jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xin Zhang | Li Sheng
Source: Spectroscopy (article, 2024-02-01)
Field: Chemistry — Analytical Chemistry

Determination of Trace Nickel(ΙΙ) Using a Kinetic Fluorescence Method with a Morin-Hydrogen Peroxide System Nickel (Ni[Ⅱ]) was used to catalyze the hydrogen peroxide oxidation of morin to weaken its fluorescence. In Na2B4O7-NaOH buffer solution at pH 10.10, Ni(Ⅱ) has an obvious catalytic effect on the oxidation of morin by H2O2 solution. At the maximum excitation and emission wavelengths (λex = 440 nm and λem = 530 nm), the fluorescence intensity of the system is linearly correlated with the concentration of Ni(II) within a certain range. The linear equation is ΔF = 11463ρ (μg/mL) − 34.70, and the R2 = 0.9992. The linear range is 1.6 × 10-2 − 9.6 × 10-2 μg/mL, and the detection limit was 2.9 × 10-4 μg/mL. Finally, the samples of tap water, Yellow River water and tea were analyzed, with the recoveries being 99.6%, 107.3%, and 104.6%, respectively. This method is suitable for the determination of trace Ni(II) in water samples. Morin ; Hydrogen peroxide ; Nickel ; TRACE (psycholinguistics) ; Chemistry ; Fluorescence ; Kinetic energy ; Organic chemistry ; Physics ; Philosophy Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors
Authors: M. E. Muratova | Ivan V. Mikheev | Dmitry S. Volkov | Мikhail А. Proskurnin
Source: Журнал аналитической химии (article, 2024-12-09)
Field: Chemistry — Analytical Chemistry

Use of two-dimensional correlation spectroscopy in chemical analysis An overview of the main applications of generalized two-dimensional correlation spectroscopy (2D-COS) in analytical chemistry is presented. 2D-COS is a method used to analyze datasets obtained from spectroscopic measurements. This approach is based on the use of two-dimensional correlation maps to identify and analyze correlations between different regions of the spectrum or data from two measurement methods. The purpose of using 2D-COS is to increase the amount of analytical information by revealing hidden data correlations. Analyzing such correlations for series of spectral data obtained for a certain range of analyte concentrations, pH, or component ratios of a mixture, as well as changes in temperature or other external factors, allows researchers to investigate and identify chemical processes and interactions that cannot be directly obtained from the spectra. Compared to one-dimensional spectra, 2D-COS offers significant analytical information for complex mixtures, particularly in identifying components and determining composition. Additionally, 2D-COS can be used to monitor changes in a sample over time, making it a valuable tool for studying dynamically changing systems. Overall, 2D-COS is a highly versatile approach that can be used in conjunction with a large number of methods for most analytical tasks and complex objects, including those without sample preparation. The review presents advancements in the application of 2D-COS as of early September 2023. Spectroscopy ; Correlation ; Materials science ; Mathematics ; Physics ; Geometry Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Feng Han | Sheng Li
Source: Spectroscopy (article, 2024-02-01)
Field: Chemistry — Analytical Chemistry

Detection of Trace Tin(II) by Salicylfluoroketone Complex Fluorescence Method Based on the Sn(II)-salicylfluorescein (SAF)-cetyltrimethylammonium bromide (CTMAB) system, the complex fluorescence method was used to detect Sn(Ⅱ). The method was established by exploring the optimal reaction conditions, and the possible reaction mechanism was discussed. The results showed that the concentration of Sn(Ⅱ) had a good linear relationship with the fluorescence intensity of the system in the range of 0~0.40 µg/mL. The linear regression equation is ΔF = - 7.457 + 715.286ρ (µg/mL), which had a good linearity; the correlation coefficient is 0.9976, and the detection limit of the method was 0.0764 µg/mL. In the detection of Sn(Ⅱ) in synthetic water samples, laboratory wastewater, and canned pineapples, the recovery rates were 95.83%, 104.17%, and 100.0%, which showed good results. TRACE (psycholinguistics) ; Tin ; Fluorescence ; Chemistry ; Environmental chemistry ; Analytical Chemistry (journal) ; Physics ; Optics ; Organic chemistry ; Philosophy Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Molecular Sensors and Ion Detection
Authors: Sang-Kwon Yun | Won Bo Lee
Source: arXiv (Cornell University) (preprint, 2024-11-29)
Field: Materials Science — Materials Chemistry

Hierarchical Framework for Retrosynthesis Prediction with Enhanced Reaction Center Localization Retrosynthesis is essential for designing synthetic pathways for complex molecules and can be revolutionized by AI to automate and accelerate chemical synthesis planning for drug discovery and materials science. Here, we propose a hierarchical framework for retrosynthesis prediction that systematically integrates reaction center identification, action prediction, and termination decision into a unified pipeline. Leveraging a molecular encoder pretrained with contrastive learning, the model captures both atom and bond level representations, enabling accurate identification of reaction centers and prediction of chemical actions. The framework addresses the scarcity of multiple reaction center data through augmentation strategies, enhancing the ability of the model to generalize to diverse reaction scenarios. The proposed approach achieves competitive performance across benchmark datasets, with notably high topk accuracy and exceptional reaction center identification capabilities, demonstrating its robustness in handling complex transformations. These advancements position the framework as a promising tool for future applications in material design and drug discovery. Retrosynthetic analysis ; Center (category theory) ; Computer science ; Artificial intelligence ; Chemistry ; Crystallography Machine Learning in Materials Science
Authors: Sakhinana Sagar Srinivas | Geethan Sannidhi | Venkataramana Runkana
Source: arXiv (Cornell University) (preprint, 2024-08-27)
Field: Materials Science — Surfaces, Coatings and Films

Multi-Modal Instruction-Tuning Small-Scale Language-and-Vision Assistant for Semiconductor Electron Micrograph Analysis We present a novel framework for analyzing and interpreting electron microscopy images in semiconductor manufacturing using vision-language instruction tuning. The framework employs a unique teacher-student approach, leveraging pre-trained multimodal large language models such as GPT-4 to generate instruction-following data for zero-shot visual question answering (VQA) and classification tasks, customizing smaller multimodal models (SMMs) for microscopy image analysis, resulting in an instruction-tuned language-and-vision assistant. Our framework merges knowledge engineering with machine learning to integrate domain-specific expertise from larger to smaller multimodal models within this specialized field, greatly reducing the need for extensive human labeling. Our study presents a secure, cost-effective, and customizable approach for analyzing microscopy images, addressing the challenges of adopting proprietary models in semiconductor manufacturing. Electron micrographs ; Micrograph ; Semiconductor ; Scale (ratio) ; Modal ; Computer science ; Optics ; Materials science ; Artificial intelligence ; Optoelectronics ; Electron microscope ; Scanning electron microscope ; Physics ; Composite material ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Image Processing Techniques and Applications
Authors: А. А. Ильин | Almas Yerzhanov | Nikolay Zobnin | Н. В. Немчинова | Victor Romanov
Source: Applied Sciences (article, 2024-09-17)
Field: Materials Science — Materials Chemistry

Know-How of the Effective Use of Carbon Electrodes with a through Axial Hole in the Smelting of Silicon Metal This article describes elements of the know-how of using carbon electrodes produced using the technology of molding around a rod when smelting silicon metal. Application of our know-how will dramatically increase the competitiveness of silicon metal production. Experts’ concerns regarding the use of such electrodes were that such electrodes have a through axial hole. This significantly reduces the mechanical strength of such electrodes, which can presumably lead to problems associated with the breakage of the working side of the electrode, which is immersed in the smelting space of the furnace under the charge layer. Industrial testing of such electrodes was carried out in a 30 MVA furnace of “Tau-Ken Temir” LLP. During testing, we used an approach previously developed by our team for working with a furnace in the process of smelting silicon metal. In particular, we used an interval between top treatments of about 30 min and adhered to the principles of balanced smelting, i.e., provided a balance between the intensity of the uniform supply of the charge into the furnace and the current active electrical power. Industrial testing carried out over four weeks confirmed the stability of the operation of cheaper carbon electrodes with a through axial hole. The recovery of silicon into finished products was also improved to 88–89% and the specific energy consumption was reduced to 11.2–12.1 MWh/t of silicon metal from the initial value 14,752 MWh/t. Thus, we received additional evidence for the effectiveness of our approach in furnace operating compared to an approach based on the ultimate provision of gas and permeability of the furnace top due to excessively intense processing of the top and an uncontrolled, uneven supply of charge to the furnace. Smelting ; Materials science ; Silicon ; Metallurgy ; Carbon fibers ; Metal ; Composite material Carbon Nanotubes in Composites ; Gas Sensing Nanomaterials and Sensors ; Nanotechnology research and applications
Source: Biointerface Research in Applied Chemistry (review, 2024-08-15)
Field: Chemistry — Organic Chemistry

A Review of the Synthesis of Dibenzofuran Derivatives that Possess Various Medicinal Activities as Potent Anticancer and Antibacterial Agents Dibenzofuran (DBF) is a typical heterocyclic aromatic compound (O-HETs) capable of coexisting with Polycyclic Aromatic Hydrocarbons (PAHs) in settings of pyrolysis and combustion. This review focuses on reports entailing the synthesis of DBF derivatives possessing anticancer and antibacterial prowess, though it is not a comprehensive discussion of the compounds in question but rather an illustration of the range of anticancer and antibacterial activity possessed them besides variables synthesis sources thereof. Compounds with diminished benzene rings, e.g., morphine and its derivatives, are omitted, besides compounds with alkylation-disrupted aromaticity, such as usnic acid. It is worth noting that one of the most important issues that we learned in this review is that dibenzofuran derivatives have many medical advantages. Dibenzofuran ; Chemistry ; Alkylation ; Combinatorial chemistry ; Antibacterial activity ; Benzene ; Organic chemistry ; Catalysis ; Bacteria ; Biology Synthesis and Biological Evaluation ; Cyclization and Aryne Chemistry ; Biological Activity of Diterpenoids and Biflavonoids
Authors: JiHyeon Song | Seung‐Mo Hong | Seok Kyu Park | Hyuk-Chan Kwon | Seok‐Ho Hwang | Jong‐Min Oh | Sang‐Mo Koo | Giwon Lee | Chulhwan Park
Source: Polymers (article, 2024-12-30)
Field: Materials Science — Polymers and Plastics

Synthesis and Characterization of UV-Curable Resin with High Refractive Index for a Luminance-Enhancing Prism Film A novel monomer, 9-bis[4-(2-hydroxyethoxy)phenyl]fluorene di(mercaptopropionate), with a highly refractive index, purity, and excellent UV-curable properties, is synthesized through an optimized Fischer esterification process, reacting 9,9-bis[4-(2-hydroxyethoxy)phenyl]fluorene with 3-mercaptopropionic acid. The structural characterization of this monomer is performed using Fourier-transform infrared spectroscopy, nuclear magnetic resonance spectroscopy, high-performance liquid chromatography, and liquid chromatography-mass spectrometry. The synthesis conditions are optimized using a design-of-experiments approach. UV-curable resins are obtained by incorporating the synthesized monomer as the thiol component. The effects of thiol content on the UV-curing behavior, refractive index, shrinkage, adhesion to the polyethylene terephthalate (PET) foil, and viscoelastic recovery are examined. The thermal properties are assessed using differential scanning calorimetry and thermogravimetric analysis. Field-emission scanning electron microscopy confirms the successful replication of the prism film. In edge-lit light-emitting diode (LED) backlight units, the prism film showed increased luminance with higher thiol monomer content in the UV-curable resin while maintaining stable color coordinates. This novel highly refractive index monomer can be utilized in luminance-enhancing prism films, thereby contributing to future innovations in the display film industry. Materials science ; Differential scanning calorimetry ; Fourier transform infrared spectroscopy ; Monomer ; Refractive index ; Polyethylene terephthalate ; Polymer chemistry ; Polymer ; Chemical engineering ; Composite material ; Optoelectronics Synthesis and properties of polymers ; Photopolymerization techniques and applications ; Silicone and Siloxane Chemistry
Authors: Julian Cremer | Tuan Le | Frank Noé | Djork-Arné Clevert | Kristof T. Schütt
Source: arXiv (Cornell University) (preprint, 2024-05-23)
Field: Materials Science — Materials Chemistry

PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance sampling The generation of ligands that both are tailored to a given protein pocket and exhibit a range of desired chemical properties is a major challenge in structure-based drug design. Here, we propose an in-silico approach for the $\textit{de novo}$ generation of 3D ligand structures using the equivariant diffusion model PILOT, combining pocket conditioning with a large-scale pre-training and property guidance. Its multi-objective trajectory-based importance sampling strategy is designed to direct the model towards molecules that not only exhibit desired characteristics such as increased binding affinity for a given protein pocket but also maintains high synthetic accessibility. This ensures the practicality of sampled molecules, thus maximizing their potential for the drug discovery pipeline. PILOT significantly outperforms existing methods across various metrics on the common benchmark dataset CrossDocked2020. Moreover, we employ PILOT to generate novel ligands for unseen protein pockets from the Kinodata-3D dataset, which encompasses a substantial portion of the human kinome. The generated structures exhibit predicted $IC_{50}$ values indicative of potent biological activity, which highlights the potential of PILOT as a powerful tool for structure-based drug design. Equivariant map ; Diffusion ; Sampling (signal processing) ; Ligand (biochemistry) ; Computer science ; Mathematics ; Medicine ; Physics ; Pure mathematics ; Thermodynamics ; Computer vision ; Receptor ; Internal medicine Crystallization and Solubility Studies ; Gaussian Processes and Bayesian Inference ; Tuberculosis Research and Epidemiology
Authors: Mukesh Shrivastava | G. Swarnabala | S. B. Khatavkar | G. S. Sadana | Anahita Bharadwaj
Source: Asian Journal of Chemical Sciences (article, 2024-12-21)
Field: Chemistry — Organic Chemistry

Structural Investigations of Some Metal Complexes of Malon-Di-(α-Naphthyl) Amide-Oxime The study investigates the structural properties of Cu(II), Ni(II), Fe(II), Co(III), Zn(II), Cd(II), and Hg(II) complexes with malon-di-(α-naphthyl)amide-oxime (isonitroso malon-di-(α-naphthyl)amine oxime). It summarizes chemicals, equipment, experimental methods, metal-ligand bonding, stereochemistry, oxidation states, and coordination modes using spectral and magnetic data. Solution studies done with reagent and metal salts assisted in predicting geometry for these complexes, to finalize the M:L ratios and have allowed the pH to be established to separate the complex formed in solid state. The green Cu(II) complex is dimeric, confirmed by its electronic and reflectance spectra, with a likely square planar structure, Bleaney-Bower’s equation helped to finalize the dimeric complex formation. Yellow Ni(II) and dark-blue Fe(II) complexes are paramagnetic, suggesting high-spin octahedral geometries. The dark-red Co(III) complex exhibits weak paramagnetism, explained by the Van Vleck term, spectral data, and its diamagnetic nature, indicative of an octahedral structure. Zn(II), Cd(II), and Hg(II) complexes are assigned tetrahedral structures based on magnetic and infrared spectral data. From the dark colours of the complexes obtained, they may be utilized as complexing agents like the well-known oxime containing ligands. Oxime ; Amide ; Metal ; Chemistry ; Stereochemistry ; Organic chemistry Inorganic and Organometallic Chemistry ; Metal complexes synthesis and properties ; Thermal and Kinetic Analysis
Authors: M. R. S. Kushwaha | Shivani Singh
Source: World journal of nanomaterials/American journal of nanomaterials (article, 2024-07-22)
Field: Materials Science — Materials Chemistry

Utilizing &lt;i&gt;Chlorophytum comosum&lt;/i&gt; Leaf Extract for Eco-friendly Synthesis of Iron-Based Nanoparticles: Assessing Their Efficacy in Methyl Orange Dye Degradation and Antimicrobial Activities The utilization of plant extracts for environmentally friendly nanoparticle synthesis has garnered significant attention from researchers due to its clean, safe, cost-efficient, and eco-conscious approach to crafting nanomaterials. In the present study, eco-friendly synthesis of iron nanoparticles by using Chlorophytum comosum leaf extract have been explored with special emphasis on its potential as Fenton-like catalyst for degrading methyl orange dye. Noticeable was almost complete disappearance of the dye's color, achieving an impressive 77% degradation efficiency within a mere 6 hours. Additionally, exploration of its antimicrobial properties revealed a potent effect against Staphylococcus aureus. This investigation highlights the straightforward green synthesis of Fe nanoparticles, showcasing their dual benefits in dye degradation and potent antibacterial action. Environmentally friendly ; Antimicrobial ; Degradation (telecommunications) ; Chemistry ; Orange (colour) ; Methyl orange ; Nuclear chemistry ; Organic chemistry ; Food science ; Biology ; Photocatalysis ; Computer science ; Catalysis ; Ecology Nanoparticles: synthesis and applications ; Moringa oleifera research and applications
Authors: Nguyen Thi Huong | Nguyễn Ngọc Sơn | Vu Minh Thanh | Ninh Duc Ha
Source: Research Square (preprint, 2024-02-13)
Field: Materials Science — Biomaterials

Glutamic Acid-Coated Zinc Oxide Nanoparticles: Synthesis, Characterization, and Anticancer Activity Abstract This paper presents a method to enhance the compatibility of zinc oxide nanoparticles (ZnO NPs) produced from guava leaf extract by modifying the nanoparticle surface with L-glutamic acid. The Glu-coated ZnO material was subjected to characterization using Fourier transform infrared spectroscopy (FTIR), X-ray scattering spectroscopy (XRD), UV-Vis spectroscopy, and electron energy scattering spectroscopy (EDS). The results corroborated the attachment of glutamic acid to the surface of the nanoparticle. The thermal density analysis (TGA) results indicate that the Glu-coated ZnO material contains around 8.998% organic content. The morphology and size of nanoparticles were assessed using scanning electron microscopy (SEM), transmission electron microscopy (TEM), and dynamic light scattering spectroscopy (DLS) both before and after modification. The findings demonstrate that the bare ZnO nanoparticles had an average size of around 25.32 nm, but the Glu-coated ZnO nanoparticles measure 41.88 nm. Their zeta values are − 9.05 mVs and − 18.6 mV, respectively. The anti-cancer effect of ZnO nanoparticles coated with glutamic acid was evaluated on various cell lines including HeLa (cervical cancer), A549 (lung cancer), and MCF7 (breast cancer). The findings demonstrated a significant enhancement in the anti-cancer efficacy of ZnO NPs with the application of Glu coating on their surface. The IC50 values of Glu-coated ZnO for the Hela, A549, and MCF7 cancer cell lines are 40.43 µg/mL, 37.20 µg/L, and 44.23 µg/mL, respectively. The findings indicate that the utilization of Glu-coated ZnO material holds significant promise in the field of cancer treatment. Zinc ; Characterization (materials science) ; Nanoparticle ; Glutamic acid ; Chemistry ; Nuclear chemistry ; Combinatorial chemistry ; Nanotechnology ; Materials science ; Biochemistry ; Amino acid ; Organic chemistry Nanoparticle-Based Drug Delivery ; Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics
Authors: Salma J. Ahmed | Mohammed, Emad A.
Source: arXiv (Cornell University) (preprint, 2024-05-10)
Field: Materials Science — Materials Chemistry

Improving Targeted Molecule Generation through Language Model Fine-Tuning Via Reinforcement Learning Developing new drugs is laborious and costly, demanding extensive time investment. In this paper, we introduce a de-novo drug design strategy, which harnesses the capabilities of language models to devise targeted drugs for specific proteins. Employing a Reinforcement Learning (RL) framework utilizing Proximal Policy Optimization (PPO), we refine the model to acquire a policy for generating drugs tailored to protein targets. The proposed method integrates a composite reward function, combining considerations of drug-target interaction and molecular validity. Following RL fine-tuning, the proposed method demonstrates promising outcomes, yielding notable improvements in molecular validity, interaction efficacy, and critical chemical properties, achieving 65.37 for Quantitative Estimation of Drug-likeness (QED), 321.55 for Molecular Weight (MW), and 4.47 for Octanol-Water Partition Coefficient (logP), respectively. Furthermore, out of the generated drugs, only 0.041% do not exhibit novelty. Reinforcement learning ; Reinforcement ; Computer science ; Artificial intelligence ; Psychology ; Social psychology Machine Learning in Materials Science ; Chemical Synthesis and Analysis