jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: MN Uddin | SK Ray | MS Islam | MM Karim | MS Jahan
Source: Bangladesh Journal of Scientific and Industrial Research (article, 2022-12-25)
Field: Chemistry — Analytical Chemistry

Quantification of components of textile fabrics by using chemometric techniques with FT-NIR spectroscopic data The study has attempted to develop chemometric modeling based method to quantify compositions of textile fabrics by FT-NIR spectroscopic data. Three calibration techniques such as: Principal Component Regression (PCR), Partial Least Square Regression (PLSR) and Artificial Neural Network (ANN) were assessed, and PLSR showed the best result. Several pretreatment techniques of spectral data have been evaluated, and Multiplicative Scatter Correction (MSC) performed the best. Results also shows that performance of PLSR was satisfactory for quantification of cotton (R2 ≈0.99), elastine (R2 ≈0.97) and polyester (R2≈0.94) when FT-NIR spectral data were pretreated with MSC. But for quantification of viscose in mixture fabric, efficiency of developed model was not upto the mark (R2≈0.75). Finally, the developed PLSR model with FT-NIR spectroscopic data pretreated with MSC could be used for quantification of cotton, elastine and polyester in textile fabrics rapidly and with comparatively low cost. Bangladesh J. Sci. Ind. Res. 57(4), 229-238, 2022 Partial least squares regression ; Principal component analysis ; Textile ; Chemometrics ; Polyester ; Near-infrared spectroscopy ; Calibration ; Principal component regression ; Viscose ; Biological system ; Chromatography ; Materials science ; Chemistry ; Mathematics ; Statistics ; Composite material ; Optics Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Huanchao Shen | Yingrui Geng | Hongfei Ni | Hui Wang | Jizhong Wu | Xianwei Hao | Jinxin Tie | Yingjie Luo | Tengfei Xu | Yong Chen | Xuesong Liu
Source: RSC Advances (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Across different instruments about tobacco quantitative analysis model of NIR spectroscopy based on transfer learning With the development of near-infrared (NIR) spectroscopy, various calibration transfer algorithms have been proposed, but such algorithms are often based on the same distribution of samples. In machine learning, calibration transfer between types of samples can be achieved using transfer learning and does not need many samples. This paper proposed an instance transfer learning algorithm based on boosted weighted extreme learning machine (weighted ELM) to construct NIR quantitative analysis models based on different instruments for tobacco in practical production. The support vector machine (SVM), weighted ELM, and weighted ELM-AdaBoost models were compared after the spectral data were preprocessed by standard normal variate (SNV) and principal component analysis (PCA), and then the weighted ELM-TrAdaBoost model was built using data from the other domain to realize the transfer from different source domains to the target domain. The coefficient of determination of prediction (<i>R</i> <sup>2</sup>) of the weighted ELM-TrAdaBoost model of four target components (nicotine, Cl, K, and total nitrogen) reached 0.9426, 0.8147, 0.7548, and 0.6980. The results demonstrated the superiority of ensemble learning and the source domain samples for model construction, improving the models' generalization ability and prediction performance. This is not a bad approach when modeling with small sample sizes and has the advantage of fast learning. Spectroscopy ; Tobacco use ; Medicine ; Environmental health ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Andrew Dickson White | Glen M. Hocky | Heta A. Gandhi | Mehrad Ansari | Sam Cox | Geemi P. Wellawatte | Subarna Sasmal | Ziyue Yang | Kangxin Liu | Yuvraj Singh | Willmor J. Peña Ccoa
Source: ChemRxiv (preprint, 2022-12-11)
Field: Materials Science — Materials Chemistry

Assessment of chemistry knowledge in large language models that generate code In this work, we investigate the question: do code-generating large language models know chemistry? Our results indicate, mostly yes. To evaluate this, we produce a benchmark set of problems, and evaluate these models based on correctness of code by automated testing and evaluation by experts. We find recent LLMs are able to write correct code across a variety of topics in chemistry and their accuracy can be increased by 30 percentage points via prompt engineering strategies, like putting copyright notices at the top of files. These dataset and evaluation tools are open source which can be contributed to or built upon by future researchers, and will serve as a community resource for evaluating the performance of new models as they emerge. We also describe some good practices for employing LLMs in chemistry. The general success of these models demonstrates that their impact on chemistry teaching and research is poised to be enormous. Correctness ; Benchmark (surveying) ; Computer science ; Code (set theory) ; Set (abstract data type) ; Variety (cybernetics) ; Resource (disambiguation) ; Data science ; Programming language ; Artificial intelligence Machine Learning in Materials Science ; Topic Modeling ; Software Engineering Research
Authors: Enesi Y. Salawu | Adeolu Adesoji Adediran | O. O. Ajayi | A.O. Inegbenebor | Joseph O. Dirisu
Source: Scientific Reports (article, 2022-10-31)
Field: Materials Science — Materials Chemistry

On the analyses of carbon atom diffused into grey cast iron during carburisation process The study employed Fick's second law of diffusion to discover some unknown aspect of carbon diffusion in grey cast iron during carburisation process. Emphasis on the experiments and theoretical modelling were established for better accomplishments. Pulverised palm kernel and eggshell additives of 70 (wt.%) and 30 (wt.%) according to the Voige law of mixture was considered as a continuous medium without considering the atomic nature of the mixture. Furthermore, a kinetic approach was described where a physical model of the substrate immersed in the carbon mixture was established while diffusion equations were modelled to establish the mechanism of carbon diffusion during carburisation. Initial composition and concentration of diffused atom remained constant which are 2.68 and 6.67% carbon. While the carburizing time used varied from 60 min, 90 min, 120 min, 150 min, 180 min and 210 min respectively at constant carburising temperature of 900° The results revealed varying composition gradient of carbon atom ranging from 5.4%, 5.42%, 5.44%, 5.46%, 5.51%, and 5.65 compared to the initial carbon content of 2.68%. The concentration of carbon atom on the substrate surface at varying time implies that the process was non-steady state diffusion which verified Fick's second law of diffusion. Hence, the composition achieved is a function of boundary conditions such as time position and temperature. This novel study will enhance the understanding of heat treat treatment of metals such that their applications in the industry will be numerous. Diffusion ; Carburizing ; Carbon fibers ; Fick's laws of diffusion ; Composition (language) ; Atom (system on chip) ; Carbon atom ; Substrate (aquarium) ; Chemistry ; Materials science ; Thermodynamics ; Chemical engineering ; Metallurgy ; Organic chemistry ; Composite material ; Computer science Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Aluminum Alloys Composites Properties
Authors: G. Venkatachalam | Arunkumar Gopu | P. Shenbaga Velu | Neelanarayanan Venkataraman | Dinesh Ramesh Salunke | Raghava Rao Mukkamala
Source: Nanomaterials (article, 2022-11-22)
Field: Materials Science — Polymers and Plastics

Study on Tensile Properties of Fly Ash, Sugarcane Fiber and Carbon Nanotube-Reinforced Polymer Matrix Composite Using Objective Evolutionary Algorithm Composite materials have a wide range of applications in emerging eco-friendly environments. Composites that created from naturally available materials are easily decomposed over time and very cost-effective. Fly ash and sugarcane fiber are widely available waste materials produced on a massive scale. This research was aimed to find an optimal mixture of reinforced composites (fly ash, sugarcane fiber and CNTs) in order to maximize yield strength, ultimate tensile strength and Young's modulus using a Multi-Objective Evolutionary Algorithm with Decomposition (MOEA/D). Optimizing one objective may have a negative impact on another objective, so the authors used the sophisticated MOEA/D algorithm to simultaneously find optimal values on all three objectives. The Design of Experiments (DOE) method was performed using ANOVA, and then regression equations were generated. The regression equations were optimized using the MOEA/D algorithm to obtain optimal values. Using the optimal compositional values produced by the algorithm, materials were fabricated. The fabricated materials were tested using a Shimadzu UTM machine to cross-validate the findings. A combination of 0.2 wt.% of fly ash, 2 wt.% of SCF, and 0.39 wt.% of CNTs showed a maximum yield strength of 7.52 MPa and Young's modulus of 1281.18 MPa, with a quite considerable ultimate tensile strength of 10.54 MPa compared with the optimized results obtained through the response surface methodology. Ultimate tensile strength ; Fly ash ; Materials science ; Response surface methodology ; Composite material ; Composite number ; Yield (engineering) ; Carbon nanotube ; Fiber ; Modulus ; Matrix (chemical analysis) ; Evolutionary algorithm ; Young's modulus ; Algorithm ; Computer science ; Mathematics ; Mathematical optimization ; Machine learning Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Additive Manufacturing and 3D Printing Technologies
Authors: Hongchuan Qin | Zhonghua Deng | Xi Li
Source: Energies (article, 2022-05-04)
Field: Materials Science — Materials Chemistry

Cooperative Control of a Steam Reformer Solid Oxide Fuel Cell System for Stable Reformer Operation Solid oxide fuel cells (SOFCs) have complex characteristics, including a long time delay, strong thermoelectrical coupling, and multiple constraints. This leads to multiple control objectives, such as efficiently controlling the power output of the stack and considering the temperature constraints of multiple high-temperature components. Dealing with multiple objectives at the same time brings challenges to the design of SOFC system control. Based on the verified high-precision system model and aiming to achieve fast response, high efficiency, and thermal management, this paper first designs a generalized predictive controller (GPC) to realize the global optimization of the system. Then, through the actual test of the individual reformer, the reformer characteristics are analyzed, the standby controller to control the reformer temperature is designed, and the thermoelectric cooperative controller is constricted with the GPC. The results show that while fast power tracking, high efficiency, and multiple temperature constraints are realized by the controller, the temperature and methane conversion rate (MCR) of the reformer are stably controlled, providing a basis for further practical experiments of the SOFC system. Steam reforming ; Stack (abstract data type) ; Solid oxide fuel cell ; Controller (irrigation) ; Model predictive control ; Temperature control ; Power (physics) ; Computer science ; Control theory (sociology) ; Automotive engineering ; Process engineering ; Engineering ; Control engineering ; Control (management) ; Anode ; Hydrogen production ; Chemistry Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Catalytic Processes in Materials Science
Authors: Girma T. Chala | Berihun Mamo Negash
Source: Energies (article, 2022-02-25)
Field: Chemistry — Analytical Chemistry

Artificial Neural Network and Regression Models for Predicting Intrusion of Non-Reacting Gases into Production Pipelines Wax deposition and gelation of waxy crude oil in production pipelines are detrimental to crude oil transportation from offshore fields. A waxy crude oil forms intra-gel voids in pipelines under cooling mode, particularly below the pour point temperature. Consequently, intrusion of non-reacting gas into production pipelines has become a promising method to lessen the restart pressure required and clear the clogged gel. A trial-and-error method is currently employed to determine the required restart pressure and restart time in response to injected gas volume. However, this method is not always accurate and requires expert knowledge. In this study, predictive models based on an Artificial Neural Network (ANN) and multilinear regression are developed to predict restart pressure and time as a function of seabed temperature and non-reacting gas injected volume. The models’ outcomes are compared against experimental results available from the literature. The empirical models predicted the response variables with an absolute error of below 5% compared to the experimental studies. Thus, such models would allow accurate estimation of restart pressure, thereby improving transportation efficiency in offshore fields. Artificial neural network ; Petroleum engineering ; Pipeline transport ; Submarine pipeline ; Volume (thermodynamics) ; Computer science ; Engineering ; Environmental science ; Machine learning ; Geotechnical engineering ; Environmental engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Yingjie Yan | Yukun Zhang | Lixian Zhao | Yongqing Chen | Rui Cao | Hong‐Hui Wu | Yang He | Yu Yan | Lijie Qiao
Source: Metals (article, 2022-09-27)
Field: Materials Science — Metals and Alloys

Effect of Applied Tensile Stress on Hydrogen-Induced Delayed Fracture Mode of Fe-Ni-Cr Austenitic Alloy Weldment Fe-Ni-Cr austenitic alloys are widely used in hydrogen environments as structural materials. Their weld normally shows higher hydrogen-embrittlement sensitivity than the base metal, endangering large-scale applications. Herein, by using electron microscopy and numerical calculations, the influence of applied load on the fracture mode of hydrogen-embrittled JBK-75 alloy weldment is revealed and correlated with a competition between hydrogen-prompted intergranular decohesion (HPID) and hydrogen-enhanced localized plasticity (HELP). Therefore, independent of the load levels, the weld featuring a lower strength and smaller grain sizes is always more vulnerable to hydrogen embrittlement than the base metal. Materials science ; Hydrogen embrittlement ; Metallurgy ; Intergranular corrosion ; Hydrogen ; Alloy ; Intergranular fracture ; Ultimate tensile strength ; Embrittlement ; Base metal ; Environmental stress fracture ; Austenite ; Fracture (geology) ; Welding ; Composite material ; Corrosion ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Nuclear Materials and Properties
Authors: Valery M. Dembitsky | Tatyana A. Gloriozova | Vladimir Poroikov | Maju Mathew Koola
Source: Journal of Applied Pharmaceutical Science (article, 2022-05-05)
Field: Chemistry — Analytical Chemistry

QSAR Study of Some Natural and Synthetic Platelet Aggregation Inhibitors and their Pharmacological Profile Several well-known synthetic drugs, such as aspirin, elinogrel, and beraprost, and natural drugs, such as eptifibatide and vorapaxar, as platelet aggregation inhibitors are widely used in clinical medicine today. The purpose of this review is a comparative pharmacological analysis of the biological activity of these drugs with natural sulfur-containing hydrocarbons isolated from bacteria, plants, and mineral oils. According to the quantitative structure-activity relationship estimates, these naturally occurring sulfur-containing hydrocarbons are more likely to exhibit antiplatelet properties than those currently used in clinical medicine. Platelet aggregation ; Quantitative structure–activity relationship ; Natural (archaeology) ; Traditional medicine ; Stereochemistry ; Chemistry ; Platelet ; Biology ; Medicine ; Internal medicine Analytical Methods in Pharmaceuticals ; Diverse Scientific Research Studies
Authors: Yichao Xu | Jinliang Chen | Dandan Yang | Yin Hu | Bo Jiang | Zourong Ruan | Honggang Lou
Source: BMC Pharmacology and Toxicology (article, 2022-07-19)
Field: Chemistry — Analytical Chemistry

Development of a particle swarm optimization-backpropagation artificial neural network model and effects of age and gender on pharmacokinetics study of omeprazole enteric-coated tablets in Chinese population The study was registered in China Drug Clinical Trial Registration and Information Publicity Platform ( http://www.chinadrugtrials.org.cn ), the registration number was CTR20170876, and the full date of registration was 04/AUG/2017. Omeprazole ; Pharmacokinetics ; Particle swarm optimization ; Artificial neural network ; Population ; Backpropagation ; Medicine ; Mathematics ; Statistics ; Internal medicine ; Computer science ; Artificial intelligence ; Algorithm Analytical Methods in Pharmaceuticals ; Gastrointestinal motility and disorders ; Helicobacter pylori-related gastroenterology studies
Authors: Thomas Van Laethem | Priyanka Kumari | Bruno Boulanger | Philippe Hubert | Marianne Fillet | Pierre‐Yves Sacré | Cédric Hubert
Source: Molecules (article, 2022-11-28)
Field: Chemistry — Spectroscopy

User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules In the pharmaceutical field, and more precisely in quality control laboratories, robust liquid chromatographic methods are needed to separate and analyze mixtures of compounds. The development of such chromatographic methods for new mixtures can result in a long and tedious process even while using the design of experiments methodology. However, developments could be accelerated with the help of in silico screening. In this work, the usefulness of a strategy combining response surface methodology (RSM) followed by multicriteria decision analysis (MCDA) applied to predictions from a quantitative structure-retention relationship (QSRR) model is demonstrated. The developed strategy shows that selecting equations for the retention time prediction models based on the pKa of the compound allows flexibility in the models. The MCDA developed is shown to help to make decisions on different criteria while being robust to the user's decision on the weights for each criterion. This strategy is proposed for the screening phase of the method lifecycle. The strategy offers the possibility to the user to select chromatographic conditions based on multiple criteria without being too sensitive to the importance given to them. The conditions with the highest desirability are defined as the starting point for further optimization steps. Flexibility (engineering) ; In silico ; Multiple-criteria decision analysis ; Computer science ; Quality by Design ; Process (computing) ; Field (mathematics) ; Biochemical engineering ; Chemistry ; Mathematics ; Operations research ; Engineering Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Analytical Methods in Pharmaceuticals
Authors: Yingcheng Zhao | Xiaoli Wang | Yifei Wang | Beilei Wang | Lihong Zhang | Wei Xiao | Xiaowei He
Source: Quantitative Imaging in Medicine and Surgery (article, 2022-09-09)
Field: Materials Science — Materials Chemistry

Application of a Markov chain Monte Carlo method for robust quantification in chemical exchange saturation transfer magnetic resonance imaging The MCMC method provided an efficient approach for parameter estimation and CEST effect quantification. The method may therefore be useful in achieving an accurate pathological diagnosis. Markov chain Monte Carlo ; Monte Carlo method ; Mean squared error ; Magnetic resonance imaging ; Bayesian probability ; Nuclear magnetic resonance ; Statistics ; Algorithm ; Mathematics ; Statistical physics ; Computer science ; Physics ; Medicine ; Radiology Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Silvia Cateni | Valentina Colla | Marco Vannucci
Source: Neural Processing Letters (article, 2022-06-10)
Field: Chemistry — Analytical Chemistry

Improving the Stability of the Variable Selection with Small Datasets in Classification and Regression Tasks Abstract Within the design of a machine learning-based solution for classification or regression problems, variable selection techniques are often applied to identify the input variables, which mainly affect the considered target. The selection of such variables provides very interesting advantages, such as lower complexity of the model and of the learning algorithm, reduction of computational time and improvement of performances. Moreover, variable selection is useful to gain a profound knowledge of the considered problem. High correlation in variables often produces multiple subsets of equally optimal variables, which makes the traditional method of variable selection unstable, leading to instability and reducing the confidence of selected variables. Stability identifies the reproducibility power of the variable selection method. Therefore, having a high stability is as important as the high precision of the developed model. The paper presents an automatic procedure for variable selection in classification (binary and multi-class) and regression tasks, which provides an optimal stability index without requiring any a priori information on data. The proposed approach has been tested on different small datasets, which are unstable by nature, and has achieved satisfactory results. Feature selection ; Stability (learning theory) ; Variable (mathematics) ; Selection (genetic algorithm) ; Computer science ; A priori and a posteriori ; Machine learning ; Artificial intelligence ; Regression analysis ; Computational intelligence ; Binary classification ; Regression ; Reduction (mathematics) ; Data mining ; Mathematics ; Statistics ; Support vector machine Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Neural Networks and Applications
Authors: Firas AL-Zuhairi | Rana Abbas Azeez | Nabil Majd Alawi | Hoang M. Nguyen
Source: Iraqi journal of oil and gas researches (article, 2022-04-01)
Field: Chemistry — Analytical Chemistry

Enhancement the Properties of Local Crude Oil using DE-asphalting Process Asphaltene precipitation is one of the main hitches in the oil sector through the production and refining stages that causes real damages every year. The object of the present work is to study the effect of the deasphalting process of crude oils using different concentrations (5, 10, and 15 wt.%) of n-heptane as a solvent, the experiments were done at a laboratory scale. The deasphalting process quality was based on the sulfur content, conradson carbon residue, density, API, and kinematic viscosity. The results have been shown that the deasphalting process quality was enhanced with increasing weight fraction of added n-heptane, the API value and kinematic viscosity were increased by about 37 and 19.5% respectively, while the density, conradson carbon residue, and sulfur content were decreased by about 5, 17 and 26.8% respectively when 15 wt.% of n-heptane was used, i.e. , the quality of deasphalting methods was enhanced with increased solvent concentrations. Heptane ; Pulp and paper industry ; Sulfur ; Refining (metallurgy) ; Residue (chemistry) ; Solvent ; Asphaltene ; Viscosity ; Materials science ; Chemistry ; Chemical engineering ; Chromatography ; Organic chemistry ; Composite material ; Metallurgy Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Corrosion Behavior and Inhibition