jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shunjie Zhang | Yudan Wang | Tianyang Liu | Xinzhi Li | Meiliang Wang | Y Qiu
Source: Journal of Food Quality (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Optimization of the Extraction Process of Effective Components of <i>Eleutherococcus senticosus</i> Using Mathematical Models Eleutheroside B, eleutheroside E, and isofraxidin are all important active ingredients for medicinal purposes of Eleutherococcus senticosus (Rupr. and Maxim).To improve the efficacy and quality of Chinese medicinal materials, optimizing the extraction process is necessary. By improving and innovating the process, the utilization efficiency of medicinal materials can be enhanced, promoting the effective utilization of Chinese medicinal materials. This experiment focused on three traditional components of Eleutherococcus senticosus, a medicinal plant known for its various bioactive properties. High‐performance liquid chromatography and response surface methodology were employed to investigate the effects of extraction parameters on the yield of these components. Mathematical model analysis was used to optimise the response surface experimental design and to determine the exact extraction conditions for each ingredient. In the extraction method prediction model, the method described in the best group extracted more of the three active ingredients. The extraction rate of the components was increased by 1.33 percent compared to the conventional extraction method. Meanwhile, the R ‐square of this prediction model was as high as 0.938. A validated method was used to quantify the three components in 21 samples of Eleutherococcus senticosus from different origins. The results revealed significant variations in the component content among the samples. The recall F1 of cross‐validation sets in the GA‐decision tree of the classifier reaches 0.833. K ‐means cluster analysis was performed to classify the samples based on their component profile, providing a basis for quality assessment. The novel content evaluation model developed in this study enabled a reliable and visual comparison of the component differences among the samples and extraction conditions, determining the optimal extraction parameters for each component of Eleutherococcus senticosus . This study offers new techniques and insights for the development and utilization of natural active ingredients from Chinese herbs, with important scientific and practical implications. Extraction (chemistry) ; Process (computing) ; Computer science ; Chemistry ; Chromatography Chromatography in Natural Products ; Natural product bioactivities and synthesis ; Phytochemistry and biological activity of medicinal plants
Authors: Anthony Berardi | Jeffery E. Raymond | Albert Chang | Ava Mauser | Joerg Lahann
Source: ACS Applied Materials & Interfaces (article, 2024-08-06)
Field: Materials Science — Biomaterials

Self-Reporting Therapeutic Protein Nanoparticles We present a modular strategy to synthesize nanoparticle sensors equipped with dithiomaleimide-based, fluorescent molecular reporters capable of discerning minute changes in interparticle chemical environments based on fluorescence lifetime analysis. Three types of nanoparticles were synthesized with the aid of tailor-made molecular reporters, and it was found that protein nanoparticles exhibited greater sensitivity to changes in the core environment than polymer nanogels and block copolymer micelles. Encapsulation of the hydrophobic small-molecule drug paclitaxel (PTX) in self-reporting protein nanoparticles induced characteristic changes in fluorescence lifetime profiles, detected via time-resolved fluorescence spectroscopy. Depending on the mode of drug encapsulation, self-reporting protein nanoparticles revealed pronounced differences in their fluorescence lifetime signatures, which correlated with burst- vs diffusion-controlled release profiles observed in previous reports. Self-reporting nanoparticles, such as the ones developed here, will be critical for unraveling nanoparticle stability and nanoparticle-drug interactions, informing the future development of rationally engineered nanoparticle-based drug carriers. Nanoparticle ; Materials science ; Fluorescence ; Micelle ; Nanotechnology ; Polymer ; Nanomedicine ; Drug delivery ; Fluorescence spectroscopy ; Biophysics ; Organic chemistry ; Chemistry ; Aqueous solution Nanoparticle-Based Drug Delivery ; Polymer Surface Interaction Studies ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Xiuwen Zhang | Jia-Xin Xiong | Alex Zunger
Source: npj Computational Materials (article, 2024-09-13)
Field: Materials Science — Polymers and Plastics

Hidden magnetism and split off flat bands in the insulator metal transition in VO2 Abstract Transition metal d -electron oxides with an odd number of electrons per unit cell are expected to form metals with partially occupied energy bands, but exhibit in fact a range of behaviors, being either insulators, or metals, or having insulator-metal transitions. Traditional explanations involved predominantly electron-electron interactions in fixed structural symmetry. The present work focuses instead on the role of symmetry breaking local structural motifs. Viewing the previously observed V-V dimerization in VO 2 as a continuous knob, reveals in density functional calculations the splitting of an isolated flat band from the broad conduction band. This leads past a critical percent dimerization to the formation of the insulating phase while lowering the total energy. In VO 2 this transition is found to have a rather low energy barrier approaching the thermal energy at room temperature, suggesting energy-efficient switching in neuromorphic computing. Interestingly, sufficient V-V dimerization suppresses magnetism, leading to the nonmagnetic insulating state, whereas magnetism appears when dimerization is reduced, forming a metallic state. This study opens the way to design novel functional quantum materials with symmetry breaking-induced flat bands. Magnetism ; Condensed matter physics ; Symmetry breaking ; Density functional theory ; Electron ; Materials science ; Negative thermal expansion ; Band gap ; Metal–insulator transition ; Metal ; Chemistry ; Physics ; Thermal expansion ; Computational chemistry ; Quantum mechanics Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing ; ZnO doping and properties
Authors: Trung Duong Ngoc Bao | Hữu Nghĩa Nguyễn | Van Thai Nguyen
Source: Tạp chí Khoa học - Công nghệ Thủy Sản Trường Đại học Nha Trang (article, 2024-03-28)
Field: Materials Science — Materials Chemistry

Mechanical and thermal transport in nanoporous graphene-like BC6N This work analyzes the thermal transport (TT) and mechanical properties (MP) of nanoporous graphene-like BC6N monolayer utilizing non-equilibrium molecular dynamics (NEMD) and molecular dynamic (MD) simulations. Herein, the heat transfer and mechanical of nanoporous graphene-like BC6N membrane with various circle-pore size defects are systematically reviewed and numerically examined. The results show that the BC6N sheet has a balance on mechanical properties in two directions under biaxial tension as changing pore size from D = 2.0 to D = 7.0 nm. Young's modulus and ultimate strength of BC6N monolayer with a circle pore decrease when the pore size increases. The trending laws are also shown to predict the mechanical parameters of the porous BC6N sheet. Besides, the thermal conductivity (TC) of the monolayer BC6N with a smaller pore size is much higher than that of the sample with a bigger pore size due to higher energy loss. Changing pore size can adjust the TC and 2D temperature distribution around the circle-pore. Furthermore, the pristine BC6N TC is more heightened than the defective ones. The study results confirm that 2D-BC6N has outstanding optical, electronic, thermal transport, and mechanical characteristics. It is particularly inspiring for further experimental and theoretical works that advance different technological applications, such as gas sensors, nanoelectronics, and optoelectronic devices. It helps for real-time monitoring, which is critical to improving safety and efficiency in marine and air transportation infrastructure. Keywords: 2D-BC6N, mechanical, thermal transport, energy. Nanoporous ; Materials science ; Graphene ; Monolayer ; Thermal conductivity ; Nanotechnology ; Porous medium ; Molecular dynamics ; Heat transfer ; Work (physics) ; Composite material ; Porosity ; Thermodynamics ; Chemistry Graphene research and applications ; Boron and Carbon Nanomaterials Research ; Graphene and Nanomaterials Applications
Authors: S. H. KIM | Do Kyung Lee | Jin Ho Seo | Jaegyoung Gwon | Sang Jin Chun
Source: Journal of Korea Technical Association of The Pulp and Paper Industry (article, 2024-12-30)
Field: Materials Science — Biomaterials

Characterization of Cellulose Acetate-based Derivative Films with Addition of Polyethylene Glycol Using Breath Figure Array Method In this study, CA-based derivative (CA, CAP, CAB)/PEG films were prepared using Breath Figure Array (BFA) method, and the surface void characteristics of films formed by the types of CA, CAB, and CAP, as well as films with PEG formulation, were analyzed.For this purpose, the chemical properties of CA-based derivatives were compared, and CA-based derivative/PEG films were prepared using BFA method using a blade coater.The changes in pore size, arrangement, and uniformity were compared via FE-SEM on the film surface according to the type of CA-based derivative and the presence or absence of PEG addition.FT-IR results confirmed the differences in CA, CAP, and CAB due to the fatty acid chain length.TGA results showed that CA showed relatively high thermal stability.In the films with the addition of PEG, both CAP and CAB showed narrower pore size distributions and more uniform pore arrangements. Polyethylene glycol ; Cellulose acetate ; Derivative (finance) ; Cellulose ; Characterization (materials science) ; Materials science ; Organic chemistry ; Chemistry ; Chromatography ; Polymer chemistry ; Nanotechnology Advanced Cellulose Research Studies ; biodegradable polymer synthesis and properties ; Hydrogels: synthesis, properties, applications
Authors: Yao Yang | Jianqiang Bi | Linjing Qiao | Guandong Liang | Shuyong Liang | Shushuai Liu | S.R. Wang | Hongyu Gong | Zhao Qian | Jinwang Shi | Weiqiang Li
Source: International Journal of Applied Ceramic Technology (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

Stabilizing MoB <sub>2</sub> with HCP structure in (Mo <i> <sub>x</sub> </i> Hf <sub>(1‒</sub> <i> <sub>x</sub> </i> <sub>)/5</sub> Zr <sub>(1‒</sub> <i> <sub>x</sub> </i> <sub>)/5</sub> Ta <sub>(1‒</sub> <i> <sub>x</sub> </i> <sub>)/5</sub> V <sub>(1‒</sub> <i> <sub>x</sub> </i> <sub>)/5</sub> Nb <sub>(1‒</sub> <i> <sub>x</sub> </i> <sub>)/5</sub> )B <sub>2</sub> via a high‐entropy strategy Abstract The properties of molybdenum diboride are intricately tied to its crystal structure, which includes two distinct phases, α‐MoB 2 and β‐MoB 2 . The α‐MoB 2 exhibits superior electrical and catalytic properties, akin to those of graphite due to the similar HCP structure. Nevertheless, the transformation between α‐MoB 2 and β‐MoB 2 is complicated and the formation of α‐MoB 2 remains challenge due to higher Gibbs free energy. The MoB 2 powder synthesized through boro/carbothermal reduction in this study suggests the β phase, transforming into α‐MoB 2 completely via a high‐entropy strategy. It founds that the introduction of high‐entropy diborides (HEB) effectively inhibits the grain growth of samples, resulting in a significant enhancement in their hardness. The potential for the formation of a single‐phase between MoB 2 and HEB is associated with Pauling electronegativity difference, δ χP . Furthermore, the formation of a single‐phase solid solution was found to contribute to the improvement in the fracture toughness of the samples. This work presents a novel strategy for stabilizing α‐MoB 2 , offering valuable insights into understanding phase‐transition behavior in MoB 2 and the solid solubility in multi‐component ceramics. Materials science ; Crystallography Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Metal and Thin Film Mechanics
Authors: Supreeya Thepparak | Chusak Pulmar | Chanwit Kaewtapee
Source: The Thai Journal of Veterinary Medicine (article, 2024-08-15)
Field: Chemistry — Analytical Chemistry

Image processing and machine learning approach for yolk color evaluation The objective of this study was to develop a predictive model for yolk color measurement using machine learning. A total of eight hundred eggs were randomly collected from thirty laying hens (Lohmann brown, 28–35 weeks). The experimental diets were formulated based on broken rice or corn, and further supplemented with canthaxanthin from 10 to 150 mg/kg. The Digital YolkFan™ was used to classify yolk color scales from 0 to 15, whereas the Hunter Lab was used to measure color values. Furthermore, yolk images were obtained from a digital camera and then extracted into red color, green color and blue (RGB) and hue, saturation and value (HSV). Machine learning, including multiple linear regression, decision tree (DT), support vector machine, artificial neural networks and deep learning were used to develop the predictive models. The accuracy of R2 was greater for the HSV (0.971) than for the Hunter Lab (0.969) and RGB (0.947) approaches. The root mean square error (RMSE) was also lower for HSV than for Hunter Lab and RGB (0.770, 0.805 and 1.055, respectively). Further improvement based on HSV with DT was achieved the highest accuracy (R2 = 0.996) and the lowest statistical error measurements (RMSE = 0.288). In conclusion, the HSV obtained from the digital yolk image provided a suitable color system, with the use of the DT model expected to improve the accuracy of prediction. Therefore, combined digital imagery and machine learning provided a rapid and highly cost-effective technique requiring little human subjectivity for yolk color evaluation. Computer science ; Artificial intelligence ; Image processing ; Computer vision ; Machine learning ; Pattern recognition (psychology) ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Animal Virus Infections Studies ; Diverse Scientific Research Studies
Authors: Emerson Colonetti | Lilian C. da Luz | Fabiano Severo Rodembusch
Source: Colorants (article, 2024-04-03)
Field: Materials Science — Materials Chemistry

Solid-State Luminescence with a Large Stokes Shift in Starch Functionalized with Low-Content ESIPT Dye Herein, we present the preparation of solid-state photoactive starches with a large Stokes shift, along with the resulting materials. In this investigation, 2-(2′-hydroxyphenyl)benzazole derivatives responsive to intramolecular proton transfer in the excited state (ESIPT) were covalently bonded to the polymeric structure of starch through a reaction involving an isothiocyanate group and the hydroxyl groups of starch. These compounds exhibit absorption at approximately 350 nm, which is related to fully spin- and symmetry-allowed π → π* electronic transitions, and solid-state fluorescence at approximately 500 nm, which features a significant separation between the absorption and emission maxima (~9000 cm−1). Due to the minimal use of fluorophores in functionalized starch preparation, this modification does not affect the original properties of the starch. Finally, photoactive starch-based films with significantly high transparency were successfully produced. Starch ; Photochemistry ; Stokes shift ; Absorption (acoustics) ; Fluorescence ; Luminescence ; Intramolecular force ; Materials science ; Modified starch ; Covalent bond ; Chemistry ; Organic chemistry ; Optics ; Optoelectronics Luminescence and Fluorescent Materials ; Photochemistry and Electron Transfer Studies ; Molecular Sensors and Ion Detection
Authors: Fery Eko Pujiono | Dwi Setyawan | Juni Ekowati
Source: Pharmacy Education (article, 2024-05-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hydrogen bond analysis of the p-coumaric acid-nicotinamide cocrystal using the DFT and AIM method Background: The molecular geometric structure of p-coumaric acid-nicotinamide has been optimised using Density Functional Theory (DFT) and Atom In Molecule (AIM). Objective: To analyse the hydrogen bond of the p-coumaric acid–nicotinamide cocrystal. Method: Structural optimisation using DFT was carried out on the basis set B3LYP/6-311G++ (d, p). The electron density topology from the optimisation results obtained was then validated using the Non-Covalent Interaction (NCI) method. Result: Optimisation results showed that there are intermolecular hydrogen bonds in the carbonyl group of p-coumaric acid and the amine group of nicotinamide, namely C1=O11∙∙∙O34 with length 1.804 Å. On the other hand, the results of the topology test with AIM showed a value of ∇2ρ = 0.1196 a.u; G = 0.0393 a.u; H = 0.0946 a.u; V = -0.0488 a.u which means there was an intermolecular Hydrogen bond with EH∙∙∙O = -64.05 a.u. Conclusion: A hydrogen bond in the cocrystal of p-coumaric acid-nicotinamide is classified as an intermolecular hydrogen bond between the carbonyl group of p-coumaric acid and the amine group in the carboxyl group. Cocrystal ; Nicotinamide ; Hydrogen bond ; p-Coumaric acid ; Chemistry ; Organic chemistry ; Biochemistry ; Molecule ; Enzyme Nonlinear Optical Materials Research ; Crystallography and molecular interactions
Authors: Chao Zhou | Zhenye Li | Dongyi Wang | Sheng Xue | Tingting Zhu | Chao Ni
Source: IEEE Access (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

SSNet: Exploiting Spatial Information for Tobacco Stem Impurity Detection With Hyperspectral Imaging Tobacco stems are a critical component in cigarette production in China. However, accurately and promptly identifying impurities in tobacco stems remains a challenge with current deep learning methods, often failing to meet industry standards. This study introduces the Spectral-Spatial Network (SSNet), which is specifically optimized for rapid and precise detection of impurities in tobacco stems, leveraging spatial information in hyperspectral imaging. Designed to align with the high-speed requirements of industrial processing, SSNet effectively overcomes the complexities associated with analyzing hyperspectral data. The integration of spatial information particularly enhances recognition accuracy, especially in areas affected by fringe patterns. Our experiments reveal that SSNet significantly outperforms traditional methods in terms of speed and accuracy, marking a substantial advancement in the shift from manual to automated impurity detection in tobacco processing. Furthermore, SSNet’s successful application highlights its potential for broader uses in various industrial image processing tasks. Hyperspectral imaging ; Computer science ; Artificial intelligence ; Spatial analysis ; Deep learning ; Data processing ; Pattern recognition (psychology) ; Remote sensing ; Database Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Remote-Sensing Image Classification
Authors: Amr Ismail | Walid Hamdy | Aya M. Al‐Zoghby | Wael A. Awad | Ahmed Ismail Ebada | Yunyoung Nam | Byeong-Gwon Kang | Mohamed Abouhawwash
Source: Computer Systems Science and Engineering (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

A Novel Deep Learning-Based Model for Classification of Wheat Gene Expression Deep learning (DL) plays a critical role in processing and converting data into knowledge and decisions. DL technologies have been applied in a variety of applications, including image, video, and genome sequence analysis. In deep learning the most widely utilized architecture is Convolutional Neural Networks (CNN) are taught discriminatory traits in a supervised environment. In comparison to other classic neural networks, CNN makes use of a limited number of artificial neurons, therefore it is ideal for the recognition and processing of wheat gene sequences. Wheat is an essential crop of cereals for people around the world. Wheat Genotypes identification has an impact on the possible development of many countries in the agricultural sector. In quantitative genetics prediction of genetic values is a central issue. Wheat is an allohexaploid (AABBDD) with three distinct genomes. The sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat, Therefore, genome sequence approaches based on techniques of Artificial Intelligence (AI) are necessary. This paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources and managing genetic variation in their breeding program, as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current constraints. In this paper, the hyperparameters of the network are optimized in the CNN to decrease the requirement for manual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional Neural Networks (CNN). Artificial intelligence ; Deep learning ; Gene ; Computational biology ; Expression (computer science) ; Gene expression ; Computer science ; Biology ; Genetics Spectroscopy and Chemometric Analyses ; Gene expression and cancer classification ; Smart Agriculture and AI
Authors: Youngbin Yoon | Yong-Ki Kim | Myunghun Shin
Source: Sensors (article, 2024-09-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Impact of Channel Thickness and Doping Concentration for Normally-Off Operation in Sn-Doped β-Ga2O3 Phototransistors We demonstrate a Sn-doped monoclinic gallium oxide (<i>β</i>-Ga<sub>2</sub>O<sub>3</sub>)-based deep ultraviolet (DUV) phototransistor with high area coverage and manufacturing efficiency. The threshold voltage (<i>V</i><sub>T</sub>) switches between negative and positive depending on the <i>β</i>-Ga<sub>2</sub>O<sub>3</sub> channel thickness and doping concentration. Channel depletion and Ga diffusion during manufacturing significantly influence device characteristics, as validated through computer-aided design (TCAD) simulations, which agree with the experimental results. We achieved enhancement-mode (e-mode) operation in <10 nm-thick channels, enabling a zero <i>V</i><sub>G</sub> to achieve a low dark current (1.84 pA) in a fully depleted equilibrium. Quantum confinement in thin <i>β</i>-Ga<sub>2</sub>O<sub>3</sub> layers enhances UV detection (down to 210 nm) by widening the band gap. Compared with bulk materials, dimensionally constrained optical absorption reduces electron-phonon interactions and phonon scattering, leading to faster optical responses. Decreasing <i>β</i>-Ga<sub>2</sub>O<sub>3</sub> channel thickness reduces <i>V</i><sub>T</sub> and <i>V</i><sub>G</sub>, enhancing power efficiency, dark current, and the photo-to-dark current ratio under dark and illuminated conditions. These results can guide the fabrication of tailored Ga<sub>2</sub>O<sub>3</sub>-based DUV phototransistors. Dark current ; Materials science ; Doping ; Photodiode ; Optoelectronics ; Quantum efficiency ; Diffusion ; Ultraviolet ; Band gap ; Optics ; Photodetector ; Physics Ga2O3 and related materials ; ZnO doping and properties ; Advanced Photocatalysis Techniques