jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hermann Muhammad | Mohamed Mézouar | Gastón Garbarino | Laura Henry | Tomasz Porȩba | Max Gerin | Matteo Ceppatelli | Manuel Serrano‐Ruiz | Maurizio Peruzzini | F. Datchi
Source: The Journal of Physical Chemistry Letters (article, 2024-08-08)
Field: Materials Science — Materials Chemistry

Melting Curve of Black Phosphorus: Evidence for a Solid–Liquid–Liquid Triple Point Black phosphorus (bP) is a crystalline material that can be seen as an ordered stacking of two-dimensional layers, which results in outstanding anisotropic physical properties. The knowledge of its pressure (<i>P</i>)-temperature (<i>T</i>) phase diagram, and in particular, of its melting curve is fundamental for a better understanding of the synthesis and stability conditions of this element. Despite the numerous studies devoted to this subject, significant uncertainties remain regarding the determination of the position and slope of its melting curve. Here we measured the melting curve of bP in an extended <i>P</i>, <i>T</i> region from 0.10(3) to 5.05(40) GPa and from 914(25) to 1788(70) K, using in situ high-pressure and high-temperature synchrotron X-ray diffraction. We employed an original metrology based on the anisotropic thermoelastic properties of bP to accurately determine <i>P</i> and <i>T</i>. We observed a monotonic increase of the melting temperature with pressure and the existence of two distinct linear regimes below and above 1.35(15) GPa, with respective slopes of 348 ± 21 and of 105 ± 12 K·GPa<sup>-1</sup>. These correspond to the melting of bP toward the low-density liquid and the high-density liquid, respectively. The triple point at which solid bP and the two liquids meet is located at 1.35(15) GPa and 1350(25) K. In addition, we have characterized the solid phases after crystallization of the two liquids and found that, while the high-density liquid transforms back to solid bP, the low-density liquid crystallizes into a more complex, partly crystalline and partly amorphous solid. The X-ray diffraction pattern of the crystalline component could be indexed as a mixture of red and violet P. Melting curve analysis ; Triple point ; Melting point ; Materials science ; Phase diagram ; Thermodynamics ; Diffraction ; Anisotropy ; Crystallography ; Analytical Chemistry (journal) ; Phase (matter) ; Chemistry ; Physics ; Composite material ; Optics ; Chromatography ; Organic chemistry 2D Materials and Applications ; Inorganic Chemistry and Materials ; MXene and MAX Phase Materials
Authors: Juan Liao | Tao Wang | Yexiong Liang | Xinying He | Hui Wang | H. Zeng | Zinan Wang | Xiwen Luo | Jun Sun | Pei Wang | Ying Zang
Source: International journal of agricultural and biological engineering (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Multi-scale monitoring for hazard level classification of Brown Planthopper damage in rice using hyperspectral technique The primary aim of this study was to classify the hazard level of brown Planthopper (BPH) damage in rice. Three datasets, including spectral reflectance corresponding to the sensitive wavelengths from rice canopy spectral wavelengths, rice stem spectral wavelengths, and fusion information of rice canopy and stem spectral wavelengths were used for BPH hazard level classification by using different algorithms. Datasets and algorithms were optimized by the BPH hazard level classification effects (which was evaluated by indices of accuracy, precision, recall, F1, k-value, etc.). The optimized algorithms combination was used to build hazard level classification model for spectral reflectance corresponding to the sensitive wavelength from the rice canopy spectral images. Results showed that: (1) The spectral reflectance corresponding to the sensitive wavelengths of fusion information dataset performed best in BPH hazard level classification, with the highest accuracy (99.08%), precision (99.31%), recall (98.83%), F1 (0.99), and k-value (0.99). (2) The optimum algorithms combination was Savitzky-Golay (S-G) smoothing, principal component analysis (PCA) for sensitive wavelength selection, and broad-learning system (BLS) for modeling. (3) The spectral reflectance corresponding to the sensitive wavelengths dataset of rice canopy spectral images achieved the accuracy (80.63%), precision (80.28%), recall (77.03%), F1 (0.79), and k-value (0.74) in classifying BPH hazard level by using the optimum algorithms combination. Keywords: brown planthopper (BPH), hazard level classification, hyperspectral technique, rice canopy, rice stem, fusion information DOI: 10.25165/j.ijabe.20241706.9199 Citation: Liao J, Tao W Y, Liang1 Y X, He X Y, Wang H, Zeng H Q, et al. Multi-scale monitoring for hazard level classification of brown planthopper damage in rice using hyperspectral technique. Int J Agric & Biol Eng, 2024; 17(6): 202-211 . Brown planthopper ; Hyperspectral imaging ; Scale (ratio) ; Hazard ; Environmental science ; Remote sensing ; Computer science ; Biology ; Artificial intelligence ; Geography ; Cartography ; Ecology Spectroscopy and Chemometric Analyses
Authors: Niranjan Shadaksharappa Jayanna | Raviprakash Madenur Lingaraju
Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (article, 2024-11-18)
Field: Chemistry — Inorganic Chemistry

Seasonal auto-regressive integrated moving average with bidirectional long short-term memory for coconut yield prediction Crop yield prediction helps farmers make informed decisions regarding the optimal timing for crop cultivation, taking into account environmental factors to enhance predictive accuracy and maximize yields. The existing methods require a massive amount of data, which is complex to acquire. To overcome this issue, this paper proposed a seasonal auto-regressive integrated moving average-bidirectional long short-term memory (SARIMA-BiLSTM) for coconut yield prediction. The collected dataset is preprocessed through a label encoder and min-max normalization is employed to change non-numeric features into numerical features and enhance model performance. The preprocessed features are selected through an adaptive strategy-based whale optimization algorithm (AS-WOA) to avoid local optima issues. Then, the selected features are given to the SARIMA-BiLSTM to predict the coconut yields. The proposed SARIMA-BiLSTM is adaptable to handling a widespread of various seasonal patterns and captures spatial features. The SARIMA-BiLSTM performance is estimated through the coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE). SARIMA-BiLSTM attains 0.84 of R2, 0.056 of MAE, 0.081 of MSE, and 0.907 of RMSE which is better when compared to existing techniques like multilayer stacked ensemble, convolutional neural network and deep neural network (CNN-DNN) and autoregressive moving average (ARIMA). Mean squared error ; Autoregressive integrated moving average ; Artificial neural network ; Term (time) ; Normalization (sociology) ; Moving average ; Computer science ; Mean absolute error ; Autoregressive model ; Statistics ; Pattern recognition (psychology) ; Artificial intelligence ; Mathematics ; Time series Coconut Research and Applications
Authors: Yuqing Meng | Junzhe Zhang | Yanqing Liu | Yongping Zhu | Hai‐Ning Lv | Fei Xia | Qiuyan Guo | Qianli Shi | Chong Qiu | Jigang Wang
Source: Journal of Advanced Research (review, 2024-05-29)
Field: Materials Science — Materials Chemistry

The biomedical application of inorganic metal nanoparticles in aging and aging-associated diseases Aging and aging-associated diseases (AAD), including neurodegenerative disease, cancer, cardiovascular diseases, and diabetes, are inevitable process. With the gradual improvement of life style, life expectancy is gradually extended. However, the extended lifespan has not reduced the incidence of disease, and most elderly people are in ill-health state in their later years. Hence, understanding aging and AAD are significant for reducing the burden of the elderly. Inorganic metal nanoparticles (IMNPs) predominantly include gold, silver, iron, zinc, titanium, thallium, platinum, cerium, copper NPs, which has been widely used to prevent and treat aging and AAD due to their superior properties (essential metal ions for human body, easily synthesis and modification, magnetism). Therefore, a systematic review of common morphological alternations of senescent cells, altered genes and signal pathways in aging and AAD, and biomedical applications of IMNPs in aging and AAD is crucial for the further research and development of IMNPs in aging and AAD. This review focus on the existing research on cellular senescence, aging and AAD, as well as the applications of IMNPs in aging and AAD in the past decade. This review aims to provide cutting-edge knowledge involved with aging and AAD, the application of IMNPs in aging and AAD to promote the biomedical application of IMNPs in aging and AAD. Successful aging ; Life expectancy ; Disease ; Medicine ; Materials science ; Gerontology ; Internal medicine ; Population Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Telomeres, Telomerase, and Senescence
Authors: Qinglong Meng | Shunan Feng | Tao Tan | Qingchun Wen | Jing Shang
Source: Journal of Food Safety (article, 2024-08-01)
Field: Chemistry — Analytical Chemistry

Application of hyperspectral imaging and chemometrics for determining quality and maturity of loquats Abstract Color, firmness, soluble solid content, and pH are important indices for assessing the quality and maturity of loquats. To explore the feasibility of rapid and non‐destructive determination of loquat quality and maturity, this study utilized hyperspectral imaging combined with chemometrics to predict four quality indices of loquats and discriminate their maturity. Partial least squares regression models were developed using both raw and pre‐processed spectral data to determine the optimal pre‐processing method of multiple scattering correction and standard normal variate (SNV). The competitive adaptive reweighted sampling (CARS) and successive projection algorithms were used to extract spectral features. Feature wavelength models were subsequently developed using multiple linear regression (MLR) and error back propagation neural network. Finally, maturity determination models for loquats were developed by partial least squares discrimination analysis (PLS‐DA), support vector machine, and random forest. The SNV‐CARS‐MLR model performed relatively better than the other models for predicting four quality indices. The PLS‐DA model exhibited superior performance, with discrimination accuracies of 99.19% and 96.67% for the calibration and prediction sets. This study demonstrates that integrating hyperspectral imaging and chemometrics enables rapid and non‐destructive determination of loquat quality and maturity. Hyperspectral imaging ; Chemometrics ; Maturity (psychological) ; Quality (philosophy) ; Environmental science ; Biochemical engineering ; Chemistry ; Computer science ; Artificial intelligence ; Chromatography ; Engineering ; Physics ; Psychology Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Botanical Research and Applications
Authors: Md Foyzur Rahman | Ariful Islam | Md. Monirul Islam | Md. Al Mamun | Lili Xu | Takumi Sakamoto | Tomohito Sato | Yutaka Takahashi | Tomoaki Kahyo | Satoka Aoyagi | Kozo Kaibuchi | Mitsutoshi Setou
Source: International Journal of Molecular Sciences (article, 2024-07-21)
Field: Chemistry — Spectroscopy

Mass Spectrometry Imaging Combined with Sparse Autoencoder Method Reveals Altered Phosphorylcholine Distribution in Imipramine Treated Wild-Type Mice Brains Mass spectrometry imaging (MSI) is essential for visualizing drug distribution, metabolites, and significant biomolecules in pharmacokinetic studies. This study mainly focuses on imipramine, a tricyclic antidepressant that affects endogenous metabolite concentrations. The aim was to use atmospheric pressure matrix-assisted laser desorption/ionization (AP-MALDI)-MSI combined with different dimensionality reduction methods to examine the distribution and impact of imipramine on endogenous metabolites in the brains of treated wild-type mice. Brain sections from both control and imipramine-treated mice underwent AP-MALDI-MSI. Dimensionality reduction methods, including principal component analysis, multivariate curve resolution, and sparse autoencoder (SAE), were employed to extract valuable information from the MSI data. Only the SAE method identified phosphorylcholine (ChoP) as a potential marker distinguishing between the control and treated mice brains. Additionally, a significant decrease in ChoP accumulation was observed in the cerebellum, hypothalamus, thalamus, midbrain, caudate putamen, and striatum ventral regions of the treated mice brains. The application of dimensionality reduction methods, particularly the SAE method, to the AP-MALDI-MSI data is a novel approach for peak selection in AP-MALDI-MSI data analysis. This study revealed a significant decrease in ChoP in imipramine-treated mice brains. Imipramine ; Mass spectrometry imaging ; Chemistry ; Autoencoder ; Pharmacology ; Internal medicine ; Endocrinology ; Mass spectrometry ; Biology ; Chromatography ; Medicine ; Pathology ; Artificial intelligence Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: G. Velmurugan | Jasgurpreet Singh Chohan | Ganga Shree Subburaj | Hariharan Panneerselvam | Kudimi Yaswanth Nagabhushanam | M. Venkatesan | Deepthi Jebasingh
Source:  (article, 2024-01-31)
Field: Materials Science — Polymers and Plastics

Experimental Analysis of Mechanical Properties of Banana Fibre/Eggshell Powder-Reinforced Hybrid Epoxy Composite Natural fibre–polymer composites are widely used because they are economical and ecologically beneficial in a variety of applications. In order to improve its performance, this study focuses on examining the mechanical characteristics of an epoxy composite material that has been reinforced with banana fibre mats that have undergone NaOH treatment. Additionally, using various configurations both with and without eggshell powder (ESP), the compression moulding method was used to fabricate and investigate the impact of ESP on these mechanical qualities. The results showed that the composite with 25 weight percent banana fibre and 2.5 weight percent ESP had the maximum tensile strength (31.21 MPa), bending strength (33.69 MPa), and impact strength (2.84 kJ/m2). Strong interfacial adhesion between the banana and eggshell components was discovered via the microscopic examination of shattered surfaces. Notably, compared to untreated banana composites, the alkaline-treated banana materials showed fewer occurrences of pull-outs and fractures, leading to noticeably better mechanical performance. Composite material ; Materials science ; Epoxy ; Ultimate tensile strength ; Composite number ; Eggshell ; Flexural strength ; Polymer ; Bending Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Additive Manufacturing and 3D Printing Technologies
Authors: Ravi Raj Purohit Purushottam Raj Purohit | Daniel Fowan | Stéphan Arnaud | Nils Blanc | Jean‐Sébastien Micha | René Guinebretière | O. Castelnau
Source: Journal of Applied Crystallography (article, 2024-03-31)
Field: Materials Science — Ceramics and Composites

Laue microdiffraction on polycrystalline samples above 1500 K achieved with the QMAX-µLaue furnace X-ray Laue microdiffraction aims to characterize microstructural and mechanical fields in polycrystalline specimens at the sub-micrometre scale with a strain resolution of ∼10<sup>-4</sup>. Here, a new and unique Laue microdiffraction setup and alignment procedure is presented, allowing measurements at temperatures as high as 1500 K, with the objective to extend the technique for the study of crystalline phase transitions and associated strain-field evolution that occur at high temperatures. A method is provided to measure the real temperature encountered by the specimen, which can be critical for precise phase-transition studies, as well as a strategy to calibrate the setup geometry to account for the sample and furnace dilation using a standard α-alumina single crystal. A first application to phase transitions in a polycrystalline specimen of pure zirconia is provided as an illustrative example. Crystallite ; Materials science ; Phase (matter) ; Phase transition ; Crystallography ; Crystal (programming language) ; X-ray crystallography ; Optics ; Mineralogy ; Diffraction ; Condensed matter physics ; Metallurgy ; Chemistry ; Physics ; Computer science Advanced ceramic materials synthesis ; X-ray Diffraction in Crystallography ; High-pressure geophysics and materials
Authors: Boon Chen Teo | Huong Yong Ting | Abdulwahab Funsho Atanda
Source: Green Intelligent Systems and Applications (article, 2024-01-18)
Field: Chemistry — Analytical Chemistry

Durian Species Classification Using Deep Learning Method Durian is a popular fruit in Southeast Asia, and the market offers various species of durians. Accurate species classification is crucial for quality control, grading, and marketing. However, the complexity of this task has led to the utilization of machine learning and deep learning methods. Traditional machine learning algorithms, such as K-Nearest Neighbors, Linear Discriminant Analysis, Support Vector Machines, and Random Forests, have demonstrated good accuracy, but they require extensive feature engineering. Deep learning algorithms, particularly Convolutional Neural Networks, can automatically extract features, making them less dependent on manual feature selection. This research aims to review deep learning classification algorithms, including Convolutional Neural Networks and Recurrent Neural Networks, to determine the most suitable algorithm for an efficient and accurate durian classification system. The objective is to enhance the precision and speed of durian species classification, presenting potential advantages for both durian producers and consumers. The literature review revealed that Convolutional Neural Networks outperformed other deep learning and traditional machine learning algorithms on datasets of varying sizes, achieving the highest accuracy of 98.96% through techniques like image resizing, color conversion, and additional parameters such as days harvested and dry weight. Deep learning emerges as a promising approach for robust and accurate durian species recognition, with future directions including developing models to classify durian species from different plant parts and even real-time video analysis. However, while Convolutional Neural Networks lead the way, a critical research gap exists in identifying optimal features, necessitating further investigation to refine durian species recognition accuracy. Artificial intelligence ; Convolutional neural network ; Deep learning ; Machine learning ; Computer science ; Support vector machine ; Feature selection ; Random forest ; Artificial neural network ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Identification and Quantification in Food
Authors: Rémi Lazzari
Source: Journal of Electron Spectroscopy and Related Phenomena (article, 2024-07-25)
Field: Materials Science — Surfaces, Coatings and Films

I4P: An Igor Pro suite for photoemission analysis Suite ; Angle-resolved photoemission spectroscopy ; Physics ; Geography ; Electronic structure ; Condensed matter physics ; Archaeology Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Ion-surface interactions and analysis
Authors: Mina Sobhaninia | Ahmad Mani‐Varnosfaderani | Mohsen Barzegar | Mohammad Ali Sahari
Source: Food Chemistry X (article, 2024-02-09)
Field: Chemistry — Analytical Chemistry

Combining ion mobility spectrometry and chemometrics for detecting synthetic colorants in black tea: A reliable and fast method Black tea (<i>Camellia sinensis</i>) is a widely consumed beverage and is subjected to adulteration. In this study, the combination of ion mobility spectrometry and machine learning techniques was employed to detect synthetic colorants in black tea. To accomplish our objective, six synthetic colorants (carmine, carmoisine, indigo carmine, brilliant blue, sunset yellow, and tartrazine) were added to pure tea at different concentrations. A qualitative model was built using partial least squares discriminant analysis (PLS-DA) for the collected data and exhibited 100% accuracy in identifying synthetic colorants in black tea. For quantitative analysis, a PLS regression model was employed. The R<sup>2</sup> values obtained for the test set ranged from 0.986 to 0.997. The method developed in this study has proven to be reliable and effective in detecting synthetic colorants in black tea. Also, this method is a simple, rapid, and trustworthy tool for identifying adulteration in black tea. Black tea ; Indigo carmine ; Tartrazine ; Chemometrics ; Ion-mobility spectrometry ; Camellia sinensis ; Partial least squares regression ; Chromatography ; Theaflavin ; Chemistry ; Mass spectrometry ; Mathematics ; Food science ; Statistics ; Botany ; Polyphenol Dye analysis and toxicity ; Tea Polyphenols and Effects ; Identification and Quantification in Food
Authors: Allison A. Kim | Milan Babu Poudel
Source: Journal of Composites Science (article, 2024-03-29)
Field: Materials Science — Biomaterials

Spiral Structured Cellulose Acetate Membrane Fabricated by One-Step Electrospinning Technique with High Water Permeation Flux A functionally graded membrane (FGM) with a special spiral-structured cellulose acetate (CA) membrane was prepared by electrospinning under different collection distances. The membrane morphology was analyzed by scanning electron microscopy (SEM). FESEM images revealed that the high concentration shows the formation of fibers with an irregular diameter, with a large diameter distribution range. The fiber collected at a short distance of 10 cm experiences the strong electrostatic force, resulting in the short flight time for the polymer jet. This causes the bending instability of the polymer jet forming the comparatively thick fiber diameters, whereas the fiber collected at 15 cm shows the presence of a smooth, homogeneous diameter. Furthermore, the water flux of the membrane was determined using 50 mL of Amicon stirred cells. The fiber collected at different distances showed diameter variation, which is used to design a special spiral structure on the membrane by auto-moving the collector between the fixed distances of 10–20 cm. This technique will reveal a new approach for the fabrication of a special spiral structure on the nanofibrous membrane for different biomedical applications from different polymers. Meanwhile, the fabricated FGM with a special spiral-structure CA membrane demonstrates high water permeation flux. Cellulose acetate ; Permeation ; Electrospinning ; Membrane ; Flux (metallurgy) ; Spiral (railway) ; Chemical engineering ; Materials science ; Cellulose ; Chemistry ; Composite material ; Polymer ; Mechanical engineering ; Engineering ; Metallurgy Electrospun Nanofibers in Biomedical Applications ; Surface Modification and Superhydrophobicity ; Electrohydrodynamics and Fluid Dynamics