jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sahar Daneshyan | Gholamhossein Sodeifian
Source: Scientific Reports (article, 2022-10-19)
Field: Chemistry — Organic Chemistry

Utilization of CO2 in supercritical conditions for the synthesis of cyclic poly (N-isopropylacrylamide) via emulsion and homogeneous reactions In this study, cyclic poly (N-isopropylacrylamide) (cPNIPAAM) was synthesized in supercritical carbon dioxide (SC-CO<sub>2</sub>) using emulsion and homogeneous reactions for the first time. This was accomplished by applying free radical polymerization and nitroxide compounds to produce low molecular weight precursors in the SC-CO<sub>2</sub> solvent. The cyclization reaction occurred in a homogeneous phase in the SC-CO<sub>2</sub> solvent, with dimethylformamide (DMF) serving as a co-solvent for dissolving the linear precursor. This reaction was also conducted in emulsion of SC-CO<sub>2</sub> in water. The effects of pressure and time on the morphology, molecular weight, and yield of a difunctionalized chain were investigated, where a higher pressure led to a higher yield. The maximum yield was 64% at 23 MPa, and the chain molecular weight (M<sub>w</sub>) was 4368 (gr/mol). Additionally, a lower pressure reduced the solubility of materials (particularly terminator) in SC-CO<sub>2</sub> and resulted in a chain with a higher molecular weight 9326 (gr/mol), leading to a lower conversion. Furthermore, the effect of cyclization reaction types on the properties of cyclic polymers was investigated. In cyclic reactions, the addition of DMF as a co-solvent resulted in the formation of a polymer with a high viscosity average molecular weight (M<sub>v</sub>) and a high degree of cyclization (100%), whereas the CO<sub>2</sub>/water emulsion resulted in the formation of a polymer with a lower M<sub>v</sub> and increased porosity. Polymers were characterized by <sup>1</sup>HNMR, FTIR, DSC, TLC, GPC, and viscometry tests. The results were presented and thoroughly discussed. Supercritical fluid ; Homogeneous ; Emulsion ; Poly(N-isopropylacrylamide) ; Chemical engineering ; Chemistry ; Aqueous solution ; Materials science ; Organic chemistry ; Thermodynamics ; Copolymer ; Polymer ; Physics Advanced Polymer Synthesis and Characterization ; Carbon dioxide utilization in catalysis ; Phase Equilibria and Thermodynamics
Authors: Yongtao Lü | Tingxiang Gong | Zhuoyue Yang | Hanxing Zhu | Yadong Liu | Chengwei Wu
Source: Frontiers in Bioengineering and Biotechnology (article, 2022-09-27)
Field: Materials Science — Materials Chemistry

Designing anisotropic porous bone scaffolds using a self-learning convolutional neural network model The design of bionic bone scaffolds to mimic the behaviors of native bone tissue is crucial in clinical application, but such design is very challenging due to the complex behaviors of native bone tissues. In the present study, bionic bone scaffolds with the anisotropic mechanical properties similar to those of native bone tissues were successfully designed using a novel self-learning convolutional neural network (CNN) framework. The anisotropic mechanical property of bone was first calculated from the CT images of bone tissues. The CNN model constructed was trained and validated using the predictions from the heterogonous finite element (FE) models. The CNN model was then used to design the scaffold with the elasticity matrix matched to that of the replaced bone tissues. For the comparison, the bone scaffold was also designed using the conventional method. The results showed that the mechanical properties of scaffolds designed using the CNN model are closer to those of native bone tissues. In conclusion, the self-learning CNN framework can be used to design the anisotropic bone scaffolds and has a great potential in the clinical application. Scaffold ; Convolutional neural network ; Biomedical engineering ; Computer science ; Anisotropy ; Materials science ; Artificial intelligence ; Engineering ; Physics Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Bone Tissue Engineering Materials
Authors: Halina Szatyłowicz | Paweł A. Wieczorkiewicz | Tadeusz M. Krygowski
Source: Sci (article, 2022-06-09)
Field: Chemistry — Organic Chemistry

Aromaticity Concepts Derived from Experiments Aromaticity, a very important term in organic chemistry, has never been defined unambiguously. Various ways to describe it come from different phenomena that have been experimentally observed. The most important examples related to some theoretical concepts are presented here. Aromaticity ; Term (time) ; Computational chemistry ; Chemistry ; Theoretical physics ; Computer science ; Chemical physics ; Physics ; Organic chemistry ; Quantum mechanics ; Molecule Synthesis and Properties of Aromatic Compounds ; Fullerene Chemistry and Applications ; Organic Chemistry Cycloaddition Reactions
Authors: Sandra Loerakker | Jay D. Humphrey
Source: Annals of Biomedical Engineering (review, 2022-08-16)
Field: Materials Science — Biomaterials

Computer Model-Driven Design in Cardiovascular Regenerative Medicine Continuing advances in genomics, molecular and cellular mechanobiology and immunobiology, including transcriptomics and proteomics, and biomechanics increasingly reveal the complexity underlying native tissue and organ structure and function. Identifying methods to repair, regenerate, or replace vital tissues and organs remains one of the greatest challenges of modern biomedical engineering, one that deserves our very best effort. Notwithstanding the continuing need for improving standard methods of investigation, including cell, organoid, and tissue culture, biomaterials development and fabrication, animal models, and clinical research, it is increasingly evident that modern computational methods should play increasingly greater roles in advancing the basic science, bioengineering, and clinical application of regenerative medicine. This brief review focuses on the development and application of computational models of tissue and organ mechanobiology and mechanics for purposes of designing tissue engineered constructs and understanding their development in vitro and in situ. Although the basic approaches are general, for illustrative purposes we describe two recent examples from cardiovascular medicine-tissue engineered heart valves (TEHVs) and tissue engineered vascular grafts (TEVGs)-to highlight current methods of approach as well as continuing needs. Mechanobiology ; Regenerative medicine ; Biofabrication ; Tissue engineering ; Computer science ; Personalized medicine ; Medicine ; Bioinformatics ; Biology ; Biomedical engineering ; Stem cell ; Anatomy Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Cardiac Valve Diseases and Treatments
Authors: Marc G. Cuxart | Daniele Perilli | Sena Tömekce | Joel Deyerling | Felix Haag | Matthias Muntwiler | Francesco Allegretti | Cristiana Di Valentin | Willi Auwärter
Source: Carbon (article, 2022-10-05)
Field: Materials Science — Materials Chemistry

Spatial segregation of substitutional B atoms in graphene patterned by the moiré superlattice on Ir(111) Graphene ; Superlattice ; Materials science ; Scanning tunneling microscope ; Condensed matter physics ; Boron ; Chemical physics ; Doping ; Density functional theory ; Fermi level ; Electronic structure ; Graphene nanoribbons ; Quantum tunnelling ; Nanotechnology ; Electron ; Optoelectronics ; Chemistry ; Computational chemistry ; Physics Graphene research and applications ; Surface and Thin Film Phenomena ; Surface Chemistry and Catalysis
Authors: Xinying Ruan | Rui Xiong | Zhou Cui | Cuilian Wen | Jiang‐Jiang Ma | Bao‐Tian Wang | Baisheng Sa
Source: Materials (article, 2022-06-06)
Field: Materials Science — Materials Chemistry

Strain-Enhanced Thermoelectric Performance in GeS2 Monolayer Strain engineering has attracted extensive attention as a valid method to tune the physical and chemical properties of two-dimensional (2D) materials. Here, based on first-principles calculations and by solving the semi-classical Boltzmann transport equation, we reveal that the tensile strain can efficiently enhance the thermoelectric properties of the GeS<sub>2</sub> monolayer. It is highlighted that the GeS<sub>2</sub> monolayer has a suitable band gap of 1.50 eV to overcome the bipolar conduction effects in materials and can even maintain high stability under a 6% tensile strain. Interestingly, the band degeneracy in the GeS<sub>2</sub> monolayer can be effectually regulated through strain, thus improving the power factor. Moreover, the lattice thermal conductivity can be reduced from 3.89 to 0.48 W/mK at room temperature under 6% strain. More importantly, the optimal ZT value for the GeS<sub>2</sub> monolayer under 6% strain can reach 0.74 at room temperature and 0.92 at 700 K, which is twice its strain-free form. Our findings provide an exciting insight into regulating the thermoelectric performance of the GeS<sub>2</sub> monolayer by strain engineering. Monolayer ; Strain engineering ; Materials science ; Thermoelectric effect ; Strain (injury) ; Thermoelectric materials ; Condensed matter physics ; Tensile strain ; Thermal conductivity ; Boltzmann equation ; Ultimate tensile strength ; Nanotechnology ; Conduction band ; Composite material ; Optoelectronics ; Thermodynamics ; Physics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications ; Perovskite Materials and Applications
Authors: Kaiyang Liu | Lixia Yin | Lin Xu | S.X. Liang
Source: Recent Progress in Materials (article, 2022-04-21)
Field: Materials Science — Materials Chemistry

Development of Low Elastic Modulus Titanium Alloys as Implant Biomaterials Biomaterials have always been the focus of material scientists and engineers. Titanium and its alloys have favorable properties, such as high strength, low density, good corrosion resistance, non-toxicity, low elastic modulus, biocompatibility, etc. Thus, Ti alloys have received much attention from scientists and engineers who work with biomaterials. Among these properties, the elastic modulus is a very important property for implant biomaterials because it avoids the “stress shielding” effect. In this study, we summarized low elastic modulus titanium alloys, which have great application potential for implant biomaterials. The major series of titanium alloys with low elastic modulus, including TiNb-based, TiMo-based, and TiZr-based series of titanium alloys, were discussed. The research status and the possible factors related to the low elastic modulus of these major titanium alloys were analyzed. Finally, the development prospects of the above series of low elastic modulus titanium alloys were compared, and the future direction of low elastic modulus Ti alloys as biomaterials was proposed. Elastic modulus ; Materials science ; Titanium ; Titanium alloy ; Stress shielding ; Modulus ; Composite material ; Corrosion ; Biocompatibility ; Young's modulus ; Implant ; Metallurgy ; Alloy ; Medicine Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Bone Tissue Engineering Materials
Authors: Yanan Xu | Weifeng Wu | Yi Chen | Tingting Zhang | Keling Tu | Yun Hao | Hailu Cao | Xuehui Dong | Qun Sun
Source: Frontiers in Plant Science (article, 2022-11-29)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging with machine learning for non-destructive classification of Astragalus membranaceus var. mongholicus, Astragalus membranaceus, and similar seeds The roots of <i>Astragalus membranaceus</i> var. <i>mongholicus</i> (AMM) and A. membranaceus (AM) are widely used in traditional Chinese medicine. Although AMM has higher yields and accounts for a larger market share, its cultivation is fraught with challenges, including mixed germplasm resources and widespread adulteration of commercial seeds. Current methods for distinguishing Astragalus seeds from similar (SM) seeds are time-consuming, laborious, and destructive. To establish a non-destructive method, AMM, AM, and SM seeds were collected from various production areas. Machine vision and hyperspectral imaging (HSI) were used to collect morphological data and spectral data of each seed batch, which was used to establish discriminant models through various algorithms. Several preprocessing methods based on hyperspectral data were compared, including multiplicative scatter correction (MSC), standard normal variable (SNV), and first derivative (FD). Then selection methods for identifying informative features in the above data were compared, including successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS). The results showed that support vector machine (SVM) modeling of machine vision data could distinguish Astragalus seeds from SM with >99% accuracy, but could not satisfactorily distinguish AMM seeds from AM. The FD-UVE-SVM model based on hyperspectral data reached 100.0% accuracy in the validation set. Another 90 seeds were tested, and the recognition accuracy was 100.0%, supporting the stability of the model. In summary, HSI data can be applied to discriminate among the seeds of AMM, AM, and SM non-destructively and with high accuracy, which can drive standardization in the Astragalus production industry. Hyperspectral imaging ; Artificial intelligence ; Pattern recognition (psychology) ; Support vector machine ; Preprocessor ; Computer science ; Astragalus ; Linear discriminant analysis ; Mathematics ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Dongchuang Wu | Xiaosheng Cui | Mohamed E. El‐Khouly | Minchao Gu | Bin Zhang | Yu Chen
Source: Advanced Electronic Materials (article, 2022-12-07)
Field: Materials Science — Materials Chemistry

Covalent Functionalization of Black Phosphorus Nanosheets with Photochromic Polymer for Transient Optoelectronic Memory Devices Abstract How to erase sensitive data stored on memory devices quickly in an emergency has become a critical issue that needs to be addressed. Using BP‐BIBB as a key template for atom transfer radical polymerization, which is prepared by the reaction of p ‐hydroxyphenyl‐functionalized black phosphorous nanosheets (C 6 H 4 OH‐BP) with 2‐bromoisobutyryl bromide (BIBB), a highly soluble poly[4‐(4‐(3,3,4,4,5,5‐hexafluoro‐2‐(2‐methyl‐5‐phenylthiophen‐3‐yl)cyclopent‐1‐en‐1‐yl)‐5‐methylthiophen‐2‐yl)phenyl methacrylate] (PHPM)‐covalently grafted BP derivative, hereafter abbreviated as PHPM‐ g ‐BP, is synthesized in situ. PHPM is a typical photochromic polymer that emits blue light under UV irradiation, while showing pale yellow under visible light illumination. By using PHPM‐ g ‐BP as an active layer, an as‐fabricated transient optoelectronic memory device exhibits a nonvolatile rewritable memory effect under UV irradiation, with an ON/OFF current ratio of 3.6 × 10 3 , a turn‐on voltage of 1.60 V, and a turn‐off voltage of −1.41 V. Upon visible light illumination for 10 s, all the information stored in the device is erased quickly. The observed device current drops down from 10 −3 to 10 −8 A rapidly. Photochromism ; Materials science ; Covalent bond ; Atom-transfer radical-polymerization ; Polymer ; Bromide ; Irradiation ; Photochemistry ; Optoelectronics ; Methacrylate ; Surface modification ; Polymerization ; Nanotechnology ; Chemical engineering ; Chemistry ; Organic chemistry Photochromic and Fluorescence Chemistry ; Radical Photochemical Reactions ; Porphyrin and Phthalocyanine Chemistry
Authors: Н. Г. Бичан | Е. Н. Овченкова | В. А. Мозгова | Alexander A. Ksenofontov | N.O. Kudryakova | I. V. Shelaev | F. E. Gostev | Т. Н. Ломова
Source: Molecules (article, 2022-12-14)
Field: Chemistry — Organic Chemistry

Donor–Acceptor Complexes of (5,10,15,20-Tetra(4-methylphenyl)porphyrinato)cobalt(II) with Fullerenes C60: Self-Assembly, Spectral, Electrochemical and Photophysical Properties The noncovalent interactions of (5,10,15,20-tetra(4-methylphenyl)porphinato)cobalt(II) (CoTTP) with C<sub>60</sub> and 1-N-methyl-2-(pyridin-4-yl)-3,4-fullero[60]pyrrolidine (PyC<sub>60</sub>) were studied in toluene using absorption and fluorescence titration methods. The self-assembly in the 2:1 complexes (the triads) (C<sub>60</sub>)<sub>2</sub>CoTTP and (PyC<sub>60</sub>)<sub>2</sub>CoTTP was established. The bonding constants for (C<sub>60</sub>)<sub>2</sub>CoTTP and (PyC<sub>60</sub>)<sub>2</sub>CoTTP are defined to be (3.47 ± 0.69) × 10<sup>9</sup> and (1.47 ± 0.28) × 10<sup>10</sup> M<sup>-2</sup>, respectively. <sup>1</sup>H NMR, IR spectroscopy, thermogravimetric analysis and cyclic voltammetry data have provided very good support in favor of efficient complex formation in the ground state between fullerenes and CoTTP. PyC<sub>60</sub>/C<sub>60</sub> fluorescence quenching in the PyC<sub>60</sub>/C<sub>60</sub>-CoTTP systems was studied and the fluorescence lifetime with various CoTTP additions was determined. The singlet oxygen quantum yield was determined for PyC<sub>60</sub> and the intensity decrease in the <sup>1</sup>O<sub>2</sub> phosphorescence for C<sub>60</sub> and PyC<sub>60</sub> with the CoTTP addition leading to the low efficiency of intercombination conversion for the formation of the <sup>3</sup>C<sub>60</sub>* triplet excited state was found. Using femtosecond transient absorption measurements in toluene, the photoinduced electron transfer from the CoTTP in the excited singlet state to fullerene moiety was established. Quantum chemical calculations were used for the determination of molecular structure, stability and the HOMO/LUMO energy levels of the triads as well as to predict the localization of frontier orbitals in the triads. Chemistry ; Photochemistry ; Fullerene ; Quantum yield ; Phosphorescence ; Excited state ; HOMO/LUMO ; Photoinduced electron transfer ; Cyclic voltammetry ; Fluorescence ; Electron transfer ; Physical chemistry ; Electrochemistry ; Molecule ; Organic chemistry Fullerene Chemistry and Applications ; Molecular Junctions and Nanostructures ; Porphyrin and Phthalocyanine Chemistry
Authors: Mahshid Moheb | Aida Iraji | Navid Dastyafteh | Minoo Khalili Ghomi | Milad Noori | Somayeh Mojtabavi | Mohammad Ali Faramarzi | Fatemeh Rasekh | Bagher Larijani | Kamiar Zomorodian | Seyed Esmaeil Sadat-Ebrahimi | Mohammad Mahdavi
Source: BMC Chemistry (article, 2022-11-15)
Field: Chemistry — Organic Chemistry

Synthesis and bioactivities evaluation of quinazolin-4(3H)-one derivatives as α-glucosidase inhibitors The development of new antidiabetes agents is necessary to obtain optimal glycemic control and overcome its complications. Different quinazolin-4(3H)-one bearing phenoxy-acetamide derivatives (7a-r) were designed and synthesized to develop α-glucosidase inhibitors. All the synthesized derivatives were evaluated against α-glucosidase in vitro and among them, compound 7b showed the highest α-glucosidase inhibition with an IC<sub>50</sub> of 14.4 µM, which was ∼53 times stronger than that of acarbose. The inhibition kinetic studies showed that the inhibitory mechanism of compound 7b was a competitive type towards α-glucosidase. Also, molecular docking studies analyzed the interaction between the most potent derivative and α-glucosidase. Current findings indicate the new potential of quinazolin-4(3H)-ones that could be used for the development of novel agents against diabetes mellitus. Acarbose ; Acetamide ; Chemistry ; Docking (animal) ; Stereochemistry ; Combinatorial chemistry ; In vitro ; Pharmacology ; Enzyme ; Biochemistry ; Biology ; Medicine ; Organic chemistry Quinazolinone synthesis and applications ; Synthesis and Characterization of Heterocyclic Compounds ; Biochemical and Molecular Research
Authors: Qinlin Xiao | Na Wu | Wentan Tang | Chu Zhang | Lei Feng | Lei Zhou | Jianxun Shen | Ze Zhang | Pan Gao | Yong He
Source: Frontiers in Plant Science (article, 2022-12-20)
Field: Chemistry — Analytical Chemistry

Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves Leaf nitrogen concentration (LNC) is a critical indicator of crop nutrient status. In this study, the feasibility of using visible and near-infrared spectroscopy combined with deep learning to estimate LNC in cotton leaves was explored. The samples were collected from cotton's whole growth cycle, and the spectra were from different measurement environments. The random frog (RF), weighted partial least squares regression (WPLS), and saliency map were used for characteristic wavelength selection. Qualitative models (partial least squares discriminant analysis (PLS-DA), support vector machine for classification (SVC), convolutional neural network classification (CNNC) and quantitative models (partial least squares regression (PLSR), support vector machine for regression (SVR), convolutional neural network regression (CNNR)) were established based on the full spectra and characteristic wavelengths. Satisfactory results were obtained by models based on CNN. The classification accuracy of leaves in three different LNC ranges was up to 83.34%, and the root mean square error of prediction (RMSEP) of quantitative prediction models of cotton leaves was as low as 3.36. In addition, the identification of cotton leaves based on the predicted LNC also achieved good results. These results indicated that the nitrogen content of cotton leaves could be effectively detected by deep learning and visible and near-infrared spectroscopy, which has great potential for real-world application. Partial least squares regression ; Support vector machine ; Linear discriminant analysis ; Artificial intelligence ; Mean squared error ; Least squares support vector machine ; Pattern recognition (psychology) ; Convolutional neural network ; Mathematics ; Spectroscopy ; Regression analysis ; Least-squares function approximation ; Regression ; Statistics ; Biological system ; Computer science ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture
Authors: Andrew Nader | Mohamed H.Khafagy | Shereen A. Hussien
Source: International Journal of Advanced Computer Science and Applications (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Grape Leaves Diseases Classification using Ensemble Learning and Transfer Learning Agriculture remains an important sector of the economy. Plant diseases and pests have a big impact on plant yield and quality. So, prevention and early detection of crop disease are some of the measures that must be implemented in farming to save the plants at an early stage and thereby reduce the overall food loss. Grapes are the most profitable fruit, but they are also vulnerable to a variety of diseases. Black Measles, Black Rot, and Leaf Blight are diseases that affect grape plants. Manual disease diagnosis can result in improper identification and use of pesticides, and it takes a long time. A variety of deep learning approaches have been used to address this issue of the identification and classification of grape leaf diseases. However, there are also limits to such approaches. Therefore, this paper uses deep learning with the concept of ensemble learning based on three famous Convolutional Neural Network (CNN) architectures (Visual Geometry Group (VGG16), VGG19, and Extreme Inception (Xception)). These three models are pre-trained with ImageNet. The performance of the proposed approach is analyzed using the Plant Village (PV) dataset of common grape leaf diseases. The Proposed model gives higher performance than the results achieved by using each Deep Learning architecture separately and compared with the recent approaches in this study. The proposed system outperformed the others with 99.82% accuracy. Computer science ; Deep learning ; Artificial intelligence ; Convolutional neural network ; Machine learning ; Transfer of learning ; Identification (biology) ; Variety (cybernetics) ; Ensemble learning ; Agriculture ; Black rot ; Horticulture ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI