jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alina Skorynina | Bogdan Protsenko | Oleg Usoltsev | С. А. Гуда | Aram L. Bugaev
Source: Inorganic Chemistry (article, 2023-04-14)
Field: Chemistry — Inorganic Chemistry

Quantitative Structural Description of Zeolites by Machine Learning Analysis of Infrared Spectra Application of machine learning (ML) algorithms to spectroscopic data has a great potential for obtaining hidden correlations between structural information and spectral features. Here, we apply ML algorithms to theoretically simulated infrared (IR) spectra to establish the structure-spectrum correlations in zeolites. Two hundred thirty different types of zeolite frameworks were considered in the study whose theoretical IR spectra were used as the training ML set. A classification problem was solved to predict the presence or absence of possible tilings and secondary building units (SBUs). Several natural tilings and SBUs were also predicted with an accuracy above 89%. The set of continuous descriptors was also suggested, and the regression problem was also solved using the ExtraTrees algorithm. For the latter problem, additional IR spectra were computed for the structures with artificially modified cell parameters, expanding the database to 470 different spectra of zeolites. The resulting prediction quality above or close to 90% was obtained for the average Si-O distances, Si-O-Si angles, and volume of TO<sub>4</sub> tetrahedra. The obtained results provide new possibilities for utilization of infrared spectra as a quantitative tool for characterization of zeolites<i>.</i> SBus ; Chemistry ; Spectral line ; Infrared spectroscopy ; Infrared ; Tetrahedron ; Set (abstract data type) ; Zeolite ; Support vector machine ; Algorithm ; Biological system ; Artificial intelligence ; Pattern recognition (psychology) ; Crystallography ; Physical chemistry ; Computer science ; Optics ; Physics Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies
Authors: Daniel C. Castro | Peter C. Chan-Andersen | Elena V. Romanova | Jonathan V. Sweedler
Source: Mass Spectrometry Reviews (review, 2023-04-03)
Field: Chemistry — Spectroscopy

Probe‐based mass spectrometry approaches for single‐cell and single‐organelle measurements Exploring the chemical content of individual cells not only reveals underlying cell-to-cell chemical heterogeneity but is also a key component in understanding how cells combine to form emergent properties of cellular networks and tissues. Recent technological advances in many analytical techniques including mass spectrometry (MS) have improved instrumental limits of detection and laser/ion probe dimensions, allowing the analysis of micron and submicron sized areas. In the case of MS, these improvements combined with MS's broad analyte detection capabilities have enabled the rise of single-cell and single-organelle chemical characterization. As the chemical coverage and throughput of single-cell measurements increase, more advanced statistical and data analysis methods have aided in data visualization and interpretation. This review focuses on secondary ion MS and matrix-assisted laser desorption/ionization MS approaches for single-cell and single-organelle characterization, which is followed by advances in mass spectral data visualization and analysis. Chemistry ; Mass spectrometry ; Organelle ; Single-cell analysis ; Chromatography ; Mass spectrometry imaging ; Analytical Chemistry (journal) ; Cell ; Biochemistry Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Analytical chemistry methods development
Authors: Chandra Mohan | Neeraj Kumari | Philippe Jeandet | Priyanka Kumari | Arvind Negi
Source: Micromachines (article, 2023-05-21)
Field: Chemistry — Inorganic Chemistry

Synthesis of Nano Pigments Using Clay Minerals and Organic Dyes and Their Application as Colorants in Polymer Matrix A new generation of clay-based nano pigments has been introduced, providing the advantage of both inorganic pigments and organic dyes. These nano pigments have been synthesized through a stepwise procedure where, initially, an organic dye is adsorbed onto the surface of the adsorbent, and then dye adsorbed adsorbent is used as pigment for further applications. The objective of the current paper was to examine the interaction of non-biodegradable toxic dyes, Crystal Violet (CV) and Indigo Carmine (IC), with clay minerals (montmorillonite (Mt), vermiculite (Vt), and clay bentonite (Bent)) and their organically modified forms (OMt, OBent, and OVt) and to develop a novel methodology for the synthesis of the value-added products and clay-based nano pigments without creating second generation waste materials. In our observation, the uptake of CV was more intense onto pristine Mt, Bent, and Vt, and the uptake of IC was more onto OMt, OBent, and OVt. CV was found to be in the interlayer region of Mt and Bent, as supported by XRD data. Zeta potential values confirmed the presence of CV on their surface. In contrast, in the case of Vt and organically modified forms, the dye was found on the surface, confirmed by XRD and zeta potential values. In the case of indigo carmine, the dye was found only on the surface of pristine Mt, Bent, Vt, and organo Mt, Bent, Vt. During the interaction of CV and IC with clay and organoclays, intense violet and blue-colored solid residues were obtained (also known as clay-based nano pigments). The nano pigments were used as colorants in a poly (methyl-methacrylate) (PMMA) polymer matrix to form transparent polymer films. Indigo carmine ; Adsorption ; Pigment ; Zeta potential ; Montmorillonite ; Bentonite ; Clay minerals ; Crystal violet ; Chemical engineering ; Chemistry ; Saponite ; Matrix (chemical analysis) ; Materials science ; Vermiculite ; Nuclear chemistry ; Nanoparticle ; Mineralogy ; Organic chemistry ; Nanotechnology ; Chromatography ; Composite material Pigment Synthesis and Properties ; Dyeing and Modifying Textile Fibers ; Adsorption and biosorption for pollutant removal
Authors: Alexander Fischereder | Markus Rödl | Markus Suta | Thomas S. Hofer | Heidi A. Schwartz
Source: The Journal of Physical Chemistry C (article, 2023-07-28)
Field: Chemistry — Inorganic Chemistry

From Blue Jeans to Luminescent Materials: Designing Thioindigo-Based Red-Fluorescent Hybrid Systems Three novel hybrid systems containing thioindigo (TI) as a guest molecule and MIL-68(In), MIL-68(Ga), and MOF-5 as host matrices were synthesized and studied with respect to their optical properties. Surprisingly, TI@metal–organic framework (MOF) systems do not exhibit photochromic response as a result of strong host–guest interactions, in which the E isomer is trapped in a parallel interaction motif between TI and the phenyl rings of the linker molecules. This binding site is assumed to be the origin of the occurring bright red solid-state fluorescence of TI inside the different MOF hosts. It was found that the corresponding decay times are significantly longer for TI@MOF than for pure TI. The emission wavelength of pure solid TI is also longer compared to its TI@MOF counterpart. These findings demonstrate that insertion into the MOF prevents aggregation between the TI molecules, which is beneficial to enhance the photoluminescence quantum yield. In addition to the experimental measurements, a series of density functional tight binding molecular dynamics (DFTB MD) simulations of E- and Z-thioindigo embedded in MOF-5 and MIL-68(Ga) have been performed, providing detailed insight into the host–guest interaction at the molecular level. Fluorescence ; Photoluminescence ; Photochromism ; Molecule ; Linker ; Luminescence ; Quantum yield ; Materials science ; Metal-organic framework ; Density functional theory ; Hybrid material ; Solid-state ; Photochemistry ; Chemical physics ; Nanotechnology ; Chemistry ; Computational chemistry ; Physical chemistry ; Optoelectronics ; Organic chemistry ; Physics ; Optics Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Polyoxometalates: Synthesis and Applications
Authors: Patrick Lambrix | Rickard Armiento | Huanyu Li | Olaf Hartig | Mina Abd Nikooie Pour | Ying Li
Source: Semantic Web (article, 2023-10-11)
Field: Materials Science — Materials Chemistry

The materials design ontology In the materials design domain, much of the data from materials calculations is stored in different heterogeneous databases with different data and access models. Therefore, accessing and integrating data from different sources is challenging. As ontology-based access and integration alleviates these issues, in this paper we address data access and interoperability for computational materials databases by developing the Materials Design Ontology. This ontology is inspired by and guided by the OPTIMADE effort that aims to make materials databases interoperable and includes many of the data providers in computational materials science. In this paper, first, we describe the development and the content of the Materials Design Ontology. Then, we use a topic model-based approach to propose additional candidate concepts for the ontology. Finally, we show the use of the Materials Design Ontology by a proof-of-concept implementation of a data access and integration system for materials databases based on the ontology.11 This paper is an extension of (In The Semantic Web – ISWC 2020 – 19th International Semantic Web Conference, Proceedings, Part II (2000) 212–227 Springer) with results from (In ESWC Workshop on Domain Ontologies for Research Data Management in Industry Commons of Materials and Manufacturing 2021 1–11) and currently unpublished results regarding an application using the ontology. Ontology ; Ontology-based data integration ; Computer science ; Upper ontology ; Interoperability ; Process ontology ; OWL-S ; Open Biomedical Ontologies ; Semantic interoperability ; Semantic Web ; Ontology engineering ; Data discovery ; Domain (mathematical analysis) ; Data integration ; Suggested Upper Merged Ontology ; World Wide Web ; Database ; Metadata ; Semantic Web Stack Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jina Wang | Yan Gao | Nan Zhang | Yundong Xie | Xinya Xu | Hui Bin Guo | Tao Bao | Meng Sun | Sicen Wang
Source: Journal of Separation Science (article, 2023-04-14)
Field: Chemistry — Analytical Chemistry

Stir‐bar sorptive extraction based on hydroxyl‐functionalized zirconium‐metal‐organic framework for the detection of three quinolones in actual samples A novel analytical method based on stir-bar sorptive extraction was proposed for the determination of three trace quinolones in fish and shrimp samples. UiO-66-(OH)<sub>2</sub> , a hydroxyl-functionalized zirconium metal-organic framework, has been coated on frosted glass rods by an in situ growth method. The product, UiO-66-(OH)<sub>2</sub> modified frosted glass rods, has been characterized and key parameters have been optimized in combination with ultra-high-performance liquid chromatography. The detection limits of enoxacin, norfloxacin, and ciprofloxacin were 0.48-0.8 ng ml<sup>-1</sup> , and the detection concentrations were in the range of 10-300 ng ml<sup>-1</sup> , showing a good linear relationship. This method was used for the determination of three quinolones in aquatic organisms, and the recoveries in spiked fish and shrimp muscle tissue samples were 74.8%-105.4% and 82.5%-115.8%, respectively. The relative standard deviations were less than 6.9%. The established method combined stir-bar sorptive extraction based on UiO-66-(OH)<sub>2</sub> modified frosted glass rods with ultra-high-performance liquid chromatography, has good application prospects for the detection of quinolone residues in fish and shrimp muscle samples. Zirconium ; Metal-organic framework ; Extraction (chemistry) ; Chemistry ; Bar (unit) ; Metal ; Environmental chemistry ; Chromatography ; Adsorption ; Organic chemistry ; Geology Analytical chemistry methods development ; Molecular Sensors and Ion Detection ; Antibiotics Pharmacokinetics and Efficacy
Authors: Cristina Chircov | Iulia Dumitru | Bogdan Ştefan Vasile | Ovidiu Oprea | Alina Maria Holban | Roxana Cristina Popescu
Source: Pharmaceutics (article, 2023-08-27)
Field: Materials Science — Biomaterials

Microfluidic Synthesis of Magnetite Nanoparticles for the Controlled Release of Antibiotics Magnetite nanoparticles (MNPs) have been intensively studied for biomedical applications, especially as drug delivery systems for the treatment of infections. Additionally, they are characterized by intrinsic antimicrobial properties owing to their capacity to disrupt or penetrate the microbial cell wall and induce cell death. However, the current focus has shifted towards increasing the control of the synthesis reaction to ensure more uniform nanoparticle sizes and shapes. In this context, microfluidics has emerged as a potential candidate method for the controlled synthesis of nanoparticles. Thus, the aim of the present study was to obtain a series of antibiotic-loaded MNPs through a microfluidic device. The structural properties of the nanoparticles were investigated through X-ray diffraction (XRD) and, selected area electron diffraction (SAED), the morphology was evaluated through transmission electron microscopy (TEM) and high-resolution TEM (HR-TEM), the antibiotic loading was assessed through Fourier-transform infrared spectroscopy (FT-IR) and, and thermogravimetry and differential scanning calorimetry (TG-DSC) analyses, and. the release profiles of both antibiotics was determined through UV-Vis spectroscopy. The biocompatibility of the nanoparticles was assessed through the MTT assay on a BJ cell line, while the antimicrobial properties were investigated against the <i>S. aureus</i>, <i>P. aeruginosa</i>, and <i>C. albicans</i> strains. Results proved considerable uniformity of the antibiotic-containing nanoparticles, good biocompatibility, and promising antimicrobial activity. Therefore, this study represents a step forward towards the microfluidic development of highly effective nanostructured systems for antimicrobial therapies. Biocompatibility ; Nanoparticle ; Fourier transform infrared spectroscopy ; Context (archaeology) ; Materials science ; Nanotechnology ; Selected area diffraction ; Differential scanning calorimetry ; Microfluidics ; Antimicrobial ; Transmission electron microscopy ; Chemical engineering ; Nuclear chemistry ; Chemistry ; Organic chemistry ; Biology Nanoparticle-Based Drug Delivery ; Innovative Microfluidic and Catalytic Techniques Innovation ; Microfluidic and Bio-sensing Technologies
Authors: Vitalyi Vladimirovich Kuznetsov | N. A. Ivantsova | Е. Н. Кузин | А. В. Пирогов | Yaroslav Mezhuev | Elena A. Filatova | Yulia Michailovna Averina
Source: Water (article, 2023-09-26)
Field: Chemistry — Electrochemistry

Study of the Process of Electrochemical Oxidation of Active Pharmaceutical Substances on the Example of Nitrofurazone ((2E)-2-[(5-Nitro-2-furyl)methylene]hydrazine Carboxamide) The effective mineralization of nitrofurazone (10–100 mg L−1) was performed in aqueous solutions in the presence of chloride ions by electrochemical treatment. The destruction of the organic pollutant molecules was due to their interaction with active oxygen- and chlorine-containing species forming at the inert anode (Pt/Ti or BDD) during electrolysis. Measurements of nitrofurazone concentration, chemical oxygen demand (COD) and total organic carbon (TOC) were used to estimate the removal efficiency of the pollutant. Both the pollutant oxidation rate and the degree of its mineralization were higher for the BDD anode due to the higher anode potentials on it in the course of electrolysis, which provides a high rate of active oxidizer species generation. As a result, practically full nitrofurazone molecule destruction (&gt;99%) was achieved in 30 min at an anodic current density of 0.1 A cm−2, a volume current density of 1.33 A L−1 and pH 2 using BDD anodes. On the other hand, the nitrafurazone degradation efficiency was about 95% for Pt/Ti anodes under the same conditions. Additionally, byproducts of nitrofurazone electrooxidation were investigated by means of liquid chromato-mass-spectrometry (LC/MS). It was found that the initial decolorization of nitrofurazone solution, which occurs during the first 5 min of electrolysis, is due to the formation of a dichloro derivative of nitrofurazone, which causes the destruction of the π−conjugated bond system. Further electrolysis resulted in the almost complete destruction of the dichloro derivative within 30 min of electrochemical treatment. Nitrofurazone ; Chemistry ; Electrolysis ; Mineralization (soil science) ; Electrochemistry ; Anode ; Inorganic chemistry ; Aqueous solution ; Oxygen ; Nuclear chemistry ; Organic chemistry ; Nitrogen ; Electrode Electrochemical Analysis and Applications ; Advanced oxidation water treatment ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: Victor M. Nazarychev | Sergey V. Lyulin
Source: Polymers (article, 2023-07-01)
Field: Materials Science — Materials Chemistry

The Effect of Mechanical Elongation on the Thermal Conductivity of Amorphous and Semicrystalline Thermoplastic Polyimides: Atomistic Simulations Over the past few decades, the enhancement of polymer thermal conductivity has attracted considerable attention in the scientific community due to its potential for the development of new thermal interface materials (TIM) for both electronic and electrical devices. The mechanical elongation of polymers may be considered as an appropriate tool for the improvement of heat transport through polymers without the necessary addition of nanofillers. Polyimides (PIs) in particular have some of the best thermal, dielectric, and mechanical properties, as well as radiation and chemical resistance. They can therefore be used as polymer binders in TIM without compromising their dielectric properties. In the present study, the effects of uniaxial deformation on the thermal conductivity of thermoplastic PIs were examined for the first time using atomistic computer simulations. We believe that this approach will be important for the development of thermal interface materials based on thermoplastic PIs with improved thermal conductivity properties. Current research has focused on the analysis of three thermoplastic PIs: two semicrystalline, namely BPDA-P3 and R-BAPB; and one amorphous, ULTEM<sup>TM</sup>. To evaluate the impact of uniaxial deformation on the thermal conductivity, samples of these PIs were deformed up to 200% at a temperature of 600 K, slightly above the melting temperatures of BPDA-P3 and R-BAPB. The thermal conductivity coefficients of these PIs increased in the glassy state and above the glass transition point. Notably, some improvement in the thermal conductivity of the amorphous polyimide ULTEM<sup>TM</sup> was achieved. Our study demonstrates that the thermal conductivity coefficient is anisotropic in different directions with respect to the deformation axis and shows a significant increase in both semicrystalline and amorphous PIs in the direction parallel to the deformation. Both types of structural ordering (self-ordering of semicrystalline PI and mechanical elongation) led to the same significant increase in thermal conductivity coefficient. Materials science ; Thermal conductivity ; Composite material ; Amorphous solid ; Crystallinity ; Thermoplastic ; Glass transition ; Polymer ; Polyimide ; Crystallography ; Chemistry Thermal properties of materials ; Synthesis and properties of polymers ; Dielectric materials and actuators
Authors: Claudio Marchesi | Monika Rani | Stefania Federici | Matteo Lancini | Laura E. Depero
Source: ACTA IMEKO (article, 2023-06-29)
Field: Chemistry — Analytical Chemistry

Evaluating chemometric strategies and machine learning approaches for a miniaturized near-infrared spectrometer in plastic waste classification Optimizing the sorting of plastic waste plays a crucial role in improving the recycling process. In this contribution, we report on a comparative study of multiple machine learning and chemometric approaches to categorize a data set derived from the analysis of plastic waste performed with a handheld spectrometer working in the Near-Infrared (NIR) spectral range. Conducting a cost-effective NIR study requires identifying appropriate techniques to improve commodity identification and categorization. Chemometric techniques, such as Principal Component Analysis (PCA) and Partial Least Squares - Discriminant Analysis (PLS - DA), and machine learning techniques such as Support- Vector Machines (SVM), fine tree, bagged tree, and ensemble learning were compared. Various pre-treatments were tested on the collected NIR spectra. In particular, Standard Normal Variate (SNV) and Savitzky-Golay derivatives as signal pre-processing tools were compared with feature selection techniques such as multiple Gaussian Curve Fit based on Radial Basis Functions (RBF). Furthermore, results were combined into a single predictor by using a likelihood-based aggregation formula. Predictive performances of the tested models were compared in terms of classification parameters such as Non-Error Rate (NER) and Sensitivity (Sn) with the analysis of the confusion matrices, giving a broad overview and a rational means for the selection of the approach in the analysis of NIR data for plastic waste sorting. Artificial intelligence ; Principal component analysis ; Pattern recognition (psychology) ; Support vector machine ; Computer science ; Linear discriminant analysis ; Feature selection ; Machine learning ; Partial least squares regression ; Sorting ; Chemometrics ; Algorithm Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Andrew E. Blanchard | Debsindhu Bhowmik | Zachary Fox | John Gounley | Jens Gläser | Belinda S. Akpa | Stephan Irle
Source: Journal of Cheminformatics (article, 2023-06-08)
Field: Materials Science — Materials Chemistry

Adaptive language model training for molecular design The vast size of chemical space necessitates computational approaches to automate and accelerate the design of molecular sequences to guide experimental efforts for drug discovery. Genetic algorithms provide a useful framework to incrementally generate molecules by applying mutations to known chemical structures. Recently, masked language models have been applied to automate the mutation process by leveraging large compound libraries to learn commonly occurring chemical sequences (i.e., using tokenization) and predict rearrangements (i.e., using mask prediction). Here, we consider how language models can be adapted to improve molecule generation for different optimization tasks. We use two different generation strategies for comparison, fixed and adaptive. The fixed strategy uses a pre-trained model to generate mutations; the adaptive strategy trains the language model on each new generation of molecules selected for target properties during optimization. Our results show that the adaptive strategy allows the language model to more closely fit the distribution of molecules in the population. Therefore, for enhanced fitness optimization, we suggest the use of the fixed strategy during an initial phase followed by the use of the adaptive strategy. We demonstrate the impact of adaptive training by searching for molecules that optimize both heuristic metrics, drug-likeness and synthesizability, as well as predicted protein binding affinity from a surrogate model. Our results show that the adaptive strategy provides a significant improvement in fitness optimization compared to the fixed pre-trained model, empowering the application of language models to molecular design tasks. Computer science ; Training (meteorology) ; Language model ; Data science ; Natural language processing ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis