jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Li Zhang | Jianming Liu | Duanjiao Li | Wenxing Sun | Zhi Li | Yongchao Liang | Qiang Fu | Nian Tang | Jie Zhang | Fei Huang | Xuelian Fan | Pengxiang Bai | Yuqi Wang | Zhaomeng Liu | Simin Zhu | Dan Qiao
Source: Crystals (article, 2025-06-05)
Field: Materials Science — Polymers and Plastics

Investigation of the Mechanical and Thermal Properties of MWCNT/SiC-Filled Ethylene–Butene–Terpolymer Rubber Rubber is widely used in daily lives, such as in automobile tires, conveyor belts, sealing rings, and gaskets. The performance of rubber determines its service life. Therefore, it is of crucial importance to improve the performance of rubber. Theoretical studies have found that the inherent properties of nanofillers themselves, the interfacial bonding force between fillers and the matrix, and the uniform dispersibility of nanofillers in the polymer matrix are the most significant factors for enhancing the performance of rubber nanocomposites. This study systematically investigated the synergistic enhancement effect of silicon carbide (SiC) and multi-walled carbon nanotubes (MWCNTs) on the mechanical and thermal properties of ethylene–butene–terpolymer (EBT) composites. By optimizing the addition amount of fillers and improving the interfacial bonding between fillers and the matrix, the influence of filler content on the properties of composites was studied. The results demonstrate that the addition of SiC and MWCNTs significantly improved the storage modulus, tensile strength, hardness, and thermal stability of the composites. In terms of mechanical properties, the tensile strength of the composites increased from 6.68 MPa of pure EBT to 8.46 MPa, and the 100% modulus increased from 2.14 MPa to 3.81 MPa. Moreover, hardness was significantly enhanced under the reinforcement of SiC/CNT fillers. In terms of thermal stability, the composites exhibited excellent resistance to deformation at high temperatures. Through the analysis of the mechanical and thermal properties of the composites, the synergistic enhancement mechanism between SiC and MWCNTs was revealed. The research results provide a theoretical basis for the design and engineering applications of high-performance ethylene–butylene rubber composites. Materials science ; Composite material ; Copolymer ; Natural rubber ; Vulcanization ; Thermal ; Ethylene ; Ethylene propylene rubber ; 1-Butene ; Polymer ; Chemistry ; Catalysis ; Organic chemistry ; Thermodynamics Polymer Nanocomposites and Properties ; Tribology and Wear Analysis ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Maksim Kuznetsov | Airat Valiev | Alex Aliper | Daniil Polykovskiy | Elena Tutubalina | Rim Shayakhmetov | Zulfat Miftahutdinov
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

nach0-pc: Multi-task Language Model with Molecular Point Cloud Encoder Recent advancements have integrated Language Models (LMs) into a drug discovery pipeline. However, existing models mostly work with SMILES and SELFIES chemical string representations, which lack spatial features vital for drug discovery. Additionally, attempts to translate chemical 3D structures into text format encounter issues such as excessive length and insufficient atom connectivity information. To address these issues, we introduce nach0-pc, a model combining domain-specific encoder and textual representation to handle spatial arrangement of atoms effectively. Our approach utilizes a molecular point cloud encoder for concise and order-invariant structure representation. We introduce a novel pre-training scheme for molecular point clouds to distillate the knowledge from spatial molecular structures datasets. After fine-tuning within both single-task and multi-task frameworks, nach0-pc demonstrates performance comparable with other diffusion models in terms of generated samples quality across several established spatial molecular generation tasks. Notably, our model is a multi-task approach, in contrast to diffusion models being limited to single tasks. Additionally, it is capable of processing point cloud-related data, which language models are not capable of handling due to memory limitations. These lead to our model having reduced training and inference time while maintaining on par performance. Encoder ; Task (project management) ; Computer science ; Point (geometry) ; Cloud computing ; Point cloud ; Artificial intelligence ; Operating system ; Engineering ; Mathematics ; Systems engineering Machine Learning in Materials Science ; Topic Modeling
Authors: Zakia Riaz | Y. Fong | Y.‐Y. CHAN | V. S. Chan | Neethu Ninan | Vi Khanh Truong
Source: Regenerative medicine and dentistry. (article, 2025-06-24)
Field: Materials Science — Biomaterials

Therapeutics-Loaded Electrospun Nanofibers: Rational Design and Mechanistic Insights on Managing Chronic Wound Infection Chronic wounds present a multifactorial clinical challenge characterized by prolonged inflammation, microbial biofilm formation, oxidative stress, and impaired vascularization. Conventional wound dressings such as films, hydrogels, and decellularized matrices often fall short due to limited bioactivity, inadequate mechanical properties, and insufficient control over therapeutic delivery. This review highlights electrospun nanofiber membranes as advanced biomimetic platforms that replicate the structural and functional attributes of the extracellular matrix while enabling localized and sustained release of therapeutic agents. The novelty of this work lies in its systematic association of bioactive compounds including antimicrobial, antioxidant, immunomodulatory, oxygen releasing, and hemostatic agents with their specific biological targets in chronic wound healing. Also, the review critically examines fabrication techniques such as coaxial, emulsion, gas assisted, and stimuli responsive electrospinning, and evaluates how key processing parameters influence fiber morphology, drug release profiles, and cellular interactions. By integrating material science with mechanistic insight, this work provides a unified framework for the rational design of responsive nanofiber based wound dressings and outlines future directions involving smart delivery systems, biosensing integration, and three dimensional bioprinting to support clinical translation and personalized therapy. Emphasis is also placed on emerging multifunctional membranes capable of real-time interaction with wound pathophysiology. Challenges related to scalability, regulatory approval, and long-term biocompatibility are discussed to bridge the gap between laboratory findings and clinical adoption. This review ultimately serves as a foundation for developing next generation wound care strategies that are both mechanistically targeted and clinically adaptable. Nanofiber ; Rational design ; Nanotechnology ; Materials science Electrospun Nanofibers in Biomedical Applications ; Wound Healing and Treatments ; Silk-based biomaterials and applications
Authors: Mehdi Razavifar | Ali Nabipourghazanfari | Seyed Borhan Mousavi | Saeed Zeinali Heris
Source: Scientific Reports (article, 2025-07-01)
Field: Chemistry — Analytical Chemistry

Modifying crude oil synergistic properties with carbon dots and ultrasonic waves Improving oil recovery from hydrocarbon reservoirs is a significant goal in the petroleum industry, particularly from an economic perspective. Over the past two decades, the influence of nanotechnology and sonic wave irradiation methods on crude oil properties has been well established. Among these, carbon nanoparticles have demonstrated a notable impact on fluid-rock interactions within reservoirs. This study thoroughly investigated the combined effects of carbon dots (CDs) and ultrasonic waves (USW) on wettability and interfacial tension (IFT) between fluids and rocks. The synergistic application of CDs and USW was found to reduce oil viscosity by 58%, from 230 cP (crude oil) to 96 cP, at an optimal CD concentration of 0.08 wt%. Additionally, the simultaneous use of CDs and USW decreased IFT by 42%, from 43 to 25 dyne/cm, enhancing the solubility between oil and water. This IFT reduction is attributed to the combined effect of CDs and USW in lowering oil viscosity, reducing heavy crude oil components, and modifying interfacial interactions between crude oil and rock. The study also revealed that the concurrent use of CDs and USW increased the contact angle between crude oil and rock surfaces from 30° to 73°, thereby reducing the rock's oil adsorption tendency. These findings highlight the dual role of CDs in breaking down heavy oil components and modifying interfacial properties at the rock-fluid interface, leading to enhanced oil mobilization. Furthermore, USW alone was found to decrease oil viscosity and IFT by 44% and 14%, respectively. The results suggest that the combined application of USW and CDs holds great potential for industrial applications, including enhanced oil recovery and crude oil transportation, to optimize oil production efficiency. Ultrasonic sensor ; Crude oil ; Carbon fibers ; Materials science ; Chemical engineering ; Petroleum engineering ; Acoustics ; Composite material ; Geology ; Physics ; Engineering Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Wei Lü | Markus J. Buehler
Source: Materials Advances (article, 2025-01-01)
Field: Materials Science — Biomaterials

Generative design and molecular mechanics characterization of silk proteins based on unfolding behavior A generative design and simulation framework reveals unfolding mechanics of silk-inspired proteins, enabling exploration of sequence–structure–property relationships and offering insights into the mechanobiological basis of fiber performance. Characterization (materials science) ; Generative grammar ; SILK ; Molecular mechanics ; Materials science ; Biological system ; Polymer science ; Computer science ; Nanotechnology ; Chemistry ; Artificial intelligence ; Molecular dynamics ; Biology ; Composite material ; Computational chemistry Silk-based biomaterials and applications ; Viral Infectious Diseases and Gene Expression in Insects ; RNA and protein synthesis mechanisms
Authors: Anna E. Rassabina | Maxim V. Fedorov
Source: Plants (article, 2025-10-24)
Field: Chemistry — Spectroscopy

Analysis of the Toxicological Profile of Heracleum sosnowskyi Manden. Metabolites Using In Silico Methods The invasive plant <i>Heracleum sosnowskyi</i> Manden. is a valuable source of a number of bioactive metabolites that can be used in the pharmaceutical industry and medicine and may have some other applications as well. Today, there is a need to summarize data on these substances as well as analyze the toxicological profile of the metabolites of <i>H. sosnowskyi</i>. In this study, we collected a dataset of 225 metabolites of <i>H. sosnowskyi</i> from different literature sources and performed cluster analysis of their chemical structures; we revealed five main clusters of compounds: terpenoids, aromatic compounds, polyaromatic compounds, fatty acids, and furanocoumarins. In order to fill the gaps in the experimental data on the toxicity of the studied substances, we used machine learning (ML) algorithms previously designed for high-accuracy prediction of toxicity end-points. The ML-based approach allowed us to fill in up to 90% of the missing median lethal dose LD50 (mouse) data for the studied molecules. The validity of each predicted value was confirmed by analyzing the applicability domain of the used ML models. For the calculations and ML modeling, we used the <i>Syntelly</i> chemoinformatics platform. For the most toxic compounds-hydroxycoumarins and furanocoumarins of <i>H. sosnowskyi</i>-the values for hepatotoxicity, drug-induced liver injury (DILI), cardiotoxicity, and carcinogenicity were predicted. Based on the analysis of LD50 values for the mouse animal model, the greatest toxicity for furanocoumarins is expected with the intravenous route of administration (62-450 mg/kg), which can cause drug-induced liver injury. At the same time, the data do not show high cardiotoxicity risks for the studied furanocoumarins. Based on the presented results, we discuss prospects of using some of the compounds as pharmaceutical agents. In silico ; Toxicity ; Cardiotoxicity ; Cheminformatics ; Median lethal dose ; Acute toxicity Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Chemical synthesis and alkaloids
Authors: Tuğçe Tüccar | Judit Knisz | Richard B. Eckert | Torben Lund Skovhus
Source: npj Materials Degradation (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Ontology study: harmonizing microbiologically influenced corrosion (MIC) terminology across disciplines Abstract Microbiologically influenced corrosion (MIC) is a pervasive yet poorly understood phenomenon, partly due to inconsistent terminology across scientific and industrial domains. Standardized terminology is essential to improve communication and advance MIC research and mitigation strategies. To address this gap, we developed a comprehensive glossary of MIC-related terms by compiling terminology from industry standards, glossaries, and scientific literature across multiple sectors. Terms were analyzed to identify consensus and discrepancies, enabling clarification and harmonization of definitions. The resulting glossary provides clear, consistent terminology to reduce misunderstandings and foster transdisciplinary dialog. This initiative represents a critical step toward unifying MIC-related language, supporting more effective diagnosis, mitigation, and control strategies. By promoting standardized communication, the glossary lays the foundation for improved knowledge exchange between academia and industry, ultimately contributing to better management of MIC in diverse applications. Terminology ; Glossary ; Harmonization ; Ontology ; Scientific literature ; Best practice Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xinzhi Zhou | Zi-Xuan Weng | Chen Zhao | Qiang Zhang | Xuye Lang | Baiyi Lu | Yuan Yao
Source: Synthetic biology and engineering (article, 2025-01-01)
Field: Materials Science — Biomaterials

Collagen Biosynthesis to Engineered Biomaterials: Molecular Design, Synthetic Strategy, and Biomedical Application Collagen, a principal component of the extracellular matrix, provides mechanical strength and stability to tissues and organs through its structural organization. Its biocompatibility has established it as a crucial material in biomedical applications such as drug delivery systems, cell culture matrices, and tissue engineering scaffolds. However, the use of animal-derived collagen carries risks of pathogen transmission, which has driven research towards developing synthetic collagen alternatives. Advances in AI-assisted protein engineering are accelerating the design of synthetic collagens and their applications in biomaterials. This review examines collagen’s structural characteristics, biosynthesis strategies, biological activities as well as AI-assisting engineering. Synthetic biology ; Biomimetic materials ; Tissue engineering ; Nanotechnology ; Biosynthesis ; Materials science ; Chemistry ; Engineering ; Computational biology ; Biomedical engineering ; Biochemistry ; Biology Collagen: Extraction and Characterization ; Bone Tissue Engineering Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Tobias Eifert | Martin Gerlach | Katharina Dahlmann | Bernhard Lendl | Matthias Rädle | Martin Jaeger
Source: Analytical and Bioanalytical Chemistry (article, 2025-06-27)
Field: Chemistry — Spectroscopy

Becoming fully circular facilitated by PAT Process analytical technologies (PAT) have contributed to helping industrial processes become more efficient, more sustainable, safer, and more reliable for more than two decades. Real-time monitoring is the key to process control but also to process knowledge. Advancements in instrument performance and in the availability of complex data analysis methodologies have made PAT a fundamental pillar in quality by design concepts. On account of the 20th anniversary of the Arbeitskreis PAT of the GDCh, this article assembles use cases for and from PAT applications that do or will contribute to and impact relevant and forward-looking processes. Five examples are presented for the monitoring of sustainable processes from the recycling of industrial process water, solvents, and plastics, from biotechnological production using disposable reactors, i.e., single-use technology, and of product safety confirmation amenable to pharmaceutical production and real-time release. The applied PAT methodology illustrates use cases of online solid-phase extraction, ion-chromatography conductivity determination, mid- and near-infrared spectroscopy, and external cavity quantum cascade laser vibrational circular dichroism. The reviewed examples are taken to a perspective on future PAT developments. SAFER ; Process (computing) ; Process analytical technology ; Process safety ; Process engineering ; Nanotechnology ; Quality (philosophy) ; Computer science ; Systems engineering ; Engineering ; Biochemical engineering ; Work in process ; Materials science ; Physics ; Operations management Analytical Chemistry and Chromatography ; Analytical chemistry methods development ; Mass Spectrometry Techniques and Applications
Authors: Lorena Silva | Joice Catiane Soares Martins | Israel Luís Diniz Carvalho | Júlio Abreu Miranda | Danilo Rodrigues de Souza | Éverton do Nascimento Alencar | Moan Jéfter Fernandes Costa | Pedro Henrique Sette‐de‐Souza
Source: Beilstein Journal of Nanotechnology (review, 2025-10-22)
Field: Materials Science — Materials Chemistry

On the road to sustainability – application of metallic nanoparticles obtained by green synthesis in dentistry: a scoping review The growing interest in green-synthesized metallic nanoparticles reflects a global shift toward sustainable, eco-friendly technologies in biomedical innovation, particularly in dentistry. This scoping review examines the rising focus on these nanoparticles regarding their antimicrobial, regenerative, and therapeutic potential in dental applications. Among the metals studied, silver and zinc oxide nanoparticles dominate because of their broad-spectrum antimicrobial activity and enhanced biocompatibility, achieved through phytochemically mediated synthesis. Conventional nanoparticle production often relies on toxic reagents and energy-intensive processes, posing environmental and clinical challenges. In contrast, green synthesis, using plant extracts, fungi, or bacteria, offers a sustainable alternative by leveraging natural reducing agents like polyphenols and flavonoids. These bioactive compounds not only facilitate nanoparticle formation but also improve stability and biological efficacy, making them ideal for dental applications such as caries prevention, endodontic disinfection, and periodontal regeneration. Our analysis of 98 studies reveals India as the leading contributor (78.6%), driven by its rich biodiversity and strong research infrastructure. Key plant families including Lamiaceae and Fabaceae were frequently employed due to their high phenolic content. Despite promising results, gaps remain, such as the predominance of in vitro studies (68.7%) and insufficient cytotoxicity assessments (47.8%), underscoring the need for translational research. This review highlights the transformative potential of green-synthesized nanoparticles in dentistry, merging technological advancement with ecological responsibility. Future work should prioritize clinical trials, long-term safety evaluations, and standardized protocols to fully realize their therapeutic benefits. Sustainability ; Transformative learning ; Nanoparticle ; Nanomedicine ; Drug delivery ; Prioritization ; Sustainable development Nanoparticles: synthesis and applications ; Healthcare and Environmental Waste Management ; Chemistry and Chemical Engineering
Authors: Marco Eckhoff | Markus Reiher
Source: Journal of Chemical Theory and Computation (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Lifelong Machine Learning Potentials for Chemical Reaction Network Explorations Recent developments in computational chemistry facilitate the automated quantum chemical exploration of chemical reaction networks for the in-silico prediction of synthesis pathways, yield, and selectivity. However, the underlying quantum chemical energy calculations require vast computational resources, limiting these explorations severely in practice. Machine learning potentials (MLPs) offer a solution to increase computational efficiency, while retaining the accuracy of reliable first-principles data used for their training. Unfortunately, MLPs will be limited in their generalization ability within chemical (reaction) space, if the underlying training data are not representative for a given application. Within the framework of automated reaction network exploration, where new reactants or reagents composed of any elements from the periodic table can be introduced, this lack of generalizability will be the rule rather than the exception. Here, we therefore evaluate the benefits of the lifelong MLP concept in this context. Lifelong MLPs push their adaptability by efficient continual learning of additional data. We propose an improved learning algorithm for lifelong adaptive data selection yielding efficient integration of new data while previous expertise is preserved. In this way, we can reach chemical accuracy in reaction search trials. Generalizability theory ; Quantum chemical ; Lifelong learning ; Generalization ; Adaptability ; Limiting ; Table (database) ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Isaiah A. Moses | Chen Chen | Joan M. Redwing | Wesley F. Reinhart
Source: Advanced Intelligent Systems (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Cross‐Modal Characterization of Thin‐Film MoS <sub>2</sub> Using Generative Models The growth and characterization of materials using empirical optimization typically requires a significant amount of expert time, experience, and resources. Several complementary characterization methods are routinely performed to determine the quality and properties of a grown sample. Machine learning (ML) can support the conventional approaches by using historical data to guide and provide speed and efficiency to the growth and characterization of materials. Specifically, ML can provide quantitative information from characterization data that is typically obtained from a different modality. In this study, the feasibility of projecting quantitative metrics from microscopy measurements, such as atomic force microscopy (AFM), using data obtained from spectroscopy measurements, like Raman spectroscopy is investigated. Generative models are also trained to generate the full and specific features of the Raman and photoluminescence spectra from each other and the AFM images of the thin‐film MoS 2 . The results are promising and have provided a foundational guide for the use of ML for the cross‐modal characterization of materials for their accelerated, efficient, and cost‐effective discovery. Characterization (materials science) ; Materials science ; Generative grammar ; Modal ; Computer science ; Composite material ; Artificial intelligence ; Nanotechnology 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing