jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hao Zhou | Janak Tiwari | Tianli Feng
Source: Physical Review Materials (article, 2024-04-25)
Field: Materials Science — Materials Chemistry

Understanding the flat thermal conductivity of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi mathvariant="normal">La</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">Zr</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>7</mml:mn></mml:msub></mml:mrow></mml:math> at ultrahigh temperatures Many crystals, such as lanthanum zirconate $({\mathrm{La}}_{2}{\mathrm{Zr}}_{2}{\mathrm{O}}_{7})$, exhibit a flat temperature dependence of thermal conductivity at elevated temperatures. This phenomenon has recently been attributed to the interband phonon tunneling (or diffuson) contribution using different formalisms. However, the contributions of finite-temperature corrections (e.g., higher-order phonon scattering, phonon renormalization, and phonon-scattering cross-section softening effects) and radiation at high temperatures remain unclear. In this work, we predict and compare the thermal conductivity of ${\mathrm{La}}_{2}{\mathrm{Zr}}_{2}{\mathrm{O}}_{7}$ using three distinct first-principles methods. The first method is Green-Kubo molecular dynamics (MD) based on temperature-dependent machine-learning interatomic potentials trained from ab initio MD simulations, which successfully predict the flat trend at ultrahigh temperatures. The second method is the Peierls Boltzmann transport equation (BTE), within the phonon particle framework, using phonon lifetime that includes all the finite-temperature corrections. Four-phonon scattering is found large but is canceled by the phonon-scattering cross-section softening effect. As a result, BTE with temperature corrections does not reproduce the flat thermal conductivity. The third method is Wigner formalism, which includes both phonon particle and wave contributions, which successfully reproduce the flat thermal conductivity. Diffuson and phonon contribute about 67 and 27% of thermal conductivity at 1800 K, respectively. The radiation contribution to thermal conductivity is calculated by the Rosseland model and found to be around 6%. The scaling laws of the phonon, diffuson, radiation, and total thermal conductivity are found to be $\ensuremath{\sim}{T}^{\ensuremath{-}0.97}$, $\ensuremath{\sim}{T}^{0.43}$, $\ensuremath{\sim}{T}^{2.01}$, and $\ensuremath{\sim}{T}^{\ensuremath{-}0.40}$, respectively. This work clarifies the thermal transport mechanisms in ${\mathrm{La}}_{2}{\mathrm{Zr}}_{2}{\mathrm{O}}_{7}$ at ultrahigh temperatures from different aspects. Thermal conductivity ; Phonon ; Condensed matter physics ; Scattering ; Phonon scattering ; Physics ; Materials science ; Boltzmann equation ; Thermodynamics ; Quantum mechanics Nuclear Materials and Properties ; Thermal properties of materials ; High-pressure geophysics and materials
Authors: Vida Gyimah Boadu | Ernest Teye | Francis Padi Lamptey | Charles Lloyd Yeboah Amuah | L.K. Sam-Amoah
Source: Heliyon (article, 2024-07-31)
Field: Chemistry — Analytical Chemistry

Novel authentication of African geographical coffee types (bean, roasted, powdered) by handheld NIR spectroscopic method African coffee is among the best traded coffee types worldwide, and rapid identification of its geographical origin is very important when trading the commodity. The study was important because it used NIR techniques to geographically differentiate between various types of coffee and provide a supply chain traceability method to avoid fraud. In this study, geographic differentiation of African coffee types (bean, roasted, and powder) was achieved using handheld near-infrared spectroscopy and multivariant data processing. Five African countries were used as the origins for the collection of Robusta coffee. The samples were individually scanned at a wavelength of 740-1070 nm, and their spectra profiles were preprocessed with mean centering (MC), multiplicative scatter correction (MSC), and standard normal variate (SNV). Support vector machines (SVM), linear discriminant analysis (LDA), neural networks (NN), random forests (RF), and partial least square discriminate analysis (PLS-DA) were then used to develop a prediction model for African coffee types. The performance of the model was assessed using accuracy and F1-score. Proximate chemical composition was also conducted on the raw and roasted coffee types. The best classification algorithms were developed for the following coffee types: raw bean coffee, SD-PLSDA, and MC + SD-PLSDA. These models had an accuracy of 0.87 and an F1-score of 0.88. SNV + SD-SVM and MSC + SD-NN both had accuracy and F1 scores of 0.97 for roasted coffee beans and 0.96 for roasted coffee powder, respectively. The results revealed that efficient quality assurance may be achieved by using handheld NIR spectroscopy combined with chemometrics to differentiate between different African coffee types according to their geographical origins. Linear discriminant analysis ; Random forest ; Support vector machine ; Mathematics ; Mobile device ; Artificial intelligence ; Partial least squares regression ; Coffee bean ; Computer science ; Pattern recognition (psychology) ; Food science ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Coffee research and impacts
Authors: Ν. Zotov | Konstantin Gubaev | J. Wörner | Blazej Grabowski
Source: Modelling and Simulation in Materials Science and Engineering (article, 2024-02-27)
Field: Materials Science — Metals and Alloys

