jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haochong Zhang | Z. Y. Deng | Yu Liu | Tao Liu | Mohan Chen | Yin Shi | Lixin He
Source: arXiv (Cornell University) (preprint, 2024-09-14)
Field: Chemistry — Inorganic Chemistry

GPU Acceleration of Numerical Atomic Orbitals-Based Density Functional Theory Algorithms within the ABACUS package With the fast developments of high-performance computing, first-principles methods based on quantum mechanics play a significant role in materials research, serving as fundamental tools for predicting and analyzing various properties of materials. However, the inherent complexity and substantial computational demands of first-principles algorithms, such as density functional theory, limit their use in larger systems. The rapid development of heterogeneous computing, particularly General-Purpose Graphics Processing Units (GPGPUs), has heralded new prospects for enhancing the performance and cost-effectiveness of first-principles algorithms. We utilize GPGPUs to accelerate the electronic structure algorithms in Atomic-orbital Based Ab-initio Computation at USTC (ABACUS), a first-principles computational package based on the linear combination of atomic orbitals (LCAO) basis set. We design algorithms on GPGPU to efficiently construct and diagonalize the Hamiltonian of a given system, including the related force and stress calculations. The effectiveness of this computational acceleration has been demonstrated through calculations on twisted bilayer graphene with the system size up to 10,444 atoms. Atomic orbital ; Acceleration ; Abacus (architecture) ; Density functional theory ; Computer science ; Algorithm ; Computational science ; Physics ; Quantum mechanics ; Electron ; Geography Inorganic Fluorides and Related Compounds ; Catalytic Processes in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Yulong Zhou | Zhiyun Xiao | Tengfei Bao | Tao Fang
Source: Preprints.org (preprint, 2024-09-18)
Field: Chemistry — Analytical Chemistry

Estimation Network for Multiple Chemical Parameters of Astragalus Leaves Based on Attention Mechanism and Multivariate Hyperspectral Features In the context of smart agriculture, accurately estimating plant leaf chemical parameters is crucial for optimizing crop management and improving agricultural yield. Hyperspectral imaging, with its ability to capture detailed spectral information across various wavelengths, has emerged as a powerful tool in this regard. However, the complex and high-dimensional nature of hyperspectral data poses significant challenges in extracting meaningful features for precise estimation. To address this challenge, this study proposes an end-to-end estimation network for multiple chemical parameters of Astragalus leaves based on attention mechanism (AM) and multivariate hyperspectral features (AM-MHENet). We leveraging HybridSN and multilayer perception (MLP) to extract prominent features from the hyperspectral data of Astragalus membranaceus var. mongholicus (AMM) leaves and stems, as well as the surface and deep soil surrounding AMM roots. This methodology allows us to capture the most significant characteristics present in these hyperspectral data with high precision. The AM is subsequently used to assign weights and integrate the hyperspectral features extracted from different parts of the AMM. The MLP is then employed to simultaneously estimate the chlorophyll content (CC) and nitrogen content (NC) of AMM leaves. Compared with estimation networks that utilize only hyperspectral data from AMM leaves as input, our proposed end-to-end AM-MHENet demonstrates superior estimation performance. Specifically, AM-MHENet achieves an R2 of 0.983 with an RMSE of 0.73 for the estimation of CC in AMM leaves. For NC estimation, AM-MHENet achieves an R2 value of 0.977 with an RMSE of 0.27. These results underscore AM-MHENet&amp;rsquo;s effectiveness in significantly enhancing the accuracy of both CC and NC estimation in AMM leaves. Moreover, these findings indirectly suggest a strong correlation between the development of AMM leaves and stems, as well as the surface and deep soil surrounding the roots of AMM, and directly highlight the ability of AM to effectively focus on the relevant spectral features within the hyperspectral data. This findings from this study could offer valuable insights into the simultaneous estimation of multiple chemical parameters in plants, thereby making a contribution to the existing body of research in this field. Hyperspectral imaging ; Multivariate statistics ; Mechanism (biology) ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Estimation ; Biological system ; Machine learning ; Biology ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Remote Sensing and Land Use
Authors: Hadda Krarcha | Ferroudj Abdelhak
Source: STUDIES IN ENGINEERING AND EXACT SCIENCES (article, 2024-12-26)
Field: Materials Science — Materials Chemistry

New formula of prediction of lattice constant in cubic perovskites: revised Jiang model’s ABX3Perovskite materials are of great interest in the new technology because of the possibility of modifying their physical properties by numerous ionic substitutions. The mesh parameter prediction model used is useful for providing the predicted structural information for estimating the physical properties of materials for which accurate structural data are not available. It is also useful as a guide for synthetic exploratory efforts as a starting point for structural characterization improvements of new materials. Our reformulation of Jiang's model allowed us to obtain a new equation of parameter prediction of cubic perovskite structures taking into account 158 ​​materials. The results obtained are in good agreement with the experimental with a relative error of the order of 1.54%. This prediction equation allowed us to predict the mail parameter of 20 new perovskite oxides. Lattice constant ; Lattice (music) ; Constant (computer programming) ; Statistical physics ; Condensed matter physics ; Materials science ; Physics ; Computer science ; Quantum mechanics ; Diffraction Thermal Expansion and Ionic Conductivity ; Magnetic and transport properties of perovskites and related materials ; Ferroelectric and Piezoelectric Materials
Authors: S. B. Katti
Source: Chettinad Health City Medical Journal (article, 2024-12-31)
Field: Chemistry — Organic Chemistry

Design, Synthesis and In Silico Evaluation of 1,3,4-Oxadiazole Derivatives for Their Nootropic Activity Introduction: Computational methods have become indispensable in modern medicinal chemistry research, enabling the rapid screening and evaluation of potential drug candidates. This study leverages in silico approaches to investigate the neuroprotective potential of 1,3,4-oxadiazole derivatives. By employing software such as PASS online, SwissADME, ProTox-III, and Autodock Vina, we aimed to predict the biological activity, pharmacological properties, and toxicity profiles of these compounds.Method: We utilised a combination of in silico techniques, including PASS online for predicting biological activity, SwissADME for assessing pharmacokinetic properties, ProTox-III for evaluating toxicity, and Autodock Vina for molecular docking studies. The predicted properties of the 1,3,4-oxadiazole derivatives were then compared with those of donepezil, a well-established neuroprotective drug. Furthermore, compounds exhibiting significant predicted activity were synthesised and subsequently characterised using analytical techniques such as TLC, FTIR, and NMR.Results: The results from the PASS online analysis revealed that compound NR1 exhibited the highest predicted activity score (0.636) compared to donepezil (0.553). The predicted activity order was determined as NR1&gt;NR7&gt;NR2&gt;NR3&gt;NR5&gt;NR6&gt;NR4. Molecular docking studies further supported these findings, indicating that 2,5 diaryl 1,3,4-oxadiazole derivatives with polar group substitutions at specific positions (3, 4, and 5; 3, 5; or 2, 5) displayed favorable docking scores.Conclusion: The in silico analyses conducted in this study suggest that 1,3,4-oxadiazole derivatives possess promising neuroprotective potential, comparable to the standard drug donepezil. These findings provide a valuable foundation for further experimental investigations and optimization of these compounds as potential therapeutic candidates for neurodegenerative disorders. How to cite this article:Katti S A, Revar N, Sonawane S, Pawar S H, Patil R A, Bhosale A P. Design, Synthesis and In Silico Evaluation of 1,3,4-Oxadiazole Derivatives for Their Nootropic Activity. Chettinad Health City Med J. 2024;13(4):43-54. DOI: https://doi.org/10.24321/2278.2044.202458 In silico ; Nootropic ; Oxadiazole ; Chemistry ; Combinatorial chemistry ; Computer science ; Computational biology ; Pharmacology ; Biochemistry ; Biology ; Organic chemistry Synthesis and biological activity
Authors: Usman A. Zahidi | Krystian Łukasik | Grzegorz Cielniak
Source: arXiv (Cornell University) (preprint, 2024-05-16)
Field: Chemistry — Analytical Chemistry

Dual-band feature selection for maturity classification of specialty crops by hyperspectral imaging The maturity classification of specialty crops such as strawberries and tomatoes is an essential agricultural downstream activity for selective harvesting and quality control (QC) at production and packaging sites. Recent advancements in Deep Learning (DL) have produced encouraging results in color images for maturity classification applications. However, hyperspectral imaging (HSI) outperforms methods based on color vision. Multivariate analysis methods and Convolutional Neural Networks (CNN) deliver promising results; however, a large amount of input data and the associated preprocessing requirements cause hindrances in practical application. Conventionally, the reflectance intensity in a given electromagnetic spectrum is employed in estimating fruit maturity. We present a feature extraction method to empirically demonstrate that the peak reflectance in subbands such as 500-670 nm (pigment band) and the wavelength of the peak position, and contrarily, the trough reflectance and its corresponding wavelength within 671-790 nm (chlorophyll band) are convenient to compute yet distinctive features for the maturity classification. The proposed feature selection method is beneficial because preprocessing, such as dimensionality reduction, is avoided before every prediction. The feature set is designed to capture these traits. The best SOTA methods, among 3D-CNN, 1D-CNN, and SVM, achieve at most 90.0 % accuracy for strawberries and 92.0 % for tomatoes on our dataset. Results show that the proposed method outperforms the SOTA as it yields an accuracy above 98.0 % in strawberry and 96.0 % in tomato classification. A comparative analysis of the time efficiency of these methods is also conducted, which shows the proposed method performs prediction at 13 Frames Per Second (FPS) compared to the maximum 1.16 FPS attained by the full-spectrum SVM classifier. Hyperspectral imaging ; Feature selection ; Selection (genetic algorithm) ; Maturity (psychological) ; Feature (linguistics) ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Remote sensing ; Geography ; Political science ; Linguistics Spectroscopy and Chemometric Analyses
Authors: R Kannabiran. | Logeshwaran Vellingiri | Dr.P. Chidambararajan | G Abilash.
Source: International Journal of Pharmaceutical Sciences Review and Research (article, 2024-02-01)
Field: Materials Science — Biomaterials

Isolation and Characterization of Polyethylene-Degrading Microbes from the Digestive System of Perionyx excavates Background: Polyethylene's resistance to degradation and adverse environmental impacts necessitates urgent intervention.This study explores microbial degradation, focusing on earthworms, particularly Perionyx excavatus, as potential agents for efficient plastic degradation.Methodology: 11 bacterial and 5 fungal strains isolated from P. excavatus were characterized for their potential in polyethylene breakdown.Polyethylene underwent pre-treatment with 30% H2O2 under UV light, inducing observable changes.Microbial degradation experiments compared bacterial and fungal rates.Microscopic imaging and FTIR analysis validated structural changes, and weight loss measurements quantified degradation.Results: Fungal strains exhibited higher polyethylene degradation rates than bacteria.Microscopic imaging and FTIR analysis confirmed successful degradation by both.Weight loss measurements quantified substantial compromise in polyethylene tensile strength. Conclusion:Microbial-mediated degradation, exemplified by bacterial isolate 6 (BAC 6), offers a sustainable approach to polyethylene waste management.BAC 6, a novel species related to Bacillus cereus strain JCM 2152, This research highlights the efficacy of earthworm-associated microbes in polyethylene degradation, offering a sustainable waste management solution with global implications for addressing plastic pollution.The findings contribute to interdisciplinary approaches for a sustainable future. Isolation (microbiology) ; Microbiology ; Biology biodegradable polymer synthesis and properties
Authors: Peijie Wang | Chuanchuan Ma | Chun Xue | Zhibing Chu
Source: Materials (article, 2024-12-29)
Field: Materials Science — Biomaterials

Study on Influence of Mechanical Behavior of AZ31 Magnesium Alloy Based on GTN Damage Modeling Damage mechanisms are a key factor in materials science and are essential for understanding and predicting the behavior of materials under complex loading conditions. In this paper, the influence of different directions, different rates and different model parameters on the mechanical behavior of AZ31 magnesium alloy during the tensile process is investigated based on the secondary development of the VUMAT user subroutine based on the GTN damage model and verified by the tensile experiments at different loading rates and in different directions. The results show that AZ31 magnesium alloy exhibits significant differences in mechanical properties in radial and axial stretching, where the yield strength is lower in the radial direction than in the axial direction, and the elongation is the opposite. Moreover, the maximum stress and elongation of the material decreased with the increasing tensile rate, revealing the importance of the loading rate on the material properties. Compared with the existing studies, this paper determines the GTN model parameters of the AZ31 magnesium alloy extruded state bar by theresponse surface method combined with the optimization algorithm and obtains the parameter set that can accurately describe the damage behavior of this material. The study also found that the nucleation-averaged plastic strain (εN) has the most significant effect on the maximum stress and fracture point of the stress-strain curve by the sensitivity analysis of six key parameters of the GTN model, while the other parameters change the shape of the curve and the local features to different degrees. Further analysis shows that the differences in yield strength and elongation can be attributed to the differences in basal slip, twinning behavior and dynamic recrystallization in the microstructure, which provides an important guidance for the optimization of the microstructure of AZ31 magnesium alloy. This study not only reveals the influence law of loading conditions on the mechanical properties of AZ31 magnesium alloy but also provides a theoretical basis and reference for understanding the damage mechanism of magnesium alloy and optimizing its mechanical properties. Materials science ; Magnesium alloy ; Ultimate tensile strength ; Elongation ; Nucleation ; Magnesium ; Yield (engineering) ; Composite material ; Fracture (geology) ; Stress (linguistics) ; Strain rate ; Alloy ; Bar (unit) ; Metallurgy ; Thermodynamics Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Metal Forming Simulation Techniques
Authors: J. Alarcon | Mateus I. O. Souza | Vinícius M. Pepino | Ben‐Hur V. Borges
Source:  (preprint, 2024-03-29)
Field: Chemistry — Analytical Chemistry

Identification and Quantification of Common Adulterants in Extra Virgin Olive Oil Using Microwave Dielectric Spectroscopy Aided by Artificial Neural Network Classifiers (This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.) In this work, we combine microwave dielectric spectroscopy and machine learning techniques to assess the authenticity of extra virgin olive oil (EVOO). We investigate four common adulterants of EVOO, namely soybean oil, corn oil, sunflower oil, and canola oil. We use a low-cost spiral shaped microwave resonator-based sensor operating at 546.8 MHz to detect changes in the complex permittivity of oil samples. A vector network analyzer (VNA) is used to extract complex scattering parameters S11 and S21 that serves as inputs for two artificial neural network models. The first model, using only the real and imaginary parts of S21, achieves an overall accuracy of 95.8% in predicting the applied adulterant in test samples. In contrast, the second model, incorporating the real and imaginary parts of both S11 and S21, attains a 100% accuracy for test samples. Additionally, we investigate the relationship between the measured |S21| (in dB) and the adulteration level, expressed as the percentile value of the volume of adulterant per volume of the sample (mL/mL). For each adulterant, a calibration equation is developed using partial least squares regression (PLSR) to predict adulteration levels up to 50%. The maximum root mean square error (RMSE) is 2.1% for canola oil adulteration prediction. PLSR yields RMSE values of 0.9% for soybean oil, 1.1% for corn oil, and 1.0% for sunflower oil adulteration. This methodology offers both qualitative and quantitative analyses of EVOO, capable of identifying adulterations as low as 5% with a simple, portable, and practical system. Artificial neural network ; Olive oil ; Identification (biology) ; Microwave ; Artificial intelligence ; Dielectric ; Pattern recognition (psychology) ; Biological system ; Materials science ; Computer science ; Chemistry ; Biology ; Food science ; Botany ; Optoelectronics ; Telecommunications Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Dominic N. McBrayer | Christina Signoretti | M. Pesce | Brianna M. Flood | Sneha Varghese | Fares Sirdah | Elena Toscano | I. A. Bhatti | Md. Shahadat Hossain
Source: PLoS ONE (article, 2024-02-16)
Field: Chemistry — Spectroscopy

Development of a Python-based electron ionization mass spectrometry amino acid and peptide fragment prediction model The increased fragmentation caused by harsher ionization methods used during mass spectrometry such as electron ionization can make interpreting the mass spectra of peptides difficult. Therefore, the development of tools to aid in this spectral analysis is important in utilizing these harsher ionization methods to study peptides, as these tools may be more accessible to some researchers. We have compiled fragmentation mechanisms described in the literature, confirmed them experimentally, and used them to create a Python-based fragment prediction model for peptides analyzed under direct exposure probe electron ionization mass spectrometry. This initial model has been tested using single amino acids as well as targeted libraries of short peptides. It was found that the model does well in predicting fragments of peptides composed of amino acids for which the model is well-defined, but several cases where additional mechanistic information needs to be incorporated have been identified. Mass spectrometry ; Fragmentation (computing) ; Python (programming language) ; Electron ionization ; Ionization ; Chemistry ; Peptide ; Amino acid ; Mass spectrum ; Protein mass spectrometry ; Electrospray ionization ; Chromatography ; Computer science ; Biochemistry ; Ion ; Organic chemistry ; Programming language Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Abdul Rahim | Agus Mujianto | Richie Feriyanto | Hery Tri Waloyo
Source: Jurnal Rekayasa Mesin (article, 2024-12-15)
Field: Materials Science — Polymers and Plastics

OPTIMIZATION OF TENSILE STRENGTH OF EMPTY OIL PALM FRUIT BUNCH FIBER REINFORCED COMPOSITES USING GENETIC ALGORITHMS The use of natural materials such as oil palm empty fruit bunch fibers can provide a solution to increase value-added and manage plantation waste. Fibers are combined with a matrix to create composite materials. Instead of glass fibers, environmentally friendly natural fibers serve as the reinforcement in the composite material. Implementing natural fiber composites must consider the primary construction requirement, which is tensile strength. Artificial intelligence like genetic algorithms (GA) can simplify and reduce costs in the search for optimal values in composite material engineering. Data is obtained through experimental testing prepared samples and subsequently used as input for GA. The input parameters consist of three variables such as soaking time, volume fraction, and fiber length. The output of the optimization process is tensile strength. The maximum tensile strength has already been achieved with genetic crossover by the 125th generation. Based on GA calculations, the optimal parameters obtained are soaking time of 6.2 hours, volume fraction of 29.6%, and fiber length of 6.9 cm. The predicted optimal tensile strength value is 4.78 MPa. Ultimate tensile strength ; Composite material ; Materials science ; Palm oil ; Fiber ; Genetic algorithm ; Algorithm ; Structural engineering ; Computer science ; Mathematics ; Mathematical optimization ; Engineering ; Environmental science Natural Fiber Reinforced Composites
Authors: Gaurav Shrimali | Gangawane Ajit | Esha Rami | Shah Hardik | Thumar Kashyap | Deepak Sahoo | Ashish Patel | Jens Ejbye Schmidt
Source: Preprints.org (preprint, 2024-09-14)
Field: Materials Science — Biomaterials

Optimized Polyhydroxybutyrate Production by &lt;em&gt;Neobacillus Niacinii&lt;/em&gt; GS1 Utilizing Corn Flour, Wheat Bran, and Peptone: A Sustainable Approach Plastic pollution is a pressing environmental challenge, necessitating the development of biodegradable alternatives like polyhydroxybutyrate (PHB). This study focuses on optimizing PHB production by Neobacillus niacinii GS1, a bacterium isolated from a municipal dumping site. By utilizing agricultural residues such as corn flour, wheat bran, and peptone as substrates, we aimed to establish an eco-friendly method for biopolymer production, contributing to sustainable waste management and bioplastic innovation. The bacterium was identified using morphological, biochemical, and molecular techniques. The optimization process involved adjusting variables such as inoculum age, inoculum size, incubation time, agitation rate, incubation temperature, pH of the medium, carbon sources and nitrogen sources. Response surface methodology (RSM) was employed to identify optimal conditions, with the highest PHB yield of 61.1% achieved under specific conditions: 37&amp;deg;C, pH 7, and an agitation rate of 150 rpm. These findings underscore the potential of Neobacillus niacinii GS1 in converting agro-industrial residues into valuable biopolymers, promoting sustainable bioplastic production, and advancing waste valorization efforts through the use of eco-friendly materials. Polyhydroxybutyrate ; Bioplastic ; Bran ; Biopolymer ; Lees ; Yield (engineering) ; Biodegradable plastic ; Food science ; Polyhydroxyalkanoates ; Pulp and paper industry ; Biotechnology ; Environmentally friendly ; Waste management ; Chemistry ; Environmental science ; Raw material ; Bacteria ; Biology ; Materials science ; Engineering ; Wine biodegradable polymer synthesis and properties