jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Juan Xu | Wei Gao | Qi Zhang | Lifeng Ning
Source: Molecules (article, 2023-05-22)
Field: Chemistry — Physical and Theoretical Chemistry

Cocrystallization of Progesterone with Nitrogen Heterocyclic Compounds: Synthesis, Characterization, Calculation and Property Evaluation Progesterone injection is oily because of its poor solubility. It is necessary to develop new dosage forms or delivery methods for Progesterone. Six cocrystals of Progesterone with nitrogen heterocyclic compounds (2,6-diaminopyridine, isonicotinamide, 4-aminopyridine, aminopyrazine, picolinamide and pyrazinamide) have been designed and prepared by ethyl acetate-assisted grinding, of which four cocrystals (2,6-diaminopyridine, isonicotinamide, 4-aminopyridine and aminopyrazine) had single crystal data in 1:1 stoichiometry. Metadynamics-genetic crossing was used to search and optimize various cluster structures to explain the reason the other two cocrystals could not be obtained with suitable size for single crystal X-ray diffraction. In contrast to the carboxyl group, the amide group and amino group were good substituents in the pyridine/pyrazine ring for cocrystallization with Progesterone, which meant inductive effect played an important role in nitrogen heterocyclic compounds containing reactive hydrogen. All cocrystals were more soluble than Progesterone in water, and Progesterone-pyrazinamide cocystal featured the best water solubility performance with an approximately six-fold increase over free Progesterone. This successful attempt provides an effective route for designing and manufacturing novel solid states of Progesterone. Isonicotinamide ; Chemistry ; Cocrystal ; Solubility ; Tetrazole ; Amide ; Pyrazine ; Pyridine ; Single crystal ; Combinatorial chemistry ; Hydrogen bond ; Crystallography ; Organic chemistry ; Molecule Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Chemical Thermodynamics and Molecular Structure
Authors: Xintong Wang | Jiang Yang | Xu Chen | Kunfeng Yan
Source: ACS Omega (article, 2023-08-16)
Field: Materials Science — Materials Chemistry

Exploring the Effectiveness of Natural Food Flavors in Protecting Carbon Steel against CO<sub>2</sub>–Induced Corrosion Two food flavors, furfuryl mercaptan (2-FFT) and difurfuryl disulfide (DFDS), were investigated as green corrosion inhibitors for the N80 steel in a CO<sub>2</sub>-saturated solution containing 3.5% NaCl. Experimental methods, quantum chemical calculations, and molecular dynamics simulation were employed to evaluate the effectiveness of 2-FFT and DFDS. The results of the study indicate that both 2-FFT and DFDS act as mixed corrosion inhibitors, with a dominant inhibition effect on the cathodic reaction. 2-FFT is physiochemically adsorbed on the steel surface in a tiled form through the furan ring and the - SH groups as adsorption sites. On the other hand, DFDS is chemisorbed on the steel surface through the - S-S- groups in a parallel manner. DFDS exhibits a higher tendency for electron transfer and stronger adsorption to steel compared to 2-FFT. Overall, this study highlights the potential of natural food flavors as effective and environmentally friendly corrosion inhibitors for carbon steel in CO<sub>2</sub>-saturated environments. The findings of this research can contribute to the development of sustainable and nontoxic corrosion inhibitors for the oil and gas industry. Corrosion ; Adsorption ; Carbon steel ; Corrosion inhibitor ; Chemistry ; Materials science ; Chemical engineering ; Organic chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Instituto Politécnico Nacional - Unidad Querétaro | Anuar Jassen Morales | I. Domínguez-López | Julio Cesar Sosa Savedra | Adrian Luis García García
Source: Revista Mexicana de Ingeniería Biomédica (review, 2023-09-01)
Field: Materials Science — Biomaterials

A Review on the Advances of Biocompatible Materials and Their Processing Via Additive Manufacturing for Tissue Engineering Applications About two decades ago, medicine experienced a revolutionary approach, driven by technological development in manufacturing techniques and scientific advances in the medical and life sciences, the field took on the challenge of regenerating tissue and organs damaged by disease, trauma, or hereditary issues, incorporating additive manufacturing as one of its strategies. Since its inception, regenerative medicine has developed techniques like tissue engineering, cellular therapy, medical devices, and artificial organs to provide wound healing and orthopedic applications. The incorporation of additive manufacturing allowed to recreate biologically appropriate environments for cell reproduction and growth that, eventually, lead to useful, regenerated tissue or organs. The objective of the present work is to review recent advances in the application of additive manufacturing techniques and ad hoc biomaterials in the field of regenerative medicine, to determine their impact in the development of new therapies for tissue engineering. Regenerative medicine ; Biocompatible material ; Tissue engineering ; Biomedical engineering ; Medicine ; Engineering ; Stem cell ; Biology Electrospun Nanofibers in Biomedical Applications ; Additive Manufacturing and 3D Printing Technologies ; 3D Printing in Biomedical Research
Authors: Georgiana Amariei | Anne Schaarup-Kjær | Pernille Klarskov | Martin Lahn Henriksen | Mogens Hinge
Source: Journal of Spectral Imaging (article, 2023-03-31)
Field: Chemistry — Analytical Chemistry

Estimation of pigment concentration in LDPE via in-line hyperspectral imaging and machine learning Due to the increasing amount of plastic waste and high-quality demands on recycled plastic interest for in-line composition estimation in plastics has grown the last few years. This study investigates pigment blue 15 : 3 with varying concentrations in LDPE. Samples are investigated with two industrial hyperspectral imaging systems where one has the hyperspectral range from 450 nm to 1050 nm and the other from 950 nm to 1750 nm. A model based on peak ratios of selected bands and model based on a principal component analysis have been tested. The models only predict pigment concentrations between 40.0 wt% and 1.7 × 10–3 wt% if both spectral ranges are combined. Unknown samples containing pigment concentration ranging from 20 wt% to 0.31 wt% were predicted and correlated to the actual pigment concentrations (R2 = :0.977) and the PC-based model outperforms the peak ratio model. The studied approach can be a part of the solution to the plastic challenge and can be transferred to other applications where concentration determination is key. Hyperspectral imaging ; Pigment ; Low-density polyethylene ; Principal component analysis ; Materials science ; Plastic waste ; Environmental science ; Analytical Chemistry (journal) ; Biological system ; Chemistry ; Polyethylene ; Environmental chemistry ; Mathematics ; Remote sensing ; Composite material ; Geology ; Statistics ; Waste management Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Dariana R. Vega-Santander | Rodinson R. Arrieta-Pérez | Daniela Rivera-Mirabal | Gabriela Del Valle-Pérez | Miguel Sepúlveda-Pagán | Juan C. Muñoz-Senmache | Yomaira J. Pagán‐Torres | Arturo J. Hernández‐Maldonado
Source: Environmental Science Advances (article, 2023-11-10)
Field: Chemistry — Inorganic Chemistry

Superior single- and multi-component siloxane removal from water using a faulted silica DON zeolite adsorbent Pure porous silica zeolite adsorbents with framework surface faults, in the form of siloxy groups, are selective toward single- or multi-component linear siloxanes for removal from water. Zeolite ; Siloxane ; Adsorption ; Component (thermodynamics) ; Porosity ; Chemical engineering ; Materials science ; Chemistry ; Organic chemistry ; Composite material ; Catalysis ; Polymer ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Catalytic Processes in Materials Science
Authors: Zheni Zeng | Bangchen Yin | Shipeng Wang | Jiarui Liu | Cheng Yang | Haishen Yao | Xingzhi Sun | Maosong Sun | Guotong Xie | Zhiyuan Liu
Source: arXiv (Cornell University) (preprint, 2023-06-21)
Field: Materials Science — Materials Chemistry

Interactive Molecular Discovery with Natural Language Natural language is expected to be a key medium for various human-machine interactions in the era of large language models. When it comes to the biochemistry field, a series of tasks around molecules (e.g., property prediction, molecule mining, etc.) are of great significance while having a high technical threshold. Bridging the molecule expressions in natural language and chemical language can not only hugely improve the interpretability and reduce the operation difficulty of these tasks, but also fuse the chemical knowledge scattered in complementary materials for a deeper comprehension of molecules. Based on these benefits, we propose the conversational molecular design, a novel task adopting natural language for describing and editing target molecules. To better accomplish this task, we design ChatMol, a knowledgeable and versatile generative pre-trained model, enhanced by injecting experimental property information, molecular spatial knowledge, and the associations between natural and chemical languages into it. Several typical solutions including large language models (e.g., ChatGPT) are evaluated, proving the challenge of conversational molecular design and the effectiveness of our knowledge enhancement method. Case observations and analysis are conducted to provide directions for further exploration of natural-language interaction in molecular discovery. Computer science ; Interpretability ; Natural language ; Property (philosophy) ; Natural language processing ; Artificial intelligence ; Comprehension ; Task (project management) ; Bridging (networking) ; Natural (archaeology) ; Natural language understanding ; Natural language user interface ; Generative grammar ; Human–computer interaction ; Programming language ; Engineering Machine Learning in Materials Science ; Academic Writing and Publishing ; Computational Drug Discovery Methods
Authors: Juha Tiihonen | Hannu Häkkinen
Source: The Journal of Chemical Physics (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Towards structural optimization of gold nanoclusters with quantum Monte Carlo We study the prospects of using quantum Monte Carlo techniques (QMC) to optimize the electronic wavefunctions and atomic geometries of gold compounds. Complex gold nanoclusters are widely studied for diverse biochemical applications, but the dynamic correlation and relativistic effects in gold set the bar high for reliable, predictive simulation methods. Here we study selected ground state properties of few-atom gold clusters by using density functional theory (DFT) and various implementations of the variational Monte Carlo (VMC) and diffusion Monte Carlo. We show that the QMC methods mitigate the exchange-correlation (XC) approximation made in the DFT approach: the average QMC results are more accurate and significantly more consistent than corresponding DFT results based on different XC functionals. Furthermore, we use demonstrate structural optimization of selected thiolated gold clusters with between 1 and 3 gold atoms using VMC forces. The optimization workflow is demonstrably consistent, robust, and its computational cost scales with nb, where b < 3 and n is the system size. We discuss the implications of these results while laying out steps for further developments. Nanoclusters ; Quantum Monte Carlo ; Monte Carlo method ; Density functional theory ; Statistical physics ; Wave function ; Diffusion Monte Carlo ; Quantum ; Variational Monte Carlo ; Physics ; Monte Carlo molecular modeling ; Computer science ; Quantum mechanics ; Materials science ; Mathematics ; Nanotechnology ; Markov chain Monte Carlo Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Siddharth Srivastava | Yusuke Terai | Jun Liu | Giovanni Capellini | Ya‐Hong Xie
Source: Biosensors (article, 2023-12-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Controlling the Nucleation and Growth of Salt from Bodily Fluid for Enhanced Biosensing Applications Surface-enhanced Raman spectroscopy (SERS) represents a transformative tool in medical diagnostics, particularly for the early detection of key biomarkers such as small extracellular vesicles (sEVs). Its unparalleled sensitivity and compatibility with intricate biological samples make it an ideal candidate for revolutionizing noninvasive diagnostic methods. However, a significant challenge that mars its efficacy is the throughput limitation, primarily anchored in the prerequisite of hotspot and sEV colocalization within a minuscule range. This paper delves deep into this issue, introducing a never-attempted-before approach which harnesses the principles of crystallization-nucleation and growth. By synergistically coupling lasers with plasmonic resonances, we navigate the challenges associated with the analyte droplet drying method and the notorious coffee ring effect. Our method, rooted in a profound understanding of crystallization's materials science, exhibits the potential to significantly increase the areal density of accessible plasmonic hotspots and efficiently guide exosomes to defined regions. In doing so, we not only overcome the throughput challenge but also promise a paradigm shift in the arena of minimally invasive biosensing, ushering in advanced diagnostic capabilities for life-threatening diseases. Nanotechnology ; Bioanalysis ; Nucleation ; Plasmon ; Biosensor ; Extracellular vesicles ; Materials science ; Computer science ; Raman spectroscopy ; Chemistry ; Biology ; Physics ; Optoelectronics ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Nanomaterials and Printing Technologies ; Plasmonic and Surface Plasmon Research
Authors: Maciej Przybyłek | Tomasz Jeliński | Magdalena Mianowana | Kinga Misiak | Piotr Cysewski
Source: Preprints.org (preprint, 2023-08-28)
Field: Materials Science — Materials Chemistry

Exploring the Solubility Limits of Edaravone in Neat Solvents and Binary Mixtures: Experimental and Machine Learning Study This study explores the Edaravone solubility space encompassing both neat and binary dissolution media. The efforts were made toward revealing the concentration limits inherently associated with common pure and mixed solvents. For this purpose, the published solubility data of the title drug were scrupulously inspected and cured. This enabled to make the dataset consistent and coherent. However, the lack of some important types of solvents in the collection called for an extension of the available pool of Edaravone solubility data. Hence, new measurements were performed for collecting Edaravone solubility in polar non-protic and polar di-protic media. Such an extended set of data was used for tuning the parameters of regressors models and formulating the ensemble for predicting new data. In both phases, namely the model training and ensemble formulation, close attention was paid not only to minimizing the deviation of computed values from the experimental ones but also to ensuring the high predictive power and computing solubility for new systems. Furthermore, the environmental friendliness characteristics determined based on the common green solvent selection criteria was included in the analysis. Our applied protocol led to the conclusion that the solubility space defined by ordinary solvents is limited, and it is unlikely to find solvents that are more suited for Edaravone dissolution than those depicted in this manuscript. The theoretical framework presented in this study provides a precise guide for conducting experiments, saving time and resources in the pursuit of new findings. Solubility ; Dissolution ; Solvent ; Polar ; Binary number ; Edaravone ; Chemistry ; Computer science ; Process engineering ; Organic chemistry ; Mathematics ; Physics ; Engineering Crystallization and Solubility Studies ; Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography
Authors: Ahmed Menevşeoğlu | Nurhan Güneş | Hüseyin Ayvaz | Sevim Beyza Öztürk Sarıkaya | Cuma Zehiroğlu
Source: Turkish Journal of Agriculture - Food Science and Technology (article, 2023-12-28)
Field: Chemistry — Analytical Chemistry

Near- and Mid-Infrared Spectroscopy Combined with Machine Learning Algorithms to Determine Minerals and Antioxidant Activity in Commercial Cheese Erzincan Tulum Cheese (ETC) holds a significant place among the most popular cheeses in Türkiye. It has been awarded Protected Geographical Indication status, which restricts the allowable milk species, its production area, and specific sheep breed used in its production. Mineral content and antioxidant activity of ETC were aimed to be predicted using conventional FT-NIR and a portable FT-MIR spectrometer combined with partial least square regression (PLSR) and machine learning algorithms based on conditional entropy. Seventy ETC samples were analyzed for their mineral (Al, Ca, Cr, Cu, Fe, K, Mg, Mn, Na, and P) content using ICP-MS. The samples' antioxidant activity was measured using the DPPH•+ scavenging activity method. PLSR combined with FT-NIR spectral data correlated with antioxidant activity (r=0.89) and minerals (as low as r=0.83) except for Cr and Fe. FT-MIR data provided a good correlation for minerals (as low as r=0.82) except for Cr and Mn and a moderate correlation with antioxidant activity (r=0.64). Information theory was applied to select wavenumbers used in machine learning algorithms, and better results were obtained compared to PLSR. Overall, FT-NIR and FT-MIR spectroscopy provided rapid (~ 1 min), non-destructive, sensitive, and reliable output for mineral and antioxidant activity predictions in commercial cheese samples. Antioxidant ; Partial least squares regression ; Chemistry ; DPPH ; Mathematics ; Chemometrics ; Near-infrared spectroscopy ; Mineral ; Food science ; Mineralogy ; Analytical Chemistry (journal) ; Algorithm ; Environmental chemistry ; Chromatography ; Statistics ; Biochemistry ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Fumiyasu Oba
Source: Journal of the Ceramic Society of Japan (article, 2023-07-31)
Field: Materials Science — Materials Chemistry

Computational design and exploration of nitride and oxide semiconductors Recent development in first-principles modeling and computer performance has made it feasible to predict various properties of inorganic materials at the level of accuracy required for their detailed understanding, design, and prediction. In this article, our computational design and exploration of nitride and oxide semiconductors and related materials are reviewed, along with relevant methodologies. The results presented and discussed here include (i) the modeling of local atomic and electronic structures of native point defects, dopants, and their complexes; (ii) the prediction of reconstructed structures and band positions of surfaces; and (iii) materials screening using high-throughput calculations, identifying promising ternary zinc nitride semiconductors. Ternary operation ; Materials science ; Nitride ; Semiconductor ; Dopant ; Oxide ; Point (geometry) ; Nanotechnology ; Wide-bandgap semiconductor ; Engineering physics ; Computer science ; Optoelectronics ; Doping ; Metallurgy ; Engineering Machine Learning in Materials Science ; Semiconductor materials and devices ; Electronic and Structural Properties of Oxides
Authors: Muhammad Saqib | Tayyaba Mubashir | Mudassir Hussain Tahir | Muqadas Javed | Asima Hameed | Atiq ur Rehman Aziz | Shaban R. M. Sayed | Hosam O. Elansary
Source: Journal of Physical Organic Chemistry (article, 2023-06-09)
Field: Materials Science — Polymers and Plastics

Designing of benzofuran‐based monomers for photodetectors through similarity analysis and library enumeration Abstract Organic molecules have been extensively utilized in various applications including materials science, chemical, and biomedical fields. Traditionally, the design of organic molecules is achieved through experimental approaches, guided by conceptual insights, intuition, and experience. Although these experimental approaches have been successfully utilized to unveil various high‐performance materials, these methods show serious limitations due to vast design space and the ever‐increasing demand for organic molecules (new materials). Artificial intelligence with computer science is used by modern researchers to design materials with better performance and for predicting the properties of new materials. Herein, benzofuran‐based building blocks are used as a standard molecule to search for new building blocks. Similarity analysis is performed to screen/search potential candidates for photodetectors based on the chemical structural similarity. Extended‐connectivity fingerprints (ECFPs) are used for the similarity analysis. The virtual libraries of unique monomers are enumerated. The breaking retro‐synthetically interesting chemical substructures (BRICS) method is also used to design building blocks by automatically decomposing and combining monomers in enumerated libraries. Moreover, this work offers a potential way to identify new monomers for photodetectors cost‐effectively and rapidly. Chemical space ; Similarity (geometry) ; Computer science ; Intuition ; Monomer ; Chemistry ; Virtual screening ; Nanotechnology ; Biochemical engineering ; Artificial intelligence ; Computational chemistry ; Materials science ; Engineering ; Drug discovery ; Organic chemistry Conducting polymers and applications ; Machine Learning in Materials Science ; Organic Electronics and Photovoltaics