jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hyun J. Kwon | Joseph H. Shiu | Celina K. Yamakawa | Elmer Ccopa Rivera
Source: Bioengineering (article, 2024-08-08)
Field: Chemistry — Analytical Chemistry

Enhancing Fermentation Process Monitoring through Data-Driven Modeling and Synthetic Time Series Generation Soft sensors based on deep learning regression models are promising approaches to predict real-time fermentation process quality measurements. However, experimental datasets are generally sparse and may contain outliers or corrupted data. This leads to insufficient model prediction performance. Therefore, datasets with a fully distributed solution space are required that enable effective exploration during model training. In this study, the robustness and predictive capability of the underlying model of a soft sensor was improved by generating synthetic datasets for training. The monitoring of intensified ethanol fermentation is used as a case study. Variational autoencoders were employed to create synthetic datasets, which were then combined with original datasets (experimental) to train neural network regression models. These models were tested on original versus augmented datasets to assess prediction improvements. Using the augmented datasets, the soft sensor predictive capability improved by 34%, and variability was reduced by 82%, based on R<sup>2</sup> scores. The proposed method offers significant time and cost savings for dataset generation for the deep learning modeling of ethanol fermentation and can be easily adapted to other fermentation processes. This work contributes to the advancement of soft sensor technology, providing practical solutions for enhancing reliability and robustness in large-scale production. Process (computing) ; Process engineering ; Process modeling ; Computer science ; Series (stratigraphy) ; Biochemical engineering ; Engineering ; Work in process ; Biology ; Operations management ; Operating system Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Time Series Analysis and Forecasting
Authors: Zijie Jiang | Zhuofei Gan | Chuwei Liang | Wen‐Di Li
Source: Nanophotonics (article, 2024-01-16)
Field: Materials Science — Surfaces, Coatings and Films

Generic characterization method for nano‐gratings using deep‐neural‐network‐assisted ellipsometry As a non-destructive and rapid technique, optical scatterometry has gained widespread use in the measurement of film thickness and optical constants. The recent advances in deep learning have presented new and powerful approaches to the resolution of inverse scattering problems. However, the application of deep-neural-network-assisted optical scatterometry for nanostructures still faces significant challenges, including poor stability, limited functionalities, and high equipment requirements. In this paper, a novel characterization method is proposed, which employs deep-neural-network-assisted ellipsometry to address these challenges. The method processes ellipsometric angles, which are measured by basic ellipsometers, as functional signals. A comprehensive model is developed to profile nano-gratings fabricated by diverse techniques, by incorporating rounded corners, residual layers, and optical constants into an existing model. The stability of the model is enhanced by implementing several measures, including multiple sets of initial values and azimuth-resolved measurements. A simple compensation algorithm is also introduced to improve accuracy without compromising efficiency. Experimental results demonstrate that the proposed method can rapidly and accurately characterize nano-gratings fabricated by various methods, with relative errors of both geometric and optical parameters well controlled under 5 %. Thus, the method holds great promise to serve as an alternative to conventional characterization techniques for <i>in-situ</i> measurement. Characterization (materials science) ; Artificial neural network ; Materials science ; Computer science ; Azimuth ; Residual ; Compensation (psychology) ; Ellipsometry ; Optics ; Nano- ; Artificial intelligence ; Nanotechnology ; Algorithm ; Thin film Optical Coatings and Gratings ; Advanced Fiber Optic Sensors ; Optical Polarization and Ellipsometry
Authors: Manuel Ballester | E. Márquez | John L. Bass | Christoph Wuersch | Florian Willomitzer | Aggelos K. Katsaggelos
Source: Measurement Science and Technology (article, 2024-12-24)
Field: Materials Science — Polymers and Plastics

Review and novel formulae for transmittance and reflectance of wedged thin films on absorbing substrates Abstract Historically, spectroscopic techniques have been essential for studying the optical properties of thin solid films. However, existing formulae for both normal transmission and reflection spectroscopy often rely on simplified theoretical assumptions, which may not accurately align with real-world conditions. For instance, it is common to assume (1) that the thin solid layers are deposited on completely transparent thick substrates and (2) that the film surface forms a specular plane with a relatively small wedge angle. While recent studies have addressed these assumptions separately, this work presents an integrated framework that eliminates both assumptions simultaneously. In addition, the current work presents a deep review of various formulae from the literature, each with their corresponding levels of complexity. Our review analysis highlights a critical trade-off between computational complexity and expression accuracy, where the newly developed formulae offer enhanced accuracy at the expense of increased computational time. Our user-friendly code, which includes several classical transmittance and reflectance formulae from the literature and our newly proposed expressions, is publicly available in both Python and Matlab at this link . Transmittance ; Reflectivity ; Materials science ; Thin film ; Optics ; Optoelectronics ; Physics ; Nanotechnology Synthesis and properties of polymers
Authors: Xiaolai Zhang | Xiaokang Wang | Fei Gao | Yue Chen | Hongyan Liu | Pengfei Zhou | Zixi Kang | Yutong Wang | Weidong Fan
Source: Materials Advances (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Metal–organic frameworks for petroleum-based platform compound separations Metal–organic frameworks for the separation of multicomponent petroleum-based platform compounds based on AST (adsorptive separation technology). Petroleum ; Separation (statistics) ; Metal-organic framework ; Metal ; Petroleum engineering ; Petroleum exploration ; Chemistry ; Environmental chemistry ; Process engineering ; Environmental science ; Materials science ; Computer science ; Organic chemistry ; Engineering ; Adsorption ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Enhanced Oil Recovery Techniques
Authors: Fangchen Ding | Sebastián Sánchez | Leiqing Pan | Weijie Lan | Juan Francisco García Martín
Source: Foods (review, 2024-12-03)
Field: Chemistry — Analytical Chemistry

Advances in Vibrational Spectroscopic Techniques for the Detection of Bio-Active Compounds in Virgin Olive Oils: A Comprehensive Review Vibrational spectroscopic techniques have gained significant attention in recent years for their potential in the rapid and efficient analysis of virgin olive oils, offering a distinct advantage over traditional methods. These techniques are particularly valuable for detecting and quantifying bio-active compounds that contribute to the nutritional and health benefits of virgin olive oils. This comprehensive review explores the latest advancements in vibrational spectroscopic techniques applied to virgin olive oils, focusing on the detection and measurement of key bio-active compounds such as unsaturated fatty acids, phenolic compounds, and other antioxidant compounds. The review highlights the improvements in vibrational spectroscopy, data processing, and chemometric techniques that have significantly enhanced the ability to accurately identify these compounds compared to conventional analytical methods. Additionally, it addresses current challenges, including the need for standardized methodologies and the potential for integrating vibrational spectroscopy with other analytical techniques to improve accuracy and reliability. Finally, findings over the last two decades, in which vibrational spectroscopy techniques were effectively used for the detailed characterization of bio-active compounds in virgin olive oils, are discussed. Olive oil ; Biochemical engineering ; Chemistry ; Materials science ; Food science ; Engineering Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Veronica Pawlik | Xiaohuan Zhao | Marc Figueras | Trenton J. Wolter | Zachary D. Hood | Yong Ding | Jingyue Liu | Miaofang Chi | Manos Mavrikakis | Younan Xia
Source: Nano Letters (article, 2024-01-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Thermal Stability of Au Rhombic Dodecahedral Nanocrystals Can Be Greatly Enhanced by Coating Their Surface with an Ultrathin Shell of Pt Rhombic dodecahedral nanocrystals have been considered particularly difficult to synthesize because they are enclosed by {110}, a low-index facet with the greatest surface energy. Recently, we demonstrated the use of seed-mediated growth for the facile and robust synthesis of Au rhombic dodecahedral nanocrystals (AuRD). While the unique shape and surface structure of AuRD are desirable for potential applications in plasmonics and catalysis, respectively, their high surface energy makes them highly susceptible to thermal degradation. Here we demonstrate that it is feasible to greatly improve the thermal stability with some sacrifice to the plasmonic properties of the original AuRD by coating their surface with an ultrathin shell made of Pt. Our <i>in situ</i> electron microscopy analysis indicates that the ultrathin Pt coating can increase the thermal stability from 60 up to 450 °C, a trend that is also supported by the results from a computational study. Dodecahedron ; Materials science ; Nanocrystal ; Thermal stability ; Coating ; Facet (psychology) ; Shell (structure) ; Nanotechnology ; Surface energy ; Thermal ; Plasmon ; Chemical engineering ; Crystallography ; Optoelectronics ; Composite material ; Chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Copper-based nanomaterials and applications ; Laser-Ablation Synthesis of Nanoparticles
Authors: Marco Schiavon | Marco Ravina | Mariachiara Zanetti | Deborah Panepinto
Source: Energies (article, 2024-01-23)
Field: Materials Science — Materials Chemistry

State-of-the-Art and Recent Advances in the Abatement of Gaseous Pollutants from Waste-to-Energy Despite their key role in integrated waste management, direct (incineration) and indirect (gasification/pyrolysis) waste combustion processes are still opposed by some of the general public due to the past emission levels of air pollutants. In fact, although the release of air pollutants (especially dioxin) to the atmosphere from waste combustion processes has gradually decreased over the years, thanks to the introduction of stricter regulations and more advanced removal technologies, there is still an unsolved problem regarding the public acceptance of waste-to-energy facilities. The aim of this paper is to provide an overview of the state-of-the-art air pollution control (APC) technologies used in waste combustion facilities. Air pollution control technologies are designed to reduce or eliminate the emissions of harmful pollutants into the atmosphere. These technologies are important for safeguarding public health, protecting ecosystems, complying with regulations, and promoting a more sustainable and resilient future for both local and global communities. This paper will highlight the complexity behind emission control and the efforts made by this sector over the years. This paper will also propose suggested configurations based on the interactions/complementarity between different APC technologies and recent findings to improve their performance. Pollutant ; Waste management ; Environmental science ; Waste-to-energy ; Engineering ; Chemistry ; Municipal solid waste Catalytic Processes in Materials Science ; Municipal Solid Waste Management ; Thermochemical Biomass Conversion Processes
Authors: Chen Qu | Paul L. Houston | Thomas C. Allison | Barry I. Schneider | Joel M. Bowman
Source: Journal of Chemical Theory and Computation (article, 2024-10-21)
Field: Materials Science — Materials Chemistry

DFT-Based Permutationally Invariant Polynomial Potentials Capture the Twists and Turns of C<sub>14</sub>H<sub>30</sub> Hydrocarbons are ubiquitous as fuels, solvents, lubricants, and as the principal components of plastics and fibers, yet our ability to predict their dynamical properties is limited to force-field mechanics. Here, we report two machine-learned potential energy surfaces (PESs) for the linear 44-atom hydrocarbon C<sub>14</sub>H<sub>30</sub> using an extensive data set of roughly 250,000 density functional theory (DFT) (B3LYP) energies for a large variety of configurations, obtained using MM3 direct-dynamics calculations at 500, 1000, and 2500 K. The surfaces, based on Permutationally Invariant Polynomials (PIPs) and using both a many-body expansion approach and a fragmented-basis approach, produce precise fits for energies and forces and also produce excellent out-of-sample agreement with direct DFT calculations for torsional and dihedral angle potentials. Going beyond precision, the PESs are used in molecular dynamics calculations that demonstrate the robustness of the PESs for a large range of conformations. The many-body PIPs PES, although more compute intensive than the fragmented-basis one, is directly transferable for other linear hydrocarbons. Invariant (physics) ; Physics ; Computer science ; Polynomial ; Combinatorics ; Mathematics ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Georgios I. Giannopoulos | Stelios K. Georgantzinos | Esmaeal Ghavanloo
Source: Advanced Theory and Simulations (article, 2024-07-19)
Field: Materials Science — Materials Chemistry

Tensile Behavior of Fullerene Nanosheets Utilizing Targeted Reactive Force Fields Abstract Fullerenes, as single crystals, present exceptional mechanical and physical properties due to their hollow spherical molecular structure consisting of carbon atoms connected by covalent bonds. The idea of linking these allotropes of carbon to create monolayer networks has now been accomplished experimentally. The question that remains to be answered is if these synthesized single‐layered nanosheets of fullerene present comparable properties with graphene monolayers. To answer this important question and to estimate the full tensile stress–strain behavior of quasi‐tetragonal as well as quasi‐hexagonal configurations of C 60 planar networks, several Molecular Dynamics simulations are performed in this work by using a new REAXFF and the AIREBO‐M potential. Various mechanical properties, such as Young's modulus, Poisson's ratio, ultimate tensile strength, ultimate tensile strain, and fracture energy at failure of C 60 monolayers of several sizes, are computed and compared with the results reported in the literature. Furthermore, a comprehensive discussion is made regarding the significant influence of the adopted potential on the numerical predictions of the elastic mechanical and fracture behavior of the fullerene nanosheets. Fullerene ; Ultimate tensile strength ; Materials science ; Nanotechnology ; Composite material ; Chemistry ; Organic chemistry Carbon Nanotubes in Composites ; Fullerene Chemistry and Applications ; Diamond and Carbon-based Materials Research
Authors: Mohammed Rahman | Hugh R. W. Dannatt | Charles D. Blundell | Leslie P. Hughes | Helen Blade | Jake Carson | Ben P. Tatman | Steven T. Johnston | Steven P. Brown
Source: The Journal of Physical Chemistry A (article, 2024-03-01)
Field: Chemistry — Spectroscopy

Polymorph Identification for Flexible Molecules: Linear Regression Analysis of Experimental and Calculated Solution- and Solid-State NMR Data The Δδ regression approach of Blade et al. [ <i>J. Phys. Chem. A</i> 2020, 124(43), 8959-8977] for accurately discriminating between solid forms using a combination of experimental solution- and solid-state NMR data with density functional theory (DFT) calculation is here extended to molecules with multiple conformational degrees of freedom, using furosemide polymorphs as an exemplar. As before, the differences in measured <sup>1</sup>H and <sup>13</sup>C chemical shifts between solution-state NMR and solid-state magic-angle spinning (MAS) NMR (Δδ<sub>experimental</sub>) are compared to those determined by gauge-including projector augmented wave (GIPAW) calculations (Δδ<sub>calculated</sub>) by regression analysis and a <i>t</i>-test, allowing the correct furosemide polymorph to be precisely identified. Monte Carlo random sampling is used to calculate solution-state NMR chemical shifts, reducing computation times by avoiding the need to systematically sample the multidimensional conformational landscape that furosemide occupies in solution. The solvent conditions should be chosen to match the molecule's charge state between the solution and solid states. The Δδ regression approach indicates whether or not correlations between Δδ<sub>experimental</sub> and Δδ<sub>calculated</sub> are statistically significant; the approach is differently sensitive to the popular root mean squared error (RMSE) method, being shown to exhibit a much greater dynamic range. An alternative method for estimating solution-state NMR chemical shifts by approximating the measured solution-state dynamic 3D behavior with an ensemble of 54 furosemide crystal structures (polymorphs and cocrystals) from the Cambridge Structural Database (CSD) was also successful in this case, suggesting new avenues for this method that may overcome its current dependency on the prior determination of solution dynamic 3D structures. Chemical shift ; Chemistry ; Solid-state nuclear magnetic resonance ; Linear regression ; Monte Carlo method ; Molecule ; Computational chemistry ; Statistical physics ; Physical chemistry ; Mathematics ; Nuclear magnetic resonance ; Statistics ; Physics ; Organic chemistry Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; X-ray Diffraction in Crystallography
Authors: Liam Desmond | Simone Margini | Emilio Barchiesi | Giuseppe Pontrelli | Anh N. Phan | Piergiorgio Gentile
Source: APL Bioengineering (article, 2024-02-29)
Field: Materials Science — Biomaterials

Layer-by-layer assembly of nanotheranostic particles for simultaneous delivery of docetaxel and doxorubicin to target osteosarcoma Osteosarcoma (OS) is a rare form of primary bone cancer, impacting approximately 3.4 × 10<sup>6</sup> individuals worldwide each year, primarily afflicting children. Given the limitations of existing cancer therapies, the emergence of nanotheranostic platforms has generated considerable research interest in recent decades. These platforms seamlessly integrate therapeutic potential of drug compounds with the diagnostic capabilities of imaging probes within a single construct. This innovation has opened avenues for enhanced drug delivery to targeted sites while concurrently enabling real-time monitoring of the vehicle's trajectory. In this study, we developed a nanotheranostic system employing the layer-by-layer (LbL) technique on a core containing doxorubicin (DOXO) and in-house synthesized carbon quantum dots. By utilizing chitosan and chondroitin sulfate as polyelectrolytes, we constructed a multilayered coating to encapsulate DOXO and docetaxel, achieving a coordinated co-delivery of both drugs. The LbL-functionalized nanoparticles exhibited an approximate size of 150 nm, manifesting a predominantly uniform and spherical morphology, with an encapsulation efficiency of 48% for both drugs. The presence of seven layers in these systems facilitated controlled drug release over time, as evidenced by <i>in vitro</i> release tests. Finally, the impact of the LbL-functionalized nanoparticles was evaluated on U2OS and Saos-2 osteosarcoma cells. The synergistic effect of the two drugs was found to be crucial in inducing cell death, particularly in Saos-2 cells treated with nanoparticles at concentrations higher than 10 <i>μ</i>g/ml. Transmission electron microscopy analysis confirmed the internalization of the nanoparticles into both cell types through endocytic mechanisms, revealing an underlying mechanism of necrosis-induced cell death. Osteosarcoma ; Docetaxel ; Doxorubicin ; Layer (electronics) ; Materials science ; Cancer research ; Cancer ; Nanotechnology ; Medicine ; Biomedical engineering ; Chemotherapy ; Internal medicine Nanoparticle-Based Drug Delivery ; Polymer Surface Interaction Studies ; Nanoplatforms for cancer theranostics
Authors: Chengzhi Wang | Xiaping Fu | Ying Zhou | Feng Fu
Source: Foods (article, 2024-03-15)
Field: Chemistry — Analytical Chemistry

Deoxynivalenol Detection beyond the Limit in Wheat Flour Based on the Fluorescence Hyperspectral Imaging Technique Deoxynivalenol (DON) is a harmful fungal toxin, and its contamination in wheat flour poses a food safety concern globally. This study proposes the combination of fluorescence hyperspectral imaging (FHSI) and qualitative discrimination methods for the detection of excessive DON content in wheat flour. Wheat flour samples were prepared with varying DON concentrations through the addition of trace amounts of DON using the wet mixing method for fluorescence hyperspectral image collection. SG smoothing and normalization algorithms were applied for original spectra preprocessing. Feature band selection was carried out by applying the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the random frog algorithm on the fluorescence spectrum. Random forest (RF) and support vector machine (SVM) classification models were utilized to identify wheat flour samples with DON concentrations higher than 1 mg/kg. The results indicate that the SG-CARS-RF and SG-CARS-SVM models showed better performance than other models, achieving the highest recall rate of 98.95% and the highest accuracy of 97.78%, respectively. Additionally, the ROC curves demonstrated higher robustness on the RF algorithm. Deep learning algorithms were also applied to identify the samples that exceeded safety standards, and the convolutional neural network (CNN) model achieved a recognition accuracy rate of 97.78% for the test set. In conclusion, this study demonstrates the feasibility and potential of the FHSI technique in detecting DON infection in wheat flour. Hyperspectral imaging ; Support vector machine ; Artificial intelligence ; Random forest ; Pattern recognition (psychology) ; Convolutional neural network ; Feature selection ; Normalization (sociology) ; Preprocessor ; Computer science ; Robustness (evolution) ; Mathematics ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Listeria monocytogenes in Food Safety
Authors: Siamak Attarian | Chen Shen | Dane Morgan | Izabela Szlufarska
Source: Computational Materials Science (article, 2024-09-24)
Field: Materials Science — Materials Chemistry

Best practices for fitting machine learning interatomic potentials for molten salts: A case study using NaCl-MgCl2 Interatomic potential ; Materials science ; Thermodynamics ; Computer science ; Chemistry ; Molecular dynamics ; Computational chemistry ; Physics Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes ; X-ray Diffraction in Crystallography