jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dengfeng Cao | Wenjie Xu | Shuangming Chen | Chongjing Liu | Beibei Sheng | Pin Song | Oyawale Adetunji Moses | Li Song | Shiqiang Wei
Source: Advanced Materials (review, 2023-02-20)
Field: Materials Science — Materials Chemistry

Visualizing Catalytic Dynamics Processes via Synchrotron Radiation Multitechniques The importance of catalysts today as workhorses in most modern industrial fields cannot be downplayed. As a result, rational design and engineering of targeted catalysts have emerged as key objectives and are dependent on in-depth understanding of complex catalytic dynamics. Synchrotron radiation (SR) light sources with rich advanced experimental methods are being recognized as a comprehensive characterization platform, which can draw a full picture on such multiparameter-involved catalysis under actual working conditions. Herein, the recent progress of catalytic dynamics process studied by the means of various SR techniques is summarized. In particular, SR-based spectroscopic, scattering, and imaging investigations on true catalysts are first introduced with the potential of in situ and operando characterizations. Apparently, the limitations from single SR technique naturally prompt a simple combination of SR techniques to better understand the whole catalysis process. Moreover, the discrepancies among various online testing facilities and batches of samples, along with random/systematic errors introduced by traditional intermittent/asynchronous measurement make it imperative to develop more prolific systems, complementary of multiple SR techniques for deep probing of dynamic catalytic processes. It is believed that the booming new light sources can further enrich the current multiple SR techniques, and thus may realize the true visualization on future catalytic dynamic processes. Materials science ; Synchrotron radiation ; Catalysis ; Dynamics (music) ; Synchrotron ; Nanotechnology ; Chemical physics ; Optics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Thomas A. R. Purcell | Matthias Scheffler | Luca M. Ghiringhelli | Christian Carbogno
Source: npj Computational Materials (article, 2023-06-24)
Field: Materials Science — Materials Chemistry

Accelerating materials-space exploration for thermal insulators by mapping materials properties via artificial intelligence Abstract Reliable artificial-intelligence models have the potential to accelerate the discovery of materials with optimal properties for various applications, including superconductivity, catalysis, and thermoelectricity. Advancements in this field are often hindered by the scarcity and quality of available data and the significant effort required to acquire new data. For such applications, reliable surrogate models that help guide materials space exploration using easily accessible materials properties are urgently needed. Here, we present a general, data-driven framework that provides quantitative predictions as well as qualitative rules for steering data creation for all datasets via a combination of symbolic regression and sensitivity analysis. We demonstrate the power of the framework by generating an accurate analytic model for the lattice thermal conductivity using only 75 experimentally measured values. By extracting the most influential material properties from this model, we are then able to hierarchically screen 732 materials and find 80 ultra-insulating materials. Computer science ; Field (mathematics) ; Thermal conductivity ; Artificial intelligence ; Machine learning ; Materials science ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Catalysis and Oxidation Reactions
Authors: Amol Thakkar | Alain C. Vaucher | Andrea Byekwaso | Philippe Schwaller | Alessandra Toniato | Teodoro Laino
Source: ACS Central Science (article, 2023-07-05)
Field: Materials Science — Materials Chemistry

Unbiasing Retrosynthesis Language Models with Disconnection Prompts Data-driven approaches to retrosynthesis are limited in user interaction, diversity of their predictions, and recommendation of unintuitive disconnection strategies. Herein, we extend the notions of prompt-based inference in natural language processing to the task of chemical language modeling. We show that by using a prompt describing the disconnection site in a molecule we can steer the model to propose a broader set of precursors, thereby overcoming training data biases in retrosynthetic recommendations and achieving a 39% performance improvement over the baseline. For the first time, the use of a disconnection prompt empowers chemists by giving them greater control over the disconnection predictions, which results in more diverse and creative recommendations. In addition, in place of a human-in-the-loop strategy, we propose a two-stage schema consisting of automatic identification of disconnection sites, followed by prediction of reactant sets, thereby achieving a considerable improvement in class diversity compared with the baseline. The approach is effective in mitigating prediction biases derived from training data. This provides a wider variety of usable building blocks and improves the end user's digital experience. We demonstrate its application to different chemistry domains, from traditional to enzymatic reactions, in which substrate specificity is critical. Disconnection ; Computer science ; Retrosynthetic analysis ; USable ; Artificial intelligence ; Set (abstract data type) ; Code refactoring ; Baseline (sea) ; Machine learning ; Language model ; Schema (genetic algorithms) ; Natural language processing ; Human–computer interaction ; Software ; Programming language ; World Wide Web ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Sukriti Manna | Yunzhe Wang | Alberto Hernández | Peter Lile | Shanping Liu | Tim Mueller
Source: Scientific Data (article, 2023-05-20)
Field: Materials Science — Materials Chemistry

A database of low-energy atomically precise nanoclusters The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but the structures of the clusters can be computationally expensive to predict. In this work, we present the largest database of cluster structures and properties determined using ab-initio methods to date. We report the methodologies used to discover low-energy clusters as well as the energies, relaxed structures, and physical properties (such as relative stability, HOMO-LUMO gap among others) for 63,015 clusters across 55 elements. We have identified clusters for 593 out of 1595 cluster systems (element-size pairs) explored by literature that have energies lower than those reported in literature by at least 1 meV/atom. We have also identified clusters for 1320 systems for which we were unable to find previous low-energy structures in the literature. Patterns in the data reveal insights into the chemical and structural relationships among the elements at the nanoscale. We describe how the database can be accessed for future studies and the development of nanocluster-based technologies. Nanoclusters ; Cluster (spacecraft) ; Atom (system on chip) ; Ab initio ; Chemical physics ; Materials science ; Nanotechnology ; Physics ; Computer science ; Quantum mechanics Nanocluster Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Aqeel Abbas | Akeem Yusuf Adesina | Rami K. Suleiman
Source: Metals (article, 2023-08-17)
Field: Materials Science — Materials Chemistry

Influence of Organic Acids and Related Organic Compounds on Corrosion Behavior of Stainless Steel—A Critical Review Stainless steel is one of the most commonly used structural materials in industry for the transportation of liquids such as water, acids, and organic compounds. Corrosion is a major concern in industry due to the use of strong mineral acids, feedstock contamination, flow, aqueous environments, and high temperatures. Stainless steel is the most commonly used material in the petrochemical industry because of its characteristics of self-protectiveness, offered by thin passive oxides, and its metallurgical composition. However, chlorides and mineral acids attack the stainless steel continuously, consequently breaking down the passivation film, causing a continuous challenge from corrosion. The corrosion in stainless steel is influenced by many factors including flow rate, temperature, pressure, ethanol concentration, and chloride ion content. This review describes the impact of organic compounds and organic acids on the degradation behavior of stainless steel. The review also summarizes the commonly used organic compounds and their applications. It has been demonstrated that organic acid concentration, temperature, and halide impurities have significant effects on susceptibility to pitting corrosion by damaging the passivation film. The phenomenon of corrosion in stainless steel is quite different in immersion tests and electrochemical potentiodynamic polarization. This review article discusses the importance of organic compounds and their corrosion behavior on steel. The article also puts emphasis on the roles of corrosion inhibitors, monitoring methods, corrosion management, and forms of corrosion. Corrosion ; Passivation ; Materials science ; Metallurgy ; Petrochemical ; Organic acid ; Chloride ; Aqueous solution ; Pitting corrosion ; Halide ; Raw material ; Inorganic chemistry ; Chemistry ; Composite material ; Organic chemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Amirsasan Gorgzadeh | Ali Hheidari | Parizad Ghanbarikondori | Mahshid Arastonejad | Tayebeh Ghasemi Goki | Mehrad Aria | Ahmadreza Allahyartorkaman | Farimah Moazzam
Source: Asian Pacific Journal of Cancer Biology (article, 2023-11-06)
Field: Materials Science — Biomaterials

Investigating the Properties and Cytotoxicity of Cisplatin-Loaded Nano-Polybutylcyanoacrylate on Breast Cancer Cells Background: This study aimed to develop a novel drug formulation using polybutylcyanoacrylate (PBCA) nanoparticles to deliver cisplatin, a commonly used chemotherapeutic agent for breast cancer treatment.Materials and Methods: PBCA nanoparticles were synthesized using a mini-emulsion polymerization method, and the resulting NPs were comprehensively characterized for their physical properties, such as size, size distribution, zeta potential, drug loading, and encapsulation efficiency. In addition, the cytotoxicity of the NPs was assessed, along with their ability to release the entrapped drugs over time. Results: The results showed that the PBCA nanoparticles had a mean size of 457 ± 7.4 nm, a size distribution of 0.253±0.011 and a negative zeta potential of -12.3 ± 1.3 mV. The drug encapsulation efficiency and loading capacity of cisplatin-PBCA were found to be 45.6 ± 2.7% and 3.5 ±0.8%, respectively; The release of the drug from the PBCA was estimated to be approximately 12.2±1.1% after 45 hours. The cytotoxic effects of the nanoparticle formulation were significantly enhanced compared to the free drug. The cytotoxicity of cisplatin-PBCA was evaluated in the T-47D breast cancer cell line, showing promising results as a potential drug formulation for breast cancer therapy. Conclusions: These findings suggest that cisplatin-PBCA may offer advantages over traditional cisplatin formulations, potentially improving the efficacy and reducing the toxicity of breast cancer treatment. Cisplatin ; Cytotoxicity ; Breast cancer ; Zeta potential ; Drug ; Pharmacology ; Nanoparticle ; Chemistry ; Distribution (mathematics) ; Cancer ; Medicine ; Materials science ; Chemotherapy ; Nanotechnology ; Surgery ; In vitro ; Internal medicine ; Mathematics ; Biochemistry Nanoparticle-Based Drug Delivery ; Cancer Treatment and Pharmacology ; Cancer Research and Treatment
Authors: Yi Li | Shuangshuang Tian | Linlin Zhong | Geng Chen | Song Xiao | Yann Cressault | Yuwei Fu | Yufeng Zheng | Christophe Prévé | Zhaolun Cui | Yin Zhang | Fanchao Ye | Daniel Piccoz | Gang Wang | Yalong Li | Youping Tu | Wenjun Zhou | Ju Tang | Xiaoxing Zhang
Source: iEnergy (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Eco-friendly gas insulating medium for next-generation SF<sub>6</sub>-free equipment Gas-insulated equipment (GIE) that utilizes the most potent greenhouse gas sulfur hexafluoride (SF <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</inf> ) as insulation and arc-quenching medium has been widely used in the power industry. Seeking eco-friendly insulating gas with advanced performance for next-generation SF <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</inf> -free GIE is significant for the “net-zero” goal and sustainable development. In this paper, the utilization, emission, and reduction policies of SF <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</inf> around the world were summarized first. Then, we systematically reviewed the latest progress in comprehensive performance evaluation of eco-friendly insulating gas in terms of molecular design, dielectric insulation, arc-quenching, stability and decomposition, materials compatibility, biosafety, etc. Further, the representative applications of eco-friendly insulating gas in medium-voltage, high-voltage GIE as well as relevant maintenance-related technologies were highlighted. Accordingly, the existing challenges and future perspectives were proposed, presenting a roadmap to hopefully steer the development of eco-friendly insulating gas and GIE. Environmentally friendly ; Materials science ; Process engineering ; Engineering High voltage insulation and dielectric phenomena ; Aerosol Filtration and Electrostatic Precipitation ; Offshore Engineering and Technologies
Authors: Mahbub Alam | Mohammed Nurul Abser | Ajoy Kumer | M. M. H. Bhuiyan | Parul Akter | Md. Emdad Hossain | Unesco Chakma
Source: Heliyon (article, 2023-05-24)
Field: Chemistry — Organic Chemistry

Synthesis, characterization, antibacterial activity of thiosemicarbazones derivatives and their computational approaches: Quantum calculation, molecular docking, molecular dynamic, ADMET, QSAR The thiosemicarbazones and their derivatives have been recognized as antimicrobial agents against human pathogenic bacteria and fungi. Regarding these prospective, this study was designed to address the new antimicrobial agents from thiosemicarbazones and their derivatives. These derivatives were synthesized by multi-step synthesis methods, such as alkylation, acidification, esterification, and formed the 4-(4'-alkoxybenzoyloxy) thiosemicarbazones and its derivatives <b>(</b>THS1, THS2, THS3, THS4, and THS5). Afterward the synthesis, compounds were characterized by <sup>1</sup>H NMR, FTIR spectra, and melting point. Later, the computational tools were applied to evaluate the drug likeness properties, bioavailability score, Lipinski rule, absorption, distribution, metabolism, excretion, and toxicity (ADMET). Secondly, the quantum calculations, for instance HOMO, LUMO and chemical descriptors, were calculated by the density functional theory (DFT). Finally, the molecular docking was performed against seven human pathogenic bacteria, black fungus (Rhizomucor mieh, Mucor lusitanicus, Mycolicibacterium smegmatis) and white fungus strains (Candida Auris, Aspergillus luchuensis, Candida albicans). To check and validate of molecular docking procedure and stability of docked complex for ligand and protein, the molecular dynamic was performed of docked complex. From the docking score with calculating the binding affinity, these derivatives could show a higher affinity than standard drug against all pathogens. From the computational details, it could be decided to do <i>in-vitro</i> test as antimicrobial activity against Staphylococcus aurious, Staphylococcus homonis, <i>Salmonella typhi</i>, and Shigella flexneria. The obtained result of antibacterial activity compared to standard drugs, and it was found that the synthesized compounds were almost same value of standard drug. Finally, it could be said from the <i>in-vitro</i> and <i>in-silico</i> study that the thiosemicarbazones derivatives are good antimicrobial agents. Quantitative structure–activity relationship ; Docking (animal) ; Computational chemistry ; Chemistry ; Combinatorial chemistry ; Quantum chemical ; Quantum chemistry ; Antibacterial activity ; Molecular dynamics ; Stereochemistry ; Organic chemistry ; Molecule ; Biology ; Bacteria Synthesis and biological activity ; Computational Drug Discovery Methods ; Synthesis and Characterization of Heterocyclic Compounds
Authors: В В Кукарцев | Anastasia Kozlova | Olga Kuimova | V. A. Nelyub | Andrei Gantimurov
Source: E3S Web of Conferences (article, 2023-01-01)
Field: Materials Science — General Materials Science

Using digital twins to create an inventory management system The article considers the application of the inventory management model on the example of a manufacturing enterprise, where it is planned to use a digital twin together with the inventory management system. Inventory management model is a mathematical model that allows you to determine the optimal level of stocks at the enterprise. In a manufacturing enterprise, this model can help optimize the inventory of raw materials, materials, and finished goods, which will reduce costs and reduce risks associated with the lack of necessary raw materials or required quantities of finished goods. With the help of data obtained from the enterprise, the input parameters of the model were obtained, namely the size of resource consumption, stock holding costs, delivery realization costs, and time required for delivery. The model was used to find the parameters such as optimal order size, delivery period, inventory level at which ordering should be carried out. Finished good ; Inventory theory ; Inventory management ; Computer science ; Stock management ; Operations management ; Perpetual inventory ; Business ; Operations research ; Inventory control ; Raw material ; Resource consumption ; Manufacturing engineering ; Production (economics) ; Economics ; Engineering ; Microeconomics Material Properties and Applications ; Economic and Technological Systems Analysis ; Ergonomics and Human Factors
Authors: Nelson Naveas | Ruth Pulido | Carlo Marini | Pierluigi Gargiani | Jacobo Hernández‐Montelongo | Iván Brito | Miguel Manso‐Silván
Source: Journal of Chemical Theory and Computation (article, 2023-11-17)
Field: Materials Science — Materials Chemistry

First-Principles Calculations of Magnetite (Fe<sub>3</sub>O<sub>4</sub>) above the Verwey Temperature by Using Self-Consistent DFT + <i>U</i> + <i>V</i> In this report, we have used the DFT + <i>U</i> + <i>V</i> approach, an extension of the DFT + <i>U</i> approach that takes into account both on-site and intersite interactions, to simulate structural, magnetic, and electronic properties together with the Fe and O K-edge XAS spectra of Fe<sub>3</sub>O<sub>4</sub> above the Verwey temperature (<i>T</i><sub>v</sub>). Moreover, we compared the simulated XAS spectra with experimental XAS data. We examined both orthogonalized and nonorthogonalized atomic orbital projectors and compared DFT + <i>U</i> + <i>V</i> to DFT, DFT + <i>U</i>, and HSE as a hybrid functional. It is noteworthy that, despite the widespread use of the same Hubbard <i>U</i> value for Fe<sub>oct</sub> and Fe<sub>tet</sub> at the DFT + <i>U</i> level in the literature, the HP code identified two distinct values for them using the Hubbard approaches (DFT + <i>U</i> and DFT + <i>U</i> + <i>V</i>). The resulting Hubbard <i>U</i> and <i>V</i> parameters are strongly dependent on the chosen orbital projectors. This study demonstrates how DFT + <i>U</i> + <i>V</i> can improve the structural, magnetic, and electronic properties of Fe<sub>3</sub>O<sub>4</sub> compared to approximate DFT and DFT + <i>U</i>. In this context, DFT + <i>U</i> + <i>V</i> supports the half-metallic character of the bulk crystal Fe<sub>3</sub>O<sub>4</sub> above <i>T</i><sub>v</sub>, since the Fermi level is found in the t<sub>2g</sub> band with a Fe<sub>oct</sub> down-spin. Thus, the observations in the current study emphasize the significance of intersite interactions in the theoretical analysis of Fe<sub>3</sub>O<sub>4</sub> above the <i>T</i><sub>v</sub>. Density functional theory ; Context (archaeology) ; Electronic structure ; Spectral line ; Condensed matter physics ; X-ray absorption spectroscopy ; Charge ordering ; Fermi level ; Physics ; Charge (physics) ; Materials science ; Chemistry ; Absorption spectroscopy ; Computational chemistry ; Quantum mechanics ; Electron Magnetic Properties and Synthesis of Ferrites ; Iron oxide chemistry and applications ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Zhiyong Zou | Jie Chen | Weijia Wu | Jinghao Luo | Tao Long | Qingsong Wu | Qianlong Wang | Jiangbo Zhen | Yongpeng Zhao | Yuchao Wang | Yongming Chen | Man Zhou | Lijia Xu
Source: Frontiers in Plant Science (article, 2023-02-27)
Field: Chemistry — Analytical Chemistry

Detection of peanut seed vigor based on hyperspectral imaging and chemometrics Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R<sup>2</sup> was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor. Hyperspectral imaging ; Chemometrics ; Mathematics ; Partial least squares regression ; Germination ; Sowing ; Random forest ; Artificial intelligence ; Pattern recognition (psychology) ; Horticulture ; Computer science ; Machine learning ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies