jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wanja Timm Schulze | Sebastian Schwalbe | Kai Trepte | Stefanie Gräfe
Source: arXiv (Cornell University) (preprint, 2024-10-25)
Field: Chemistry — Organic Chemistry

eminus -- Pythonic electronic structure theory In current electronic structure research endeavors such as warm dense matter or machine learning applications, efficient development necessitates non-monolithic software, providing an extendable and flexible interface. The open-source idea offers the advantage of having a source code base that can be reviewed and modified by the community. However, practical implementations can often diverge significantly from their theoretical counterpart. Leveraging the efforts of recent theoretical formulations and the features of Python, we try to mitigate these problems. We present eminus, an education- and development-friendly electronic structure package designed for convenient and customizable workflows, yet built with intelligible and modular implementations. Computer science Chemistry and Stereochemistry Studies ; Chemical Thermodynamics and Molecular Structure ; History and advancements in chemistry
Authors: Samir F. Matar | Vladimir L. Solozhenko
Source: Crystals (article, 2024-04-11)
Field: Materials Science — Materials Chemistry

Novel Tetragonal Boron Pnictides BX (X = N, P, As, Sb, Bi) with Square B2X2 Motifs from Crystal Chemistry and First Principles Novel tetragonal (P42/mnm) boron pnictides BX (X = N, P, As, Sb, Bi) with chromium boride (crb) topology exhibiting a square B2X2 motif with resulting edge- and corner-sharing tetrahedra were predicted from crystal chemistry and extensively characterized by density functional theory (DFT) calculations. All new BX phases were found to be cohesive with decreasing cohesive energy along the series. Mechanically stable with positive sets of elastic constants, all crb phases exhibit slightly lower hardness than other BX polymorphs due to increased openness of the crystal structures. All-positive phonon frequencies characterize the crb BX family except for X = Bi, which shows a slight acoustic instability; also, the shape of the phonon spectra changes from band-like for X = N, P, As to flat bands for the heavier elements. The electronic band structures reveal insulating to semiconducting properties for crb BX, depending on the pnictogen nature along the series. Tetragonal crystal system ; Pnictogen ; Boron ; Crystallography ; Boride ; Crystal chemistry ; Density functional theory ; Phonon ; Chemistry ; Crystal structure ; Condensed matter physics ; Materials science ; Computational chemistry ; Physics ; Superconductivity ; Inorganic chemistry Boron and Carbon Nanomaterials Research ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: A. Mayer | Olivier Deparis | Michaël Lobet
Source:  (article, 2024-06-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimization by a genetic algorithm of nanopyramidal broadband quasi-perfect absorbers with deeper insight into the stability of optimal solutions We use a genetic algorithm to optimize periodic arrays of truncated square-based pyramids made of alternating stacks of metal/dielectric layers. The objective is to achieve broadband quasi-perfect absorption of normally incident radiations in the visible and near-infrared ranges (wavelengths comprised between 420 and 1600nm). We compare the results one can obtain by considering one, two or three stacks of (i) Ni, Ti, Al or Cu for the metal, and (ii) poly(methyl methacrylate) (PMMA) for the dielectric. The parameters to determine for each metal/dielectric combination are (i) the period of the system, (ii) the lateral dimensions of each stack of metal/dielectric layers and (iii) the width of each dielectric layer. The Rigorous CoupledWaves Analysis (RCWA) is used to compute the absorptance spectrum of these different structures. The Genetic Algorithm is used to find the geometrical parameters that maximize the integrated absorptance. This approach provides stability maps with respect to the geometrical parameters, which leads to additional physical insight regarding practical implementation. The study shows that Ni/PMMA and Ti/PMMA provide high-quality solutions associated with broad optima (stability with respect to variations of the geometrical parameters). On the contrary, Al/PMMA and Cu/PMMA provide poor-quality solutions associated with sharp optima. We find an interesting correlation between the robustness of the solutions found by the genetic algorithm (stability with respect to variations of the geometrical parameters) and the stability of these solutions with respect to the number of plane waves used in the RCWA calculations. Dielectric ; Materials science ; Stack (abstract data type) ; Robustness (evolution) ; Stability (learning theory) ; Genetic algorithm ; Absorptance ; Broadband ; Wavelength ; Optoelectronics ; Algorithm ; Optics ; Computer science ; Physics ; Mathematics ; Mathematical optimization ; Chemistry ; Reflectivity Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Advanced Antenna and Metasurface Technologies
Authors: Ahmed Abdulrazzaq Hadi AlGburi | Vikram Rama Uttam Pandit | Ali Khalil kadom | Talib M. Albayati
Source: University of Thi-Qar Journal of Science (article, 2024-12-31)
Field: Chemistry — Analytical Chemistry

Comparative Study of Physicochemical Properties and Metal Contents in Crude Oil from Different Fields in Iraq This research evaluates the physical and chemical properties and concentration of trace elements and heavy metals in Iraqi crude oil. The way in which geographical locations affect oil quality is not widely known, especially in the Middle East. Therefore, crude oils from three locations (East Baghdad, Khurmala, and Nahr Omar) were selected. The physical and chemical data showed apparent variations in the properties of crude oils from different fields. Nahr Umr oil was the lightest (D = 0.8199 g/cm3, API = 40.57°) and the lowest in sulfur content (1.327 wt%), East Baghdad oil was the heaviest (D = 0.9067 g/cm3, API = 24.39°) and the highest in sulfur content (3.842 wt%), while Khurmala was (D = 0.8518 g/cm3, API = 34.12°) and (2.44 wt%) of sulfur content, reflecting the differences in the quality of crude oils. Advanced techniques such as the rotating disc electrode optical emission spectrometer (RDE-OES) and inductively coupled plasma (ICP-OES/MS) were used to measure the element content after sample preparation using microwave acid digestion. The content of metals in crude oil indicates that East Baghdad oil contains the highest rate of V, Al, Ag, and Hg. At the same time, Ni, Fe, Na, Ca, Pb, Se, and Li were higher in Khurmala oil, while Mo, Ti, Sr, Bi, Ba, and Hg were higher in Nahr Umr. The statistical analysis of the least significant difference (LSD) test was used to show the significant differences between the measured element concentrations at the 0.05 level Crude oil ; Chemistry ; Petroleum engineering ; Environmental science ; Engineering Petroleum Processing and Analysis
Authors: Patrick Stollenwerk | S. H. Southworth | F. Granato | Amy Renne | B. Mustapha | K. Bailey | Peter R. Müeller | J. A. Nolen | Thomas P. O’Connor | Junqi Xie | Linda Young | Matthew R. Dietrich
Source: arXiv (Cornell University) (preprint, 2024-10-30)
Field: Materials Science — Surfaces, Coatings and Films

The Auger-Meitner Radioisotope Microscope: an instrument for characterization of Auger electron multiplicities and energy distributions We describe a new instrument, the Argonne Auger Radioisotope Microscope (ARM), capable of characterizing the Auger electron emission of radionuclides, including candidates relevant in nuclear medicine. Our approach relies on event-by-event ion-electron coincidence, time-of-flight, and spatial readout measurement to determine correlated electron multiplicity and energy distributions of Auger decays. We present a proof-of-principle measurement with the ARM using X-ray photoionization of stable krypton beyond the K-edge and identify a bifurcation in the electron multiplicity distribution depending on the emission of K-LX electrons. Extension of the ARM to the characterization of radioactive sources of Auger electron emissions is enabled by the combination of two recent developments: (1) cryogenic buffer gas beam technology to introduce Auger emitters into the detection region with well-defined initial conditions, and (2) large-area micro-channel plate detectors with multi-hit detection capabilities to simultaneously detect multiple electrons emitted in a single decay. The ARM will generate new experimental data on Auger multiplicities that can be used to benchmark atomic relaxation and decay models. This data will provide insight into the low-energy regime of Auger electrons where intensity calculations are most challenging and experimental data is limited. In particular, accurate multiplicity data of the low-energy regime can be used to inform oncological dosimetry models, where electron energies less than 500 eV are known to be most effective in damaging DNA and cell membranes. Auger ; Characterization (materials science) ; Nuclear physics ; Auger electron spectroscopy ; Energy (signal processing) ; Electron microscope ; Atomic physics ; Auger effect ; Materials science ; Physics ; Optics ; Nanotechnology Electron and X-Ray Spectroscopy Techniques ; Inorganic Fluorides and Related Compounds ; Advanced Chemical Physics Studies
Authors: Slavica Oljačić | Mitja Križman | Marija R. Popović-Nikolić | Irena Vovk | Katarina Nikolić | Danica Agbaba
Source: ADMET & DMPK (article, 2024-05-04)
Field: Chemistry — Spectroscopy

Modelling of chromatographic and electrophoretic behaviour of imidazoline and alpha adrenergic receptors ligands under different acid-base conditions Background and Purpose: The ligands of the imidazoline and α-adrenergic receptors are mainly imidazoline and guanidine derivatives, known as centrally-acting antihypertensives and compounds with potential use in various neurological disorders. The extent of their ionisation has a major influence on their behaviour in the different analytical systems. The main objective of this work was to compare the mechanism of chromatographic retention and electrophoretic mobility under acidic, neutral and basic conditions. Experimental Approach: Multiple Linear Regression and Partial Least Squares Regression were applied for the QSRR (quantitative structure-retention relationship) and QSMR (quantitative structure-mobility relationship) modelling and to select the most important molecular parameters describing the chromatographic and electrophoretic behaviour of the investigated compounds. Key Results: The most important molecular descriptors, such as the chemical composition of the compounds, lipophilicity, polarizability and molecular branching, in the selected QSRR models showed that an important insight into the retention behaviour can be derived from the 0D-, 1D- and 2D-descriptors. The electrophoretic mobility could be explained by 2D- and 3D-descriptors, which provide information on the molecular mass, size and complexity, as well as on the influence of charge transfer and electronic properties on the migration behaviour. Conclusion: All created QSRR/QSMR models met the stringent validation criteria and showed high potential in describing the chromatographic and electrophoretic behaviour of investigated compounds. Imidazoline receptor ; Electrophoresis ; Adrenergic receptor ; Alpha (finance) ; Alpha-2 adrenergic receptor ; Chemistry ; Chromatography ; Base (topology) ; Receptor ; Adrenergic ; Biochemistry ; Endocrinology ; Biology ; Medicine ; Mathematics Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Pharmacological Effects and Assays
Authors: Qiwen Qiu | Stephen Yue | Jun Song
Source: arXiv (Cornell University) (preprint, 2024-03-31)
Field: Materials Science — Biomaterials

First Principles Studies of Stacking Fault Energies in Ternary Magnesium Alloys Magnesium (Mg) alloys have emerged as promising materials due to their low density and high strength-to-weight ratio, offering a wide range of applications across multiple industries. Nevertheless, the inherent brittleness of Mg alloys poses a significant hurdle, necessitating innovative approaches to enhance their mechanical performance. Among the various strategies, manipulating stacking fault energy (SFE) has been a key focus, although primarily within the realm of binary alloys. This study investigates SFE in Mg alloys, focusing on ternary compositions. Utilizing first-principles DFT calculations, we analyze solute interactions and their influence on SFE, particularly in Mg-Al-X and Mg-Zn-X configurations. Predictive models are developed for estimating SFE effects, revealing solute pairs that mimic rare earth elements and show potential for improved ductility. The findings contribute to fundamental insights into Mg alloy behavior, offering practical directions for designing advanced materials with superior mechanical properties. Ternary operation ; Magnesium ; Stacking-fault energy ; Stacking fault ; Stacking ; Materials science ; Crystallography ; Metallurgy ; Chemistry ; Computer science ; Microstructure Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties
Authors: Yulong Zhou | Zhiyun Xiao | Tengfei Bao | Tao Fang
Source: Agriculture (article, 2024-10-22)
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 leverage HybridSN and multilayer perceptron (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, an RMSE of 0.73, an MAE of 0.49, and an RPD of 7.63 for the estimation of CC in AMM leaves. For NC estimation, AM-MHENet achieves an R2 value of 0.977, an RMSE of 0.27, an MAE of 0.16, and an RPD of 6.62. These results underscore AM-MHENet’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. The 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) ; Biological system ; Astragalus ; Multivariate analysis ; Environmental science ; Pattern recognition (psychology) ; Artificial intelligence ; Biology ; Statistics ; Botany ; Mathematics ; Computer science ; Physics ; Medicine Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Remote Sensing and Land Use
Authors: Aishwarya Jagdale | Bhavana Mitkari | Abhijeet Sutar
Source: Research Square (preprint, 2024-03-25)
Field: Chemistry — Analytical Chemistry

A Chemometric Analysis of Montelukast Sodium And Levocetirizine Dihydrochloride In Pharmaceutical Formulations Using Artificial Neural Networks, Partial Least Squares, And Principal Component Regression Models Partial least squares regression ; Levocetirizine ; Principal component analysis ; Principal component regression ; Artificial neural network ; Component (thermodynamics) ; Mathematics ; Statistics ; Chromatography ; Artificial intelligence ; Chemistry ; Computer science ; Thermodynamics ; Organic chemistry Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Pesticide Residue Analysis and Safety
Authors: Juvissan Aguedo | Marián Vojs | Martin Vrška | Marek Nemčovič | Zuzana Pakanová | Kateřina Aubrechtová Dragounová | Oleksandr Romanyuk | Alexander Kromka | M. Varga | Michal Hatala | Marián Marton | Jan Tkáč
Source: Nanomaterials (article, 2024-07-24)
Field: Chemistry — Spectroscopy

What Are the Key Factors for the Detection of Peptides Using Mass Spectrometry on Boron-Doped Diamond Surfaces? We investigated the use of boron-doped diamond (BDD) with different surface morphologies for the enhanced detection of nine different peptides by matrix-assisted laser desorption/ionisation mass spectrometry (MALDI-MS). For the first time, we compared three different nanostructured BDD film morphologies (Continuous, Nanograss, and Nanotips) with differently terminated surfaces (-H, -O, and -F) to commercially available Ground Steel plates. All these surfaces were evaluated for their effectiveness in detecting the nine different peptides by MALDI-MS. Our results demonstrated that certain nanostructured BDD surfaces exhibited superior performance for the detection of especially hydrophobic peptides (e.g., bradykinin 1-7, substance P, and the renin substrate), with a limit of detection of down to 2.3 pM. Further investigation showed that hydrophobic peptides (e.g., bradykinin 1-7, substance P, and the renin substrate) were effectively detected on hydrogen-terminated BDD surfaces. On the other hand, the highly acidic negatively charged peptide adrenocorticotropic hormone fragment 18-39 was effectively identified on oxygen-/fluorine-terminated BDD surfaces. Furthermore, BDD surfaces reduced sodium adduct contamination significantly. Detection limit ; Mass spectrometry ; Materials science ; Substrate (aquarium) ; Boron ; Diamond ; Desorption ; Adduct ; Chemistry ; Analytical Chemistry (journal) ; Chromatography ; Adsorption ; Organic chemistry ; Metallurgy Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Pharmacological Effects and Assays
Authors: Zongze Li | Jingru Xiao
Source: Highlights in Science Engineering and Technology (article, 2024-03-13)
Field: Chemistry — Analytical Chemistry

Prediction Of Raman Spectra and Adulteration Concentration of Wheat Flour Based on Neural Network Modelling In order to quickly detect the adulteration of flour containing wheat flour quality testing, the Raman spectrum of wheat flour as the object of study, based on a number of data-based neural network to identify and determine the concentration of wheat adulteration. Firstly, the neural network system in this study borrowed the Raman wave number and Raman intensity curve from "Non-contact Detection of Benzoyl Peroxide in Flour Based on Raman Spectroscopy" as the training and testing set. Next, we created the neural network, set the training parameters, trained the network and simulated the test. Finally, the supervised learning process resulted in error analysis and comparison of results, and the Raman spectra of wheat flour and adulteration concentration were plotted visually. Artificial neural network ; Wheat flour ; Raman spectroscopy ; Food science ; Biological system ; Artificial intelligence ; Chemistry ; Computer science ; Biology ; Physics ; Optics Spectroscopy and Chemometric Analyses
Authors: AMM Nurul Alam | Chan‐Jin Kim | So-Hee Kim | Swati Kumari | Eun-Yeong Lee | Young-Hwa Hwang | Seon-Tea Joo
Source: arXiv (Cornell University) (preprint, 2024-01-05)
Field: Materials Science — Biomaterials

Scaffolding fundamentals and recent advances in sustainable scaffolding techniques for cultured meat development In cultured meat (CM) products the paramount significance lies in the fundamental attributes like texture and sensory of the processed end product. To cater to the tactile and gustatory preferences of real meat, the product needs to be designed to incorporate its texture and sensory attributes. Presently CM products are mainly grounded products like sausage, nugget, frankfurter, burger patty, surimi, and steak with less sophistication and need to mimic real meat to grapple with the traditional meat market. The existence of fibrous microstructure in connective and muscle tissues has attracted considerable interest in the realm of tissue engineering. Scaffolding plays an important role in CM production by aiding cell adhesion, growth, differentiation, and alignment. A wide array of scaffolding technologies has been developed for implementation in the realm of biomedical research. In recent years researchers also focus on edible scaffolding to ease the process of CM. However, it is imperative to implement cutting edge technologies like 3D scaffolds, 3D printing, electrospun nanofibers in order to advance the creation of sustainable and edible scaffolding methods in CM production, with the ultimate goal of replicating the sensory and nutritional attributes to mimic real meat cut. This review discusses recent advances in scaffolding techniques and biomaterials related to structured CM production and required advances to create muscle fiber structures to mimic real meat. Keywords: Cultured meat, Scaffolding, Biomaterials, Edible scaffolding, Electrospinning, 3D bioprinting, real meat. Scaffold ; Computer science ; Food science ; Nanotechnology ; Biochemical engineering ; Chemistry ; Engineering ; Materials science Electrospun Nanofibers in Biomedical Applications ; Protein Hydrolysis and Bioactive Peptides ; Animal Genetics and Reproduction
Authors: Wahyu Pebrianto | Ahmad Hoirul Basori | Hendra Yufit Riskiawan | Andi Besse Firdausiah Mansur | Taufiq Rizaldi | Nouf Atiahallah Alghanmi | Alanoud Subahi | Hermawan Arief Putranto | Hanadi Alkhudhayr | Arwa Mashat | Yogiswara Yogiswara
Source: International Journal of ADVANCED AND APPLIED SCIENCES (article, 2024-09-01)
Field: Chemistry — Analytical Chemistry

Efficient batch size detection of apple fruit in a plantation environment There is growing interest in using deep learning for object recognition in robots to enhance the efficiency of apple farming. While deep learning-based object detection has shown promising results in various visual tasks, more research is needed to accurately recognize apples in orchard environments. During the training phase, it is important to determine the optimal values of hyperparameters. This research aims to develop a deep learning model, YOLOv7, to reliably identify apples in orchards, using four different batch size values for training. The MinneApple dataset, trained with these batch sizes, serves as our reference model. To assess the model’s ability to work in different situations, we evaluate it using test data with varying input scales. Our results show that the optimal batch size for detecting apples in orchards is 16, achieving a mean average precision (mAP) of 50%. Furthermore, our findings suggest that increasing the batch size does not improve the efficiency of apple detection in orchard environments. Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Greenhouse Technology and Climate Control