jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohammad Ebrahimi | Omid Deymi | Fahimeh Hadavimoghaddam | Abdolhossein Hemmati‐Sarapardeh
Source: Scientific Reports (article, 2025-09-30)
Field: Materials Science — Biomaterials

Derivation of explicit mathematical equations for gypsum solubility in aqueous electrolyte solutions using GP, GEP, and GMDH techniques The accumulation of mineral deposits on industrial equipment surfaces poses a major concern in a variety of processes. Gypsum (CaSO<sub>4</sub>·2H<sub>2</sub>O) is one of the most widely produced minerals in both natural and industrial environments. Currently, intelligent white-box models can serve as a suitable alternative to time-consuming and high-priced experiments, enabling the identification of possible gypsum scaling issues in the chemical and petroleum industries. In this regard, the current study focused on the development of robust mathematical correlations to estimate the solubility of gypsum in aqueous electrolyte solutions. For this purpose, three rigorous techniques of Genetic Programming (GP), Gene Expression Programming (GEP), and Group Method of Data Handling (GMDH) were implemented on two distinct data banks, including 2288 experimental data-points taken from previously published literature. Solution temperature (T), solution molecular weight (MW), and molal concentrations of monovalent, divalent, and trivalent compounds (m<sub>I</sub>, m<sub>II</sub>, and m<sub>III</sub>) were the input/independent variables employed in the first data bank, whereas solution temperature (T), solution molecular weight (MW), and solution ionic strength (I) were included in the second data bank. The performance and accuracy of correlations were evaluated using various statistical indicators such as Mean Bias Error (MBE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R<sup>2</sup>). Following multiple statistical and graphical analyses on the novel correlations' outcomes, it was found that the correlation established by implementing the GMDH technique onto the first data bank (i.e., GMDH-1) performed significantly better than all other correlations, with MAE = 0.01095, RMSE = 0.01482, and R<sup>2</sup> = 0.8508. The correlations obtained by applying the GEP and GMDH techniques to the second data bank (i.e., GEP-2 and GMDH-2) also revealed a satisfactory level of performance. By comparing the new correlations developed in this study with models reported in previous studies, a reasonable level of agreement was found. Gypsum ; Molality ; Mean squared error ; Coefficient of determination ; Aqueous solution ; Solubility ; Experimental data ; Electrolyte ; Correlation coefficient ; Approximation error Calcium Carbonate Crystallization and Inhibition ; Chemical and Physical Properties in Aqueous Solutions ; Crystallization and Solubility Studies
Authors: Halah K Mohsin Khafaji
Source: International Journal of Chemical Studies (article, 2025-09-01)
Field: Chemistry — Analytical Chemistry

Study the effect of various temperatures on product yields of hydrocracking reactor Hydrocracking is one of the most versatile and rapidly expanding processing steps in modern petroleum refineries. In recent years, the demand for middle distillates and gasoline has risen significantly, whereas the demand for heavy gas has declined. The hydrocracking process involves complex chemistry due to the participation of high molecular weight hydrocarbons, making process modeling and simulation particularly challenging. In this work, a mathematical model was developed to evaluate the performance of a hydrocracking unit in terms of product yields. The model considers five lumped fractions: heavy oil, vacuum gas oil, middle distillates, naphtha, and gases. First-order kinetics were assumed for all conversions, and the system was modeled as an isothermal tubular reactor incorporating axial dispersion. The effect of operating temperature (380 °C, 400 °C, 420 °C, and 430 °C) on product distribution was systematically investigated. The results indicate that the yield of heavy oil feed decreases steadily with increasing temperature, while the yields of middle distillates, naphtha, and gases increase. The yield of vacuum gas oil shows a distinct trend: it rises with temperature from 380 °C to 420 °C, but decreases at 430 °C due to secondary cracking reactions. The developed model provides a robust tool for predicting the performance of hydrocracking units and can assist in optimizing operating conditions to maximize the production of valuable middle distillates and light products. Cracking ; Isothermal process ; Gasoline ; Yield (engineering) ; Product distribution ; Dry gas ; Distillation ; Vacuum distillation Petroleum Processing and Analysis ; Catalysis and Hydrodesulfurization Studies
Authors: Jesús Bladimir Cepero-Rodríguez | F. Ramos‐Brito | Jorge Angulo‐Rocha | M. A. Sánchez-Alejo | R. Martínez‐Martínez | Enrique Camarillo-García | Erika Lizárraga-Medina | F. J. Sánchez Rodríguez | C. Alejo-Armenta | Adrián Canizalez‐Román | S. J. Castillo | Jesus J. Molina-Duarte | M. García‐Hipólito
Source: Coatings (article, 2025-10-18)
Field: Materials Science — Materials Chemistry

Electric Field-Assisted Chemical Bath Deposition of ZnO Thin Films: Effects of Field Intensity, Polarity Inversion, and Air Agitation on Film Properties This study presents an innovative modification to the chemical bath deposition method for synthesizing zinc oxide thin films by incorporating a high-voltage electric field, with and without electrical polarity inversion, to influence film growth dynamics. Two configurations were developed to assess the effects of electric field strength, periodic inversion, air agitation, and solution pH on the morphological, structural, and optical properties of ZnO coatings. Morphology studies revealed that particle size, shape, and distribution were strongly dependent on synthesis parameters, with electric field and air injection enabling higher surface coverage and finer nanostructures. Crystalline structural analysis confirmed the formation of the wurtzite ZnO phase, with reduced interplanar spacing and crystallite size under electric fields, especially when polarity was inverted. Optical measurements showed a consistent increase in the band gap (blue shift) and reduced defect-related absorption when electric field is applied. These findings are evidence that controlled electric field application during chemical bath deposition enables precise tuning of ZnO film properties. Electric field ; Chemical bath deposition ; Wurtzite crystal structure ; Crystallite ; Polarity (international relations) ; Thin film ; Deposition (geology) ; Band gap ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Copper-based nanomaterials and applications
Authors: Nur Khotimah | Awaludin Martin | Erman Taer | Nasruddin Nasruddin | Ishkrizat Taib
Source: Journal of Advanced Research in Micro and Nano Engieering (article, 2025-04-30)
Field: Materials Science — Biomaterials

Synthesis and Characterization of a Novel Microporous Material from Polyethylene Terephthalate Plastic Waste Plastic waste, especially polyethylene terephthalate (PET), is one of the world's biggest environmental challenges. Its non-biodegradable nature has led to the accumulation of PET plastic waste, causing ecosystem damage and environmental pollution, including water and soil contamination. Effective and sustainable plastic waste management is a priority to reduce environmental impact. This study aims to utilize PET plastic waste as a microporous material and conduct characterization to understand and identify the physical and chemical properties of the material. PET plastic is synthesized by carbonization and physical activation. The carbonization process was carried out at a temperature of 400ºC for 240 minutes with a heating rate of 3°C/minute. Then, the process continued with physical activation using CO2 gas, at a temperature of 800°C for 240 minutes with a heating rate of 10°C/minute. Characterization of PET-activated carbon was obtained with an iodine number of 895.78 mg/g. The TGA-DTA graph shows a curve peak at a temperature of 367.80°C due to the decomposition of PET plastic caused by the carbonization process. Characterization of PET-activated carbon has a surface area of 428.141 m2/g with a pore volume of 0.4083 cm3/g and an average pore size of 19.07 Å (micropore category). Polyethylene terephthalate ; Microporous material ; Materials science ; Characterization (materials science) ; Plastic waste ; Polyethylene ; Polymer science ; Composite material ; Waste management ; Nanotechnology ; Engineering biodegradable polymer synthesis and properties ; Chemical Synthesis and Characterization
Authors: Gergely Laczkó | Imre Pápai | Péter R. Nagy
Source: ChemRxiv (preprint, 2025-06-18)
Field: Materials Science — Materials Chemistry

Understanding DFT uncertainties for more reliable reactivity predictions by advancing the analysis of error sources Decades of advancements and thousands of successful applications contributed to the reliability of density functional theory (DFT) methods. Especially in main group chemistry, DFT predictions tend to be increasingly more predictive. In this study we deeply analyze unexpected (ca. 8–13 kcal/mol) DFT disagreements obtained for a few organic reactions using only widely adopted, modern, hybrid and higher rung DFT methods. To understand the underlying causes, here, we move beyond conventional statistics-based benchmarks by combining recent advances in DFT error decomposition with affordable gold-standard references. This approach helps to characterize and disentangle multiple functional and density-based error types and enables us to find functional(s) suitable for broad mechanistic studies in all studied examples. The proposed tools are cost-efficient, readily accessible, and easy to integrate into routine thermochemistry workflows. While the focus is on main group reactions, the approach is applicable also for transition metal, bio-, and surface chemistry to assist more predictive reactivity modeling. Thermochemistry ; Density functional theory ; Computer science ; Reactivity (psychology) ; Workflow ; Decomposition ; Reliability (semiconductor) ; Computational chemistry ; Chemistry ; Biochemical engineering ; Thermodynamics ; Physical chemistry ; Physics ; Database ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Bonneau, Klara | Lederer, Jonas | Templeton, Clark | Rosenberger, David | Giambagli, Lorenzo | Müller, Klaus-Robert | Clementi, Cecilia
Source: Refubium (Universitätsbibliothek der Freien Universität Berlin) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Peering inside the black box by learning the relevance of many-body functions in neural network potentials Machine learned potentials based on artificial neural networks are becoming a popular tool to define an effective energy model for complex systems, either incorporating electronic structure effects at the atomistic resolution, or effectively renormalizing part of the atomistic degrees of freedom at a coarse-grained resolution. One main criticism regarding neural network potentials is that their inferred energy is less interpretable than in traditional approaches, which use simpler and more transparent functional forms. Here we address this problem by extending tools recently proposed in the nascent field of explainable artificial intelligence to coarse-grained potentials based on graph neural networks. With these tools, neural network potentials can be practically decomposed into n-body interactions, providing a human understandable interpretation without compromising predictive power. We demonstrate the approach on three different coarse-grained systems including two fluids (methane and water) and the protein NTL9. The obtained interpretations suggest that well-trained neural network potentials learn physical interactions, which are consistent with fundamental principles. Artificial neural network ; Black box ; Relevance (law) ; Field (mathematics) ; Peering ; Interpretation (philosophy) ; Energy (signal processing) ; Degrees of freedom (physics and chemistry) Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: Zuo Xiaoge
Source: Theory and Practice of Science and Technology (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Review of Application Expansion, Technical Bottlenecks and Optimization Path of Graphene Supercapacitors Graphene, with its exceptional electrical conductivity, large specific surface area, and outstanding mechanical properties, has emerged as a pivotal material driving technological innovation in supercapacitors. Graphene supercapacitors combine the rapid charging/discharging capabilities of traditional capacitors with the high energy storage efficiency of batteries, featuring fast charging, high power density, and long cycle life. These advantages make them highly valuable in critical fields such as new energy transportation, defense equipment, and smart grids. This paper systematically reviews the global R&amp;D progress and industrial applications of graphene supercapacitors, while analyzing three core bottlenecks hindering large-scale development: low energy density, high production costs, and inadequate environmental adaptability. Through dimensions like material system optimization, preparation process innovation, and device structure design, the study outlines targeted technical breakthrough pathways. Research indicates that innovations in composite electrode materials, development of low-cost production processes, and multi-dimensional environmental adaptation upgrades are key to overcoming existing challenges. This work provides valuable references for technological iteration, industrial transformation, and application promotion of graphene supercapacitors, helping them achieve broader scenario coverage under the "dual carbon" strategy. Supercapacitor ; Graphene ; Process (computing) ; Capacitor ; Efficient energy use ; Path (computing) ; Key (lock) ; Energy storage Supercapacitor Materials and Fabrication ; Graphene research and applications ; Advancements in Battery Materials
Authors: Lukas M. Bongartz | Garrett LeCroy | Tyler J. Quill | Nicholas Siemons | Gerwin Dijk | Adam Marks | Christina Cheng | Hans Kleemann | Karl Leo | Alberto Salleo
Source: Communications Materials (preprint, 2025-11-10)
Field: Materials Science — Polymers and Plastics

Electron–ion coupling breaks energy symmetry in bistable organic electrochemical transistors Abstract Organic electrochemical transistors are extensively studied for applications ranging from bioelectronics to analog and neuromorphic computing. Despite significant advances, the fundamental interactions between the polymer semiconductor channel and the electrolyte, which critically determine the device performance, remain underexplored. Here, we examine the coupling between the benchmark semiconductor PEDOT:PSS and an ionic liquid to explain the bistable and non-volatile behavior observed in OECTs. Using X-ray scattering and spectroscopy techniques, we demonstrate how the electrolyte modifies the channel composition, enhances molecular order, and reshapes the energetic landscape. Notably, the observed bistability arises from asymmetric and path-dependent energetics during doping and dedoping, resulting in two distinct paths, driven by a direct interaction between the electronic and ionic charge carriers. These findings highlight the electrolyte’s role in tuning charge carrier dynamics, positioning it as a powerful yet underutilized lever for enabling novel device functionalities. Electrochemistry ; Ion ; Coupling (piping) ; Symmetry (geometry) ; Transistor ; Electron ; Materials science ; Chemical physics ; Condensed matter physics ; Atomic physics ; Physics ; Quantum mechanics ; Electrode ; Voltage Conducting polymers and applications ; Advanced Memory and Neural Computing ; Analytical Chemistry and Sensors
Authors: Brandi Ransom | Riccardo Bosio | Dmitry Yu. Zubarev | James L. Hedrick | Nathaniel H. Park
Source: ChemRxiv (preprint, 2025-01-27)
Field: Materials Science — Polymers and Plastics

High-throughput computational analysis of kinetic barriers to ring-closing depolymerization for aliphatic polycarbonates The chemical reversion of polymers via ring-closing depolymerization (RCD) to their monomeric constituents is a highly promising avenue to enable end of life-cycle recycling and reuse. However, most reported systems using RCD revolve around bespoke monomer designs to facilitate facile depolymerization and there exists relatively few investigations into influence of functional groups on the ability of a particular monomer to cleanly undergo depolymerization. Here, we perform computational investigations into the energy barriers for RCD of 6-membered aliphatic carbonates in different solvents. The results demonstrate highlight clear trends observed in prior experimental studies, validating the utility of computational investigations towards understanding RCD. Experimental evaluation of thermal depolymerization of two monomers confirmed the ability of the computationally investigated monomers to cleanly undergo RCD. Overall, this work highlights the utility of high-throughput energy barrier computations to provide meaningful insight into broad reactivity trends that would be highly laborious to access experimentally. Depolymerization ; Closing (real estate) ; Kinetic energy ; Throughput ; Ring (chemistry) ; Chemistry ; Materials science ; Computational chemistry ; Combinatorial chemistry ; Computer science ; Polymer chemistry ; Organic chemistry ; Physics ; Business Polymer crystallization and properties ; biodegradable polymer synthesis and properties ; Synthesis and properties of polymers
Authors: Shinyoung Kang | Jihan Kim
Source: npj Computational Materials (article, 2025-10-28)
Field: Chemistry — Inorganic Chemistry

Property-guided inverse design of metal-organic frameworks using quantum natural language processing In this study, we explore the potential of using quantum natural language processing (QNLP) for property-guided inverse design of metal-organic frameworks (MOFs) with targeted properties. Specifically, by analyzing 450 hypothetical MOF structures consisting of 3 topologies, 10 metal nodes and 15 organic ligands, we categorize these structures into four distinct classes for pore volume and CO2 Henry’s constant values. We then compare various QNLP models (i.e., the bag-of-words, DisCoCat (Distributional Compositional Categorical), and sequence-based models) to identify the most effective approach to process the MOF dataset. Using a classical simulator provided by the IBM Qiskit, the bag-of-words model is identified to be the optimum model, achieving validation accuracies of 88.6% and 78.0% for binary classification tasks on pore volume and CO2 Henry’s constant, respectively. Further, we developed multi-class classification models tailored to the probabilistic nature of quantum circuits, with average test accuracies of 92% and 80% across different classes for pore volume and CO2 Henry’s constant datasets. Finally, the performance of generating MOF with target properties showed accuracies of 97.75% for pore volume and 90% for CO2 Henry’s constant, respectively. Although our investigation covers only a fraction of the vast MOF search space, it marks a promising first step towards using quantum computing for materials design, offering a new perspective through which to explore the complex landscape of MOFs. Volume (thermodynamics) ; Probabilistic logic ; Constant (computer programming) ; Quantum ; Inverse ; Quantum computer ; Process (computing) ; Binary number Metal-Organic Frameworks: Synthesis and Applications ; Surface Chemistry and Catalysis ; Supramolecular Self-Assembly in Materials
Authors: Neelam Vijaya Durga | K. Gayathri | Kasani Durga Mallesh | Yalamati Lokesh | Gogulamanda Sudhkar
Source: International Journal of Novel Research and Development (article, 2025-09-01)
Field: Chemistry — Organic Chemistry

Efficient Green Synthesis of Biologically Active Benzimidazole Derivatives for Pharmaceutical Applications Benzimidazole is a bicyclic heterocyclic compound composed of a benzene ring fused with the imidazole ring. It is a versatile scaffold with various applications, particularly in medicinal chemistry, due to its diverse pharmacological activities. Benzimidazole derivatives are used in the treatment of a wide range of disorders, including cancer and helminthiasis. They have favourable properties like high stability, bioavailability, and significant biological activity. The Conventional ways of synthesizing Benzimidazole lead to toxic effects, use harmful solvents, and also need a lot of heat and time. Green chemistry avoids all these Consequences, which leads to an eco-friendly and long-lasting way to make Benzimidazole derivatives. This process uses deep eutectic Solvents and microwave irradiation as an alternative, so for this we used a novel type-III eutectic mixture of Choline chloride & o-phenylenediamine, which acts as both reagent and solvent. Along with this, microwave irradation is used, which in turn saves time and energy. The DES was used in the ratio of 1:1 molar Concentration, and it was created by direct heating without the use of any catalysts or harmful chemical solvents. Benzaldehyde was used in this condensation process as an aldehyde component. This approach follows the twelve principles of green chemistry, which have multiple uses like Safety, Scalability, and environmental impact. Overall, the process gives us a quick, Safe, and eco-friendly way to synthesise benzimidazole derivatives where they can be used in research and health care purposes. Benzimidazole contains antibacterial, antiviral, anti-inflammatory, anticancer properties. Due to its heterocyclic structure, it holds a great place in the pharmaceutical Industry. Benzimidazole ; Imidazole ; Reagent ; Green chemistry ; Ring (chemistry) ; Choline chloride ; Active ingredient ; Chemical synthesis Synthesis and biological activity ; Multicomponent Synthesis of Heterocycles ; Synthesis and Biological Evaluation