jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Myasar Abdulkareem Mohammed Jaffar | Ahmed Abdulrasool Ahmed Alkhafaji
Source: Journal of Engineering (article, 2023-02-01)
Field: Materials Science — General Materials Science

Study the Effect of Adding Aluminum Nanoparticles to a Smart Alloy (Cu-Al-Ni) on Hardness and Porosity This work deals with the effect of adding aluminum nanoparticles on the mechanical properties, micro-hardness and porosity of memory-shape alloys (Cu-Al-Ni). These alloys have wide applications in various industrial fields such as (high damping compounds and self-lubricating applications). The samples are manufactured using the powder metallurgy method, which involved pressing in only one direction and sintered in a furnace surrounded by an inert gas. Four percentages (0%, 5%, 10%, and 15%) of aluminum nanoparticles were fabricated, which depended on the weight of aluminum powder (13%) in the sample under study. To find out which phase is responsible for the reliability of the formation of this type of alloy and its porosity, X-ray diffraction (XRD) and scanning electron microscopy (SEM) tests are used. The Vickers micro-hardness and porosity properties of these alloys were studied using a Vickers micro-hardness and porosity tester according to ASTM b328-1996. The results showed that increasing the concentration of aluminum nanoparticles in the alloy led to an increase in hardness with a decrease in the porosity, and the sample (15%) gave the best hardness (190.8 HV). The sample (0%) gave the highest porosity (19.573) %. Materials science ; Porosity ; Alloy ; Vickers hardness test ; Scanning electron microscope ; Metallurgy ; Powder metallurgy ; Indentation hardness ; Nanoparticle ; Aluminium ; Microstructure ; Composite material ; Nanotechnology Material Properties and Applications ; Polymer Science and Applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Chi Ho Wong | Yan Ming Yeung | Xin Zhao | Wing‐Cheung Law | Chak Yin Tang | Chee Leung Mak | Chi Wah Leung | Lei Shi | Rolf Lortz
Source: Nanomaterials (article, 2023-03-14)
Field: Materials Science — Materials Chemistry

A Simulation of the Effect of External and Internal Parameters on the Synthesis of a Carbyne with More than 6000 Atoms for Emerging Continuously Tunable Energy Barriers in CNT-Based Transistors Transistors made up of carbon nanotube CNT have demonstrated excellent current-voltage characteristics which outperform some high-grade silicon-based transistors. A continuously tunable energy barrier across semiconductor interfaces is desired to make the CNT-based transistors more robust. Despite that the direct band gap of the carbyne inside a CNT can be widely tuned by strain, the size of the carbyne cannot be controlled easily. The production of a monoatomic chain with more than 6000 carbon atoms is an enormous technological challenge. To predict the optimal chain length of a carbyne in different molecular environments, we have developed a Monte Carlo model in which a finite-length carbyne with a size of 4000-15,000 atoms is encapsulated by a CNT at finite temperatures. Our simulation shows that the stability of the carbyne@nanotube is strongly influenced by the nature and porosity of the CNT, the external pressure, the temperature, and the chain length. We have observed an initiation of the chain-breaking process in a compressed carbyne@nanotube. Our work provides much-needed input for optimizing the carbyne length to produce carbon chains much longer than 6000 atoms at ~300 K. Design rules are proposed for synthesizing ~1% strained carbyne@(6,5)CNT as a component in CNT-based transistors to tune the energy barriers continuously. Carbyne ; Materials science ; Carbon nanotube ; Nanotechnology ; Nanotube ; Optoelectronics ; Chemistry ; Organic chemistry Boron and Carbon Nanomaterials Research ; Diamond and Carbon-based Materials Research ; Graphene research and applications
Authors: Touhami Lanez | Hacen Benamara | Aicha Adaika | Elhafnaoui Lanez
Source: Journal of Fundamental and Applied Sciences (article, 2023-06-04)
Field: Chemistry — Organic Chemistry

In vitro and in silico antioxydant activity, toxicity prediction, and molecular docking study of 3- and 3,3'-nitrophenylferrocene and their reduced amines The antioxidant activity of 3-nitrophenylferrocene (3NPF) and 3,3'-nitrophenylferrocene (3,3'NPF) and their reduced amines was measured using superoxide anion radical (). Binding parameters such as binding free energies and binding constants were also calculated. sign and values suggest respectively the spontaneity and a strong interaction between the radical and all studied compounds. Molecular docking study showed that 3NPF is most inreactive compound against glutathione reductase enzyme having the the lowest docking scores of -16.96 kJ/mol. The two reduced forms were predected to be non-toxic and are not inhibitors of CYP450 2C19, 2D6 isoenzymes which suggests a decrease in their plasma concentrations and a rapid elimination route. Chemistry ; Docking (animal) ; In silico ; Glutathione ; Antioxidant ; Enzyme ; Superoxide ; In vitro ; Glutathione reductase ; Stereochemistry ; Binding site ; Biochemistry Free Radicals and Antioxidants ; Metal complexes synthesis and properties ; Chemical Reaction Mechanisms
Authors: Wenmei Huang | Zeyuan Zhang | Pingping Guo | Xiaobo Feng | Ling Weng
Source: AIP Advances (article, 2023-11-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Measurement and calculation for high frequency magnetic losses of Terfenol-D alloy rod under coupled stress and DC bias fields Compressive stress, a DC bias magnetic field, and an AC excitation field significantly affect hysteresis characteristics and magnetic energy losses of magnetostrictive materials. Therefore, studying high frequency magnetic losses of magnetostrictive materials under coupling of these three conditions is of great importance for improving the output performance of high power magnetostrictive transducers. In this paper, a magnetic property testing system for magnetostrictive materials has been constructed. It enables the magnetic property testing of Terfenol-D alloy rods under the simultaneous application of stress and DC bias at high frequency excitation (a maximum AC magnetic flux density of 0.5 T at an excitation frequency of 9 kHz). Based on Bertotti separation theory and experimental data, considering the coupling effect of stress and DC bias, a computational model for the high frequency magnetic losses of Terfenol-D materials is proposed. The model incorporates relevant parameters of stress and DC bias to modify loss coefficients, and the expressions for loss coefficients are identified using the symbolic regression method. A comparative analysis between experimental data and model calculation shows good agreement, with the maximum and average errors of 4.68% and 1.74%, respectively. Magnetostriction ; Terfenol-D ; Materials science ; Excitation ; Magnetic field ; DC bias ; Coupling (piping) ; Stress (linguistics) ; Magnetic hysteresis ; Hysteresis ; Magnetic reactance ; Electrical steel ; Nuclear magnetic resonance ; Condensed matter physics ; Magnetic energy ; Acoustics ; Mechanics ; Magnetization ; Composite material ; Physics ; Voltage ; Electrical engineering ; Engineering Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Hoa Xuan Mac | Nga Thi Thanh Ha | Lien Le Phuong Nguyen | László Baranyai
Source: Progress in Agricultural Engineering Sciences (article, 2023-11-24)
Field: Chemistry — Analytical Chemistry

A novel approach for determining coconut drink adulteration by means of laser light backscattering imaging Abstract In this work, the simulated adulteration of coconut drink by dilution with water was investigated using laser-light backscattering (LLB) imaging. The laser vision system consisted of six low power laser modules, emitting 1 mm diameter beams at wavelengths of 532, 635, 780, 808, 850 and 1,064 nm. The backscattering images were acquired by a grey scale camera with 12 bit resolution. Eight parameters were extracted to describe the backscattering profile. The methods of linear discriminant analysis (LDA) and partial least squares (PLS) regression were performed on LLB parameters for classifying and predicting dilution level of adulterated coconut drink samples. Based on the results, LLB signals responded sensitively to adulteration. LDA results showed that adulterated samples were correctly recognized with accuracies between 60 and 100%. PLS models were able to estimate the adulteration level of samples with coefficients of determination of 0.57–0.97 in validation. This result demonstrated the potential of laser-light backscattering imaging as a rapid and non-destructive optical technique for evaluation of coconut drink adulteration. Laser ; Dilution ; Partial least squares regression ; Linear discriminant analysis ; Materials science ; Laser light ; Optics ; Analytical Chemistry (journal) ; Chromatography ; Chemistry ; Mathematics ; Artificial intelligence ; Computer science ; Physics ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Frederick Cropp | Lauren Moos | Alexander Scheinker | Antonio Gilardi | Dan Wang | Sergio Paiagua | Carlos Serrano | P. Musumeci | D. Filippetto
Source: arXiv (Cornell University) (preprint, 2023-02-09)
Field: Chemistry — Spectroscopy

Virtual-Diagnostic-Based Time Stamping for Ultrafast Electron Diffraction In this work, non-destructive virtual diagnostics are applied to retrieve the electron beam time of arrival and energy in a relativistic ultrafast electron diffraction (UED) beamline using independently-measured machine parameters. This technique has the potential to improve temporal resolution of pump and probe UED scans. Fluctuations in time of arrival have multiple components, including a shot-to-shot jitter and a long-term drift which can be separately addressed by closed loop feedback systems. A linear-regression-based model is used to fit the beam energy and time of arrival and is shown to be able to predict accurately behavior for both on long and short time scales. More advanced time-series analysis based on machine learning techniques can be applied to improve this prediction further. Jitter ; Beamline ; Computer science ; Ultrafast electron diffraction ; Ultrashort pulse ; Temporal resolution ; Diffraction ; Optics ; Physics ; Beam (structure) ; Electron diffraction ; Laser Spectroscopy and Laser Applications ; Scientific Computing and Data Management ; Mass Spectrometry Techniques and Applications
Authors: Hongyuan Qi | Juan Hu | Yiyi Ju | Huayi Jiang | Mei Liu
Source: Processes (article, 2023-07-19)
Field: Materials Science — Surfaces, Coatings and Films

An Experimental Study on the Influence of the Fractal Characteristics of X80 Steel Surface Morphology on Water Ring Stability The surface morphology of X80 steel with hydrophilic underwater oleophobic characteristic is described greater comprehensively and quantitatively in this work by combining fractal dimension and multifractal. X80 steel with hydrophilic underwater oleophobic surface characteristics was constructed using a chemical etching method. Then, with the aid of three wettability parameters—contact angle, rolling angle, and adhesion work—this study investigated the relationship between the surface fractal dimension of X80 and the stability of the water ring in the core annular flow. The results showed that: (1) the fractal dimension of X80 steel specimens increased first and then decreased with the increase of reaction time. Besides that, the value of it was greater than 2, indicating that the surface had obvious fractal characteristics. The spectral difference, Δf(α), and the spectral width, Δα, supplemented the description of the X80 steel surface morphology, which was consistent with the scanning electron microscope results. (2) When the maximum fractal dimension was 2.0808, the minimum contact angle of distilled water on its surface was 50.2°, and the maximum contact angle of underwater oil droplets was 166.4°. The larger the fractal dimension of X80 steel with hydrophilic underwater oleophobic properties, the more hydrophilic and underwater oleophobic it is. This illustrated that there was a strong binding force between the water and the X80 steel pipe wall, and hence the quality and efficiency of the core annular flow was improved, which was more conducive to the promotion of this technology in the field of heavy oil transportation. Fractal dimension ; Materials science ; Wetting ; Scanning electron microscope ; Contact angle ; Morphology (biology) ; Fractal ; Composite material ; Underwater ; Mathematics ; Geology Surface Modification and Superhydrophobicity ; Fluid Dynamics and Heat Transfer ; Adhesion, Friction, and Surface Interactions
Authors: Jiale Mao | Yuanjie Fu | Yao Cheng | Qiuju He | Lingyu Zhao | Yunchang Xin | Gang Chen | Peidong Wu | Qing Liu
Source: Metals (article, 2023-04-25)
Field: Materials Science — Biomaterials

Biaxial Deformation Behavior of AZ31 Magnesium Alloy along RD and Diagonal Direction Degree between TD and ND The multiaxial deformation behavior of magnesium alloys is an important factor in understanding the service performance of structures. In the present research, the deformation mechanism of a Mg AZ31 sheet under biaxial tension with various stress ratios (σRD:σ45) along the rolling direction (RD) and the diagonal direction (45° direction) between the normal direction (ND) and transverse direction (TD) was systematically studied for the first time using cruciform specimens. The impacts of the stress ratio, σRD:σ45, on the mechanical response, twinning behavior, texture and slip behavior were investigated. The results showed that the contribution of twinning to plastic deformation was limited and governed by the Schmid law. The activation of twinning induced a twin texture component with c-axes largely parallel to the 45° between ND and TD. The deformation induced by biaxial tension was accommodated mainly by prismatic and basal slips under the stress ratio of σRD:σ45=4:1, and the fraction of grains favoring basal slip increased with lower stress ratio along the RD. The characteristics of flow stress can be effectively explained by the relative activities of twinning and slip with stress ratio. Crystal twinning ; Materials science ; Slip (aerodynamics) ; Magnesium alloy ; Deformation mechanism ; Deformation (meteorology) ; Flow stress ; Composite material ; Alloy ; Metallurgy ; Physics ; Microstructure ; Thermodynamics Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Grzegorz Kazek | Monika Głuch‐Lutwin | Barbara Mordyl | Monika Kubacka | Anna Jurowska | Dariusz Cież | Bartosz Trzewik | Janusz Szklarzewicz | Monika Papież
Source: Preprints.org (preprint, 2023-05-26)
Field: Chemistry — Inorganic Chemistry

Vanadium Complexes with Thioanilide Derivatives of Amino Acids: Inhibition of Human Phosphatases and Specificity in Various Cell Models of Metabolic Disturbances In the text, the synthesis and characteristics of the novel ONS-type vanadium(V) complexes with thioanilide derivatives of amino acids are described. They have shown inhibition of human protein tyrosine phosphatases (PTP1B, LAR, SHP1 and SHP2) in the submicromolar range, as well as inhibition of non-tyrosine phosphatases (CDC25A and PPA2) similar to bis(maltolato)oxovanadium(IV)(BMOV). The ONS complexes increased [14C]-deoxy-D-glucose transport into C2C12 myocytes, and one of them, VC070, also enhanced this transport in 3T3-L1 adipocytes. These complexes inhibited gluconeogenesis in hepatocytes HepG2, but none of them decreased lipid accumulation in the non-alcoholic fatty liver disease model using the same cells. Compared to the tested ONO-type vanadium complexes with 5-bromosalicylaldehyde and substituted benzhydrazides as Schiff base ligand components, the ONS complexes revealed stronger inhibition of protein tyrosine phosphatases, but the ONO complexes showed greater activity in the cell models in general. Moreover, the majority of the active complexes from both groups showed better effects than VOSO4 and BMOV. Complexes from both groups activated AKT and ERK signaling pathways in hepatocytes to a comparable extent. One of the ONO complexes, VC068, showed activity in all of the above models, also including glucose utilization in the myocytes and glucose transport in insulin-resistant hepatocytes. The discussion section explicates the results within the wider scope of the knowledge about vanadium complexes. Protein tyrosine phosphatase ; Chemistry ; Phosphatase ; Biochemistry ; Vanadium ; Vanadate ; Tyrosine ; Enzyme Vanadium and Halogenation Chemistry ; Metal-Catalyzed Oxygenation Mechanisms ; Neutrophil, Myeloperoxidase and Oxidative Mechanisms
Authors: Swati A. Sakhare | Anurag A. Kale | Deva G. More | Prajwal A. Kumkar | Jay M. Mirase | Udaykumar S. Sahu
Source: International Journal of Advanced Research in Science Communication and Technology (article, 2023-04-22)
Field: Materials Science — Polymers and Plastics

Smart Wearable Safety Jacket Technology is now pervasive; it surrounds us and ingrains itself into our daily lives. By balancing usefulness and the joy that fashion brings, the Internet of Things (IoT) paradigm and its supporting smart wearables and IoT-based clothing have the potential to make a significant impact. In order to develop technologies that can predict wants and desires, smart garments seek a balance between fashion, engineering, interaction, user experience, cybersecurity, design, and science. These days, seamless and widespread integration of sensors into textiles as well as the creation of conductive yarn are made possible by the fast-moving convergence of textiles and electronics. Potential for processing biometric data such as heart rate, temperature, breathing, stress, movement, acceleration, or even fingerprints using smart fabrics that can interact with smartphones. hormone levels, suggests the dawn of a new shopping era. This article discusses the primary need for creating smart, Internet of Things-enabled clothing and illustrates the potential long-term effects of smart clothing on business models. The basic types and components of smart IoT wearables and clothes are described, their key needs are examined, and some of the most current smart clothing applications are evaluated. Additionally, a worldwide IoT architecture is offered. This article examines the history and current state of smart clothing in order to offer recommendations for the designers of a network that will connect clothing to other IoT devices in the future: Smart Clothing on the Internet. Clothing ; Wearable computer ; Wearable technology ; Computer science ; Internet of Things ; Computer security ; Key (lock) ; Order (exchange) ; Smart objects ; Yarn ; The Internet ; Internet privacy ; Architectural engineering ; Human–computer interaction ; Engineering ; World Wide Web ; Embedded system ; Business Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials
Authors: Zhibin Sun | Xinyue Jiang | Xuehai Tang | Lipeng Yan | Kuang–Chao Fan | Xiaozhou Li | Min Dou | Bin Wang | Xiang Gao
Source: Remote Sensing (article, 2023-06-15)
Field: Chemistry — Analytical Chemistry

Identifying Core Wavelengths of Oil Tree’s Hyperspectral Data by Taylor Expansion The interference of background noise leads to the extremely high spatial complexity of hyperspectral data. Sensitive band selecting is an important task to minimize or eliminate the influence of non-target elements. In this study, Taylor expansion is innovatively used to identify core wavelengths/bands of hyperspectral data. Unlike other traditional methods, this proposed Taylor-CC method considers more local and global information of spectral function to estimate the linear/nonlinear correlation between two wavelengths. Using samples of hyperspectral data with a wavelength range of 350–2500 nm and SPAD for Camellia oleifera, this Taylor-CC method is compared with the traditional PCC method derived from the Pearson correlation coefficient. Using the 240 samples with their different 57 core wavelengths identified by the Taylor-CC method and PCC method, three machine models (i.e., random forest-RF, linear regression-LR, and artificial neural network-ANN) are trained to compare their performances. Their results show that the correlation matrix from the Taylor-CC method represents a clear diagonal pattern with near zero values at most locations away from the diagonal, and all three models confirm that the Taylor-CC method is superior to the PCC method. Moreover, the SPAD spectral response relationship based on machine learning algorithms is constructed, and ANN is the best prediction performance among the three models when using the core wavelengths identified by the Taylor-CC method. The Taylor-CC method proposed in this study not only lays a mathematical foundation for the next analysis of the response mechanism between spectral characteristics and nutrient content of Camellia leaf, but also provides a new idea for the correlation analysis of adjacent spectral bands for hyperspectral signals in many applications. Taylor series ; Hyperspectral imaging ; Computer science ; Correlation coefficient ; Wavelength ; Diagonal ; Algorithm ; Pattern recognition (psychology) ; Mathematics ; Artificial intelligence ; Optics ; Physics ; Machine learning ; Mathematical analysis ; Geometry Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Remote-Sensing Image Classification
Authors: Tom W. A. Montgomery | Péter Pogány | Alice Purdy | Mike Harris | Marek Kowalik | A. Ferraro | Hikmatyar Hasan | Darren V. S. Green | Sam Genway
Source: arXiv (Cornell University) (preprint, 2023-07-18)
Field: Materials Science — Materials Chemistry

Data-driven reactivity prediction of targeted covalent inhibitors using computed quantum features for drug discovery We present an approach to combine novel molecular features with experimental data within a data-driven pipeline. The method is applied to the challenge of predicting the reactivity of a series of sulfonyl fluoride molecular fragments used for drug discovery of targeted covalent inhibitors. We demonstrate utility in predicting reactivity using features extracted from a workflow which employs quantum embedding of the reactive warhead using density matrix embedding theory, followed by Hamiltonian simulation of the resulting fragment model from an initial reference state. These predictions are found to improve when studying both larger active spaces and longer evolution times. The calculated features form a `quantum fingerprint' which allows molecules to be clustered with regard to warhead properties. We identify that the quantum fingerprint is well suited to scalable calculation on future quantum computing hardware, and explore approaches to capture results on current quantum hardware using error mitigation and suppression techniques. We further discuss how this general framework may be applied to a wider range of challenges where the potential for future quantum utility exists. Computer science ; Quantum ; Drug discovery ; Embedding ; Scalability ; Quantum computer ; Chemistry ; Computational chemistry ; Algorithm ; Computational science ; Physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics