jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fatima Musbah Abbas | Selma Elsheikh Abdelrahman | Ammar H. Elsheikh
Source: Material and Mechanical Engineering Technology (article, 2025-09-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Structures, Dielectric Constant, Impedance and Electrical Conductivity of Carbon/Cu2O Nanowire (C/CONW) Composite Electrode Derived from Self-Adhesive Carbon Grain from Pre-Carbonized Date Palm leaves (Phoenix Dactylifera L.), Carbon Black and Cu Powder Composite electrodes, especially carbon-metal oxide composites, hold promising potential in numerous industrial applications such as supercapacitors and electrochemical energy storage. This study focuses on developing high-performance composite electrodes utilizing pre-carbonized date palm leaves, carbon black and copper powder. The main objective of this research is to enhance the mechanical and electrical properties of composite electrodes while exploring the potential of agricultural waste utilization. The methodology involves ball-milling pre-carbonized date palm leaves (PCDPLs), followed by manual mixing with carbon black (CB) and copper (Cu) powder, pressing into pellets, and carbonization at 1000°C in a nitrogen environment. X-ray diffraction (XRD) analysis confirmed the polycrystalline structure of the composite electrode with EDXD confirming the presence of Cu2O nanowires. Frequency-dependent dielectric parameters, impedance and electrical conductivity were analyzed utilizing the Cole–Cole plot in a range of (0-8) MHz. Results demonstrate improved crystallinity and performance of the composite electrodes, emphasizing the potential of utilizing agricultural waste for sustainable, high-performance electrode materials. This research highlights the importance of utilizing agricultural waste for developing advanced composite electrodes with enhanced performance capabilities, contributing to sustainable waste management and industrial applications in energy storage technologies Carbon black ; Supercapacitor ; Composite number ; Electrode ; Carbonization ; Dielectric ; Electrical resistivity and conductivity Supercapacitor Materials and Fabrication ; Copper-based nanomaterials and applications ; Nanomaterials and Printing Technologies
Authors: Kirill Anatolyevich Sinelnikov | Baurzhan Moldabaev | Zhanara ZHUNUSBEKOVA | Nina Georgievna Gorshkova
Source: Material and Mechanical Engineering Technology (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Method of Ultrasonic Cleaning of Internal Combustion Engine Radiator Tubes by Transverse Exposure This article is dedicated to the study of the ultrasonic cleaning method for vehicle cooling system radiators using transverse exposure to ultrasonic waves. The conducted experiment confirmed the high efficiency of the method based on water cavitation inside the radiator tubes. During the study, key liquid parameters were measured, including density, mass, outflow velocity, and scale content. The results demonstrated that ultrasonic cleaning with transverse exposure effectively removes scale without damaging the radiator structure, thereby improving heat exchange efficiency. An analysis of the dynamic changes in the mass of the removed scale showed that transverse ultrasonic exposure ensures a more uniform energy distribution across the entire radiator surface, increasing the degree of cleaning compared to the longitudinal method. This approach enhances the operational characteristics of the cooling system, extends its service life, and reduces maintenance costs, while also providing an additional environmental benefit by eliminating the need for aggressive chemical reagents Radiator (engine cooling) ; Ultrasonic sensor ; Transverse plane ; Cavitation ; Water cooling ; Ultrasonic testing ; Transducer Ultrasound and Cavitation Phenomena ; Aerosol Filtration and Electrostatic Precipitation ; Mechanical Systems and Engineering
Authors: T.M.M. Al-Abboodi | Yaser Alaiwi | Al-Khafaji Zainab | Falah Mayadah
Source: Material and Mechanical Engineering Technology (article, 2025-09-30)
Field: Materials Science — Polymers and Plastics

Enhancement of Mechanical and Dynamic Properties of Elastomeric Nanocomposites for Structural Vibration Isolation: A Review of Materials, Mechanisms, and Applications Elastomeric materials, particularly natural rubber (NR), exhibit exceptional mechanical resilience, energy absorption, and damping characteristics, making them vital in various engineering applications involving dynamic loading and vibration control. However, inherent limitations such as poor ozone resistance, thermal instability, and insufficient mechanical stiffness necessitate material modification to meet the demands of advanced structural systems. This review presents a comprehensive examination of recent advancements in the reinforcement of elastomeric matrices with nanostructured fillers, focusing primarily on zinc oxide (ZnO), multi-walled carbon nanotubes (MWCNTs), and sodium bicarbonate (NaHCO₃). The effects of these additives on the tensile strength, elongation at break, tear resistance, fatigue life, dynamic stiffness, and damping capacity of rubber-based composites are critically analyzed. Additionally, the study explores the integration of optimized rubber formulations into viscoelastic foundations for structural elements such as plates subjected to free and transient vibration loading. Emphasis is placed on the interplay between molecular chain structure, cross-linking mechanisms, filler dispersion, and their collective impact on the macroscopic behavior of the composite system. Numerical simulation approaches, including finite element modeling using SOLIDWORKS, are also reviewed to highlight their role in predicting the structural response of plate-foundation systems. The article consolidates experimental findings and theoretical models to guide future research and development of high-performance elastomeric systems in vibration mitigation and structural damping applications Elastomer ; Natural rubber ; Vibration ; Finite element method ; Stiffness ; Viscoelasticity ; Nanocomposite ; Carbon nanotube ; Composite number Polymer Nanocomposites and Properties ; Vibration Control and Rheological Fluids ; Polymer composites and self-healing
Authors: Naoki Noto | T. NAGANO | Mikito Fujinami | Ryosuke Kojima | Susumu Saito
Source: Communications Chemistry (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Transfer learning from custom-tailored virtual molecular databases to real-world organic photosensitizers for catalytic activity prediction The scarcity of experimental training data restricts the integration of machine learning into catalysis research. Here, we report on the effectiveness of graph convolutional network (GCN) models pretrained on a molecular topological index, which is not used in typical organic synthesis, for estimating the catalytic activity, a task that usually requires high levels of human expertise. For pretraining, we used custom-tailored virtual molecular databases that can be readily constructed using either a systematic generation method or a molecular generator developed in our group. Although 94%-99% of the employed virtual molecules are unregistered in the PubChem database, the resulting pretrained GCN models improve the prediction of catalytic activity for real-world organic photosensitizers. The results demonstrate the efficiency of the present transfer-learning strategy, which leverages readily obtainable information from self-generated virtual molecules. PubChem ; Virtual screening ; Cheminformatics ; Task (project management) ; Catalysis ; Relational database ; Molecular descriptor ; Convolutional neural network ; Organic molecules ; Generator (circuit theory) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Vaidotas Šimkus | Anders Christensen | Steven Bennett | I. Johnson | M. Neumann | James Gin | Jonathan Godwin | Bradley J. Rhodes
Source: ArXiv.org (preprint, 2025-12-01)
Field: Chemistry — Inorganic Chemistry

Mofasa: A Step Change in Metal-Organic Framework Generation Mofasa is an all-atom latent diffusion model with state-of-the-art performance for generating Metal-Organic Frameworks (MOFs). These are highly porous crystalline materials used to harvest water from desert air, capture carbon dioxide, store toxic gases and catalyse chemical reactions. In recognition of their value, the development of MOFs recently received a Nobel Prize in Chemistry. In many ways, MOFs are well-suited for exploiting generative models in chemistry: they are rationally-designable materials with a large combinatorial design space and strong structure-property couplings. And yet, to date, a high performance generative model has been lacking. To fill this gap, we introduce Mofasa, a general-purpose latent diffusion model that jointly samples positions, atom-types and lattice vectors for systems as large as 500 atoms. Mofasa avoids handcrafted assembly algorithms common in the literature, unlocking the simultaneous discovery of metal nodes, linkers and topologies. To help the scientific community build on our work, we release MofasaDB, an annotated library of hundreds of thousands of sampled MOF structures, along with a user-friendly web interface for search and discovery: https://mofux.ai/ . Generative grammar ; Generative model ; Interface (matter) ; Chemical space ; Lattice (music) ; Space (punctuation) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Morteza Eskandani
Source: Bioimpacts (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence–guided nanoparticle design for advanced targeted drug delivery This editorial aimed to explore the critical role of artificial intelligence (AI) in accurately predicting the structural design of nanoparticles (NPs) during targeted therapy of diseases. Based on experience, it is always surprising that perfect control of NP properties-including size, zeta potential, type, and surface modifications-using smart tools, will be more critical for optimal outcomes than trial and error. It is envisioned that the AI will change the game by predicting NPs' behavior, optimizing formulations, and speeding up clinical trials via the use of supervised learning, deep neural networks, graph neural networks, and generative models. In this context, various AI have led to an increase in drug loading efficiency and mRNA medication delivery. To achieve personalized therapy using NPs, however, issues including data quality, model interpretability, ethical frameworks, and multidisciplinary cooperation should be resolved. To enhance human knowledge and facilitate safer and more precise advancements in healthcare, this editorial urges the proper integration of AI in pharmaceutical/medical nanotechnology. SAFER ; Targeted drug delivery ; Multidisciplinary approach ; Applications of artificial intelligence ; Drug delivery ; Deep learning ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Graphene and Nanomaterials Applications
Authors: Cooley, Isabel | Wang, Weiling | Kozyrev, Vladimir | Wildman, Ricky D. | Johnston, Blair F. | Croft, Anna K.
Source: Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Predictive approaches for 3D ‐printing : Methods and approaches for polymeric materials <p dir="ltr">By bridging molecular-level insights with macroscopic performance metrics, computational strategies are poised to transform how we design next-generation 3D-printable materials with enhanced precision, functionality, and sustainability. We present a critical overview examining the role of computational methods in advancing the design and application of 3D-printable polymers. We cover key considerations—including solvation behavior, viscosity, gel point, mechanical properties, and polymer structure—as well as the design of new polymer functionalities. We highlight how a spectrum of physics-based methods, ranging from quantum chemical to coarse-grained simulations, can be leveraged to interrogate relevant polymer properties at multiple scales. In particular, we illustrate the growing impact of machine learning in accelerating polymer discovery and optimization. Such methods, whether applied independently or integrated into multi-scale modeling frameworks, offer powerful tools for pre-screening and selecting optimal formulations tailored to diverse 3D printing technologies and applications. Although challenges remain to integrate different approaches into workable prediction pipelines, the rate of advance and improvements in methods, data interoperability, and data quality, offer great promise of a ‘concept to print’ pipeline in the future. This article is categorized under: Structure and Mechanism > Computational Materials Science Data Science > Artificial Intelligence/Machine Learning Structure and Mechanism > Molecular Structures. Bridging (networking) ; Pipeline (software) ; Computational model ; Key (lock) ; Mechanism (biology) ; Big data Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Juyang Weng | Christian Schmidt | Dongshu Wang | Ming Xie
Source:  (article, 2025-05-29)
Field: Chemistry — Physical and Theoretical Chemistry

Questioning the Experimental Protocol in Two Nobel Prizes For discussions in the scientific community, we question the Post-Selection protocols (also known as p-hacking) in works that received the Nobel Prize in Physics 2024 and the Nobel Prize in Chemistry 2024, as well as the current flood of Post-Selection in Artificial Intelligence (AI) and machine learning within many other disciplines of science and engineering. We hypothesize that the Post-Selection protocol has three mistakes: (a) missing a test, (b) hiding bad-looking data, and (c) exaggerating prediction accuracy. Nobel Prizes must not escape the well-known scientific laws: (1) Different initial guesses in nonlinear problems lead to different local minima. (2) Papers about nonlinear problems must explain how the authors avoided local minima. Evidence: None of the questioned papers discuss (1) and (2). Therefore, they are not scientifically ethical. However, the future of AI and machine learning seems to be bright. Protocol (science) ; Scientific discovery ; Scientific reasoning History and advancements in chemistry
Authors: Jen-Sung Hsu | Chih‐Huang Lai | Chia‐Chen Li
Source: ACS Materials Au (article, 2025-12-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Flexible Ferrite Magnetic Composite Films for Electromagnetic Applications Anisotropic magnetic composite films composed of MnZn ferrite powders embedded in an epoxy matrix are investigated for flexible thin-film inductor applications. Commercial MnZn ferrites typically contain α-Fe<sub>2</sub>O<sub>3</sub> impurities that impair magnetic performance; in this study, these impurities are effectively eliminated by thermal annealing at 600 °C under argon. To improve particle dispersion and processability within the composite, the ferrite is surface-modified with 3-glycidoxypropyltrimethoxysilane and coated with SiO<sub>2</sub>, yielding a more dispersible MZ@SiO<sub>2</sub>. This treatment enables the formation of well-aligned particle chains under an external magnetic field, resulting in pronounced magnetic anisotropy and stable permeability. Incorporating FeNi alloy particles into the MZ@SiO<sub>2</sub>-based films further increases saturation magnetization and permeability, though finite element simulations indicate that the performance of larger FeNi particles is relatively insensitive to orientation. Inductor devices fabricated with the hybrid MZ@SiO<sub>2</sub>-FeNi films exhibit improved inductance and quality factor, underscoring their promise for next-generation high-performance magnetic components with tunable anisotropy. Ferrite (magnet) ; Composite number ; Ferrite core ; Welding ; Electromagnetic field Electromagnetic wave absorption materials ; Magnetic Properties and Synthesis of Ferrites ; Metallic Glasses and Amorphous Alloys
Authors: Arturo Alcorta | Rosa M. Claramunt | José Elguero | Ibón Alkorta
Source: Magnetic Resonance in Chemistry (article, 2025-10-20)
Field: Chemistry — Organic Chemistry

A Computational Study of the NMR Chemical Shifts of Polynitropyrazoles The <sup>1</sup>H, <sup>13</sup>C, <sup>15</sup>N, and <sup>17</sup>O chemical shifts of 91 nitro-1H-pyrazoles, as well as 1-amino-1H-pyrazole, were calculated using the Gauge-Invariant Atomic Orbital/Becke Three-parameter Lee-Yang-Parr [GIAO/B3LYP/6-311++G(d,p)] method. The majority of these compounds are polynitropyrazoles, including substituents such as N-nitro, C-nitro, and N-trinitromethyl groups. The calculated chemical shifts were compared with available experimental data using an empirical equation that relates absolute shieldings to chemical shifts. However, a new equation was required to accurately account for the oxygen atoms in the nitro groups. Additionally, some conformational studies were performed to better understand the chemical shifts, particularly those of the OH and NO<sub>2</sub> substituents at position 1. Different statistical methods were used to analyze the calculated <sup>15</sup>N chemical shifts. Chemical shift ; Chemical equation ; Position (finance) ; Chemical solution ; Chemical bond ; Carbon-13 NMR ; Nuclear magnetic resonance spectroscopy Structural and Chemical Analysis of Organic and Inorganic Compounds ; Inorganic and Organometallic Chemistry ; Chemical Reaction Mechanisms
Authors: Yusuf Sani | Raba'ah Syahidah Azis | Ismayadi Ismail | Yazid Yaakob
Source: Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia) (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Tailoring the synthesis, structural, thermal, magnetic and morphological properties of Cu0.9Cr0.1Fe2-xCoxO4 (x = 0.1–0.2) spinel ferrites by the substitution of cobalt ions using sol-gel method Spinel ; Coercivity ; Cobalt ; High-resolution transmission electron microscopy ; Crystallite ; Lattice constant ; Ion ; Ferrite (magnet) ; Octahedron Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Metallic Glasses and Amorphous Alloys
Authors: Giorgio Cagossi | Beatrice Piombo | Andrea Daolio | Paolo P. Mazzeo | Alessia Bacchi | Paolo Pelagatti
Source: RSC Mechanochemistry (article, 2025-12-04)
Field: Chemistry — Physical and Theoretical Chemistry

Shaping coordination polymers by ball milling Efficient and selective mechanochemical syntheses, post-synthetic modifications and dimensional-growth of 1D-coordination polymers under milling conditions are shown. Polymer ; Ball mill ; Mechanochemistry ; Ball (mathematics) ; Grinding Crystallography and molecular interactions ; Force Microscopy Techniques and Applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Kamron Fazel | Jacob M. Clary | Pritom Bose | Sally Brown | Ravishankar Sundararaman | Derek Vigil‐Fowler
Source: ArXiv.org (preprint, 2025-05-04)
Field: Materials Science — Materials Chemistry

Development of machine-learned interatomic potentials to predict structure, transport, and reactivity in platinum-based fuel cells Machine-learned interatomic potentials (MLIPs) have rapidly progressed in accuracy, speed, and data efficiency in recent years. However, training robust MLIPs in multicomponent systems still remains a challenge. In this work, we train a MLIP to describe hydrated Nafion ionomers and platinum catalysts, which are important components of fuel cells, by constructing a diverse training set to describe the bulk polymer and interfacial catalyst-polymer interactions well. We find that active learning improves the initial dataset little in terms of reducing uncertainty and error, pointing towards a need for more effective methods to efficiently explore the relevant interactions in complex, multicomponent systems. We use our trained MLIP to study the properties of the platinum-Nafion system, including polymer structure, proton mobility in a bulk Nafion polymer and near a platinum-Nafion interface, and reactions near and far from the interface, finding excellent results for structure and reactions contained within our training set. Transport seems well described, with both vehicular transport and Grotthuss hopping captured, although converged calculations of diffusivities were not computed because they require calculations of tens of nanoseconds that are challenging with current state-of-the-art MLIPs. The combined insights that this model provides can be leveraged to optimize fuel cell performance, and the approach can be applied to other chemical processes and devices where structure, transport, and reactivity all contribute to overall observed performance. Interatomic potential ; Reactivity (psychology) ; Density functional theory ; Polymer ; Experimental data ; Training set Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Fuel Cells and Related Materials
Authors: Wyatt C. Powell
Source: Organic Syntheses (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Synthesis of Serine Derived Oxazolines Using a Molybdenum Dioxide Catalyst [Image: see text] Chemistry ; Catalysis ; Molybdenum ; Organic chemistry ; Serine ; Carbon dioxide Synthesis and Catalytic Reactions ; Chemical Synthesis and Reactions ; Chemical Synthesis and Analysis
Authors: José Miguel Monzón-Verona | Santiago García–Alonso | Francisco Jorge Santana-Martín
Source: Applied Sciences (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Fine-Tuning a Local LLM for Thermoelectric Generators with QLoRA: From Generalist to Specialist This work establishes a large language model (LLM) specialized in the domain of thermoelectric generators (TEGs), for deployment on local hardware. Starting with the generalist JanV1-4B model and Qwen3-4B-Thinking-2507 models, an efficient fine-tuning (FT) methodology using quantized low-rank adaptation (QLoRA) was employed, modifying only 3.18% of the total parameters of thee base models. The key to the process is the use of a custom-designed dataset, which merges deep theoretical knowledge with rigorous instruction tuning to refine behavior and mitigate catastrophic forgetting. The dataset employed for FT contains 202 curated questions and answers (QAs), strategically balanced between domain-specific knowledge (48.5%) and instruction-tuning for response behavior (51.5%). Performance of the models was evaluated using two complementary benchmarks: a 16-question multilevel cognitive benchmark (94% accuracy) and a specialized 42-question TEG benchmark (81% accuracy), scoring responses as excellent, correct with difficulties, or incorrect, based on technical accuracy and reasoning quality. The model’s utility is demonstrated through experimental TEG design guidance, providing expert-level reasoning on thermal management strategies. This study validates the specialization of LLMs using QLoRA as an effective and accessible strategy for developing highly competent engineering support tools, eliminating dependence on large-scale computing infrastructures, achieving specialization on a consumer-grade NVIDIA RTX 2070 SUPER GPU (8 GB VRAM) in 263 s. Benchmark (surveying) ; Process (computing) ; Key (lock) ; Software deployment ; Adaptation (eye) ; Domain (mathematical analysis) ; Work (physics) Advanced Thermoelectric Materials and Devices ; Ferroelectric and Negative Capacitance Devices ; Machine Learning in Materials Science