jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anastasia Yu. Sholokhova | Dmitriy D. Matyushin | М. В. Шашков
Source: Journal of Chromatography A (article, 2024-07-05)
Field: Chemistry — Spectroscopy

Quantitative structure-retention relationships for pyridinium-based ionic liquids used as gas chromatographic stationary phases: convenient software and assessment of reliability of the results Ionic liquid ; Pyridinium ; Chemistry ; Stationary phase ; Molecular descriptor ; Chromatography ; Ionic bonding ; Reproducibility ; Quantitative structure–activity relationship ; Organic chemistry Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Mohsen Rezaeian | Yaser Bahari | Davood Bandi | Arash Montazeri | Mahyar Jahaninasab | Ali Rajabpour
Source: Physica B Condensed Matter (article, 2024-08-10)
Field: Materials Science — Materials Chemistry

Thermal conductivity of boronated-holey graphene under mechanical strain: Insights from molecular dynamics Thermal conductivity ; Materials science ; Graphene ; Strain (injury) ; Monolayer ; Molecular dynamics ; Thermal ; Atmospheric temperature range ; Conductivity ; Composite material ; Nanotechnology ; Thermodynamics ; Chemistry ; Computational chemistry ; Physical chemistry ; Physics Graphene research and applications ; Thermal properties of materials ; Diamond and Carbon-based Materials Research
Authors: Lei Chen | Carlos Bornes | Oscar Bengtsson | Andreas Erlebach | Ben Slater | Lukáš Grajciar | Christopher J. Heard
Source: Faraday Discussions (article, 2024-06-03)
Field: Chemistry — Spectroscopy

A machine learning approach for dynamical modelling of Al distributions in zeolites <i>via</i><sup>23</sup>Na/<sup>27</sup>Al solid-state NMR One of the main limitations in supporting experimental characterization of Al siting/pairing <i>via</i> modelling is the high computational cost of <i>ab initio</i> calculations. For this reason, most works rely on static or very short dynamical simulations, considering limited Al pairing/siting combinations. As a result, comparison with experiment suffers from a large degree of uncertainty. To alleviate this limitation we have developed neural network potentials (NNPs) which can dynamically sample across broad configurational and chemical spaces of sodium-form aluminosilicate zeolites, preserving the level of accuracy of the <i>ab initio</i> (dispersion-corrected metaGGA) training set. By exploring a wide range of Al/Na arrangements and a combination of experimentally relevant Si/Al ratios, we found that the <sup>23</sup>Na NMR spectra of dehydrated high-silica CHA zeolite offer an opportunity to assess the distribution and pairing of Al atoms. We observed that the <sup>23</sup>Na chemical shift is sensitive not only to the location of sodium in 6- and 8MRs, but also to the Al-Si<sub><i>n</i></sub>-Al sequence length. Furthermore, neglect of thermal and dynamical contributions was found to lead to errors of several ppm, and has a profound influence on the shape of the spectra and the dipolar coupling constants, thus necessitating the long-term dynamical simulations made feasible by NNPs. Finally, we obtained a predictive regression model for the <sup>23</sup>Na chemical shift in CHA (Si/Al = 35, 17, 11) that circumvents the need for expensive NMR density functional calculations and can be easily extended to other zeolite frameworks. By combining NNPs and regression methods, we can expedite the simulations of NMR properties and capture the effect of dynamics on the spectra, which is often overlooked in computational studies despite its clear manifestation in experimental setups. Chemistry ; Physical chemistry ; Materials science Advanced NMR Techniques and Applications ; Zeolite Catalysis and Synthesis ; X-ray Diffraction in Crystallography
Authors: Natalie Rozman | Rixi Peng | Willie J. Padilla
Source: Advanced Optical Materials (article, 2024-09-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Inverse Design of an Infrared Metasurface Diffuser Abstract Machine learning (ML) algorithms have become invaluable tools for tackling design challenges associated with achieving unique scattering effects in artificial electromagnetic materials (AEMs). However, their effectiveness is reliant on substantial, well‐constructed training datasets. Building such datasets using traditional methods becomes impractical for increasingly complex and large‐scale geometric models. Achieving a specific diffuse scattering is one example and this often requires electrically large and diverse AEM arrays. Unfortunately, while numerical simulations offer high accuracy by utilizing fine meshing, their computational limitations render them incapable of handling such large structures and computing their scattering parameters efficiently. This work proposes a new approach to overcome these limitations by replacing conventional numerical simulations with a hybrid method that combines electromagnetic simulations with an analytical model, enabling the rapid and accurate generation of datasets for electrically large metamaterial arrays. Utilizing this approach, an optimized metasurface geometry for the mid‐infrared range is successfully identified and tested that exhibits desirable diffuse scattering effects. This innovative method paves the way for significantly faster design and optimization of metamaterials, while also unlocking the potential for a new generation of large‐scale, high‐quality ML datasets for AEM problems. Diffuser (optics) ; Materials science ; Infrared ; Inverse ; Optics ; Optoelectronics ; Physics ; Geometry Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Thermal Radiation and Cooling Technologies
Authors: Saba Sareminia | Zahra Ghayoumian | Fatemeh Haghighat
Source: International Journal of Clothing Science and Technology (article, 2024-02-19)
Field: Materials Science — Polymers and Plastics

Developing a data-driven operational guide for the texturized yarn production process: data mining and intelligence approach Purpose The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials. Design/methodology/approach This research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021. Findings The results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation. Originality/value This paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity. Yarn ; Production (economics) ; Engineering ; Process (computing) ; Quality (philosophy) ; Raw data ; Manufacturing engineering ; Industrial engineering ; Artificial intelligence ; Computer science ; Mechanical engineering ; Economics Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: V. N. Senthil Kumaran | M. Venkatesh | Abdulrahman Saad Alqahtani | Azath Mubarakali | P. Parthasarathy
Source: Materials Science in Semiconductor Processing (article, 2024-11-10)
Field: Materials Science — Materials Chemistry

Scalable graphene nanogrid FET biosensors: Fabrication &amp; characterization for sub-femtomolar detection of viral proteins Biosensor ; Graphene ; Materials science ; Fabrication ; Characterization (materials science) ; Nanotechnology ; Scalability ; Computer science ; Medicine Graphene research and applications ; Graphene and Nanomaterials Applications ; Nanowire Synthesis and Applications
Authors: Minwoo Kim | Seungtae Kim | Changbong Hyeon | Ji Woong Yu | Siyoung Q. Choi | Won Bo Lee
Source: The Journal of Physical Chemistry Letters (article, 2024-10-24)
Field: Materials Science — Materials Chemistry

Anomalous Water Penetration in Al<sup>3+</sup> Dissolution The physicochemical characterization of trivalent ions is limited due to a lack of accurate force fields. By leveraging the latest machine learning force field to model aqueous AlCl<sub>3</sub>, we discover that upon dissolution of Al<sup>3+</sup>, water molecules beyond the second hydration shell are involved in the hydration process. A combination of scissoring of coordinating water is followed by synchronized secondary motion of water in the second solvation shell due to hydrogen bonding. Consequently, the water beyond the second solvation penetrates through the second solvation shell and coordinates to the Al<sup>3+</sup>. Our study reveals a novel microscopic understanding of solvation dynamics for the trivalent ion. Dissolution ; Penetration (warfare) ; Materials science ; Chemistry ; Mathematics ; Physical chemistry Chemical and Physical Properties of Materials ; Electrostatics and Colloid Interactions
Authors: Wei Li | Feng-ning Xue | Peng-bo Zhao | Yong Lu
Source: Physica Scripta (article, 2024-08-21)
Field: Materials Science — Materials Chemistry

Electronic structure and phonon transport properties of HfSe<sub>2</sub> under in-plane strain and finite temperature Abstract The electronic structure and phonon transport properties of HfSe 2 under different in-plane strains at finite temperatures are systematically investigated by combining first-principles calculations with machine learning force field molecular dynamics simulations. Within a strain range of -3% to 3%, the electronic band gap value of HfSe 2 varies between 0.39 and 0.87 eV. Under compressive strain, the conduction band minimum moves towards the Fermi level, with the distribution of electrons near the valence band maximum becoming more delocalized. This will reduce the scattering of electrons during the transport process, helping to improve the carrier mobility. Under tensile strain, the localization of the density of states near the valence band maximum is strengthened, accompanied by enhanced metallic properties of the Hf-Se bonds, which facilitates the enhancement of the thermoelectric power factor. Both compressive and tensile strains intensify the coupling of phonon normal modes with phonon scattering, and elevating the temperature amplifies this impact. The anharmonicity-induced reduction in phonon frequencies is especially pronounced for modes in the vicinity of the Debye frequency. This not only curtails the phonon lifetimes but also diminishes the lattice thermal conductivity through the enhancement of vibrational coupling among optical branches and the reduction of the group velocity in acoustic branches. These results demonstrate the synergistic effects of strain and temperature on the electronic structure and phonon transport of HfSe 2 . Condensed matter physics ; Phonon ; Strain (injury) ; Materials science ; Plane (geometry) ; Physics ; Geometry ; Mathematics ; Biology 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; MXene and MAX Phase Materials
Authors: Asfakujjaman | Mainak Ghosh | Suman Chowdhury | Debnarayan Jana
Source: Journal of Physics D Applied Physics (article, 2024-07-02)
Field: Materials Science — Materials Chemistry

Harnessing BN co-doping for superior thermal transport in phagraphene monolayer Abstract In this study, we thoroughly explored the thermal transport and thermoelectric properties of BN-co-doped phagraphene structures in the context of first-principles computations combined with machine learning interatomic potential (MLIP) techniques. Our results demonstrate that doping positions can critically tune the thermal properties, offering potential advantages for both thermoelectric and heat transfer applications. Notably, enhanced thermal conductivity has been obtained for phagraphene with 10 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi mathvariant="normal">%</mml:mi> </mml:mrow> </mml:math> co-doping. Additionally, the rectangular structural symmetry of phagraphene plays an important role for targeted thermal transport. Further, we observe negative Grüneisen parameter in these structures, suggesting negative thermal expansion. This will serves as an unique mechanism for controlling the thermal conductivity at different frequencies. Interestingly, n-type behavior of the structures is indicative of its negative Seebeck coefficient within the temperature range of 300–900 K. Moreover, these structures display significantly larger electrical conductivity compared to other two-dimensional (2D) materials. Apart from that, the calculated figure of merit of the structures under a constant relaxation time at 300 K shows considerably better response for some specific doped structures. We believe this study will play an important role in understanding the importance of structural modifications in tailoring the thermal properties of carbon-based 2D systems. Doping ; Business ; Materials science ; Optoelectronics Graphene research and applications ; Boron and Carbon Nanomaterials Research ; Molecular Junctions and Nanostructures
Authors: Somaye Vali Zade | Hamid Abdollahi
Source: Journal of Chemometrics (article, 2024-09-28)
Field: Chemistry — Analytical Chemistry

Evaluation of Classical Least Squares Discriminant Analysis (CLS‐DA) as a Novel Supervised Pattern Recognition Technique ABSTRACT Multivariate calibration techniques and machine learning algorithms are inextricably linked within the realm of chemometrics and data analysis. Classical least squares (CLS) modeling, a fundamental multivariate regression approach, has traditionally been utilized for quantitative analysis tasks, establishing relationships between predictor variables (e.g., spectroscopic data) and response variables (e.g., chemical concentrations). However, a unique feature of CLS is its ability to handle scenarios with partial knowledge of the independent variable matrix, making it an intriguing candidate for qualitative pattern recognition and discriminant analysis applications. This study proposes a novel approach, Classical Least Squares Discriminant Analysis (CLS‐DA), which combines the principles of CLS modeling with discriminant analysis objectives. The performance of CLS‐DA is comprehensively evaluated using two real‐world datasets: chemical analysis of three wine cultivars and mid‐infrared spectroscopy of minced meat samples (pork, chicken, and turkey). The results are compared against the well‐established Partial Least Squares Discriminant Analysis (PLS‐DA) method, a widely adopted technique for classification tasks in chemometrics. For both sets of experimental data, CLS‐DA and PLS‐DA showed comparable efficiency. For the classification of three types of wine, the accuracy of the proposed method was 94.3%, while the accuracy of the reference method was 98.1%. For the classification of minced meat samples, the accuracies of CLS‐DA and PLS‐DA were 97.2% and 94%, respectively for all three groups. The findings demonstrate the potential of CLS‐DA as a straightforward and interpretable supervised pattern recognition technique, exhibiting comparable classification performance to PLS‐DA. The study highlights the advantages of CLS‐DA, including its ability to operate within the original data space and its flexibility in accommodating partial knowledge scenarios. The proposed CLS‐DA approach presents a promising alternative for discriminant analysis, offering new perspectives on the applications of classical least squares modeling in chemometrics. Linear discriminant analysis ; CLs upper limits ; Pattern recognition (psychology) ; Artificial intelligence ; Mathematics ; Chemometrics ; Discriminant ; Least-squares function approximation ; Chromatography ; Computer science ; Statistics ; Chemistry ; Medicine Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Haiou Guan | Yiwen Zhao | Xiaodan Ma | Jiao Yang | Yulan Huang
Source: Infrared Physics & Technology (article, 2024-07-27)
Field: Chemistry — Analytical Chemistry

A detection method of Saposhnikovia divaricata seed vigor based on near-infrared spectral feature extraction Infrared ; Extraction (chemistry) ; Feature (linguistics) ; Pattern recognition (psychology) ; Materials science ; Computer science ; Chromatography ; Artificial intelligence ; Optics ; Physics ; Chemistry Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; GABA and Rice Research
Authors: Shihao Zhang | Shihao Zhu | Fan-Shun Meng | Shigenobu Ogata
Source: International Journal of Hydrogen Energy (article, 2024-10-04)
Field: Materials Science — Metals and Alloys

Hydrogen and vacancy concentrations in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg" display="inline" id="d1e964"><mml:mi>α</mml:mi></mml:math>-iron under high hydrogen gas pressure and external stress: A first-principles neural network simulation study The metal-hydrogen-vacancy interaction in α -iron is crucial for understanding hydrogen embrittlement behavior and developing reliable materials for green gaseous hydrogen applications; however, it remains underexplored, particularly in contexts involving external stress and high hydrogen gas pressure. In this study, we performed quantitative analyses of metal-vacancy–hydrogen interactions in α -iron, measured by hydrogen solubility and the thermodynamics of vacancy–hydrogen complexes, under such challenging conditions using a reliable first-principles neural network potential. High hydrogen gas pressures reaching up to 2 GPa were investigated. As the atomic concentration surpasses approximately 0.01, hydrogen solubility is dominated by hydrogen-induced lattice distortion (primarily volumetric expansion) and the interactions between hydrogen atoms within the metal matrix, resulting in significant deviations from Sieverts’ law. Our results reveal a significant effect of shear stress on hydrogen solubility, deviating from previously used equations. Moreover, the influence of external stress on the thermodynamics of the vacancy–hydrogen complex, particularly vacancy formation free energy, is uniquely characterized by hydrogen solubility, regardless of the external stress magnitude. It thereby offers a rapid method to estimate the vacancy properties under external stress based on readily accessible external-stress-free data. • Hydrogen solubility exhibits deviations from Sieverts’ law as atomic c H > 0.01. • Hydrogen-induced expansion and H H interactions in iron should be considered. • External stress effect on solubility is shaped by both tensile and shear stresses. • Stress effect on vacancy–H complex thermodynamics is defined by H solubility. Scalable Vector Graphics ; Mathematics ; Materials science ; Computer science ; World Wide Web Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Fusion materials and technologies
Authors: Niraj Bhatt | Pravin Karna | Sandip Thakur | Ashutosh Giri
Source: International Journal of Heat and Mass Transfer (article, 2024-05-15)
Field: Materials Science — Materials Chemistry

Pressure-driven enhancement of phonon contribution to the thermal conductivity of Iridium Thermal conductivity ; Iridium ; Phonon ; Materials science ; Condensed matter physics ; Physics ; Chemistry ; Composite material Thermal properties of materials ; High-pressure geophysics and materials ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Christèle Aubry | Carole Kebbi‐Beghdadi | Amanda Luraschi-Eggemann | Gino Cathomen | Danuta Cichocka | Alexander Sturm | Gilbert Greub
Source: Journal of Medical Microbiology (article, 2024-11-08)
Field: Chemistry — Physical and Theoretical Chemistry

Nanomotion technology: an innovative method to study cell metabolism in Escherichia coli, as a potential indicator for tolerance <b>Introduction.</b> Antibiotic tolerance corresponds to the bacterial ability to survive a transient exposure to antibiotics and is often associated with treatment failure. Current methods of identifying tolerance based on bacterial growth are time-consuming. This study explores the use of a growth-independent method utilizing nanomotion technology to detect antibiotic-tolerant bacteria.<b>Hypothesis.</b> The nanomotion signal obtained from a nanomechanical sensor measures real-time metabolic activity and cellular processes and could provide valuable information about the tolerance of bacteria to antibiotics that cannot be detected by standard antibiotic susceptibility tests.<b>Aim.</b> The aim of this study is to investigate the potential of nanomotion technology to record antibiotic-tolerant bacteria.<b>Methodology.</b> We generated a slow-growing <i>Escherichia coli</i> strain by manipulating <i>mazF</i> expression levels and confirmed its viability by several standard methods. We subsequently measured its nanomotion and the nanomotion of the WT <i>E. coli</i> in the presence or absence of antibiotics. Supervised machine learning was employed to distinguish slow-growing from exponentially growing bacteria. Observations for bacterial nanomotions were confirmed by standard kill curves.<b>Results.</b> We distinguished slow-growing from exponentially growing bacteria using specific features from the nanomotion signal. Furthermore, the exposition of both growth phenotypes to polymyxin decreased the nanomotion signal indicating cell death. Similarly, when exponentially growing cells were exposed to ampicillin, an antibiotic whose efficacy depends on the growth rate, the nanomotion signal also decreased. In contrast, the nanomotion signal remained unchanged for slow-growing bacteria upon exposure to ampicillin. In addition, antibiotic exposure can cause bacterial elongation, in which the biomass of a cell increases without cell division. By overexpressing <i>sulA</i>, we mimicked antibiotic-induced elongation. Differences in the nanomotion signal were observed when comparing elongating and non-elongating phenotypes.<b>Conclusion.</b> This work shows that nanomotion signals entail information about the reaction to antibiotics that standard MIC-based antibiotic susceptibility tests cannot detect. In the future, nanomotion-based antibiotic tolerance tests could be developed for clinical use in chronic or relapsing infections. Escherichia coli ; Metabolism ; Biology ; Microbiology ; Biotechnology ; Biochemistry thermodynamics and calorimetric analyses ; Lipid Membrane Structure and Behavior ; Bacterial Genetics and Biotechnology