the environment. The goal of this project is to explore the capability of Raman Spectroscopy in the analysis of TiO2-NPs and apply this technique for the analysis of TiO2-NPs in food and environmental samples. Two approaches, i.e. the ligand-based and the mapping-based, were evaluated. The ligand-based approach utilized the surface enhanced Raman scattering (SERS) property of the TiO2 NPs as a substrate to enhance the signal of a surface bound ligand, gallocyanin (GLN). This SERS property is only specific to NPs of TiO2, thus based on the R-value obtained from the ratio of TiO2 to GLN peak intensities, the R-value can be used to differentiate the NPs from their counterparts. Particles raning from 65-8 nm had a similar R-value ranged from 2.4 to 3.4 with no statistical difference, however, the R was found significantly higher for 93 nm and 173 nm samples. We then evaluated the second approach that is based on Raman mapping in combination with filtration to analyze TiO2-NPs. The results revealed linear correlation of both Raman intensity and map area occupied by the particles with particle size at different concentrations. We then evaluated both SERS and Raman mapping methods to determine the mean particle size and the amount of NPs from commercial E171 and food samples. Using R obtained from SERS analysis we were able to predict the mean particle size of chewing gum samples, however, due to the lack of R-values from standards between 65 to 200 nm, the SERS approach was not successfully able to estimate the mean particle size of the samples that contained a higher percentage of particles in that range. Consequently, using the correlation established between the map area and Raman intensity with the particle size, we were able to successfully estimate the particle size of TiO2 particles from both E171 and food samples. We then estimated the amount of NPs from the map area obtained by applying the Raman intensity threshold for the cut-off intensity of 93 nm particles. Titanium dioxide ; Raman spectroscopy ; Nanoparticle ; Materials science ; Nanotechnology ; Chemistry ; Chemical engineering ; Metallurgy ; Optics ; Physics ; Engineering Analytical chemistry methods development ; Extraction and Separation Processes ; Electrochemical Analysis and Applications
Authors: Gokul Mohanraj | Gagan Jain | Pratyush Agarwal | Vaibhavkumar Patel
Source: International Journal of Engineering and Advanced Technology (article, 2021-02-28)
Field: Chemistry — Analytical Chemistry

Application of Deep Convolutional Neural Networks and IR Spectroscopy for the Detection of Drugs and Toxins This paper explores the use of deep learning architectures to identify and categorize infrared spectral data with the objective of classifying drugs and toxins with a high level of accuracy. The model proposed uses a custom convolutional neural network to learn the spectrum of 192 drugs and 207 toxins. Variations in the architecture and number of blocks were iterated to find the best possible fit. A real-time implementation of such a model faces a lot of issues such as noise from different sources, spectral magnitude off-setting, and wavelength rotation. This paper aims to tackle some of these problems. Another common issue is the use of extensive pre-processing which makes it difficult to automate the entire process. We have aimed to side-step this issue with the architecture proposed. The focus is on 2 applications - detection of drugs and toxins. The data sets used are from different sources, each with its own noise factor and sampling rate. Some of the traditional models like Principal Component Analysis (PCA) and Support Vector Machines (SVM) were also tested on the datasets. The model works with minimal input data of two spectra (and three augmentations of the same) to learn the features and classifies the data from a source independent of the input. The proposed model showed a significant improvement in accuracy when compared to the other models currently in use, achieving an overall accuracy of 96.55\%. The model proposed performs extremely well with a minimal sampling rate and shows no loss in accuracy of classification even with an increase in the number of classes. The research conducted has the scope of being extended to the identification of counterfeit drugs which is a growing cause for concern. Another application could be in the detection of the presence of harmful toxins. Computer science ; Artificial intelligence ; Convolutional neural network ; Support vector machine ; Pattern recognition (psychology) ; Noise (video) ; Process (computing) ; Data mining ; Machine learning ; Principal component analysis ; Sampling (signal processing) ; Deep learning ; Categorization ; Focus (optics) ; Detector Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Gabriel César Pereira | Rogério Custódio
Source: Revista Chemkeys (article, 2021-09-09)
Field: Chemistry — Analytical Chemistry

Redes neurais para regressão uni- e multivariada Redes Neurais Artificiais têm ganhado notoriedade na aproximação de funções uni e multivariadas em virtude a alta capacidade aproximativa desse tipo de modelo. Neste artigo é apresentada uma descrição dos modelos de regressão baseados em redes neurais juntamente com os algoritmos comumente usados para otimizá-los. A performance deste tipo de modelo é exemplificada através da aproximação de uma função univariada que relaciona a fração em mol na fase líquida de um dos componentes de uma mistura água-acetona com sua fração em mol na fase de vapor. O desempenho do modelo é, ainda, comparado com o desempenho de outros modelos baseados em métodos de regressão clássicos utilizados para solucionar o mesmo problema. Ao final do texto, é apresentado o código PYTHON para a criação do modelo de rede neural discutido aqui. Humanities ; Physics ; Philosophy Spectroscopy and Chemometric Analyses
Authors: Abbas Saifee Valsadwala | Sriram Sriniva | B. Surya Rajan | S. Shamshath Begum | Asit Baran Samui | Sandeep K. Shukla
Source: Research Square (preprint, 2021-11-22)
Field: Materials Science — Polymers and Plastics

Use of eco-benign periwinkle shell particle for making composites and study their effect on the thermal and mechanical properties of novel HDPE composites Abstract In this investigation, the shell powder of Littorina littorea commonly called periwinkle was used as an eco-benign filler in High-Density Polyethylene (HDPE) to form periwinkle/HDPE composites (PHPC). Understanding the effect of different particle sizes of periwinkle shell powder (PSP) and optimizing their influence on PHPC is the main scope of work. Periwinkle shell (PS) particle sizes from &lt;53 μm to 150 μm were chosen as reinforcement. The different PSP size like &lt;53 μm, 53 μm, 75 μm, 90 μm, 105 μm and 150 μm chosen in this study were named as PHPCL53, PHPC53, PHPC75, PHPC90, PHPC105, and PHPC150 respectively. The composites were fabricated by incorporating 1 weight % of PSP into HDPE matrix using compression molding technique and then subjected to morphological, thermal, and mechanical characterizations. Morphology studies using scanning electron microscope (SEM) confirms 150 μm PSP had best dispersion whereas 75 μm PSP resulted with agglomeration. PSP had little influence on the thermal stability of HDPE except for PHPC150 which showed rise in degradation temperature when compared to the neat sample. Mechanical properties such as hardness, Young’s modulus, impact strength, and flexural modulus were enhanced by the addition of PSP. Whereas, a decrease was noted in elongation at break (%) and flexural strength of PHPC indicating the stiffening effect of filler on HDPE. In order to understand the particle size influence better, the extension evaluation method (EEM) was performed for all samples and PHPC150 was found to be the best performing among all particle sizes. Composite material ; High-density polyethylene ; Materials science ; Shell (structure) ; Thermal ; Particle (ecology) ; Polyethylene Natural Fiber Reinforced Composites ; biodegradable polymer synthesis and properties ; Polymer Nanocomposites and Properties
Authors: Deniz Uzunoğlu | Ayla Özer
Source: DergiPark (Istanbul University) (article, 2021-08-10)
Field: Chemistry — Organic Chemistry

Synthesis of Iron-containing Nanoparticles from Iron-Steel Industrial Waste for Adsorption of Malachite Green For the synthesis of iron-containing nanoparticles (FeNPs), the iron ions were recovered from iron-steel industrial waste by leaching method followed by the chemical precipitation of ferric hydroxide with NH3. The ferric hydroxide was reduced pyrometallurgical to iron-containing nanoparticles by the reducing agent of CO generated from charcoal. FeNPs were characterized by XRD and SEM analysis. XRD results showed that FeNPs had predominantly face-centered cubic Fe3O4 structure, besides, the peaks of Fe2O3 and Fe0 structures were also observed. The particle size of FeNPs was calculated as 57.75 nm using Williamson–Hall equation. According to SEM images, FeNPs had homogeneous and spherical-like structures; also, the structures partially decomposed and the particle size increased due to the agglomeration of the particles after adsorption. FeNPs were used as an adsorbent for the removal of Malachite Green (MG). The experimental design and the optimization of experimental conditions were investigated by using response surface methodology (RSM) according to central composite design (CCD). The optimum experimental conditions were determined as 180 min contact time, 75 °C temperature, and 300 mg/L initial MG concentration. The agreement between the adsorbed MG concentrations at the equilibrium (qe) calculated from the model (148.43 mg/g) and determined experimentally (146.63 mg/g) under the optimum conditions showed that the selected model was suitable for MG adsorption by FeNPs. Langmuir isotherm model had higher R2 and lower ARE values than Freundlich isotherm model, showing that the equilibrium data of MG adsorption with FeNPs was the best agreement to Langmuir isotherm model with the maximum monolayer coverage capacity of 175.44 mg dye/g FeNPs. The thermodynamic studies suggested that the adsorption process was endothermic, spontaneous at the optimum conditions, and the positive ΔS value indicated the increased disorder at the solid-solution interface during the adsorption. Malachite green ; Adsorption ; Malachite ; Nanoparticle ; Metallurgy ; Chemistry ; Materials science ; Nuclear chemistry ; Chemical engineering ; Nanotechnology ; Organic chemistry ; Copper ; Engineering Nanomaterials for catalytic reactions