jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Debiao Wu | Yaying Xu | Feng Xu | Minghao Shao | Mingzhi Huang
Source: Research Square (preprint, 2023-03-01)
Field: Chemistry — Analytical Chemistry

Machine Learning algorithms for in-line monitoring during Yeast Fermentations based on Raman Spectroscopy Abstract Given the nonlinear and complex industrial fermentation system, the process analytical technology offers significant advantages for direct and real-time monitoring, control and evaluation of synthetic processes. Here, we introduced a Raman spectroscopy in-line monitoring method for ethanol production by Saccharomyces cerevisiae . First, the feature selection methods in machine learning were used to reduce the dimension of Raman spectral data. The results showed that the model training time is reduced by more than 90% after feature selection, while the prediction performance of glycerol and cell concentration is improved by 14.20% and 17.10% at the RMSE level. Then, 15 machine learning algorithms were called to retrain the model, and hyperparameters were adjusted by grid search. The results demonstrated that the model after adjusting the hyperparameters improved the RMSE of ethanol, glycerol, glucose, and biomass by 9.73%, 4.33%, 22.22%, and 13.79%, respectively. Finally, BaggingRegressor, Support vector regression, BayesianRidge, and VotingRegressor are suitable machine learning algorithms corresponding to the models for predicting glucose, ethanol, glycerol, and cell concentrations, respectively. In addition, the R-squared values were 0.89–0.97, and the RMSE values were 0.06–2.59 g/L on the testing datasets, respectively. The results suggested that machine learning methods can be effectively applied in the modeling and analysis of Raman spectroscopy. Moreover, it is conducive to promoting the optimization of Raman spectroscopy in biological process monitoring, thereby improving industrial production efficiency, and providing novel modeling ideas. Artificial intelligence ; Hyperparameter ; Machine learning ; Hyperparameter optimization ; Mean squared error ; Support vector machine ; Algorithm ; Feature selection ; Glycerol ; Computer science ; Raman spectroscopy ; Biological system ; Chemistry ; Mathematics ; Biochemistry ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Viral Infectious Diseases and Gene Expression in Insects
Authors: Pinelopi Moutzouri | Manuel Cordova | Bruno Simões de Almeida | Daria Torodii | Lyndon Emsley
Source: Angewandte Chemie (article, 2023-03-16)
Field: Chemistry — Spectroscopy

Two‐dimensional Pure Isotropic Proton Solid State NMR Abstract One key bottleneck of solid‐state NMR spectroscopy is that 1 H NMR spectra of organic solids are often very broad due to the presence of a strong network of dipolar couplings. We have recently suggested a new approach to tackle this problem. More specifically, we parametrically mapped errors leading to residual dipolar broadening into a second dimension and removed them in a correlation experiment. In this way pure isotropic proton (PIP) spectra were obtained that contain only isotropic shifts and provide the highest 1 H NMR resolution available today in rigid solids. Here, using a deep‐learning method, we extend the PIP approach to a second dimension, and for samples of L‐tyrosine hydrochloride and ampicillin we obtain high resolution 1 H‐ 1 H double‐quantum/single‐quantum dipolar correlation and spin‐diffusion spectra with significantly higher resolution than the corresponding spectra at 100 kHz MAS, allowing the identification of previously overlapped isotropic correlation peaks. Isotropy ; Solid-state nuclear magnetic resonance ; Chemistry ; Spectral line ; Proton ; Nuclear magnetic resonance spectroscopy ; NMR spectra database ; Two-dimensional nuclear magnetic resonance spectroscopy ; Dipole ; Carbon-13 NMR satellite ; Spectroscopy ; Nuclear magnetic resonance ; Fluorine-19 NMR ; Physics ; Stereochemistry ; Organic chemistry ; Optics ; Quantum mechanics Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; Advanced Battery Materials and Technologies
Authors: M.J. Jiménez–Come | Francisco Javier González Gallero | Pascual Álvarez Gómez | Victoria Matres
Source: Research Square (preprint, 2023-09-07)
Field: Materials Science — Materials Chemistry

Prediction and detection of localised corrosion attack of stainless steel in biogas production: Machine Learning Classification Approach Abstract Biogas contributes to environmental protection by reducing greenhouse gas emissions and promoting the recycling of organic waste. Its utilization plays a crucial role in addressing the challenges of climate change and sustainability. However, the deterioration of process plants involved in biogas production due to corrosion has a critical impact on the safety and durability of their operations. In order to maintain safety of structures in service life with respect to corrosion, it is essential to develop effective corrosion engineering control methods. The electrochemical techniques have become a useful tool to evaluate corrosion resistance. However, these techniques may require microscopic analysis of the material surface and the analysis may be influenced by subjective factors. To solve this drawback, this work proposes the use of SVM models to predict corrosion status of the material used in biogas production with no need to perform microscopic analysis after the electrochemical test. The obtained results of sensitivity and specificity equal to 0.94 and 0.97, respectively, revealed the utility of the proposed stochastic models to assure the corrosion state of the equipment involved in biogas production. SVM-based models become an effective alternative to evaluate material durability accurately. Corrosion ; Durability ; Biogas ; Production (economics) ; Sustainability ; Biogas production ; Process engineering ; Greenhouse gas ; Environmental science ; Computer science ; Materials science ; Waste management ; Engineering ; Metallurgy ; Composite material ; Anaerobic digestion Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques
Authors: Khalida Abbas Omran
Source: International Journal of Drug Delivery Technology (article, 2023-09-25)
Field: Materials Science — Polymers and Plastics

Synthesis and Characterization of Colloidally Stable Self-crosslinking Water-based Anticorrosive Polymer at Room Temperature The emulsion polymerization of butyl acrylate (BuA), styrene (St), and 3-isopropenyl-α,α-dimethylbenzyl isocyanate (IPMBI) is presented in this work. Obtain colloidally stable latexes for the poly (BuA/St/IPMBI) system during polymerization and storage for use in a water-based coating formulation. A series of semicontinuous polymerizations were also performed to investigate the effect of comonomer composition (w/w) in the feed (BuA/St) on conversion. The particle diameter (Dp), particle number (Np), hydrolysis of the IPMBI’s isocyanate groups, stability and storage properties. Films were created in which the effect of comonomer composition was also investigated, the glass transition temperature (Tg) was determined, the gel content was selected, and the mechanical properties and capacity as a barrier to water vapour and oxygen were assessed. Another series of reactions, on the other hand, was carried out in which the effect of the IPMBI content (1.5, 3.5, and 5.5%) was studied using a constant composition BuA/St=60/40. The studies conducted are the same as those mentioned for the effect of comonomer composition in feed. With increasing IPMBI percentage, slight differences in conversion, Dp, and Np were observed; the latices undergo hydrolysis during polymerization and storage. Colloidal stability, on the other hand, was preserved. The mechanical properties of the films improved as the percentage of IPMBI increased, while the boundary characteristics to moisture and oxygen improved when compared to the white film (0% of IPMBI). Finally, the formulation of a self-crosslinking water-based coating at room temperature using the previously obtained latex as a binder is reported; the coating demonstrated excellent adhesion, scratch hardness, and gloss regardless of the type of latex used. Comonomer ; Materials science ; Emulsion polymerization ; Glass transition ; Polymerization ; Polymer chemistry ; Chemical engineering ; Polymer ; Particle size ; Composite material Polymer composites and self-healing ; Advanced Polymer Synthesis and Characterization ; Photopolymerization techniques and applications
Authors: Sanmun Kim | Chanhyung Park | Shinho Kim | Hsiao L. Chung | Min Seok Jang
Source: arXiv (Cornell University) (preprint, 2023-06-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design parameters of free-form color routers for subwavelength pixelated CMOS image sensors Metasurface-based color routers are emerging as next-generation optical components for image sensors, replacing classical color filters and microlens arrays. In this work, we report how the design parameters such as the device dimensions and refractive indices of the dielectrics affect the optical efficiency of the color routers. Also, we report how the design grid resolution parameters affect the optical efficiency and discover that the fabrication of a color router is possible even in legacy fabrication facilities with low structure resolutions. Microlens ; Fabrication ; Router ; Color gel ; Computer science ; Color filter array ; Optoelectronics ; Materials science ; Image sensor ; Optics ; Artificial intelligence ; Physics ; Nanotechnology ; Computer network ; Lens (geology) Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Plasmonic and Surface Plasmon Research
Authors: Giuseppe Lovarelli | Fabrizio Mazziotti | Demetrio Logoteta | Giuseppe Iannaccone
Source: arXiv (Cornell University) (preprint, 2023-11-29)
Field: Materials Science — Materials Chemistry

Two-dimensional field-effect transistors based on lateral heterojunctions of transition metal dichalcogenides: dissipative quantum transport modeling Reducing the contact resistance of field-effect transistors based on two-dimensional materials is one of the key improvements required to enable the integration of such transistors in an advanced semiconductor manufacturing process. Suitably designed lateral heterojunctions provide an opportunity to independently tailor the contact and channel properties in order to optimize contact resistance. Inspired by the recent experimental demonstration of a two-dimensional $p$-type Schottky barrier, here we use quantum transport simulations to estimate the performance of $p$-type transistors in which the channel consists of a lateral heterostructure of NbS$_2$/MoS$_2$/NbS$_2$ (semimetal-semiconductor-semimetal). We find that the gate alignment with the channel is a critical design parameter, strongly influencing the capability of the gate to modulate the Schottky barrier at the MoS$_2$/NbS$_2$ interface. This effect is also found to significantly affect the scaling behavior of the device. Heterojunction ; Schottky barrier ; Contact resistance ; Transistor ; Optoelectronics ; Materials science ; Semiconductor ; Field-effect transistor ; Channel (broadcasting) ; Schottky diode ; Scaling ; MOSFET ; Nanotechnology ; Electronic engineering ; Electrical engineering ; Engineering ; Voltage ; Layer (electronics) ; Mathematics ; Diode Graphene research and applications ; Surface and Thin Film Phenomena ; Semiconductor materials and interfaces
Authors: Linxue Zhang | Nuernisha Alifu | Zhongwen Lan | Zhong Yu | Qifan Li | Xiaona Jiang | Chuanjian Wu | Ke Sun
Source: PubMed (review, 2023-04-25)
Field: Materials Science — Biomaterials

[Surface modification of multifunctional ferrite magnetic nanoparticles and progress in biomedicine]. Magnetic ferrite nanoparticles (MFNPs) have great application potential in biomedical fields such as magnetic resonance imaging, targeted drugs, magnetothermal therapy and gene delivery. MFNPs can migrate under the action of a magnetic field and target specific cells or tissues. However, to apply MFNPs to organisms, further modifications on the surface of MFNPs are required. In this paper, the common modification methods of MFNPs are reviewed, their applications in medical fields such as bioimaging, medical detection, and biotherapy are summarized, and the future application directions of MFNPs are further prospected. Biomedicine ; Nanotechnology ; Surface modification ; Magnetic nanoparticles ; Magnetic resonance imaging ; Materials science ; Ferrite (magnet) ; Nanoparticle ; Magnetic field ; Biomedical engineering ; Medicine ; Engineering ; Bioinformatics ; Biology ; Physics ; Chemical engineering ; Radiology Nanoparticle-Based Drug Delivery ; Advanced Nanomaterials in Catalysis ; Nanomaterials for catalytic reactions
Authors: Boqin Zhang | Xin Jing | Qimen Xu | Shashikant Kumar | Abhiraj Sharma | Lucas Erlandson | Sushree Jagriti Sahoo | Edmond Chow | Andrew J. Medford | John E. Pask | Phanish Suryanarayana
Source: arXiv (Cornell University) (preprint, 2023-05-12)
Field: Chemistry — Inorganic Chemistry

Version 2.0.0 -- SPARC: Simulation Package for Ab-initio Real-space Calculations SPARC is an accurate, efficient, and scalable real-space electronic structure code for performing ab initio Kohn-Sham density functional theory calculations. Version 2.0.0 of the software provides increased efficiency, and includes spin-orbit coupling, dispersion interactions, and advanced semilocal as well as hybrid exchange-correlation functionals, where it outperforms state-of-the-art planewave codes by an order of magnitude and more, with increasing advantages as the number of processors is increased. These new features further expand the range of physical applications amenable to first principles investigation. Ab initio ; Scalability ; Space (punctuation) ; Density functional theory ; Computational science ; Hybrid functional ; Computer science ; Dispersion (optics) ; Coupling (piping) ; Range (aeronautics) ; Software ; Code (set theory) ; Electronic structure ; Ab initio quantum chemistry methods ; Software package ; Physics ; Computational chemistry ; Statistical physics ; Materials science ; Chemistry ; Quantum mechanics ; Programming language ; Molecule ; Database Inorganic Fluorides and Related Compounds ; Catalysis and Oxidation Reactions ; Zeolite Catalysis and Synthesis
Authors: Anoop Kumar Pandey | V.K. SINGH | Uttar Pradesh Higher Education Service Comission, Prayagraj (UP), India | Ankit K. Sharma | Satyendra Nath Tiwari | Gaurav Kumar Mishra | Rolly Yadav | V.N. MISHRA
Source: Romanian Journal of Biophysics (article, 2023-09-13)
Field: Chemistry — Organic Chemistry

