jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rana Jaafar Jawad | Mohd Halim Shah Ismail | Shamsul Izhar
Source: IIUM Engineering Journal (article, 2018-06-01)
Field: Chemistry — Organic Chemistry

ADSORPTION OF HEAVY METALS AND RESIDUAL OIL FROM PALM OIL MILL EFFLUENT USING NOVEL ADSORBENT OF ALGINATE AND MANGROVE COMPOSITE BEADS COATED BY CHITOSAN IN A PACKED BED COLUMN In this study, a novel adsorbent was prepared, namely alginate and mangrove composite beads coated with chitosan (AMCBCC), and performed well to remove heavy metals and residual oil from palm oil mill effluent (POME) using continuous adsorption studies in a fixed bed column under different bed heights. Thomas and Yoonâ€“Nelson models were applied to predict the breakthrough curve and to compute the characteristic parameters such as maximum capacity and the time required for 50% of adsorbate breakthrough. It was found that the total removal percentage of iron ions, zinc ions, and residual oil were 26.75%, 76.74%, and 83% respectively at pH 3, and a bed height 27 cm. Both models can well describe the breakthrough curve of heavy metals and residual oil. These results indicated that AMCBCC can be used to remove heavy metals and residual oil from industrial wastewater. ABSTRAK: Kajian ini menyediakan tentang penjerap baharu iaitu alginat dan manik komposit bakau bersalut kitosan (AMCBCC), berfungsi menyingkirkan logam berat dan sisa minyak daripada efluen kilang minyak sawit (POME) dengan sempurna, menggunakan kajian penjerapan berterusan dalam lapisan lajur tetap pada ketinggian berbeza. Model Thomas dan Yoon-Nelson telah digunakan bagi meramal lengkungan terbaik dan mengira parameter cirian seperti keupayaan maksimum dan tempoh diperlukan bagi 50% lengkungan terbaik penjerap. Jumlah peratusan penyingkiran ion besi, ion zink, dan sisa minyak masing-masing sebanyak 26.75%, 76.74%, danÂ 83% pada pH 3, dan pada ketinggian lapisan 27 cm. Kedua-dua model mampu menggambarkan lengkungan terbaik bagi logam berat dan sisa minyak dengan sempurna. Dapatan ini menunjukkan AMCBCC sesuai digunakan bagi menyingkirkan logam berat dan sisa minyak daripada air sisa industri. Adsorption ; Residual oil ; Chitosan ; Heavy metals ; Packed bed ; Mangrove ; Nuclear chemistry ; Composite number ; Chemistry ; Pulp and paper industry ; Materials science ; Chromatography ; Composite material ; Environmental chemistry ; Engineering ; Organic chemistry Edible Oils Quality and Analysis ; Oil Palm Production and Sustainability ; Biodiesel Production and Applications
Authors: David B. Stephenson | James R. Kermode | Duncan A. Lockerby
Source: Microfluidics and Nanofluidics (article, 2018-11-16)
Field: Materials Science — Materials Chemistry

Accelerating multiscale modelling of fluids with on-the-fly Gaussian process regression We present a scheme for accelerating hybrid continuum-atomistic models in multiscale fluidic systems by using Gaussian process regression as a surrogate model for computationally expensive molecular dynamics simulations. Using Gaussian process regression, we are able to accurately predict atomic-scale information purely by consideration of the macroscopic continuum-model inputs and outputs and judge on the fly whether the uncertainty of our prediction is at an acceptable level, else a new molecular simulation is performed to continually augment the database, which is never required to be complete. This provides a substantial improvement over the current generation of hybrid methods, which often require many similar atomistic simulations to be performed, discarding information after it is used once. We apply our hybrid scheme to nano-confined unsteady flow through a high-aspect-ratio converging-diverging channel, and make comparisons between the new scheme and full molecular dynamics simulations for a range of uncertainty thresholds and initial databases. For low thresholds, our hybrid solution is highly accurate-around that of thermal noise. As the uncertainty threshold is raised, the accuracy of our scheme decreases and the computational speed-up increases (relative to a full molecular simulation), enabling the compromise between accuracy and efficiency to be tuned. The speed-up of our hybrid solution ranges from an order of magnitude, with no initial database, to cases where an extensive initial database ensures no new MD simulations are required. Computer science ; Uncertainty quantification ; Molecular dynamics ; Gaussian process ; Gaussian ; Statistical physics ; Regression ; Surrogate model ; Kriging ; Range (aeronautics) ; Algorithm ; Mathematics ; Physics ; Machine learning ; Materials science ; Statistics Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Protein Structure and Dynamics
Authors: Peng Yu | Tao Hu | Hong Hui Chen | Fangfang Wu | Hui Liu
Source: Scanning (article, 2018-06-04)
Field: Chemistry — Analytical Chemistry

Effective Removal of Congo Red by Triarrhena Biochar Loading with TiO<sub>2</sub> Nanoparticles A composite of pyrolytic Triarrhena biochar loading with TiO<sub>2</sub> nanoparticles has been synthesized by the sol-gel method. The composite shows a well-developed hollow mesoporous and macropore structure as characterized by XRD, BET, and SEM. When used as an absorbent to remove Congo red from aqueous solution, it was found that as-prepared composite performed better absorption capacity than single biochar or TiO<sub>2</sub>. The results suggest that biochar loading with TiO<sub>2</sub> could be promisingly implemented as an environmentally friendly and inexpensive adsorbent for Congo red removal from wastewater. Biochar ; Congo red ; Nanoparticle ; Chemical engineering ; Materials science ; Nanotechnology ; Chemistry ; Adsorption ; Pyrolysis ; Organic chemistry Dye analysis and toxicity ; Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal
Authors: Tao Zhang | Biyao Wang | Pengtao Yan | Kunlun Wang | Xu Zhang | Huihui Wang | Yan Lv
Source: Journal of Food Quality (article, 2018-12-26)
Field: Chemistry — Analytical Chemistry

Nondestructive Identification of Salmon Adulteration with Water Based on Hyperspectral Data For the identification of salmon adulteration with water injection, a nondestructive identification method based on hyperspectral images was proposed. The hyperspectral images of salmon fillets in visible and near-infrared ranges (390–1050 nm) were obtained with a system. The original hyperspectral data were processed through the principal-component analysis (PCA). According to the image quality and PCA parameters, a second principal-component (PC2) image was selected as the feature image, and the wavelengths corresponding to the local extremum values of feature image weighting coefficients were extracted as feature wavelengths, which were 454.9, 512.3, and 569.1 nm. On this basis, the color combined with spectra at feature wavelengths, texture combined with spectra at feature wavelengths, and color-texture combined with spectra at feature wavelengths were independently set as the input, for the modeling of salmon adulteration identification based on the self-organizing feature map (SOM) network. The distances between neighboring neurons and feature weights of the models were analyzed to realize the visualization of identification results. The results showed that the SOM-based model, with texture-color combined with fusion features of spectra at feature wavelengths as the input, was evaluated to possess the best performance and identification accuracy is as high as 96.7%. Hyperspectral imaging ; Principal component analysis ; Feature (linguistics) ; Artificial intelligence ; Pattern recognition (psychology) ; Weighting ; Identification (biology) ; Texture (cosmology) ; Wavelength ; Computer science ; Feature extraction ; Computer vision ; Remote sensing ; Image (mathematics) ; Optics ; Geography ; Physics ; Acoustics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Devender Sharma | Jasvir Singh | Pushap Raj
Source: International Journal of Pharmacy and Pharmaceutical Sciences (article, 2018-02-01)
Field: Chemistry — Analytical Chemistry

SPECTROPHOTOMETRIC DETERMINATION OF PROPRANOLOL HYDROCHLORIDE AND METOPROLOL TARTRATE IN PHARMACEUTICAL DOSAGE FORMS, SPIKED WATER AND BIOLOGICAL FLUIDS Objective: A new spectrophotometric method for the determination of propranolol hydrochloride (PRO) and metoprolol tartrate (MTP), beta blocker drugs, has been developed for their analysis in pharmaceutical dosage forms for the purpose of quality control and water samples for monitoring impact on environmental water quality of natural sources and in biological fluids for ascertaining their physiological performance.Methods: The method is based on the derivatization of the amino function present in these drugs to the corresponding yellow copper (I) drug dithiocarbamate derivative through reaction with carbon disulphide, pyridine and copper (I) perchlorate in aqueous acetonitrile and measuring absorbance at 406 nm for propranolol and 400 nm for metoprolol. The different experimental parameters affecting the development and stability of the colour were carefully studied and optimized.Results: The Beer’s law is obeyed in the range of 1.0-40.0 μg/ml of each drug solution with a correlation coefficient 0.999. The maximum relative standard deviations (RSDs) in the analysis of pure PRO and MTP were 1.01 and 1.52 % respectively. The recoveries of the drugs from pharmaceutical formulations, spiked water samples and biological fluids were in the range 98.0-100.5 % with RSDs in the range 0.23-1.94% indicating good accuracy and precision of the method.Conclusion: The instantaneous development of colour and its stability, well-established stoichiometry of the reaction and above simplicity and rapidity of procedures are some special attributes of the proposed method. Chemistry ; Metoprolol Tartrate ; Dosage form ; Chromatography ; Derivatization ; Pharmaceutical formulation ; Propranolol Hydrochloride ; Absorbance ; Aqueous solution ; Metoprolol ; Propranolol ; High-performance liquid chromatography ; Organic chemistry Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Sensors ; Analytical chemistry methods development
Authors: Lin Yang | Jikai Bi | Yanpeng Hao | Lupeng Nian | Zijun Zhou | Licheng Li | Yifan Liao | Fuzeng Zhang
Source: Energies (article, 2018-03-28)
Field: Materials Science — Materials Chemistry

A Recognition Method of the Hydrophobicity Class of Composite Insulators Based on Features Optimization and Experimental Verification The hydrophobicity of composite insulators is a great significance to the safe and stable operation of transmission lines. In this paper, a recognition method of the hydrophobicity class (HC) of composite insulators based on features optimization was proposed. Through the spray method, many hydrophobic images of water droplets on the insulator surface at various hydrophobicity classes (HCs) were taken. After processing of the hydrophobic images, seven features were extracted: the number n, mean eccentricity Eav and coverage rate k1 of the water droplets, and the coverage rate k2, perimeter Lmax, shape factor fc, and eccentricity Emax of the maximum water droplet. Then, the maximum value Δxmax, the minimum value Δxmin, and the average value Δxav of the change rate of each feature value between adjacent HCs, and the volatility Δs of each feature value, were used as the evaluation indexes for features optimization. After this features optimization, the five features that are most closely related to the HC were obtained. Lastly, a recognition model of the HC with the five features as input and the seven HCs as output was established. When compared with the spray method and the contact angle method, the correct rate of the proposed recognition method was 98.1% and 95.2%, respectively. The influence of subjective factors on the spray method was effectively overcome. Composite number ; Contact angle ; Insulator (electricity) ; Threshold limit value ; Pattern recognition (psychology) ; Materials science ; Transmission rate ; Artificial intelligence ; Feature (linguistics) ; Biological system ; Computer science ; Mathematics ; Composite material ; Chemistry ; Transmission (telecommunications) High voltage insulation and dielectric phenomena ; Image Enhancement Techniques ; Thermal Analysis in Power Transmission
Authors: N. Vijay | Gunja Mishra
Source: International Journal of Current Microbiology and Applied Sciences (article, 2018-12-10)
Field: Chemistry — Analytical Chemistry

Time Series Forecasting Using ARIMA and ANN Models for Production of Pearl Millet (BAJRA) Crop of Karnataka, India Time series prediction is a vital problem in many applications in nature science, agriculture, engineering and economics. The objective of the study is to examine the flexibility of artificial neural network model (ANN) in time series forecasting by comparing with classical time series ARIMA model. The data consist of area and production of Pearl millet (bajra) crop area (‘000 ha) and production (‘000 MT) from 1955-56 to 2014-15 were collected from “Agricultural Statistics at a Glance 2014-15, Karnataka, India were used in the study to demonstrate the effectiveness of the model. The experiment shows that ANN model outperform the ARIMA Models based on root mean (RMSE), MAPE and MSE. Autoregressive integrated moving average ; Time series ; Pearl ; Production (economics) ; Agriculture ; Series (stratigraphy) ; Statistics ; Mathematics ; Mean squared error ; Flexibility (engineering) ; Crop production ; Geography ; Biology ; Economics Spectroscopy and Chemometric Analyses ; Stock Market Forecasting Methods
Authors: Thaís Alves Pereira Gonçalves | Júlia Sonsin‐Oliveira | Silvana Nisgoski | Carmen Regina Marcati | Adriano Wagner Ballarin | Graciela Inês Bolzón de Muñiz
Source: Australian Journal of Botany (article, 2018-06-19)
Field: Chemistry — Organic Chemistry

A contribution to the identification of charcoal origin in Brazil III: microscopic identification of 10 Cerrado species Brazil has one of the richest biodiversities in the world. The Brazilian savanna is a hotspot for conservation priorities and its deforestation is of global concern. Conservation in this biome is threatened by unsustainable use of forests, such as illegal logging for charcoal production. Thus, government agents need to verify whether charcoal loads follow the Document of Forestry Origin (DOF). To improve charcoal identification, our study presents the microscopic description of 10 Cerrado species and provides an identification key to aid government agents during surveillance. We analysed charcoal samples with a scanning electron microscope. The method of carbonisation simulated real conditions. We chose species with similar wood anatomy (sparse axial parenchyma and narrow rays), which increases misidentification by forest controllers because of their difficulty to identify these features. Also, paratracheal scanty, diffuse and diffuse-in-aggregates parenchyma were harder to recognise in charcoal than in wood. Other features, such as vessels, rays and abundant axial parenchyma, were easily identified. The present work can be used as a part of a charcoal anatomy database focussed on preventing deforestation in Brazil and in other countries with similar problems. Charcoal ; Deforestation (computer science) ; Biome ; Parenchyma ; Identification (biology) ; Agroforestry ; Biology ; Logging ; Geography ; Botany ; Ecology ; Computer science ; Chemistry ; Ecosystem Wood and Agarwood Research ; Cultural Heritage Materials Analysis ; Identification and Quantification in Food
Authors: Kylie Ariel Bemis | Dan Guo | April Harry | Mathew Thomas | Ingela Lanekoff | Mary P. Stenzel‐Poore | Susan L. Stevens | Julia Laskin | Olga Vitek
Source: International Journal of Mass Spectrometry (article, 2018-08-03)
Field: Chemistry — Spectroscopy

Statistical detection of differentially abundant ions in mass spectrometry-based imaging experiments with complex designs Chemistry ; Workflow ; Analyte ; Mass spectrometry ; Statistical analysis ; Design of experiments ; Statistical power ; Abundance (ecology) ; Mass spectrometry imaging ; Variation (astronomy) ; Statistical model ; Biological system ; Computer science ; Chromatography ; Statistics ; Artificial intelligence ; Ecology Mass Spectrometry Techniques and Applications ; Pesticide Residue Analysis and Safety ; Analytical Chemistry and Chromatography
Authors: Moisés Roberto Vallejo-Pérez | Hugo Ricardo Navarro Contreras | Jesús A. Sosa Herrera | José Pablo Lara-Ávila | Hugo M. Ramírez‐Tobías | Fernando Díaz‐Barriga | Rogelio Flores‐Ramírez | A.G. Rodríguez
Source: The Plant Pathology Journal (article, 2018-10-01)
Field: Chemistry — Analytical Chemistry

Detection of Clavibacter michiganensis subsp. michiganensis Assisted by Micro-Raman Spectroscopy under Laboratory Conditions <i>Clavibacter michiganensis</i> subsp. <i>michiganesis</i> (<i>Cmm</i>) is a quarantine-worthy pest in México. The implementation and validation of new technologies is necessary to reduce the time for bacterial detection in laboratory conditions and Raman spectroscopy is an ambitious technology that has all of the features needed to characterize and identify bacteria. Under controlled conditions a contagion process was induced with <i>Cmm</i>, the disease epidemiology was monitored. Micro-Raman spectroscopy (532 nm λ laser) technique was evaluated its performance at assisting on <i>Cmm</i> detection through its characteristic Raman spectrum fingerprint. Our experiment was conducted with tomato plants in a completely randomized block experimental design (13 plants × 4 rows). The <i>Cmm</i> infection was confirmed by 16S rDNA and plants showed symptoms from 48 to 72 h after inoculation, the evolution of the incidence and severity on plant population varied over time and it kept an aggregated spatial pattern. The contagion process reached 79% just 24 days after the epidemic was induced. Micro-Raman spectroscopy proved its speed, efficiency and usefulness as a non-destructive method for the preliminary detection of <i>Cmm</i>. Carotenoid specific bands with wavelengths at 1146 and 1510 cm<sup>-1</sup> were the distinguishable markers. Chemometric analyses showed the best performance by the implementation of PCA-LDA supervised classification algorithms applied over Raman spectrum data with 100% of performance in metrics of classifiers (sensitivity, specificity, accuracy, negative and positive predictive value) that allowed us to differentiate <i>Cmm</i> from other endophytic bacteria (<i>Bacillus</i> and <i>Pantoea</i>). The unsupervised KMeans algorithm showed good performance (100, 96, 98, 91 y 100%, respectively). Raman spectroscopy ; Clavibacter michiganensis ; Horticulture ; Population ; Hyperspectral imaging ; Artificial intelligence ; Materials science ; Veterinary medicine ; Food science ; Biology ; Analytical Chemistry (journal) ; Chemistry ; Microbiology ; Computer science ; Chromatography ; Pathogen ; Optics ; Physics ; Medicine Spectroscopy and Chemometric Analyses ; Plant Pathogenic Bacteria Studies ; Essential Oils and Antimicrobial Activity
Authors: Wen Si | Simeng Li | Huaishuo Xiao | Qingquan Li | Yalin Shi | Tongqiao Zhang
Source: Energies (article, 2018-03-08)
Field: Materials Science — Materials Chemistry

Defect Pattern Recognition Based on Partial Discharge Characteristics of Oil-Pressboard Insulation for UHVDC Converter Transformer The ultra high voltage direct current (UHVDC) transmission system has advantages in delivering electrical energy over long distance at high capacity. UHVDC converter transformer is a key apparatus and its insulation state greatly affects the safe operation of the transmission system. Partial discharge (PD) characteristics of oil-pressboard insulation under combined AC-DC voltage are the foundation for analyzing the insulation state of UHVDC converter transformers. The defect pattern recognition based on PD characteristics is an important part of the state monitoring of converter transformers. In this paper, PD characteristics are investigated with the established experimental platform of three defect models (needle-plate, surface discharge and air gap) under 1:1 combined AC-DC voltage. The different PD behaviors of three defect models are discussed and explained through simulation of electric field strength distribution and discharge mechanism. For the recognition of defect types when multiple types of sources coexist, the Random Forests algorithm is used for recognition. In order to reduce the computational layer and the loss of information caused by the extraction of traditional features, the preprocessed single PD pulses and phase information are chosen to be the features for learning and test. Zero-padding method is discussed for normalizing the features. Based on the experimental data, Random Forests and Least Squares Support Vector Machine are compared in the performance of computing time, recognition accuracy and adaptability. It is proved that Random Forests is more suitable for big data analysis. Pressboard ; Partial discharge ; Transformer ; Electrical engineering ; Engineering ; Voltage ; High voltage ; Insulation system ; Three-phase ; Random forest ; Electronic engineering ; Computer science ; Artificial intelligence High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; High-Voltage Power Transmission Systems
Authors: K. Meka | A. Giridhar | D. V. S. S. Siva Sarma
Source: Radioengineering (article, 2018-09-14)
Field: Materials Science — Materials Chemistry

PD Source Location Utilizing Acoustic TDOA Signals in Power Transformer by Fuzzy Adaptive Particle Swarm Optimization Partial discharge (PD) source location using acoustic emission (AE) is widely utilized by many transformer manufacturers and power utility engineers in routine and critical situation for optimal operation of the electrical power system as well as further risk management and repair planning. The PD detection is not enough to take solution, so identification of PD source is essential to restore apparatus condition. This work aim is to localize the defect geometrically by means of TDOA (time difference of arrival) signals from the sensors fixed on the power transformer. The solution for PD source location is acquired by making these nonlinear equations as optimization problem. In this technique, the inertia weight is effectively regulated by using 49 and 9 simple IF-THEN fuzzy rules to improve the global optimal solution and impairs the local convergence problem and improves the accuracy in estimating the PD source location. The simulation results reveal that PD location accuracy with minimum of maximum deviation error, absolute error and relative error is better when compared to other constant parameter intelligent methods which were reported in the literature. Particle swarm optimization ; Multilateration ; Transformer ; Fuzzy logic ; Computer science ; Acoustics ; Electronic engineering ; Electrical engineering ; Engineering ; Algorithm ; Physics ; Artificial intelligence ; Voltage High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Vibration and Dynamic Analysis