jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lisheng Tang | Xiaoyan He | Ran Huang
Source: International Journal of Molecular Sciences (review, 2025-01-16)
Field: Materials Science — Biomaterials

Advancements and Perspectives in Biodegradable Polyester Elastomers: Toward Sustainable and High-Performance Materials While the traditional rubber industry faces the severe pressure of environmental pollution and carbon emissions, bio-based and biodegradable elastomers have become a hot topic in the field and drawn intensive research interest. Inspired by polyester resin, incorporating polyol or polycarboxylic acid as a branching unit into aliphatic polyester and/or introducing a monomer with a C=C bond to provide open-bond cross-linking in the fashion of common vulcanization to form three-dimensional network structures are two mainstream strategies for designing biodegradable polyester elastomers (BPEs). Both methods encounter more or fewer problems, such as poor mechanical and thermal properties due to the easy hydrolysis of the ester bond and space hinderance, or the potential harm of the remaining degraded small molecules with olefin bonds. This article provides an overview of recent endeavors aimed at addressing these challenges and prospects the probable future advancements in the field. Polyester ; Vulcanization ; Elastomer ; Polymer science ; Materials science ; Monomer ; Polyol ; Natural rubber ; Organic chemistry ; Nanotechnology ; Biochemical engineering ; Polymer ; Chemistry ; Composite material ; Engineering ; Polyurethane biodegradable polymer synthesis and properties ; Polymer composites and self-healing ; Electrospun Nanofibers in Biomedical Applications
Authors: London Varalli | Reed Berlet | Eric Abenojar | John McDaid | David A. Gascoigne | Julian E. Bailes | Daniil P. Aksenov
Source: Pharmaceutics (review, 2025-04-09)
Field: Materials Science — Biomaterials

Applications and Efficacy of Iron Oxide Nanoparticles in the Treatment of Brain Tumors Cancers of the central nervous system are particularly difficult to treat due to a variety of factors. Surgical approaches are impeded by the skull-an issue which is compounded by the severity of possible harm that can result from damage to the parenchymal tissue. As a result, chemotherapeutic agents have been the standard of care for brain tumors. While some drugs can be effective on a case-by-case basis, there remains a critical need to improve the efficacy of chemotherapeutic agents for neurological cancers. Recently, advances in iron oxide nanoparticle research have highlighted how their unique properties could be leveraged to address the shortcomings of conventional therapeutics. Iron oxide nanoparticles combine the advantages of good biocompatibility, magnetic susceptibility, and functionalization via a range of coating techniques. Thus, iron oxide nanoparticles could be used in both the imaging of brain cancers with magnetic resonance imaging, as well as acting as trafficking vehicles across the blood-brain barrier for targeted drug delivery. Moreover, their ability to support minimally invasive therapies such as magnetic hyperthermia makes them particularly appealing for neuro-oncological applications, where precision and safety are paramount. In this review, we will outline the application of iron oxide nanoparticles in various clinical settings including imaging and drug delivery paradigms. Importantly, this review presents a novel approach of combining surface engineering and internal magnetic targeting for deep-seated brain tumors, proposing the surgical implantation of internal magnets as a next-generation strategy to overcome the limitations of external magnetic fields. Iron oxide nanoparticles ; Drug delivery ; Nanomedicine ; Magnetic resonance imaging ; Nanotechnology ; Magnetic hyperthermia ; Targeted drug delivery ; Magnetic nanoparticles ; Medicine ; Biocompatibility ; Biomedical engineering ; Computer science ; Materials science ; Nanoparticle ; Radiology Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Iron oxide chemistry and applications
Authors: Chunmei Jia | Qazi Mohammad Junaid | Guoying Han | Yu Gai | Francisco G. Cirujano | Xiao Feng
Source: Advanced Functional Materials (article, 2025-04-26)
Field: Chemistry — Inorganic Chemistry

Unraveling the Role of Open Metal Sites and Their Capping Ligands in MOFs Stability and Liquid‐Phase Separation Abstract The stability of metal–organic frameworks (MOFs) is crucial for their practical applications. Open metal sites (OMS), essential active sites in various applications, also significantly impact stability, yet their role remains insufficiently understood. This study investigates MOF‐808, a zirconium‐based MOF with six OMS per cluster, revealing its higher stability in liquid water compared to gaseous water. Coordination of formic acid at OMS notably enhances water stability, while recrystallization experiments elucidate the mechanisms of instability in gaseous water. Thermal stability is determined by the decomposition temperature of capping ligands on OMS. In liquid‐phase adsorption of high‐value bio‐oil compound creosol, uptake results demonstrate that capping ligand polarity modulates pore hydrophilicity/hydrophobicity, affecting adsorption selectivity. Remarkably, MOF‐808 capped with pentafluorobenzoic acid achieves 100% selective pore occupancy during adsorption. This study highlights OMS as critical to MOF stability and emphasizes the role of capping ligands in improving stability and tuning adsorption properties. It underscores nonstructural ligand modifications as a powerful strategy to enhance MOF performance, broadening their application potential in challenging environments. Materials science ; Metal ; Metal-organic framework ; Phase (matter) ; Chemical engineering ; Liquid phase ; Nanotechnology ; Organic chemistry ; Adsorption ; Metallurgy ; Thermodynamics ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Errui Li | Arvind Ganesan | Hongjun Liu | Alexander S. Ivanov | Lilin He | Phattananawee Nalaoh | David M. Jenkins | Carlos A. Steren | Narges Mokhtarinori | Jian Zhi Hu | Bo Li | De‐en Jiang | Shannon M. Mahurin | Zhenzhen Yang | Sheng Dai
Source: Angewandte Chemie International Edition (article, 2025-01-06)
Field: Chemistry — Organic Chemistry

Sub‐5 Ångstrom Porosity Tuning in Calixarene‐Derived Porous Liquids via Supramolecular Complexation Construction Sub-Ångstrom-level porosity engineering, which is appealing in gas separations, has been demonstrated in solid carbon, polymer, and framework materials but rarely achieved in the liquid phase. In this work, a gas molecular sieving effect in the liquid phase at sub-5 Ångstrom scale is created via sophisticated porosity tuning in calixarene-derived porous liquids (PLs). Type II PLs are constructed via supramolecular complexation between the sodium salts of calixarene derivatives and crown ether solvents. The chemical structure variation and assembly behavior of the porous host upon PL construction are monitored by spectroscopy-, X-ray-, and neutron-scattering techniques. The presence of permanent porosity in calixarene-derived PLs is verified by pressure swing gas uptake, altered CO<sub>2</sub> physisorption behavior, and molecular simulations. Sub-5 Ångstrom porosity tuning within the PL phase is achieved by introducing bulky substituted groups on the benzene ring of the calixarene host, which then greatly affects the dynamic motion and transport behavior of CO<sub>2</sub> molecules and the Xe uptake performance. The approach being demonstrated in this work represents a promising pathway to tune and leverage the porosity effect for enhanced gas uptake capacity and selectivity in liquid sorbents. Calixarene ; Porosity ; Supramolecular chemistry ; Materials science ; Chemical engineering ; Chemistry ; Organic chemistry ; Composite material ; Molecule ; Engineering Supramolecular Chemistry and Complexes ; Covalent Organic Framework Applications ; Nanopore and Nanochannel Transport Studies
Authors: Sumitava Mallik | Hailong Wang | Nunzio Matera | Bi‐Xiao Li | Stefano Stagni | Paolo Melchiorre
Source: Angewandte Chemie International Edition (article, 2025-06-26)
Field: Chemistry — Organic Chemistry

A Bifunctional Organic Photocatalyst for Efficient Single‐Electron and Energy Transfer Activation Bifunctional photocatalysts capable of mediating both single-electron transfer (SET) and energy transfer (EnT) processes are rare and typically metal based. Here, we present 3-thioaryl-4-hydroxycoumarins, a new family of cost-effective organic photocatalysts that leverage a stabilized charge-transfer (CT) excited state to achieve both strong reducing power and efficient energy transfer. The spatial separation of the HOMO and LUMO stabilizes the CT state, enhancing SET reactivity (E*<sub>red</sub> = -3.08 V vs. SCE) while maintaining a sufficiently high triplet energy (E<sub>T</sub> = 67 kcal mol<sup>-1</sup>) for EnT-driven transformations. This dual reactivity enables the activation of redox-inert substrates (E<sub>red</sub> < -2.8 V vs. SCE) via SET reduction, generating radicals suitable for diverse C─S, C-─P, C─B, and C─C bond-forming transformations, alongside EnT-based processes such as E/Z olefin isomerization and [2 + 2] photocycloadditions. Mechanistic studies, supported by photophysical and theoretical analyses, confirmed the catalyst's bifunctionality. Bifunctional ; Reactivity (psychology) ; Electron transfer ; Photochemistry ; Redox ; Chemistry ; Photocatalysis ; Intersystem crossing ; HOMO/LUMO ; Catalysis ; Excited state ; Organic chemistry ; Molecule Radical Photochemical Reactions ; CO2 Reduction Techniques and Catalysts ; Advanced Photocatalysis Techniques
Authors: Pratik Tiwari | Susmita Naskar | T. Mukhopadhyay
Source: Thin-Walled Structures (article, 2025-01-06)
Field: Materials Science — Materials Chemistry

Nonlinear functionally graded metamaterials for hydrogen storage and enhanced sustainability under extreme environments Functionally graded materials can exhibit remarkable tolerance towards extreme hot or cold environments and chemical surface degradation. This article exploits such properties of functionally graded materials to propose a new class of transversely curved metamaterial architectures with high specific stiffness for operations under extreme surrounding conditions. We envisage the next-generation concept design of hydrogen storage tanks with functionally graded metamaterial core for aerospace and automotive applications. Based on such innovative lattice metamaterial based design of hydrogen storage tanks it is possible to enhance the storage capability in terms of internal pressure and resistance to external loads and impacts. Most importantly the proposed concept would lead to a breakthrough in developing load-bearing energy storage devices. For the metamaterial core, hexagonal bending-dominated unit cell architecture with transversely curved connecting beam-like geometries would ensure the dual functionality of high specific stiffness and energy absorption capability which are mutually exclusive in traditional lattice metamaterials. The functionally graded beams, a periodic network of which constitutes the lattice, are modelled here using 3D degenerated shell elements in a finite element framework. Geometric nonlinearity using Green–Lagrange strain tensor is considered for an accurate analysis. The beam-level nonlinear deformation physics is integrated with the unit cell mechanics following a semi-analytical framework to obtain the effective in-plane and out-of-plane elastic moduli of the metamaterials. The numerical results show that the curved beam lattice metamaterials have significantly enhanced in-plane elastic properties than straight lattices along with a reduced disparity among the in-plane and out-of-plane elastic moduli. • Functionally graded metamaterials for extreme surrounding environments. • Next-generation concept design of hydrogen storage tanks with functionally graded metamaterial core. • Enhanced storage capability in terms of internal pressure and resistance to external loads and impacts. • Load-bearing energy storage devices with high specific stiffness and energy absorption capability. • 3D degenerated shell element-based unit cell mechanics for nonlinear in-plane and out-of-plane elastic moduli. Metamaterial ; Nonlinear system ; Sustainability ; Hydrogen storage ; Materials science ; Hydrogen ; Computer science ; Physics ; Optoelectronics ; Biology ; Ecology ; Quantum mechanics Hydrogen Storage and Materials ; Cellular and Composite Structures ; Vibration Control and Rheological Fluids
Authors: Guanjie Wang | Jingjing Hu | Jian Zhou | Sen Liu | Qi Li | Zhimei Sun
Source: Review of Materials Research (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

Knowledge-guided large language model for material science With ChatGPT starting a storm of transformative applications worldwide, the advent of large language models (LLMs) has revolutionized the paradigm of scientific research, shifting from data-driven methods to AI-driven science. While LLMs have demonstrated significant promise in many fields of science, the development of material knowledge-guided, domain-specific LLMs remains challenges. In this review, the key milestones of LLMs are discussed, and guidelines for building LLMs are provided, including determining objectives, designing model architectures, data curation, and establishing training and evaluation frameworks. Furthermore, methodologies for creating domain-specific models through fine-tuning, retrieval-augmented generation, prompt engineering, and AI agents are explored. Additionally, the applications of LLMs in materials science are investigated, ranging from structured information extraction and property prediction to autonomous laboratories and robotics. Finally, challenges such as resource demands, dataset quality, benchmarking, hallucination mitigation, and AI safety are reported alongside emerging opportunities, positioning LLMs as a pivotal tool in advancing materials discovery and innovation. Computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Aroog Fatima | Samina Aslam | Sara Janiad | Shah Faisal | Ali Irfan | Javed Iqbal | Gamal A. Shazly | Ansa Madeeha Zafar | Aqeela Shaheen | Sobia Noreen | Emilio Mateev | Yousef A. Bin Jardan
Source: Molecular Diversity (article, 2025-01-23)
Field: Chemistry — Organic Chemistry

Synthesis and biological evaluation of rationally designed pyrazoles as insecticidal agents The current research focused on the synthesis of two series of pyrazole derivatives and evaluation of their insecticidal effectiveness. In the first series, seven pyrazole Schiff bases 3a-g were successfully synthesized with yields (79-95%) by condensing phenylfuran-2-carbaldehyde with substituted pyrazole rings. In the second series, eleven amino acid-pyrazole conjugates 6a-k were synthesized utilizing acetic acid, sulfuric acid, morpholine, and EDC. HCl achieving yields of 59% to 94%. The synthesized compounds were assessed for their chemotherapeutic efficacy against locusts and termites by calculating LC<sub>50</sub> values, thereby determining their potential as anti-termite and anti-locust agents. Among the eighteen synthesized pyrazole compounds, the Schiff base pyrazole molecules 3f (LC<sub>50</sub> = 0.001 μg/mL) and 3d (LC<sub>50</sub> = 0.006 μg/mL) demonstrated excellent anti-termite activity compared to the reference drug fipronil (LC<sub>50</sub> = 0.038 μg/mL). Pyrazole derivative 6 h with LC<sub>50</sub> = 47.68 μg/mL exhibited superior anti-locust activity than the reference drug fipronil (LC<sub>50</sub> = 63.09 μg/mL). Additionally, compound 3b, containing NO<sub>2</sub> functionality at the meta position, exhibited notable and significant anti-locust activity with an LC<sub>50</sub> values of 100.00 μg/mL. However, the highest mortality was caused by the glycine conjugate of fipronil 6 h of the 2nd series with an LC<sub>50</sub> value of 47.68 μg/mL, which also proved to be a potent anti-locust agent. This study explores the efficacy of biologically active pyrazole structures as potential insecticidal agents through a combination of virtual molecular docking analysis and biological experimental investigations. The results demonstrate a strong correlation between the computational predictions and experimental outcomes, suggesting that the pyrazole derivatives exhibit significant insecticidal properties. The findings highlight the potential of these compounds in the development of innovative insecticides, paving the way for future research in pest control strategies. Combinatorial chemistry ; Biochemical engineering ; Chemistry ; Computer science ; Engineering Synthesis and biological activity ; Computational Drug Discovery Methods ; Fungal Plant Pathogen Control
Authors: Uzoma Nwabara | Kunran Yang | Akshay Talekar | Varinia Bernales | Jorge Ernesto González Mesa | Stuart Miller | Jeff Wu
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

High throughput computational and experimental methods for accelerated electrochemical materials discovery The full integration of sustainable technologies to combat climate change relies on discovering cost-competitive, safe, and durable materials, specifically for electrochemical systems that generate energy, store energy, and produce chemicals. Throughput ; Drug discovery ; Computer science ; Biology ; Bioinformatics ; Telecommunications Machine Learning in Materials Science
Authors: Wenhao Yuan | Guangyao Chen | Zhilong Wang | Fengqi You
Source: Advanced Materials (review, 2025-05-12)
Field: Materials Science — Materials Chemistry

Empowering Generalist Material Intelligence with Large Language Models Large language models (LLMs) are steering the development of generalist materials intelligence (GMI), a unified framework integrating conceptual reasoning, computational modeling, and experimental validation. Central to this framework is the agent-in-the-loop paradigm, where LLM-based agents function as dynamic orchestrators, synthesizing multimodal knowledge, specialized models, and experimental robotics to enable fully autonomous discovery. Drawing from a comprehensive review of LLMs' transformative impact across representative applications in materials science, including data extraction, property prediction, structure generation, synthesis planning, and self-driven labs, this study underscores how LLMs are revolutionizing traditional tasks, catalyzing the agent-in-the-loop paradigm, and bridging the ontology-concept-computation-experiment continuum. Then the unique challenges of scaling up LLM adoption are discussed, particularly those arising from the misalignment of foundation LLMs with materials-specific knowledge, emphasizing the need to enhance adaptability, efficiency, sustainability, interpretability, and trustworthiness in the pursuit of GMI. Nonetheless, it is important to recognize that LLMs are not universally efficient. Their substantial resource demands and inconsistent performance call for careful deployment based on demonstrated task suitability. To address these realities, actionable strategies and a progressive roadmap for equitably and democratically implementing materials-aware LLMs in real-world practices are proposed. Transformative learning ; Knowledge management ; Interpretability ; Computer science ; Function (biology) ; Artificial intelligence ; Management science ; Engineering ethics ; Engineering ; Psychology Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Electronic and Structural Properties of Oxides
Authors: Zhilong Song | Shuaihua Lu | Ming‐Gang Ju | Qionghua Zhou | Jinlan Wang
Source: Nature Communications (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models Accessing the synthesizability of crystal structures is crucial for transforming theoretical materials into real-world applications. Nevertheless, there is a significant gap between actual synthesizability and thermodynamic or kinetic stability commonly used to screen synthesizable structures. Herein, we develop the Crystal Synthesis Large Language Models (CSLLM) framework, which utilizes three specialized LLMs to predict the synthesizability of arbitrary 3D crystal structures, possible synthetic methods, and suitable precursors, respectively. We construct a comprehensive dataset including synthesizable/non-synthesizable crystal structures and develop an efficient text representation for crystal structures to fine-tune LLMs. Our Synthesizability LLM achieves state-of-the-art accuracy (98.6%), significantly outperforming traditional synthesizability screening based on thermodynamic and kinetic stability. Its outstanding generalization ability is further demonstrated in experimental structures with complexity considerably exceeding that of the training data. Furthermore, both the Method and Precursor LLMs exceed 90% accuracy in classifying possible synthetic methods and identifying solid-state synthetic precursors for common binary and ternary compounds, respectively. Leveraging CSLLM, tens of thousands of synthesizable theoretical structures are successfully identified, with their 23 key properties predicted using accurate graph neural network models. Computer science ; Computational biology ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Topic Modeling
Authors: Conor Cleeton | Lev Sarkisov
Source: Nature Communications (article, 2025-05-23)
Field: Chemistry — Inorganic Chemistry

Inverse design of metal-organic frameworks using deep dreaming approaches Exploring the expansive and largely untapped chemical space of metal-organic frameworks (MOFs) holds promise for revolutionising the field of materials science. MOFs, hailed for their modular architecture, offer unmatched flexibility in customising functionalities to meet specific application needs. However, navigating this chemical space to identify optimal MOF structures poses a significant challenge. Traditional high-throughput computational screening (HTCS), while useful, is often limited by a distribution bias towards materials not aligned with the desired functionalities. To overcome these limitations, this study adopts a "deep dreaming" methodology to optimise MOFs in silico, aiming to generate structures with systematically shifted properties that are closer to target functionalities from the outset. Our approach integrates property prediction and structure optimisation within a single interpretable framework, leveraging a specialised chemical language model augmented with attention mechanisms. Focusing on a curated set of MOF properties critical to applications like carbon capture and energy storage, we demonstrate how deep dreaming can be utilised as a tool for targeted material design. Flexibility (engineering) ; Expansive ; Computer science ; Modular design ; Chemical space ; Set (abstract data type) ; Field (mathematics) ; Architecture ; Nanotechnology ; Data science ; Artificial intelligence ; Systems engineering ; Materials science ; Drug discovery ; Engineering ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography