jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ibrahim Elganidi | Basem Elarbe | Norida Ridzuan | Norhayati Abdullah
Source: Journal of Petroleum Exploration and Production Technology (article, 2021-10-29)
Field: Chemistry — Analytical Chemistry

Optimisation of reaction parameters for a novel polymeric additives as flow improvers of crude oil using response surface methodology Abstract In recent years, polymeric additives have received considerable attention as a wax control approach to enhance the flowability of waxy crude oil. Furthermore, the satisfactory model for predicting maximum yield in free radical polymerisation has been challenging due to the complexity and rigours of classic kinetic models. This study investigated the influence of operating parameters on a novel synthesised polymer used as a wax deposition inhibitor in a crude oil pipeline. Response surface methodology (RSM) was used to develop a polynomial regression model and investigate the effect of reaction temperature, reaction time, and initiator concentration on the polymerisation yield of behenyl acrylate- co- stearyl methacrylate -co- maleic anhydride (BA-co-SMA-co-MA) polymer by using central composite design (CCD) approach. The modelled optimisation conditions were reaction time of 8.1 h, reaction temperature of 102 °C, and initiator concentration of 1.57 wt%, with the corresponding yield of 93.75%. The regression model analysis (ANOVA) detected an R 2 value of 0.9696, indicating that the model can clarify 96.96% of the variation in data variation and does not clarify only 3% of the total differences. Three experimental validation runs were carried out using the optimal conditions, and the highest average yield is 93.20%. An error of about 0.55% was observed compared with the expected value. Therefore, the proposed model is reliable and can predict yield response accurately. Furthermore, the regression model is highly significant, indicating a strong agreement between the expected and experimental values of BA -co- SMA -co- MA yield. Consequently, this study’s findings can help provide a robust model for predicting maximum polymerisation yield to reduce the cost and processing time associated with the polymerisation process. Response surface methodology ; Yield (engineering) ; Wax ; Acrylate ; Central composite design ; Maleic anhydride ; Materials science ; Polymer ; Methacrylate ; Chemical engineering ; Biological system ; Polymerization ; Chemistry ; Chromatography ; Composite material ; Monomer ; Copolymer Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Heat transfer and supercritical fluids
Authors: Chongze Hu | Yanwen Li | Zhiyang Yu | Jian Luo
Source: npj Computational Materials (article, 2021-10-01)
Field: Materials Science — Materials Chemistry

Computing grain boundary diagrams of thermodynamic and mechanical properties Abstract Computing the grain boundary (GB) counterparts to bulk phase diagrams represents an emerging research direction. Using a classical embrittlement model system Ga-doped Al alloy, this study demonstrates the feasibility of computing temperature- and composition-dependent GB diagrams to represent not only equilibrium thermodynamic and structural characters, but also mechanical properties. Specifically, hybrid Monte Carlo and molecular dynamics (MC/MD) simulations are used to obtain the equilibrium GB structure as a function of temperature and composition. Simulated GB structures are validated by aberration-corrected scanning transmission electron microscopy. Subsequently, MD tensile tests are performed on the simulated equilibrium GB structures. GB diagrams are computed for not only GB adsorption and structural disorder, but also interfacial structural and chemical widths, MD ultimate tensile strength, and MD tensile toughness. This study suggests a research direction to investigate GB composition–structure–property relationships via computing GB diagrams of thermodynamic, structural, and mechanical (or potentially other) properties. Materials science ; Molecular dynamics ; Ultimate tensile strength ; Monte Carlo method ; Phase diagram ; Thermodynamics ; Grain boundary ; Embrittlement ; Scaling ; Microstructure ; Phase (matter) ; Chemistry ; Physics ; Composite material ; Computational chemistry ; Geometry ; Mathematics Microstructure and mechanical properties ; High Temperature Alloys and Creep ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Youngwook Seo | Giyoung Kim | Jongguk Lim | Ahyeong Lee | Balgeum Kim | Jaekyung Jang | Changyeun Mo | Moon S. Kim
Source: Sensors (article, 2021-04-21)
Field: Chemistry — Analytical Chemistry

Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques Contamination is a critical issue that affects food consumption adversely. Therefore, efficient detection and classification of food contaminants are essential to ensure food safety. This study applied a visible and near-infrared (VNIR) hyperspectral imaging technique to detect and classify organic residues on the metallic surfaces of food processing machinery. The experimental analysis was performed by diluting both potato and spinach juices to six different concentration levels using distilled water. The 3D hypercube data were acquired in the range of 400-1000 nm using a line-scan VNIR hyperspectral imaging system. Each diluted residue in the spectral domain was detected and classified using six classification methods, including a 1D convolutional neural network (CNN-1D) and five pre-processing methods. Among them, CNN-1D exhibited the highest classification accuracy, with a 0.99 and 0.98 calibration result and a 0.94 validation result for both spinach and potato residues. Therefore, in comparison with the validation accuracy of the support vector machine classifier (0.9 and 0.92 for spinach and potato, respectively), the CNN-1D technique demonstrated improved performance. Hence, the VNIR hyperspectral imaging technique with deep learning can potentially afford rapid and non-destructive detection and classification of organic residues in food facilities. VNIR ; Hyperspectral imaging ; Spinach ; Support vector machine ; Artificial intelligence ; Convolutional neural network ; Pattern recognition (psychology) ; Remote sensing ; Environmental science ; Computer science ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Sarah Hettal | Simone Vincenzo Suraci | Sébastien Roland | Davide Fabiani | Xavier Colin
Source: Polymers (article, 2021-12-16)
Field: Materials Science — Materials Chemistry

Towards a Kinetic Modeling of the Changes in the Electrical Properties of Cable Insulation during Radio-Thermal Ageing in Nuclear Power Plants. Application to Silane-Crosslinked Polyethylene The radio-thermal ageing of silane-crosslinked polyethylene (Si-XLPE) was studied in air under different γ dose rates (6.0, 8.5, 77.8, and 400 Gy·h<sup>-1</sup>) at different temperatures (21, 47, and 86 °C). The changes in the physico-chemical and electrical properties of Si-XLPE throughout its exposure were determined using Fourier transform infrared spectroscopy coupled with chemical gas derivatization, hydrostatic weighing, differential scanning calorimetry, dielectric spectroscopy and current measurements under an applied electric field. From a careful analysis of the oxidation products, it was confirmed that ketones are the main oxidation products in Si-XLPE. The analytical kinetic model for radio-thermal oxidation was thus completed with relatively simple structure-property relationships in order to additionally predict the increase in density induced by oxidation, and the adverse changes in two electrical properties of Si-XLPE: the dielectric constant ε' and volume resistivity R. After having shown the reliability of these new kinetic developments, the lifetime of Si-XLPE was determined using a dielectric end-of-life criterion deduced from a literature compilation on the changes in R with ε' for common polymers. The corresponding lifetime was found to be at least two times longer than the lifetime previously determined with the conventional end-of-life criterion, i.e., the mechanical type, thus confirming the previous literature studies that had shown that fracture properties degrade faster than electrical properties. Polyethylene ; Materials science ; Silane ; Nuclear power ; Composite material ; Ageing ; Thermal ; Thermal insulation ; Kinetic energy ; Power cable ; Nuclear engineering ; Engineering ; Nuclear physics ; Thermodynamics ; Physics High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Electrostatic Discharge in Electronics
Authors: Louise Hamborg | Daniele Granata | Johan G. Olsen | Jennifer Virginia Roche | Lasse Ebdrup Pedersen | Alex Toftgaard Nielsen | Kresten Lindorff‐Larsen | Kaare Teilum
Source: Communications Biology (article, 2021-08-18)
Field: Materials Science — Materials Chemistry

Synergistic stabilization of a double mutant in chymotrypsin inhibitor 2 from a library screen in E. coli Most single point mutations destabilize folded proteins. Mutations that stabilize a protein typically only have a small effect and multiple mutations are often needed to substantially increase the stability. Multiple point mutations may act synergistically on the stability, and it is often not straightforward to predict their combined effect from the individual contributions. Here, we have applied an efficient in-cell assay in E. coli to select variants of the barley chymotrypsin inhibitor 2 with increased stability. We find two variants that are more than 3.8 kJ mol<sup>-1</sup> more stable than the wild-type. In one case, the increased stability is the effect of the single substitution D55G. The other case is a double mutant, L49I/I57V, which is 5.1 kJ mol<sup>-1</sup> more stable than the sum of the effects of the individual mutations. In addition to demonstrating the strength of our selection system for finding stabilizing mutations, our work also demonstrate how subtle conformational effects may modulate stability. Mutant ; Chymotrypsin ; Chemistry ; Biochemistry ; Enzyme ; Trypsin ; Gene Enzyme Structure and Function ; Bacterial Genetics and Biotechnology ; RNA and protein synthesis mechanisms
Authors: Hailong Yuan | Luyuan Qi | Michaël Paris | Fei Chen | Qiang Shen | E. Faulques | Florian Massuyeau | Romain Gautier
Source: Advanced Science (article, 2021-07-14)
Field: Materials Science — Materials Chemistry

Machine Learning Guided Design of Single–Phase Hybrid Lead Halide White Phosphors Designing new single-phase white phosphors for solid-state lighting is a challenging trial-error process as it requires to navigate in a multidimensional space (composition of the host matrix/dopants, experimental conditions, etc.). Thus, no single-phase white phosphor has ever been reported to exhibit both a high color rendering index (CRI - degree to which objects appear natural under the white illumination) and a tunable correlated color temperature (CCT). In this article, a novel strategy consisting in iterating syntheses, characterizations, and machine learning (ML) models to design such white phosphors is demonstrated. With the guidance of ML models, a series of luminescent hybrid lead halides with ultra-high color rendering (above 92) mimicking the light of the sunrise/sunset (CCT = 3200 K), morning/afternoon (CCT = 4200 K), midday (CCT = 5500 K), full sun (CCT = 6500K), as well as an overcast sky (CCT = 7000 K) are precisely designed. Phosphor ; Color rendering index ; Sunrise ; Color temperature ; High color ; Materials science ; Computer science ; Glazing ; Rendering (computer graphics) ; Halide ; Dopant ; Luminescence ; Optics ; Optoelectronics ; Environmental science ; Artificial intelligence ; Doping ; Chemistry ; Physics ; Image processing ; Composite material Luminescence Properties of Advanced Materials ; Perovskite Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Faris M. AL‐Oqla | M. H. Alaaeddin | Y.A. El-Shekeil
Source: Engineering Solid Mechanics (article, 2021-01-01)
Field: Materials Science — Polymers and Plastics

Thermal stability and performance trends of sustainable lignocellulosic olive / low density polyethylene biocomposites for better environmental green materials The current trend in deteriorating mechanical performance of green polymeric-based materials has made it essential for designers to establish more reliable and sustainable bio-products. Here, the mechanical performance of Jordanian lignocellulosic olive fibers in polymeric-based composites has been methodically investigated. The outcomes of different reinforcement conditions on the desired mechanical performance of the olive leaf’s lignocellulosic fibers with low-density polyethylene (LDPE) composites have been examined, including the properties of tensile strength, tensile modulus, mechanical strain, impact strength, and the intensity per composite volume. This has been accomplished to determine the optimum reinforcement condition for the desired mechanical behavior as well as to establish the performance deterioration and enhancement trends of such bio-materials in a more consistent manner. The results signify that lignocellulosic olive fibers have exhibited various enhancements in terms of mechanical performance. Both the tensile strength and modulus of elasticity have been dramatically improved at 20 wt.% fiber content. This was the most desired reinforcement condition among all considered cases. The olive fibers also possess the capability of maintaining relatively high ductility and impact strength properties, making them suitable for various industrial applications where high ductility is necessary. Thermal stability analysis using TGA and DTG has been employed to obtain accurate results. Materials science ; Ultimate tensile strength ; Composite material ; Low-density polyethylene ; Ductility (Earth science) ; Young's modulus ; Thermal stability ; Izod impact strength test ; Dynamic mechanical analysis ; Polyethylene ; Polymer ; Creep Natural Fiber Reinforced Composites ; Plant Surface Properties and Treatments ; Tree Root and Stability Studies
Authors: Sara Mantach | Ahmed Ashraf | Hamed Janani | Behzad Kordi
Source: Energies (article, 2021-03-02)
Field: Materials Science — Materials Chemistry

A Convolutional Neural Network-Based Model for Multi-Source and Single-Source Partial Discharge Pattern Classification Using Only Single-Source Training Set Classification of the sources of partial discharges has been a standard procedure to assess the status of insulation in high voltage systems. One of the challenges while classifying these sources is the decision on the distinct properties of each one, often requiring the skills of trained human experts. Machine learning offers a solution to this problem by allowing to train models based on extracted features. The performance of such algorithms heavily depends on the choice of features. This can be overcome by using deep learning where feature extraction is done automatically by the algorithm, and the input to such an algorithm is the raw input data. In this work, an enhanced convolutional neural network is proposed that is capable of classifying single sources as well as multiple sources of partial discharges without introducing multiple sources in the training phase. The training is done by using only single-source phase-resolved partial discharge (PRPD) patterns, while testing is performed on both single and multi-source PRPD patterns. The proposed model is compared with single-branch CNN architecture. The average percentage improvements of the proposed architecture for single-source PDs and multi-source PDs are 99.6% and 96.7% respectively, compared to 96.2% and 77.3% for that of the traditional single-branch CNN architecture. Convolutional neural network ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Partial discharge ; Set (abstract data type) ; Multi-source ; Feature (linguistics) ; Feature extraction ; Artificial neural network ; Machine learning ; Voltage ; Engineering ; Mathematics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics
Authors: Xin Chen | Pengli Xu | Huijun Zhang | Xiaosan Su | Lihua Guo | Xuhong Zhou | Jun‐Liang Wang | Peng Huang | Qingzhi Zhang | Ruifen Sun
Source: Oncology Letters (article, 2021-08-31)
Field: Chemistry — Organic Chemistry

EGFR and ERK activation resists flavonoid quercetin‑induced anticancer activities in human cervical cancer cells <i>in vitro</i> In the present study, due to the complex and numerous targets of Sarcandrae Herb (also known as Zhong Jie Feng), network pharmacology was performed to analyze its therapeutic effect on 2 cervical cancer cell lines, which could assist with the development of novel therapies. The results suggested that the natural flavonoid quercetin (Que), the effective antitumor ingredient in SH, which is widely present in a variety of plants, may depend on the target, EGFR. Previous studies have shown that EGFR serves a crucial role in the occurrence and development of cervical cancer, but its downstream molecules and regulatory mechanisms remain unknown. The anti-cervical cancer cell properties of Que, which are present in ubiquitous plants, were examined <i>in vitro</i> to identify the association between Que and its underlying pathway using MTT assays, flow cytometry, western blot analysis and Transwell assays. It was found that Que reduced cervical cancer cell viability, promoted G<sub>2</sub>/M phase cell cycle arrest and cell apoptosis, as well as inhibited cell migration and invasion. The Tyr1068 phosphorylation site of EGFR and the corresponding ERK target were also examined and the 2 kinases were markedly activated by Que. Furthermore, the EGFR inhibitor, afatinib and the ERK inhibitor, U0126 blocked the increase of EGFR and ERK phosphorylation, and resulted in a notable enhancement of apoptosis and cell cycle arrest. Therefore, to the best of our knowledge, the current results provided the first evidence that EGFR and ERK activation induced by Que could resist Que-induced anticancer activities. On this basis, the present study determined the role of EGFR and the underlying signaling pathways involved in the anti-cervical cancer malignant behavior induced by Que and identified the negative regulatory association. Cell cycle ; MAPK/ERK pathway ; Cancer ; Viability assay ; Cancer cell ; Apoptosis ; Cancer research ; Cell cycle checkpoint ; Cell growth ; Cell ; Kinase ; Biology ; Pharmacology ; Chemistry ; Cell biology ; Biochemistry Synthesis and biological activity ; Bioactive Compounds and Antitumor Agents ; Computational Drug Discovery Methods
Authors: David Tjandra Nugraha | John‐Lewis Zinia Zaukuu | Juan Pablo Aguinaga Bósquez | Zsanett Bodor | Flóra Vitális | Zoltán Kovács
Source: Sensors (article, 2021-01-17)
Field: Chemistry — Analytical Chemistry

Near-Infrared Spectroscopy and Aquaphotomics for Monitoring Mung Bean (Vigna radiata) Sprout Growth and Validation of Ascorbic Acid Content Mung bean is a leguminous crop with specific trait in its diet, namely in the form of anti-nutrient components. The sprouting process is commonly done for better nutritional acceptance of mung bean as it presents better nutritional benefits. Sprouted mung bean serves as a cheap source of protein and ascorbic acid, which are dependent on the sprouting process, hence the importance of following the biological process. In larger production scale, there has not been a definite standard for mung bean sprouting, raising the need for quick and effective mung bean sprout quality checks. In this regard, near-infrared spectroscopy (NIRS) has been recognized as a highly sensitive technique for quality control that seems suitable for this study. The aim of this paper was to describe quality parameters (water content, pH, conductivity, and ascorbic acid by titration) during sprouting using conventional analytical methods and advanced NIRS techniques as correlative methods for modelling sprouted mung beans' quality and ascorbic acid content. Mung beans were sprouted in 6 h intervals up to 120 h and analyzed using conventional methods and a NIR instrument. The results of the standard analytical methods were analyzed with univariate statistics (analysis of variance (ANOVA)), and the NIRS spectral data was assessed with the chemometrics approach (principal component analysis (PCA), discriminant analysis (DA), and partial least squares regression (PLSR)). Water content showed a monotonous increase during the 120 h of sprouting. The change in pH and conductivity did not describe a clear pattern during the sprouting, confirming the complexity of the biological process. Spectral data-based discriminant analysis was able to distinctly classify the bean sprouts with 100% prediction accuracy. A NIRS-based model for ascorbic acid determination was made using standard ascorbic acid to quantify the components in the bean extract. A rapid detection technique within sub-percent level was developed for mung bean ascorbic acid content with R<sup>2</sup> above 0.90. The NIR-based prediction offers reliable estimation of mung bean sprout quality. Ascorbic acid ; Vigna ; Sprouting ; Partial least squares regression ; Chemometrics ; Mung bean ; Food science ; Mathematics ; Horticulture ; Biotechnology ; Chemistry ; Biology ; Chromatography ; Statistics Spectroscopy and Chemometric Analyses ; Food composition and properties ; GABA and Rice Research
Authors: Tomasz Lipiński
Source: Production Engineering Archives (article, 2021-06-01)
Field: Materials Science — Materials Chemistry

Investigation of corrosion rate of X55CrMo14 stainless steel at 65% nitrate acid at 348 K Abstract A number of factors determine the mechanical, but also physical and chemical properties. One of the most important are the steel microstructure and its working conditions. A few corrosion processes in crevices and awkward corners can be avoided at the design stage (low roughness parameters, round-section and other). But still the construction material is exposed to corrosion. These steels often come into contact with an aggressive environment based on nitric acid. Stainless steel is more and more often used in many sectors of industry. The purpose of this article is to investigate corrosion resistance in different time (48, 96, 144, 192, 240, 288, 336, 384 and 432 hours) using weight loss and profile roughness parameters of martensitic steel in grade X55CrMo14 in nitric acid 65% pure-basic at temperature 348 K. Corrosion tests show that the tested steel in nitric acid as a corrosive environments is characterized through continuous corrosion process whose measure may be surface roughness. Corrosion ; Nitric acid ; Materials science ; Metallurgy ; Martensite ; Surface roughness ; Microstructure ; Surface finish ; Martensitic stainless steel ; Composite material Material Properties and Failure Mechanisms ; Material Properties and Applications ; Engineering Diagnostics and Reliability