SPECTROSCOPIC, NLO, NBO, BIOCHEMICAL INVESTIGATION OF 3-(HYDROXYMETHYL)-3-METHYL-2,6-DIPHENYLPIPERIDIN-4-ONE The piperidine put deep attention to research due to their pharmacological activity and hence drug designing. The derivatives of piperidine have important activities against cancers and tumor disease. Cancer is the most difficult disease in the world (WHO) which is basically considered to be the uncontrolled growth of abnormal cells. In present communication structural and vibrational features of piperidine derivatives 3-(hydroxymethyl)-3-methyl-2,6-diphenylpiperidin-4-one has been computed based on theoretical quantum chemical approach. The primary aspects of piperidine derivatives 3-(hydroxymethyl)-3-methyl-2,6-diphenylpiperidin-4-one structure has been calculated by using optimized geometry, spectroscopic behavior, chemical reactivity and molecular docking analysis. The info about coupled of modes were founded in vibrational features of the molecule. The stability of the chemical reactivity of 3-(hydroxymethyl)-3-methyl-2,6-diphenylpiperidin-4-one was predicted by using HOMO-LUMO energy gap. The molecular electrostatic surface potential (MESP) plot utilizes overall picture of accretion of charges on separate atoms in molecule which very useful predict the nucleophilic and electrophilic charge center. The pharmacological importance of piperidine derivatives 3-(hydroxymethyl)-3-methyl-2,6-diphenylpiperidin-4-one is established by calculated biological activity. The inhibition properties and chemical reactivity of piperidine derivatives 3-(hydroxymethyl)- 3-methyl-2,6-diphenylpiperidin-4-one are well-known by chemical reactivity descriptors by using HOMO-LUMO energies. The inhibition potentials of piperidine derivatives 3-(hydroxymethyl)- 3-methyl-2,6-diphenylpiperidin-4-one are established by calculated lipophilicity, aqueous solubility, and binding affinity. The docking of molecule has been also performed with suitable target. Piperidine ; Hydroxymethyl ; Chemistry ; HOMO/LUMO ; Reactivity (psychology) ; Molecule ; Computational chemistry ; Heteroatom ; Docking (animal) ; Natural bond orbital ; Stereochemistry ; Organic chemistry Free Radicals and Antioxidants ; Synthesis and biological activity ; Computational Drug Discovery Methods
Authors: MinHao HU | Sheng Sun | Tong‐Yi Zhang
Source: Scientia Sinica Technologica (article, 2023-04-10)
Field: Materials Science — Materials Chemistry

Theoretical derivation and MD simulation of size dependent mechanical properties of grains and grain boundaries in nanocrystalline nickel Bi-crystal <p indent="0mm">This paper studied the ∑5 grain boundaries of FCC pure nickel and derived the size-dependent analytical formulas of the equivalent mechanical properties of grains and grain boundaries in the anisotropic bi-crystal model based on the interface eigenstress model and nonlinear elastic constitutive model. Molecular dynamics simulations were performed to validate the theoretical analytical formulas and determine the material parameters of the grain boundary. These analytical formulas can accurately predict pure nickel (∑5 grain boundary) with different sizes and thicknesses. The results show that the grain boundary has a lower biaxial modulus than the grain, resulting in an increase in the apparent biaxial modulus of the bi-crystal with model thickness. The theoretical model can accurately describe the relaxation process caused by the eigenstress of the grain boundary in the bi-crystal model and the size-dependent behavior of the equivalent mechanical properties. The theoretical model can be directly applied to other nanocrystalline bi-crystal systems. Nanocrystalline material ; Grain size ; Materials science ; Grain boundary ; Nickel ; Crystal (programming language) ; Grain boundary strengthening ; Nanocrystal ; Molecular dynamics ; Metallurgy ; Crystallography ; Nanotechnology ; Computer science ; Chemistry ; Microstructure ; Computational chemistry Microstructure and mechanical properties ; Surface Treatment and Residual Stress ; Microstructure and Mechanical Properties of Steels
Authors: Ashish Tiwari
Source: International Journal of Multidisciplinary Research in Science, Engineering and Technology. (article, 2023-11-25)
Field: Materials Science — Polymers and Plastics

Enhancing the Optical Properties of Polymethyl Methacrylate (PMMA) Through Metamaterial Integration: Mechanisms, Techniques, and Future Directions Polymethyl methacrylate (PMMA), known as acrylic or plexiglass, is a transparent thermoplastic often used as a lightweight, shatter-resistant alternative to glass. Due to its excellent optical clarity, ease of processing, and favorable mechanical properties, PMMA has found extensive applications in various industries, including automotive, electronics, medical devices, and optical instruments. However, despite its widespread use, the inherent optical properties of PMMA have certain limitations, such as limited light absorption and scattering capabilities, which can restrict its performance in advanced photonic applications. Polymethyl methacrylate ; Metamaterial ; Materials science ; Composite material ; Optoelectronics ; Polymer Synthesis and properties of polymers ; Semiconductor Lasers and Optical Devices
Authors: Okezzi F. Ukorigho | Opeoluwa Owoyele
Source: arXiv (Cornell University) (preprint, 2023-07-19)
Field: Materials Science — Materials Chemistry

A Competitive Learning Approach for Specialized Models: A Solution for Complex Physical Systems with Distinct Functional Regimes Complex systems in science and engineering sometimes exhibit behavior that changes across different regimes. Traditional global models struggle to capture the full range of this complex behavior, limiting their ability to accurately represent the system. In response to this challenge, we propose a novel competitive learning approach for obtaining data-driven models of physical systems. The primary idea behind the proposed approach is to employ dynamic loss functions for a set of models that are trained concurrently on the data. Each model competes for each observation during training, allowing for the identification of distinct functional regimes within the dataset. To demonstrate the effectiveness of the learning approach, we coupled it with various regression methods that employ gradient-based optimizers for training. The proposed approach was tested on various problems involving model discovery and function approximation, demonstrating its ability to successfully identify functional regimes, discover true governing equations, and reduce test errors. Computer science ; Physical system ; Range (aeronautics) ; Limiting ; Identification (biology) ; Function (biology) ; Machine learning ; Artificial intelligence ; Set (abstract data type) ; Complex system ; Engineering Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Computational Drug Discovery Methods
Authors: Julio J. Valdés | Alain Tchagang
Source: arXiv (Cornell University) (preprint, 2023-09-25)
Field: Materials Science — Materials Chemistry

Understanding the Structure of QM7b and QM9 Quantum Mechanical Datasets Using Unsupervised Learning This paper explores the internal structure of two quantum mechanics datasets (QM7b, QM9), composed of several thousands of organic molecules and described in terms of electronic properties. Understanding the structure and characteristics of this kind of data is important when predicting the atomic composition from the properties in inverse molecular designs. Intrinsic dimension analysis, clustering, and outlier detection methods were used in the study. They revealed that for both datasets the intrinsic dimensionality is several times smaller than the descriptive dimensions. The QM7b data is composed of well defined clusters related to atomic composition. The QM9 data consists of an outer region predominantly composed of outliers, and an inner core region that concentrates clustered, inliner objects. A significant relationship exists between the number of atoms in the molecule and its outlier/inner nature. Despite the structural differences, the predictability of variables of interest for inverse molecular design is high. This is exemplified with models estimating the number of atoms of the molecule from both the original properties, and from lower dimensional embedding spaces. Cluster analysis ; Predictability ; Inverse ; Outlier ; Curse of dimensionality ; Dimension (graph theory) ; Computer science ; Molecule ; Embedding ; Quantum ; Statistical physics ; Mathematics ; Physics ; Artificial intelligence ; Quantum mechanics ; Combinatorics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics