jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stefane N. Costa | Francisco Almeida-Neto | Emmanuel Silva Marinho | Othon Campos | Adriana N. Correia | Pedro de Lima‐Neto
Source: Journal of the Brazilian Chemical Society (article, 2022-08-16)
Field: Materials Science — Materials Chemistry

Inhibition of Copper Corrosion in Acid Medium by Imidazole-Based Compounds: Electrochemical and Molecular Approaches Chemically modified imidazole molecules: 4-(1H-imidazol-1-yl)aniline, 4-(1H-imidazol1-yl)benzaldehyde, 4-(1H-imidazol-1-yl)phenol and (4-(1H-imidazol-1-yl)phenyl)methanol were investigated as inhibitors of the copper (Cu0) corrosion in 0.5 mol L-1 H2SO4 medium. The electrochemical corrosion data were obtained by monitoring open circuit potential, linear potentiodynamic polarization and electrochemical impedance spectroscopy techniques, while the computational density functional theory (DFT) method was applied to correlate the electronic properties of the molecules with corrosion inhibition efficiencies. All molecules had inhibited the Cu corrosion, and the inhibition values lied between 80 and 94%. A good correlation between the inhibition efficiencies values and Gibbs adsorption energy was found, showing that the more negative Gibbs energy, better interaction between the corrosion inhibitor with the Cu0 surface, diminishing its corrosion in 0.5 mol L-1 H2SO4 medium. The DFT calculations showed significative differences in electronic and reactivity properties of imidazole and other molecules. The higher corrosion inhibition of imidazole derivates could be explained by electrophilic characteristic of these molecules, since there are empty molecular orbitals spread over mainly in benzene rings that make a metal-ligand charge transfer, receiving electronic density from the copper surface by backbonding, according to the electronic Fukui functions and the potential charge distribution considering the map of electrostatic potential. Imidazole ; Chemistry ; Density functional theory ; Corrosion ; Gibbs free energy ; Molecule ; Inorganic chemistry ; Corrosion inhibitor ; Electrochemistry ; Copper ; Dielectric spectroscopy ; Erosion corrosion of copper water tubes ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Electrode ; Thermodynamics Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Electrochemical Analysis and Applications
Authors: Dagmara Pietkiewicz | Szymon Plewa | Mikołaj Piotr Zaborowski | Timothy J. Garrett | Eliza Matuszewska | Zenon J. Kokot | Jan Matysiak
Source: Cancer Cell International (review, 2022-12-19)
Field: Chemistry — Spectroscopy

Mass spectrometry imaging in gynecological cancers: the best is yet to come Mass spectrometry imaging (MSI) enables obtaining multidimensional results simultaneously in a single run, including regiospecificity and m/z values corresponding with specific proteins, peptides, lipids, etc. The knowledge obtained in this way allows for a multifaceted analysis of the studied issue, e.g., the specificity of the neoplastic process and the search for new therapeutic targets. Despite the enormous possibilities, this relatively new technique in many aspects still requires the development or standardization of analytical protocols (from collecting biological material, through sample preparation, analysis, and data collection, to data processing). The introduction of standardized protocols for MSI studies, with its current potential to extend diagnostic and prognostic capabilities, can revolutionize clinical pathology. As far as identifying ovarian cancer subtypes can be challenging, especially in poorly differentiated tumors, developing MSI-based algorithms may enhance determining prognosis and tumor staging without the need for extensive surgery and optimize the choice of subsequent therapy. MSI might bring new solutions in predicting response to treatment in patients with endometrial cancer. Therefore, MSI may help to revolutionize the future of gynecological oncology in terms of diagnostics, treatment, and predicting the response to therapy. This review will encompass several aspects, e.g., contemporary discoveries in gynecological cancer research utilizing MSI, indicates current challenges, and future perspectives on MSI. Standardization ; Medicine ; Medical physics ; Endometrial cancer ; Cancer ; Computer science ; Bioinformatics ; Computational biology ; Data science ; Internal medicine ; Biology Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Molecular Biology Techniques and Applications
Authors: Dong Zhao | Michael Benson | Tiankai Yao | Kun Yang | Fidelma Giulia Di Lemma | Bowen Gong | Jie Lian
Source: Journal of Nuclear Materials (article, 2022-12-17)
Field: Materials Science — Materials Chemistry

Manufacturing porous U10Zr fuels with controlled porosities by SPS and thermal properties Materials science ; Porosity ; Microstructure ; Sintering ; Pellets ; Composite material ; Spark plasma sintering ; Metallurgy Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Yaping Zhao | Fu Liping | Jinbai Chen | Junhao Zhou | Chongmei Tian | Daotang Zhou | Rui Zhu
Source: Journal of Oncology (article, 2022-09-21)
Field: Chemistry — Organic Chemistry

Eupalinolide O Induces Apoptosis in Human Triple-Negative Breast Cancer Cells via Modulating ROS Generation and Akt/p38 MAPK Signaling Pathway EO may induce cell apoptosis in TNBC via regulating ROS generation and Akt/p38 MAPK pathway, indicating EO may be a candidate drug for TNBC. Medicine ; Protein kinase B ; Triple-negative breast cancer ; p38 mitogen-activated protein kinases ; MAPK/ERK pathway ; Apoptosis ; Cancer research ; Signal transduction ; Cell biology ; Human breast ; Breast cancer ; Cancer ; Internal medicine ; Biology ; Genetics Synthesis and biological activity ; Histone Deacetylase Inhibitors Research ; Synthesis of Organic Compounds
Authors: Marzieh Abdollahi | Pedro A. Segura | Francis Beaudry
Source: Biomedical Chromatography (article, 2022-10-21)
Field: Chemistry — Spectroscopy

Is nontargeted data acquisition for target analysis (nDATA) in mass spectrometry a forward‐thinking analytical approach? Targeted mass spectrometry is extensively used for the quantitative measurement of various molecules present in complex matrices. It is certainly one of the most important analytical duties in a mass spectrometry laboratory. Systematic development of selected-reaction monitoring (SRM), multiple-reaction monitoring (MRM) and parallel-reaction monitoring (PRM) methods for targeted mass spectrometry-based analysis was performed without considering future opportunities. The advancement of hardware and software technologies has resulted in greater resolution, accuracy, speed and depth. For sure, SRM, MRM or PRM acquisitions can quantify molecules very accurately at trace levels. However, they do not provide datasets allowing future data mining. Obviously, we cannot truly quantify something that we do not know is there. However, using non-targeted data acquisition for target analysis, we can generate a MS<sup>1</sup> and MS<sup>2</sup> digital libraries of each sample, providing future proof datasets. This is instrumental for data mining following new questions potentially arising in time permitting new and deeper processing and interpretation. This perspective article provides thoughts on why we believe it is time to question the status quo in targeted mass spectrometry. Mass spectrometry ; Chemistry ; Selected reaction monitoring ; Software ; Resolution (logic) ; Data acquisition ; Perspective (graphical) ; TRACE (psycholinguistics) ; Computer science ; Data mining ; Data science ; Chromatography ; Tandem mass spectrometry ; Artificial intelligence Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Pesticide Residue Analysis and Safety
Authors: Yufan Zhang | Wenxiu Wang | Fan Zhang | Qianyun Ma | Shuang Gao | Jie Wang | Jianfeng Sun | Yuanyuan Liu
Source: International journal of agricultural and biological engineering (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Rapid and non-destructive decay detection of Yali pears using hyperspectral imaging coupled with 2D correlation spectroscopy The black spot disease caused by Alternaria alternata on Yali pears is a great concern as it compromises their edible quality and commercial value. To realize rapid and non-destructive classification of this disease, hyperspectral imaging (HSI) technology was combined with two-dimensional correlation spectroscopy (2DCOS) analysis. A total of 150 pear samples at different decay grades were prepared. After obtaining the HSI images, the whole sample was demarcated as the region of interest, and the spectral information was extracted. Seven preprocessing methods were applied and compared to build the classification models. Thereafter, using the inoculation day as an external perturbation, 2DCOS was used to select the feature-related wavebands for black spot disease identification, and the result was compared to those obtained using competitive adaptive reweighting sampling and the successive projections algorithm. Results demonstrated that the simplified least squares support vector model based on 2DCOS-identified feature wavebands yielded the best performance with the identification accuracy, precision, sensitivity, and specificity of 97.30%, 94.60%, 96.16%, and 98.21%, respectively. Therefore, 2DCOS can effectively interpret the feature-related wavebands, and its combination with HSI is an effective tool to predict black spot disease on Yali pears. Keywords: hyperspectral imaging technology, black spot disease, two-dimensional correlation spectroscopy, Yali pear DOI: 10.25165/j.ijabe.20221505.7313 Citation: Zhang Y F, Wang W X, Zhang F, Ma Q Y, Gao S, Wang J, et al. Rapid and non-destructive decay detection of Yali pears using hyperspectral imaging coupled with 2D correlation spectroscopy. Int J Agric & Biol Eng, 2022; 15(5): 236–244. Hyperspectral imaging ; Mathematics ; PEAR ; Artificial intelligence ; Pattern recognition (psychology) ; Horticulture ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Essential Oils and Antimicrobial Activity
Authors: Etienne Palos | Eleftherios Lambros | Saswata Dasgupta | Francesco Paesani
Source: ChemRxiv (preprint, 2022-03-10)
Field: Materials Science — Materials Chemistry

Density Functional Theory of Water with the Machine-Learned DM21 Functional The delicate interplay between functional-driven and density-driven errors in density functional theory (DFT) has hindered traditional density functional approximations (DFAs) from providing an accurate description of water for over 30 years. Recently, the deep-learned DeepMind 21 (DM21) functional has been shown to overcome the limitations of traditional DFAs as it is free of delocalization error. To determine if DM21 can enable a molecular-level description of the physical properties of aqueous systems within Kohn-Sham DFT, we assess the accuracy of the DM21 functional for neutral, protonated, and deprotonated water clusters. We find that the ability of DM21 to accurately predict the energetics of aqueous clusters varies significantly with cluster size. Additionally, we introduce the many-body MB-DM21 potential derived from DM21 data within the many-body expansion of the energy and use it in simulations of liquid water as a function of temperature at ambient pressure. We find that size-dependent functional-driven errors identified in the analysis of the energetics of small clusters calculated with the DM21 functional result in the MB-DM21 potential systematically overestimating the hydrogen-bond strength and, consequently, predicting a more ice-like local structure of water at room temperature. Density functional theory ; Energetics ; Cluster (spacecraft) ; Hybrid functional ; Coupled cluster ; Delocalized electron ; Statistical physics ; Function (biology) ; Computer science ; Aqueous solution ; Protonation ; Chemical physics ; Computational chemistry ; Molecule ; Physics ; Thermodynamics ; Chemistry ; Physical chemistry ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Peyman Narjabadifam | Mohammad Noori | Ertuǧrul Taciroğlu | Jian Zhang | Behrokh Khoshnevis | Donatello Cardone | Dipanjan Basu | Tao Wang | Eltahry Elghandour | Ehsan Noroozinejad Farsangi | Reza Lotfi | Mahdi Chavoshi | Davood Sattarian | Orlando Fabio Stirnimann
Source: Sensors (article, 2022-09-12)
Field: Materials Science — Materials Chemistry

Sustainable Earthquake Resilience with the Versatile Shape Memory Alloy (SMA)-Based Superelasticity-Assisted Slider Earthquakes threaten humanity globally in complex ways that mainly include various socioeconomic consequences of life and property losses. Resilience against seismic risks is of high importance in the modern world and needs to be sustainable. Sustainable earthquake resilience (SER) from the perspective of structural engineering means equipping the built environment with appropriate aseismic systems. Shape memory alloys (SMAs) are a class of advanced materials well suited for fulfilling the SER demand of the built environment. This article explores how this capability can be realized by the innovative SMA-based superelasticity-assisted slider (SSS), recently proposed for next-generation seismic protection of structures. The versatility of SSS is first discussed as a critical advantage for an effective SER. Alternative configurations and implementation styles of the system are presented, and other advantageous features of this high-tech isolation system (IS) are studied. Results of shaking table experiments, focused on investigating the expected usefulness of SSS for seismic protection in hospitals and conducted at the structural earthquake engineering laboratory of the University of Bonab, are then reported. SSS is compared with currently used ISs, and it is shown that SSS provides the required SER for the built environments and outperforms other ISs by benefitting from the pioneered utilization of SMAs in a novel approach. Pseudoelasticity ; Shape-memory alloy ; Resilience (materials science) ; Slider ; Earthquake shaking table ; Engineering ; Earthquake engineering ; SMA* ; Computer science ; Structural engineering ; Mechanical engineering ; Materials science ; Artificial intelligence Shape Memory Alloy Transformations ; Masonry and Concrete Structural Analysis ; Structural Engineering and Vibration Analysis
Authors: A. Shalwan | Talal Alsaeed | B.F. Yousif
Source: Journal of Materials Science and Chemical Engineering (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

Machinability of Polymeric Composites and Future Reinforcements—A Review This paper reviews the machinability and mechanical properties of natural fiber-reinforced composites. Coupling agents, operating parameters, as well as chemical treatment effects on natural fiber-reinforced composites’ machinability are also reviewed. Moreover, the impacts of fibers’ physical properties on the machinability of the composite are mentioned. Fiber volume fraction (Vf), fiber orientation as well as chemical treatment effects on mechanical properties are also defined. Conclusively, the effect of fibers’ physical properties as well as mechanical properties is described. It was discovered that chemical treatment of natural fibers improved their compatibility with the matrix by removing their surface tissues, increasing the roughness average (Ra), and reducing moisture absorption. Also, the Orientation of the fiber plays an important role in controlling the mechanical properties of the composite. Moreover, some physical properties of the fibers, including quality of fiber distributed in the matrix; fiber size, length, and diameter; moisture absorption; porosity and the way fibers break during compounding with the matrix, were found to affect the mechanical properties of the composites formed. Materials science ; Composite material ; Machinability ; Composite number ; Natural fiber ; Compounding ; Fiber ; Porosity ; Absorption of water ; Surface roughness ; Machining Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Additive Manufacturing and 3D Printing Technologies
Authors: Zhenye Li | Jun Fu | Zhi Chen | Qiankun Fu | Xiwen Luo
Source: Frontiers in Plant Science (article, 2022-11-29)
Field: Chemistry — Analytical Chemistry

Hyperspectral detection of fresh corn peeling damage using germinating sparse classification method Peeling damage reduces the quality of fresh corn ear and affects the purchasing decisions of consumers. Hyperspectral imaging technique has great potential to be used for detection of peeling-damaged fresh corn. However, conventional non-machine-learning methods are limited by unsatisfactory detection accuracy, and machine-learning methods rely heavily on training samples. To address this problem, the germinating sparse classification (GSC) method is proposed to detect the peeling-damaged fresh corn. The germinating strategy is developed to refine training samples, and to dynamically adjust the number of atoms to improve the performance of dictionary, furthermore, the threshold sparse recovery algorithm is proposed to realize pixel level classification. The results demonstrated that the GSC method had the best classification effect with the overall classification accuracy of the training set was 98.33%, and that of the test set was 95.00%. The GSC method also had the highest average pixel prediction accuracy of 84.51% for the entire HSI regions and 91.94% for the damaged regions. This work represents a new method for mechanical damage detection of fresh corn using hyperspectral image (HSI). Hyperspectral imaging ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Set (abstract data type) ; Training set ; Pixel ; Data set ; Test set ; Support vector machine ; Germination ; Mathematics ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: D. L. Muccignat | Peter W. Stokes | Daniel Cocks | Jason R. Gascooke | D. B. Jones | M. J. Brunger | Ronald D. White
Source: International Journal of Molecular Sciences (article, 2022-03-20)
Field: Materials Science — Surfaces, Coatings and Films

Simulating the Feasibility of Using Liquid Micro-Jets for Determining Electron–Liquid Scattering Cross-Sections The extraction of electron-liquid phase cross-sections (surface and bulk) is proposed through the measurement of (differential) energy loss spectra for electrons scattered from a liquid micro-jet. The signature physical elements of the scattering processes on the energy loss spectra are highlighted using a Monte Carlo simulation technique, originally developed for simulating electron transport in liquids. Machine learning techniques are applied to the simulated electron energy loss spectra, to invert the data and extract the cross-sections. The extraction of the elastic cross-section for neon was determined within 9% accuracy over the energy range 1-100 eV. The extension toward the simultaneous determination of elastic and ionisation cross-sections resulted in a decrease in accuracy, now to within 18% accuracy for elastic scattering and 1% for ionisation. Additional methods are explored to enhance the accuracy of the simultaneous extraction of liquid phase cross-sections. Scattering ; Electron ; Electron scattering ; Materials science ; Physics ; Nuclear physics ; Optics Electron and X-Ray Spectroscopy Techniques ; Power Transformer Diagnostics and Insulation ; Electrohydrodynamics and Fluid Dynamics
Authors: G.M. Abo-Lila | T.Z.N. Sokkar | E.A. Seisa | E.Z. Omar
Source: Applied Physics B (article, 2022-01-01)
Field: Materials Science — General Materials Science

Investigation of the opto-thermo-mechanical properties of antimicrobial PET/TiO2 fiber using the transport of intensity equation technique Materials science ; Polyethylene terephthalate ; Refractive index ; Fiber ; Annealing (glass) ; Optical fiber ; Composite material ; Optics ; Optoelectronics Material Properties and Applications ; Thermography and Photoacoustic Techniques ; Additive Manufacturing and 3D Printing Technologies
Authors: Yun Jun Yang | Zhong Feng Gao
Source: Frontiers in Bioengineering and Biotechnology (review, 2022-03-28)
Field: Materials Science — Surfaces, Coatings and Films

Superwettable Biosensor for Disease Biomarker Detection Bioinspired superwettable materials have aroused wide interests in recent years for their promising application fields from service life to industry. As one kind of emerging application, the superwettable surfaces used to fabricate biosensors for the detection of disease biomarkers, especially tumor biomarkers, have been extensively studied. In this mini review, we briefly summarized the sensing strategy for disease biomarker detection based on superwettable biosensors, including fluorescence, electrochemistry, surface-enhanced Raman scattering, and visual assays. Finally, the challenges and direction for future development of superwettable biosensors are also discussed. Biosensor ; Biomarker ; Nanotechnology ; Raman scattering ; Computer science ; Materials science ; Computational biology ; Chemistry ; Biology ; Raman spectroscopy ; Physics ; Optics ; Biochemistry Surface Modification and Superhydrophobicity ; Fluid Dynamics and Thin Films ; Adhesion, Friction, and Surface Interactions
Authors: Hao Jiang | Zegang Yu | Yonghua Wang | Baowei Zhang | Jiuxiang Song | Jingdian Wei
Source: Scientific Reports (article, 2022-08-26)
Field: Chemistry — Analytical Chemistry

The state prediction method of the silk dryer based on the GA-BP model Considering the under-maintenance and over-maintenance of existing equipment maintenance methods, this paper studies a Condition Based Maintenance method for silk dryers. The entropy method is used to eliminate the influence of subjective factors to more objectively reflect the weight of different input parameters; optimizing the number of nodes in the hidden layer of the network to improve the prediction accuracy; and using the GA-BP neural network to establish a state prediction model of the equipment to solve the disadvantages of the BP neural network, for example, unstable prediction, easily falling into local optimum, and slow global search ability. Simulation experiments show that this method can effectively compensate for the shortcomings of the existing maintenance methods, and provide an effective scientific basis for dryer state maintenance. SILK ; State (computer science) ; Computer science ; Algorithm ; Telecommunications Spectroscopy and Chemometric Analyses ; Viral Infectious Diseases and Gene Expression in Insects ; Advanced Algorithms and Applications