jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nasir Khan | Mehdi Razavifar | Qazi Adnan Ahmad | Muhammad Siyar | Masoud Riazi | Waqar A. Khan | Jafar Qajar
Source: Scientific Reports (article, 2025-08-14)
Field: Chemistry — Analytical Chemistry

Machine learning model to predicting synergy of ultrasonication and solvation impacts on crude oil viscosity The growing energy demand is attracting a lot of interest in heavy crude oil. While various conventional methods have been employed to reduce the viscosity of crude oil, the challenges associated with these techniques have driven researchers to seek economical and environmentally sustainable alternatives. In this context, combining traditional methods, such as solvent treatment, with ultrasonic radiation presents a promising, yet complex solution. In this study, we develop a machine learning-based algorithm to rigorously predict the synergistic effects of ultrasonication and solvation on crude oil viscosity. This research is divided into two parts. First, we conducted experimental measurements of crude oil viscosity following treatment with ultrasound and/or n-heptane solvent. It was found that the optimum irradiation period for samples containing 0% to 16% n-heptane was 8 min. Notably, after 8 min of treatment, the viscosity decreased by over 34% for samples with 16% n-heptane and by more than 47% for those with 0% n-heptane. When the n-heptane concentration was increased to 22% and 30%, the required sonication time increased by 2 to 10 min. In the former case, viscosity was reduced by more than 50%, while in the latter, it decreased by over 48%. In the second part of the study, a Machine Learning (ML) model was developed using the experimental data. In particular, a Random Forest Regressor (RFR) model was applied, with results demonstrating high reliability based on RMSE and R<sup>2</sup> values for training (3.3395, 0.9764), validation (3.0166, 0.9602), and testing (2.4778, 0.9557). Considering the significance of features, n-Heptane (0.54) and irradiation time (0.45) were the key predictors, with n-heptane showing slightly greater impact on error reduction. The application of this model to additional datasets from other oil fields shows significant promise for future research and practical implementation. Heptane ; Viscosity ; Context (archaeology) ; Sonication ; Solvent ; Solvation ; Crude oil ; Oil viscosity ; Machine learning ; Materials science ; Computer science ; Chemistry ; Petroleum engineering ; Chromatography ; Organic chemistry ; Composite material ; Engineering ; Geology Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Ultrasound and Cavitation Phenomena
Authors: Lauren A. Viarengo‐Baker | Adrian Whitty
Source: ChemRxiv (preprint, 2025-01-16)
Field: Chemistry — Organic Chemistry

Macrocycles for Conventionally Druggable Targets: Lessons from Macrocyclic Kinase Inhibitors Macrocycles are emerging as a prominent modality in drug discovery, including for conventionally druggable targets for which simpler, acyclic ligands are readily discoverable. Given the additional synthetic challenges of macrocyclic chemotypes, we ask what benefits macrocycles provided for these highly druggable targets. To do this, we examine the effects of macrocyclization on inhibitors of highly druggable kinase targets. For each example, we isolate closely matched acyclic/macrocyclic compound pairs, allowing us to precisely identify the effects of macrocyclization on binding affinity, selectivity, and ADME properties absent other confounding factors. Our findings show that, while the impact of macrocyclization on potency is variable, a profound effect on selectivity is common. Macrocyclization can also bring benefits for membrane permeability, efflux ratio, blood-brain barrier penetrance, and metabolic stability. These findings lead us to propose specific circumstances in which a drug discoverer targeting kinases or other conventionally druggable target classes should consider a macrocycle approach. Druggability ; Kinase ; Stereochemistry ; Chemistry ; Computational biology ; Pharmacology ; Biology ; Biochemistry Synthesis and biological activity ; Advanced Breast Cancer Therapies ; Synthesis of Tetrazole Derivatives
Authors: Quentin Tencé | E. Meslin | Isabelle Mouton | B. Décamps | Raphaëlle Guillou | Jean-Luc Béchade | Erik Elkaı̈m | Anna Fraczkiewicz | Maylise Nastar
Source: Acta Materialia (article, 2025-09-23)
Field: Materials Science — Materials Chemistry

Mechanisms of radiation-induced G-phase precipitation in Fe-MnNiSi ferritic model alloys at 400 °C A high-purity Fe- 0 . 7 Mn- 1 . 8 Ni- 0 . 8 Si model reactor pressure vessel alloy was irradiated with 22.5 MeV Fe 9＋ ions at 400 °C, producing a radiation dose of 1–2 dpa at a depth of 1 μ m , following by annealing at the same temperature for up to 34 weeks. Irradiation led to the formation of four Mn-Ni-Si-enriched features: spherical precipitates in the bulk and on dislocation lines, sandwich-like precipitates, and toroidal segregation on dislocation loops. High-resolution transmission electron microscopy and synchrotron X-ray diffraction identified bulk precipitates as G-phase, exhibiting L2 1 symmetry, a cube-on-cube orientation with the matrix, and semi-coherent interfaces. Atom probe tomography showed that the bulk G-phase precipitates have the formula Ni 16 Mn x Fe 1 − x 6 Si 7 , with increased Fe substitution in the Mn sublattice under irradiation. G-phase precipitates exhibited thermodynamic stability during post-irradiation annealing, while sandwich-like phases dissolved, indicating metastability. Irradiation reduces the matrix solubility limit, which is evidenced by the reduced volume fraction of G-phase precipitates observed in post-irradiation annealed samples. Heterogeneous precipitation of the G-phase on dislocation lines likely occurs through radiation-induced segregation (RIS) of Ni, Si, and Mn. Bulk sandwich-like precipitates with a dense Ni-Si-enriched phase in the center are strongly evidenced to form through a two-step mechanism: the SIAs clustering into a dense NiSi phase, followed by the kinetics of vacancy-solute clustering, leading to the precipitation of the less dense G-phase. Atom probe ; Precipitation ; Irradiation ; Transmission electron microscopy ; Alloy ; Annealing (glass) ; Dislocation ; Volume fraction Nuclear Materials and Properties ; Fusion materials and technologies ; High Temperature Alloys and Creep
Authors: Neiba Yadira Echeagaray-Solorza | Yennifer Díaz-Romero | Lucila Guadalupe Tobón Galicia | Sebastián Tejada Paniagua
Source: Revista Colombiana de Ciencias Químico Farmacéuticas (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

Predicting drug solubility in cosolvent systems using artificial intelligence algorithms Introduction: Solubility is one of the most important physicochemical properties in the pharmaceutical sciences and is involved in processes ranging from drug development to environmental biotoxicity assessment. Accurate prediction of solubility, especially aqueous solubility, has been a scientific challenge due to the complexity of the dissolution process, which involves lattice energies, solvation, solute ionization, and solute-solvent interactions. While various approaches have been used to predict solubility, such as semi-empirical models and group contribution methods, accurate prediction remains difficult. Statistical and machine learning approaches often use numerous descriptors, making rational interpretation and refinement of prediction models difficult. Purpose: This study proposes an approach based on autonomous learning, specifically using a neural network model. Experimental and theoretical descriptors are carefully selected according to their relevance to physicochemical aspects of the dissolution process. Methodology: A solubility data set of four structurally related sulfonamides (sulfadiazine, sulfamerazine, sulfamethazine, sulfamethazine, and sulfacetamide) in different solvents under different temperature conditions and cosolvent composition is used to develop an artificial neural network model using less than 37 descriptors. Results: The developed AI algorithm shows acceptable correlation with experimental data, suggesting its potential as an approximation tool for optimizing solubility-related processes in the pharmaceutical industry. Solubility ; Artificial neural network ; Dissolution ; Quantitative structure–activity relationship ; Interpretation (philosophy) ; Experimental data ; Relevance (law) Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Drug Solubulity and Delivery Systems
Authors: A. G. Shcherbinin | Henning Finkenzeller | Fariba Partovi | Netta Vinkvist | Jussi Kontro | Matthew Boyer | Joona Mikkilä | Siddharth Iyer | Jyri Mikkilä | Paxton Juuti | Nina Sarnela | Juha Kangasluoma | Matti Rissanen
Source: ChemRxiv (preprint, 2025-06-05)
Field: Chemistry — Inorganic Chemistry

Uronium from X-ray Desorbed Solid Urea Enables Attomole Sensitivity to Amines and Semivolatiles Comprehensive mass spectrometric detection requires multiple ionization schemes. Chemical ionization at low pressure is suitable for the detection of weakly polar volatile organic compounds (VOCs). Negative mode ionization at ambient pressure delivers superior performance for polar acidic compounds. Positive mode chemical ionization has been explored to detect basic and polar neutral compounds, for which negative polarity and low-pressure ionization techniques have shown insufficient performance. Several ion attachment reagents have been proposed for more sensitive and soft ionization. These reagents are often reactive, toxic, and difficult to control, impeding their applicability and operability. Inspired by these challenges, we explored uronium as a sensitive and robust alternative for ionizing moderately oxygenated, basic, and polar neutral compounds at ambient pressure. Urea, a non-toxic solid with negligible vapor pressure, is desorbed by X-ray irradiation, forming the uronium ion. We experimentally determined the calibration factors and behavior of uronium ionization under different humidities for several semivolatile organic compounds (SVOCs), amines, and ammonia, and explored the ionization characteristics using theory. In laboratory measurements of a-pinene and dimethyl sulfide (DMS) oxidation systems, we characterized how uronium complements other ionization schemes. Excellent sensitivities were achieved to several key components (including amines, dimethyl sulfoxide (DMSO), N-Methyl-2-pyrrolidone (NMP), verbenone and dimethylformamide (DMF)) allowing detection limits at the low to mid parts per quadrillion per volume (ppqv) level. Uronium exhibits the tendency to selectively form strong ion--molecular clusters which renders the ionization robust against sample humidity changes. X-ray desorption of solid urea enables trivial handling of the reagent supply and long-term stability of the ion production system. This represents an innovative technique with significant potential for standardization and wide applicability, demonstrating efficiency, safety, and low maintenance requirements. Urea ; Sensitivity (control systems) ; Chemistry ; Materials science ; Organic chemistry ; Engineering Radioactive element chemistry and processing ; Chemical Synthesis and Characterization
Authors: Ilias Darsaklis
Source: International Journal of Scientific Research in Chemical Sciences (article, 2025-02-28)
Field: Chemistry — Organic Chemistry

Identification of an Unknown Organic Compound by Determination of its Melting Point The melting point is very useful information for identifying an unidentified organic substance. It is also helpful to define the purity of the substance. Pure substances have a melting point range of 1-2oC, while the impurities lower the melting point and increase the melting range. In a mixture of two substances, the melting point can be calculated by creating a two-phase melting diagram. The lowest temperature at which the melting point of a mixture can be reduced is called the eutectic point. The melting point is measured on a special heated apparatus using a glass capillary tube. The sample is placed in the tube, which is heated in the device. In the following study, the melting point of an unknown organic compound was measured by the fast and the slow rate heating method. The melting point of the sample showed that the chemical compound to be identified was either acetanilide or 4-nitrophenol. Equal amounts of the unknown substance and acetanilide were mixed, and the melting point was estimated for the mixture. If the mixture has a lower melting point compared to that of the unknown compound, then the two compounds are different, and one acts as an impurity to the other. The mixture had a similar melting point to that of the unknown compound, and therefore the unknown compound was identified as acetanilide C6H5NH(COCH3). Identification (biology) ; Melting point ; Point (geometry) ; Analytical Chemistry (journal) ; Chromatography ; Chemistry ; Mathematics ; Organic chemistry ; Biology ; Geometry Chemical Thermodynamics and Molecular Structure ; Analytical Chemistry and Chromatography ; thermodynamics and calorimetric analyses
Authors: Pai Wang | Wenxiang Zhao | Xibin Wang | Shuyao Liu | Yifan Bai | Hongtao Chen | Z. D. Liu
Source: Chinese Journal of Mechanical Engineering (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

Research on the Microstructure Characterization and Fatigue Behavior of Nickel-Based Superalloy Subjected to Short-Arc and Milling Composite Processing Abstract Short-arc machining is a novel electrical discharge machining method that utilizes high-energy arc discharge as the energy carrier. Due to its low cost and high processing efficiency, it has been widely applied in the efficient processing of superalloys. To address the challenges of efficient and high-precision processing of superalloys, a processing method combining short-arc machining with precision milling is employed. Advanced material characterization techniques such as electron backscatter diffraction (EBSD) are utilized to analyze the physical properties of the recast layer and surface crystal characteristics. High-temperature low-cycle fatigue life tests are conducted to investigate the correlation between fatigue life and typical surface integrity parameters (surface roughness, residual stress), as well as crystallographic parameters (grain size, grain orientation spread, geometrically necessary dislocations). Processing parameter optimization is achieved with fatigue life as the target. The results indicate that at high temperatures during short-arc machining, the surface material underwent recrystallisation to form a recast layer with a grain size reduction of 85.5% and a heat affected layer depth of over 400 μm. The trends in fatigue life are consistent with changes in residual stress, grain orientation spread and geometrically necessary dislocations. Selecting a larger axial depth of cut and lower feed per tooth is advantageous for achieving a higher fatigue life. The proposed research provides an instruction for high efficient precision machining of superalloys. Electron backscatter diffraction ; Machining ; Microstructure ; Superalloy ; Characterization (materials science) ; Residual stress ; Surface integrity ; Grain size ; Thermomechanical processing ; Layer (electronics) Metal Alloys Wear and Properties ; Additive Manufacturing Materials and Processes ; Aluminum Alloy Microstructure Properties
Authors: Frank J. Tucci | Madeline B. Ho | Aaron A. B. Turner | Lisa Y. Stein | Brian M. Hoffman | Amy C. Rosenzweig
Source: Chemical Science (article, 2025-12-18)
Field: Chemistry — Inorganic Chemistry

Simultaneous occupancy of Cu <sub>C</sub> and Cu <sub>D</sub> in the ammonia monooxygenase active site Ammonia monooxygenase (AMO), a copper-dependent membrane enzyme, catalyzes the first and rate-limiting step of nitrification: the oxidation of ammonia to hydroxylamine. Despite its central role in the global nitrogen cycle and its biotechnological relevance, structural characterization of AMO has lagged behind that of its homolog, particulate methane monooxygenase (pMMO), due to the slow growth rates of ammonia-oxidizing bacteria and the instability of AMO upon purification. Recent cryoEM studies of <i>Nitrosomonas europaea</i> AMO and <i>Methylococcus capsulatus</i> (Bath) pMMO in native membranes revealed new structural features, including two adjacent copper-binding sites in the transmembrane region, Cu<sub>C</sub> and Cu<sub>D</sub>, believed to constitute the active site. Although multiple structures were determined under various conditions, simultaneous occupancy of Cu<sub>C</sub> and Cu<sub>D</sub> was never observed, leaving their potential functional interplay unresolved. Here we report the 2.6 Å resolution cryoEM structure of AMO from <i>Nitrosospira briensis</i> C-128 in isolated native membranes. This structure reveals the first instance of simultaneous copper occupancy of the Cu<sub>C</sub> and Cu<sub>D</sub> sites, along with occupancy of the periplasmic Cu<sub>B</sub> site. Electron paramagnetic resonance (EPR) spectroscopic data indicate that the Cu<sub>B</sub> site is primarily occupied by Cu(ii), while Cu<sub>C</sub> and Cu<sub>D</sub> are primarily occupied by diamagnetic ions, presumably Cu(i). Notably, a lipid molecule is bound between the Cu<sub>C</sub> and Cu<sub>D</sub> sites, separating them by ∼8.0 Å. The results underscore the importance of studying these enzymes in their native environments across species to resolve conserved and divergent molecular features. Methane monooxygenase ; Periplasmic space ; Active site ; Ammonia monooxygenase ; Copper protein ; Ammonia ; Transmembrane protein ; Membrane Metal-Catalyzed Oxygenation Mechanisms ; Ammonia Synthesis and Nitrogen Reduction ; Photosynthetic Processes and Mechanisms
Authors: Diptesh Gayen | Yannik Schütze | Sébastien Groh | Joachim Dzubiella
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Optimizing cation–π force fields for molecular dynamics studies of competitive solvation in conjugated organosulfur polymers for lithium–sulfur batteries Lithium-sulfur (Li/S) batteries are emerging as a next-generation energy storage technology due to their high theoretical energy density and cost-effectiveness. π-Conjugated organosulfur polymers, such as poly(4-(thiophene-3-yl)benzenethiol) (PTBT), have shown promise in overcoming challenges such as the polysulfide shuttle effect by providing a conductive framework and enabling sulfur copolymerization. In these cathodes, cation-π interactions significantly influence Li<sup>+</sup> diffusion and storage properties in π-conjugated cathodes, but classical OPLS-AA force fields fail to capture these effects. This study employs a bottom-up approach based on density functional theory (DFT) to optimize the nonbonded interaction parameters (OPLS-AA/corr.), particularly for the Li<sup>+</sup>-π interactions with the PTBT polymer. Following prior work, we used an ion-induced dipole potential to model the cation-π interactions. The impact of the solvent on the PTBT monomers was examined by computing the potential of mean force (PMF) between PTBT monomers and Li<sup>+</sup> ions in both explicit and implicit solvents using the Boltzmann inversion of probability distributions close to room temperature. In the implicit solvent case, the magnitude of the binding free energy decreased with increasing dielectric constant, as the dominant electrostatics scaled with the dielectric constant. In contrast, in the explicit solvent case, considering the mixtures of organic solvent DME and DOL, the binding free energy shows minimal dependence on solvent composition due to the competing interaction of TBT and Li<sup>+</sup> with the solvent molecules. However, increasing salt concentration decreases the binding free energy due to Debye-Hückel screening effects. In general, this work suggests that the optimized parameters can be widely used in the simulation of polymers in electrolytes for the Li/S battery to enhance the representation of cation-π interactions for a fixed charge force field. Organosulfur compounds ; Solvation ; Conjugated system ; Sulfur ; Chemistry ; Lithium (medication) ; Molecular dynamics ; Polymer ; Force field (fiction) ; Computational chemistry ; Organic chemistry ; Nanotechnology ; Materials science ; Molecule Conducting polymers and applications ; Solid-state spectroscopy and crystallography ; Nonlinear Optical Materials Research
Authors: Nan Sun | Yang Gao | Jiali Cheng | Li Xia | Xuepin Liao
Source: Collagen and Leather (article, 2025-06-30)
Field: Materials Science — Biomaterials

Synthesis and retanning performance of hollow polymer microspheres for lightweight leather processing Microsphere ; Polymer ; Materials science ; Glass microsphere ; Materials processing ; Chemical engineering ; Polymer science ; Composite material ; Process engineering ; Engineering Collagen: Extraction and Characterization ; biodegradable polymer synthesis and properties ; Bone Tissue Engineering Materials
Authors: Vedant Tripathi | Jhuma Bhadra | Santanu Bhattacharya
Source: Journal of Surfactants and Detergents (article, 2025-11-16)
Field: Chemistry — Organic Chemistry

Structure–Activity Relationships and Practical Aspects of Cationic Gemini Surfactants as Antimicrobial Agents ABSTRACT This mini‐review summarizes current knowledge on cationic gemini surfactants as antimicrobial agents, with a focus on the structure–activity relationships that link their molecular architecture including headgroup type, alkyl chain length, spacer structure, and counter‐ion identity to antimicrobial activity. Analyses of these relationships reveal consistent trends, such as the characteristic alkyl chain length cut‐off and the influence of charge density and critical micellar concentration on biological activity. Compared with monomeric surfactants, gemini surfactants generally exhibit greater potency and broader antimicrobial spectra, although systematic investigations into resistance mechanisms remain limited. Documented mechanisms such as efflux pumps and biofilm tolerance highlight the importance of monitoring resistance development. Collectively, this review offers a mechanistic framework and practical insights for guiding the design, evaluation, and safe application of gemini surfactants in antimicrobial contexts. Antimicrobial ; Cationic polymerization ; Alkyl ; Efflux ; Monomer ; Biofilm Surfactants and Colloidal Systems ; Environmental Chemistry and Analysis ; Antimicrobial agents and applications