Moment tensor potential for static and dynamic investigations of screw dislocations in bcc Nb Abstract A new machine-learning interatomic potential, specifically a moment tensor potential (MTP), is developed for the study of screw-dislocation properties in body-centered-cubic (bcc) Nb in the thermally- and stress-assisted temperature regime. Importantly, configurations with straight screw dislocations and with kink pairs are included in the training set. The resulting MTP reproduces with near density-functional theory (DFT) accuracy a broad range of physical properties of bcc Nb, in particular, the Peierls barrier and the compact screw-dislocation core structure. Moreover, it accurately reproduces the energy of the easy core and the twinning-anti-twinning asymmetry of the critical resolved shear stress (CRSS). Thereby, the developed MTP enables large-scale molecular dynamics simulations with near DFT accuracy of properties such as for example the Peierls stress, the critical waiting time for the onset of screw dislocation movement, atomic trajectories of screw dislocation migration, as well as the temperature dependence of the CRSS. A critical assessment of previous results obtained with classical embedded atom method potentials thus becomes possible. Materials science ; Moment tensor ; Moment (physics) ; Condensed matter physics ; Tensor (intrinsic definition) ; Classical mechanics ; Composite material ; Geometry ; Deformation (meteorology) ; Physics ; Mathematics Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Microstructure and mechanical properties
Authors: Roberto Laganà Vinci | Katia Arena | Francesca Rigano | Francesco Calabrò | Paola Dugo | Luigi Mondello
Source: Journal of Chromatography A (article, 2024-07-09)
Field: Chemistry — Spectroscopy

Prediction of retention data of phenolic compounds by quantitative structure retention relationship models under reverse-phase liquid chromatography Quantitative Structure-Retention Relationship models were developed to identify phenolic compounds using a typical LC- system, with both UV and MS detection. A new chromatographic method was developed for the separation of fifty-two standard phenolic compounds. Over 5000 descriptors for each standard were calculated using AlvaDesc software and then selected through Genetic Algorithm. The selected descriptors were used as variables for models construction and to obtain a better understanding of the retention behaviour of phenols during reverse-phase separation. Three distinct molecule sets, including fifty-two phenolic compounds (Set 1), 32 flavonoids (Set 2) and 15 mono-substituted flavonoids were divided into training and validation sets to build Partial Least Square, Multiple Linear Regression and Partial Least Square-Artificial Neural Network models. To assess the predictivity of the models, these were tested on a bergamot juice sample. Partial Least Square and Partial Least Square-Artificial Neural Network exhibit the lowest prediction error, and the latter showed the best predictive power in real sample recognition. The building and implementation of such predictive models showed to be a powerful tool to identify phenolic compounds based on retention data and avoiding the use of expensive and sophisticated detectors such as tandem MS. Chemistry ; Partial least squares regression ; Artificial neural network ; Chromatography ; Linear regression ; Mean squared error ; Molecular descriptor ; Phenols ; Data set ; Kovats retention index ; Set (abstract data type) ; Regression analysis ; Biological system ; Quantitative structure–activity relationship ; Artificial intelligence ; Machine learning ; Statistics ; Computer science ; Gas chromatography ; Mathematics ; Organic chemistry Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Phytochemicals and Antioxidant Activities
Authors: Isadora Fernandez Brazolin | Felipe Matheus Mota Sousa | Jackson Wesley Silva dos Santos | Víktor Oswaldo Cárdenas Concha | Flávio Vasconcelos da Silva | Cristiana Maria Pedroso Yoshida
Source: Journal of Applied Polymer Science (article, 2024-04-29)
Field: Materials Science — Biomaterials

A method for pH food dependent shelf‐life prediction of intelligent sustainable packaging device using artificial neural networks Abstract Intelligent packaging systems can contribute to easily informing the quality of products. Tools such as artificial neural networks (ANN) have the potential to be used in the development of intelligent packaging. The objective of this work was the development and training of an ANN to facilitate the control of foods from the pH variation based on a sustainable colorimetric indicator of chitosan‐anthocyanin. The pH intelligent films were prepared with different concentrations of chitosan (Cch, 0.5%, 1.0%, and 2.0%, w/w) and anthocyanin (Cath, 0.5%, 1.0%, and 2.0%, w/w). The films were characterized by water solubility, mechanical properties, and thermal analyses. The colorimetric efficiency of the pH intelligent films was measured by immersing the material in a wide pH range (1.20–12.58) buffer solution, determining the color parameters L*, a*, and b*. From the experimental results, a database was built to develop an empirical multivariable model based on ANN. Higher Cch increases the solubility and resistance of intelligent films. Color variation was better identified in films containing Cath = 0.5%. The ANN presented assertiveness of 79%, showing that classification algorithms based on colorimetric measurements can be exploited to indicate alterations in food products resulting from pH variation. Active packaging ; Anthocyanin ; Solubility ; Artificial neural network ; Chitosan ; Shelf life ; Polymer ; Materials science ; Computer science ; Chemical engineering ; Biological system ; Food packaging ; Process engineering ; Chemistry ; Artificial intelligence ; Nanotechnology ; Organic chemistry ; Composite material ; Food science ; Engineering Nanocomposite Films for Food Packaging ; Postharvest Quality and Shelf Life Management ; Meat and Animal Product Quality
Authors: Muhamad Akrom | Supriadi Rustad | Hermawan Kresno Dipojono
Source: Journal of Materials Engineering and Performance (article, 2024-09-23)
Field: Materials Science — Materials Chemistry

Investigation of Corrosion Inhibition Capability of Pyridazine Compounds via Ensemble Learning Pyridazine ; Materials science ; Corrosion ; Nanotechnology ; Metallurgy ; Organic chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Electrochemical Analysis and Applications
Authors: Junyi Zhang | Liping Chen | Liwei Luo | Zhonglei Cai | Ruiyao Shi | Letian Cai | Xuhai Yang | Jiangbo Li
Source: Postharvest Biology and Technology (article, 2024-09-14)
Field: Chemistry — Analytical Chemistry

Construction of a stable YOLOv8 classification model for apple bruising detection based on physicochemical property analysis and structured-illumination reflectance imaging Reflectivity ; Property (philosophy) ; Remote sensing ; Geology ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Plant Surface Properties and Treatments ; Plant Pathogens and Fungal Diseases
Authors: Yinjie Zhou | Sibei Ji | Songyang He | Wei Fan | Liang Zan | Li Zhou | Xu Ji | Ge He
Source: Industrial & Engineering Chemistry Research (article, 2024-09-13)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Assisted High-Throughput Screening of Metal–Organic Frameworks for CO<sub>2</sub> Separation from CO<sub>2</sub>-Rich Natural Gas Under the appeal of carbon peaking and carbon neutrality goals, it is highly advisable to develop green chemical technologies. Based on this, it is even more attractive to synthesize methanol with the H2 generated from water electrolysis by offshore wind power and the CO2 separated from offshore CO2-rich natural gas. Therefore, the separation and adsorption of CO2-rich natural gas in this context is of great socioeconomic significance. However, the conventional high-throughput screening methods for metal–organic frameworks (MOFs) in separating natural gas components and CO2 suffer from great challenges such as high model complexity and long computation time. To address the aforementioned problems, a machine learning-assisted modeling and screening strategy is proposed herein for the rapid and efficient separation of CO2 from the actual natural gas of six components (N2, CO2, CH4, C2H6, C3H8, and H2S). First, structural analysis is used to eliminate the MOFs that cannot adsorb CO2 from the Computation-Ready Experimental Metal–Organic Frameworks (CoRE-MOFs) database. Six structural and 17 chemical descriptors of the remaining MOFs were calculated. Grand Canonical Monte Carlo (GCMC) simulations were applied to evaluate the separation performance metrics of the randomly selected training and testing MOF samples. By combining 23 descriptors and separation performance metrics, a Random Forest (RF) regression model was obtained with R2 exceeding 0.92 on the test samples, which was employed to predict the separation performance of the remaining MOFs. As a result, 10 MOF candidates with the best CO2 separation performance were obtained. Furthermore, a structure–property relationship of MOFs with satisfactory regenerability was conducted. Three design strategies were proposed to guide the development of high-performance novel MOFs for CO2 separation. This study offers a high-throughput screening framework for MOFs to facilitate the separation of CO2 from a CO2-rich natural gas. Metal-organic framework ; Throughput ; Natural gas ; Separation (statistics) ; Computer science ; Chemistry ; Materials science ; Chemical engineering ; Environmental science ; Process engineering ; Adsorption ; Physical chemistry ; Organic chemistry ; Machine learning ; Telecommunications Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Aydin Larestani | Ahmadreza Jafari-Sirizi | Fahimeh Hadavimoghaddam | Saeid Atashrouz | Dragutin Nedeljković | Ahmad Mohaddespour | Abdolhossein Hemmati‐Sarapardeh
Source: Adsorption (article, 2024-09-21)
Field: Chemistry — Inorganic Chemistry

Toward reliable prediction of CO2 uptake capacity of metal–organic frameworks (MOFs): implementation of white-box machine learning Metal-organic framework ; White box ; White (mutation) ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis
Authors: Yuya Takakura | Suryateja Ravutla | Jinsu Kim | Keisuke Ikeda | Hiroshi Kajiro | Tomoyuki Yajima | Junpei Fujiki | Fani Boukouvala | Matthew J. Realff | Yoshiaki Kawajiri
Source: International journal of greenhouse gas control (article, 2024-10-01)
Field: Chemistry — Inorganic Chemistry

Surrogate model optimization of vacuum pressure swing adsorption using a flexible metal organic framework with hysteretic sigmoidal isotherms • Adsorption process using a flexible metal-organic framework ELM-11 is optimized. • Surrogate models are developed for vacuum pressure swing adsorption process. • Process models with hysteresis and sigmoidal isotherms are successfully optimized. • Pareto front is analyzed for recovery, energy consumption, and bed size factor. • ELM-11 shows high purity consistently owing to high selectivity. This study presents a process optimization study for a vacuum pressure swing adsorption (VPSA) process using a flexible metal-organic framework (MOF), which is gaining attention as a material to realize energy-efficient carbon dioxide capture processes. Many flexible MOFs exhibit sigmoidal adsorption isotherms with hysteresis, posing a challenge for simulation and optimization using a rigorous process model. In this study, we employ surrogate model optimization, where surrogate models using machine-learning algorithms were constructed from simulation of 903 operating conditions generated by Latin hypercube sampling. The surrogate models with the best performance were identified from 18 different surrogate options considering four design variables—adsorption pressure, desorption pressure, adsorption time, and desorption time. Using the best surrogate models, a multi-objective optimization problem was solved to identify the Pareto front among recovery, energy consumption, and bed size factor. Our analysis identified a distinct characteristic of VPSA using a flexible-MOF where purity and recovery are hardly affected by the feed volume. Adsorption ; Swing ; Pressure swing adsorption ; Sigmoid function ; Materials science ; Surrogate model ; Chemistry ; Engineering ; Mechanical engineering ; Computer science ; Mathematics ; Mathematical optimization ; Artificial intelligence ; Organic chemistry ; Artificial neural network Metal-Organic Frameworks: Synthesis and Applications ; Phase Equilibria and Thermodynamics ; Enhanced Oil Recovery Techniques
Authors: Wael A. Mahdi | Ahmad J. Obaidullah
Source: Chemometrics and Intelligent Laboratory Systems (article, 2024-08-23)
Field: Materials Science — Materials Chemistry

Combination of machine learning and COSMO-RS thermodynamic model in predicting solubility parameters of coformers in production of cocrystals for enhanced drug solubility Solubility ; COSMO-RS ; Chemistry ; Thermodynamics ; Process engineering ; Organic chemistry ; Physics ; Engineering Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography ; Thermal and Kinetic Analysis
Authors: Marta Barea-Sepúlveda | José Luis P. Calle | Marta Ferreiro‐González | Miguel Palma
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-01-18)
Field: Chemistry — Analytical Chemistry

Machine learning-based approaches to Vis-NIR data for the automated characterization of petroleum wax blends Wax ; Partial least squares regression ; Support vector machine ; Regression analysis ; Petroleum ; Computer science ; Characterization (materials science) ; Feature selection ; Linear regression ; Artificial intelligence ; Machine learning ; Biological system ; Materials science ; Chemistry ; Nanotechnology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ilane Schroetter | Olivier Berné | C. Joblin | Amélie Canin | Ryan Chown | Ameek Sidhu | E. Habart | E. Peeters | Thomas S. -Y. Lai | Alessandra Candian | Shubhadip Chakraborty | Annemieke Petrignani | Boris Trahin | Dries Van De Putte | Felipe Alarcón
Source: Astronomy and Astrophysics (preprint, 2024-03-06)
Field: Chemistry — Spectroscopy

PDRs4All Aromatic infrared bands (AIBs) are a set of broad emission bands at 3.3, 6.2, 7.7, 8.6, 11.2, and 12.7 μm, seen in the infrared spectra of most galaxies. With the James Webb Space Telescope (JWST), the 3.3 μm AIB can in principle be detected up to a redshift of ~7. Relating the evolution of the 3.3 μm AIB to local physical properties of the interstellar medium (ISM) is thus of paramount importance. By applying a dedicated machine learning algorithm to JWST NIRSpec observations of the Orion Bar photodissociation region obtained as part of the PDRs4All Early Release Science (ERS) program, we extracted two template spectra capturing the evolution of the AIB-related emission in the 3.2–3.6 μm range, which includes the AIB at 3.3 μm and its main satellite band at 3.4 μm. In the Orion Bar, we analyzed the spatial distribution of the templates and their relationship with the ro-vibrational H 2 line at 2.12 μm, the pure rotational line of H 2 at 4.69 μm and the Pfund δ line at 3.29 μm. We find that one template (AIB Irrad ) traces regions of neutral atomic gas with strong far-UV fields, while the other template (AIB Shielded ) corresponds to shielded regions with lower FUV fields and a higher molecular gas fraction. We then show that these two templates can be used to fit the NIRSpec AIB-related spectra of nearby galaxies. The relative weight of the two templates (AIB Irrad / Shielded ) is a tracer of the radiative feedback from massive stars on the ISM. We derive an estimate of AIB Irrad/Shielded in a z = 4.22 lensed galaxy and find that it has a lower value than for local galaxies. This pilot study illustrates how a detailed analysis of AIB emission in nearby regions can be used to probe the physical conditions of the extragalactic ISM. Physics ; Galaxy ; Astrophysics ; Redshift ; Shielded cable ; Stars ; Spectral line ; Infrared ; Emission spectrum ; Astronomy Spectroscopy and Laser Applications ; Astronomy and Astrophysical Research ; Spectroscopy and Chemometric Analyses
Authors: B.R. Radha Krushna | I.S. Pruthviraj | S.C. Sharma | S. Vijayanand | C. Krithika | Liza Mohapatra | R. Pappa Ammal | Sanjan Kumar | K. Manjunatha | Sheng Yun Wu | SeGi Yu | H. Nagabhushana
Source: Optical Materials (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

Synergistic enhancement of photoluminescence and advanced deep learning model through YOLOv8x in combined effects of carbon dots and Sr₉Al₆O₁₈:Sm³⁺ phosphors Photoluminescence ; Phosphor ; Materials science ; Carbon fibers ; Chemical engineering ; Nanotechnology ; Optoelectronics ; Engineering physics ; Physics ; Composite material ; Engineering Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Luminescence and Fluorescent Materials
Authors: Handong Wang | Duo Zhang | Xinzijian Liu | Xiangyu Zhang | Chengqian Zhang | Chun Cai | Hangrui Bi | Yiming Du | Xuejian Qin | Jiameng Huang | Bowen Li | Yifan Shan | Jinzhe Zeng | Yuzhi Zhang | Siyuan Liu | Yifan Li | Junhan Chang | Xinyan Wang | Shuo Zhou | Jianchuan Liu | Xiaoshan Luo | Zhenyu Wang | Wanrun Jiang | Jing Wu | Yudi Yang | Jiyuan Yang | Manyi Yang | Fu‐Qiang Gong | Linshuang Zhang | Mengchao Shi | Fu‐Zhi Dai | Darrin M. York | Shi Liu | Tong Zhu | Zhicheng Zhong | Jian Lv | Jun Cheng | Weile Jia | Mohan Chen | Guolin Ke | E Weinan | Linfeng Zhang
Source: Research Square (preprint, 2024-04-23)
Field: Materials Science — Materials Chemistry

DPA-2: Towards a universal large atomic model for molecular and materials simulation Computer science ; Nanotechnology ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties