jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Büşra Uzun | Mustafa Özgür Yaylı | Ömer Cívalek
Source: ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik (article, 2024-05-02)
Field: Materials Science — Materials Chemistry

An investigation on the torsional vibration of a FG strain gradient nanotube Abstract In this work, an attention is paid to the prediction of torsional vibration frequencies of functionally graded porous nanotubes based on the Lam strain gradient elasticity theory. The nanotubes are formed of functionally graded porous nanomaterials that vary in the radial direction. This study also aims to obtain the analytical solution of the strain gradient model presented by Lam for torsional vibration response, in a simple manner, for different rigid or restrained boundary conditions. The torsion angle of a functionally graded nanotube is defined by an infinite Fourier series. Then, the Stokes’ transformation is applied to force the boundary conditions to the desired state. An eigenvalue problem is established with the help of the two systems of equations obtained. This eigenvalue problem, which includes deformable springs at both ends of the nanotube, appears as a general analytical solution that can find torsional vibration frequencies. It is shown that the vibrational responses can be significantly influenced by the through‐radius gradings of material, material length scale parameters and deformable springs of the functionally graded nanotubes and consequently can be predicted by giving proper values to torsional spring parameters. Torsion (gastropod) ; Vibration ; Materials science ; Boundary value problem ; Eigenvalues and eigenvectors ; Torsional vibration ; Nanotube ; Elasticity (physics) ; Torsion spring ; Mechanics ; Fourier series ; Mathematical analysis ; Classical mechanics ; Composite material ; Physics ; Mathematics ; Acoustics ; Carbon nanotube Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Thermoelastic and Magnetoelastic Phenomena
Authors: Fengxun Zheng | Guodong Sun | Yuchen Suo | Hao Ma | Tao Feng
Source: Sensors (article, 2024-10-18)
Field: Chemistry — Spectroscopy

Research on Through-Flame Imaging Using Mid-Wave Infrared Camera Based on Flame Filter High-temperature furnaces and coal-fired boilers are widely employed in the petrochemical and metal-smelting sectors. Over time, the deterioration, corrosion, and wear of pipelines can lead to equipment malfunctions and safety incidents. Nevertheless, effective real-time monitoring of equipment conditions remains insufficient, primarily due to the interference caused by flames generated from fuel combustion. To address this issue, in this study, a through-flame infrared imager is developed based on the mid-wave infrared (MWIR) radiation characteristics of the flame. The imager incorporates a narrowband filter that operates within the wavelength range of 3.80 μm to 4.05 μm, which is integrated into conventional thermal imagers to perform flame filtering. This configuration enables the radiation from the background to pass through the flame and reach the detector, thereby allowing the infrared imager to visualize objects obscured by the flame and measure their temperatures directly. Our experimental findings indicate that the imager is capable of through-flame imaging; specifically, when the temperature of the target exceeds 50 °C, the imager can effectively penetrate the outer flame of an alcohol lamp and distinctly capture the target's outline. Importantly, as the temperature of the target increases, the clarity of the target's contour in the images improves. The MWIR through-flame imager presents considerable potential for the real-time monitoring and preventive maintenance of high-temperature furnaces and similar equipment, such as detecting the degradation of refractory materials and damage to pipelines. Infrared ; Filter (signal processing) ; Acoustics ; Materials science ; Computer science ; Environmental science ; Artificial intelligence ; Computer vision ; Engineering ; Optics ; Physics Spectroscopy and Laser Applications ; Combustion and flame dynamics ; Fire Detection and Safety Systems
Authors: Yiming Tan | Zian Jia | Zhifei Deng | Ling Li
Source: Interface Focus (article, 2024-06-07)
Field: Materials Science — Biomaterials

Elasmoid fish scales as a natural fibre composite: microscopic heterogeneities in structure, mineral distribution, and mechanical properties The elasmoid scales in teleost fish serve as exemplary models for natural fibre composites with integrated flexibility and protection. Yet, limited research has been focused on the potential structural, chemical, and mechanical heterogeneity within individual scales. This study presents systematic characterizations of the elasmoid scales from black drum fish (<i>Pogonias cromis</i>) at different zones within individual scales as a natural fibre composite, focusing on the microscopic structural heterogeneities and corresponding mechanical effects. The focus field at the centre of the scales exhibits a classical tri-layered collagen-based composite design, consisting of the mineralized outermost limiting layer, external elasmodine layer in the middle, and the unmineralized internal elasmodine layer. In comparison, the rostral field at the anterior end of the scales exhibits a two-layered design: the mineralized outermost limiting layer exhibits radii sections on the outer surface, and the inner elasmodine layer consists of collagen fibre-based sublayers with alternating mineralization levels. Chemical and nanoindentation analysis suggests a close correlation between the mineralization levels and the local nanomechanical properties. Comparative finite element modelling shows that the rostral-field scales achieve increased flexibility under both concave and convex bending. Moreover, the evolving geometries of isolated Mandle's corpuscles in the internal elasmodine layer, transitioning from irregular shapes to faceted octahedrons, suggest the mechanisms of mineral growth and space-filling to thicken the mineralized layers in scales during growth, which enhances the bonding strength between the adjacent collagen fibre layers. This work offers new insights into the structural variations in individual elasmoid scales, providing strategies for bioinspired fibre composite designs with local-adapted functional requirements. Fish <Actinopterygii> ; Composite number ; Mineral ; Natural (archaeology) ; Distribution (mathematics) ; Computer science ; Materials science ; Geology ; Composite material ; Biology ; Ecology ; Mathematics ; Fishery ; Paleontology Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials
Authors: Yujing Zhang | Zhongkang Han | Beien Zhu | Xiaojuan Hu | Maria Troppenz | Santiago Rigamonti | Hui Li | Claudia Draxl | M. V. Ganduglia-Pirovano | Yi Gao
Source: Nanoscale (article, 2024-12-23)
Field: Materials Science — Materials Chemistry

Decoupling many-body interactions in the CeO<sub>2</sub>(111) oxygen vacancy structure with statistical learning and cluster expansion Oxygen vacancies (V<sub>O</sub>'s) are of paramount importance in influencing the properties and applications of ceria (CeO<sub>2</sub>). Yet, comprehending the distribution and nature of V<sub>O</sub>'s poses a significant challenge due to the vast number of electronic configurations and intricate many-body interactions among V<sub>O</sub>'s and polarons (Ce<sup>3+</sup> ions). In this study, we established a cluster expansion model based on first-principles calculations and statistical learning to decouple the interactions among the Ce<sup>3+</sup> ions and V<sub>O</sub>'s, thereby circumventing the limitations associated with sampling electronic configurations. By separating these interactions, we identified specific electronic configurations characterized by the most favorable V<sub>O</sub>-Ce<sup>3+</sup> attractions and the least favorable Ce<sup>3+</sup>-Ce<sup>3+</sup>/V<sub>O</sub>-V<sub>O</sub> repulsions, which are crucial in determining the stability of vacancy structures. Through more than 10<sup>8</sup> Metropolis Monte Carlo samplings of V<sub>O</sub>'s and Ce<sup>3+</sup> ions in the near surface of CeO<sub>2</sub>(111), we explored potential configurations within an 8 × 8 supercell. Our findings revealed that oxygen vacancies tend to aggregate and are abundant in the third oxygen layer with an elevated V<sub>O</sub> concentration primarily due to extensive geometric relaxation, an aspect previously overlooked. This work introduces a novel theoretical framework for unraveling the complex vacancy structures in metal oxides, with potential applications in redox and catalytic chemistry. Cluster expansion ; Decoupling (probability) ; Cluster (spacecraft) ; Vacancy defect ; Oxygen ; Materials science ; Chemical physics ; Statistical physics ; Nanotechnology ; Computer science ; Chemistry ; Crystallography ; Physics ; Thermodynamics ; Engineering ; Quantum mechanics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Ahrum Son | H. S. Kim | Jolene K. Diedrich | Casimir Bamberger | Daniel B. McClatchy | Stuart A. Lipton | John R. Yates
Source: Nature Communications (article, 2024-10-29)
Field: Chemistry — Spectroscopy

Using in vivo intact structure for system-wide quantitative analysis of changes in proteins Mass spectrometry-based methods can provide a global expression profile and structural readout of proteins in complex systems. Preserving the in vivo conformation of proteins in their innate state is challenging during proteomic experiments. Here, we introduce a whole animal in vivo protein footprinting method using perfusion of reagents to add dimethyl labels to exposed lysine residues on intact proteins which provides information about protein conformation. When this approach is used to measure dynamic structural changes during Alzheimer's disease (AD) progression in a mouse model, we detect 433 proteins that undergo structural changes attributed to AD, independent of aging, across 7 tissues. We identify structural changes of co-expressed proteins and link the communities of these proteins to their biological functions. Our findings show that structural alterations of proteins precede changes in expression, thereby demonstrating the value of in vivo protein conformation measurement. Our method represents a strategy for untangling mechanisms of proteostasis dysfunction caused by protein misfolding. In vivo whole-animal footprinting should have broad applicability for discovering conformational changes in systemic diseases and for the design of therapeutic interventions. In vivo ; Computational biology ; Computer science ; Biology ; Genetics Advanced Proteomics Techniques and Applications ; Protein Structure and Dynamics ; Bioinformatics and Genomic Networks
Authors: Lin Luo | Honghua Zhang | Yu Wang | Jianliang Zhang | Rui Zhang | Shan Gao | Yuanyong Dian | Zijin Bai | Chunhui Feng | Ze Zhang
Source: Horticulturae (article, 2024-10-27)
Field: Chemistry — Analytical Chemistry

A Model for Fat Content Detection in Walnuts Based on Near-Infrared Spectroscopy Near-infrared spectroscopy (NIR) is an efficient and accurate method for fat content detection in walnuts. ‘Wen 185’ walnut is grown in large quantities in southern Xinjiang, and its fat content is an important indicator for evaluating the intrinsic quality. The excessive pursuit of yield efficiency, combined with the neglect of quality, agricultural product safety and other factors, has led to the production of poor-quality walnuts. Moreover, research on predicting walnut kernel fat content based on near-infrared spectroscopy technology is rarely reported. Therefore, a technical framework for walnut kernel detection based on near-infrared spectroscopy and the technical standards for ‘Wen 185’ are urgently needed. After first optimizing the initial spectrum data using five preprocessing methods, we established separate prediction models for walnut kernel fat content based on either a back propagation neural network or a support vector regression (SVR) algorithm. The results show that the correction set and validation set coefficients of determination of the walnut kernel fat content prediction model using the back propagation neural network algorithm were 0.86 and 0.89, respectively, with root mean square errors of 1.56 and 1.58, and an RPD value of 2.57; the coefficients of determination for the calibration and validation sets of SVR were 0.90 and 0.83, respectively, with root mean square errors of 1.76 and 1.70, respectively, and an RPD value of 1.70. Thus, near-infrared spectroscopy can be used to establish a foundation for realizing the rapid detection of walnut kernel fat content. Near-infrared spectroscopy ; Kernel (algebra) ; Mathematics ; Mean squared error ; Calibration ; Content (measure theory) ; Backpropagation ; Support vector machine ; Spectroscopy ; Artificial neural network ; Artificial intelligence ; Computer science ; Statistics ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Nuts composition and effects ; Meat and Animal Product Quality
Authors: Hassan Fazayeli | María Luisa Amodio | Danial Fatchurrahman | Francesco Serio | Francesco Fabiano Montesano | I. Burud | Andrea Peruzzi | Giancarlo Colelli
Source: Scientia Horticulturae (article, 2024-01-30)
Field: Chemistry — Analytical Chemistry

Potential application of hyperspectral imaging and FT-NIR spectroscopy for discrimination of soilless tomato according to growing techniques, water use efficiency and fertilizer productivity This study aimed to test the potential of nondestructive optical techniques for classifying sustainable-produced tomatoes according to a) growing practices; b) levels of water use (WUE) and partial factor productivity of nutrients (PFP). Three distinct hydroponic growing techniques for water and fertilizer use were applied over two cultivation cycles for two varieties (cv ‘Carminio’, and cv ‘Mose’): i) free drain open cycle cultivation (OPEN); ii) open cycle cultivation with on-demand sensor-based fertigation (SMART); iii) closed cycle cultivation (CLOSED). Hyperspectral images (HSI) in the Vis-NIR and NIR range, as well as reflectance spectra obtained through Fourier Transform (FT)-NIR spectroscopy, were acquired throughout the harvesting period, with approximately 300 fully ripe tomatoes obtained per variety. For each variety, partial least squares discriminant analysis (PLS-DA) was initially employed to discriminate the three cultivation systems and subsequently distinguish two levels of WUE and PFP, per variety. Finally, the data obtained from both varieties were combined and PLS-DA utilized to categorize three levels of WUE: LOW, MEDIUM, and HIGH. The PLS-DA models applied in external prediction for discriminating tomatoes of each variety according the three cultural practices achieved accuracy higher than 79.55 % for data obtained with FT-NIR and HSI in the Vis-NIR range, and lower for HSI (NIR). The performance increased when considering only two distinct classes (based on WUE and nutrients PFP) with accuracy, specificity, and sensitivity higher than 86 %. General models based on spectra obtained with HSI Vis-NIR, using data over the two varieties to classify tomato according to three levels of WUE, yielded accuracy and specificity of 89.8 % and 91.7 %, respectively, utilizing only 20 variables. Results of this study indicated the effectiveness of FT-NIR and HSI (Vis-NIR) techniques for discrimination of tomato fruits cultivated with varying levels of water and fertilizers. Further validation of these methods is needed before their widespread application to support the adoption of low input growing techniques. Hyperspectral imaging ; Fertilizer ; Productivity ; Environmental science ; Agronomy ; Hydroponics ; Horticulture ; Remote sensing ; Mathematics ; Agricultural engineering ; Biology ; Geography ; Engineering ; Economics Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Water Quality Monitoring and Analysis
Authors: Wenyi Ma | Huolei Feng | Yushan Ni
Source: Journal of Applied Physics (article, 2024-03-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A novel interfacial resistance-free bifunctional camouflage device in thermal–electric fields A novel interfacial resistance-free (IRF) bifunctional camouflage (transparent and invisible) device is proposed in this paper. The thermal and electric conductivities of the shell and background are the same to eliminate the interfacial resistance. The IRF bifunctional camouflage device can operate in thermal–electric fields based on the neutral inclusion method. The distribution of isotherm and equipotential lines are studied quantitatively by the simulations. It is confirmed that the IRF bifunctional camouflage device with arbitrary natural materials can effectively achieve not only the invisible function but also the transparent function in thermal–electric fields. This method provides a window to the realization of bifunctions and the development of multi-physics fields. Electric field ; Bifunctional ; Camouflage ; Materials science ; Thermal ; Electrical resistance and conductance ; Thermal resistance ; Physics ; Computer science ; Chemistry ; Composite material ; Thermodynamics ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Photonic Crystals and Applications
Authors: Zicheng Zhang | Tianshuo Wang | Hanhan Fan
Source: Applied Sciences (article, 2024-03-13)
Field: Chemistry — Analytical Chemistry

Neural Network-Based Analysis and Its Application to Spectroscopy for Mango Sugar derived from crops is a crucial organic energy source studied in the Earth sciences, serving as a renewable and clean energy alternative. Biofuels produced from crop sugars are more environmentally friendly than traditional fossil fuel sources and contribute to solar energy storage and conversion within the Earth’s cycle. Using mangoes as a case study, this research employs near-infrared spectral analysis technology to develop an algorithm for a mango brix detection device. The study investigates the relationship between brix and absorbance, as well as changes in brix levels, and their application for on-site mango brix detection. Near-infrared spectral data in the range of 1300 nm to 2300 nm were collected during the mango ripening season in summer and preprocessed using various techniques. A neural network-based least squares modeling approach was utilized to develop a mango sugar content detection model, resulting in a correlation coefficient of 0.9055 and a root-mean-square error of 0.2192. To enhance model accuracy and avoid local optimization issues, this study incorporated the simulated annealing algorithm for model optimization, leading to a correlation coefficient of 0.9854 and a root-mean-square error of 0.0431. The findings demonstrate that the non-destructive testing model of mangoes based on near-infrared spectroscopy effectively detects brix changes and storage potential post-harvest, offering valuable insights for mango quality assessment, optimal picking and selling times, and market selection. Artificial neural network ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Ziyi Li | Cory A. Weller | Syed Islamuddin Shah | Nicholas L. Johnson | Ying Hao | Paige Jarreau | Jessica P. Roberts | Deyaan Guha | Colleen Bereda | Sydney Klaisner | Pedro Machado | Matteo Zanovello | Mercedes Prudencio | Björn Oskarsson | Nathan P. Staff | Dennis W. Dickson | Pietro Fratta | Leonard Petrucelli | Priyanka Narayan | Mark Cookson | Michael E. Ward | Andrew Singleton | Mike A. Nalls | Yue Qi
Source: Genomics Proteomics & Bioinformatics (article, 2024-11-22)
Field: Chemistry — Spectroscopy

ProtPipe: A Multifunctional Data Analysis Pipeline for Proteomics and Peptidomics Mass spectrometry (MS) is a technique widely employed for the identification and characterization of proteins, with personalized medicine, systems biology, and biomedical applications. The application of MS-based proteomics advances our understanding of protein function, cellular signaling, and complex biological systems. MS data analysis is a critical process that includes identifying and quantifying proteins and peptides and then exploring their biological functions in downstream analyses. To address the complexities associated with MS data analysis, we developed ProtPipe to streamline and automate the processing and analysis of high-throughput proteomics and peptidomics datasets with DIA-NN preinstalled. The pipeline facilitates data quality control, sample filtering, and normalization, ensuring robust and reliable downstream analyses. ProtPipe provides downstream analyses, including protein and peptide differential abundance identification, pathway enrichment analysis, protein-protein interaction analysis, and major histocompatibility complex (MHC)-peptide binding affinity analysis. ProtPipe generates annotated tables and visualizations by performing statistical post-processing and calculating fold changes between predefined pairwise conditions in an experimental design. It is an open-source, well-documented tool available at https://github.com/NIH-CARD/ProtPipe, with a user-friendly web interface. Pipeline (software) ; Proteomics ; Computational biology ; Computer science ; Biology ; Genetics ; Operating system ; Gene Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Ubiquitin and proteasome pathways