jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zeinab Amiri | Mohammad Bayat | Davood Gheidari
Source: BMC Chemistry (article, 2025-04-05)
Field: Chemistry — Organic Chemistry

Synthesis of thiazoloquinolinone derivatives: molecular docking, MD simulation, and pharmacological evaluation as VEGFR-2 inhibitors We synthesized a series of novel thiazoloquinolinone derivatives, achieving moderate to high yields ranging from 74 to 96%, and assessed their efficacy against Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) using in silico methodologies. The structures of these compounds were characterized through various spectroscopic techniques, including <sup>1</sup>H-NMR, <sup>13</sup>C-NMR, IR, and mass spectrometry. Comprehensive computational analyses, encompassing molecular docking, molecular dynamics (MD) simulations, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling, were conducted. Docking studies with VEGFR-2 revealed that all synthesized compounds exhibited docking scores between - 3.24 and - 6.63, indicating varying degrees of binding affinity. Notably, compound (5e) demonstrated the strongest binding affinity with an energy of - 6.63 kcal/mol. The MD simulations indicated that Lys868 was one of the amino acids exhibiting the highest frequency of interaction throughout the simulation. Analysis of the ADMET and physicochemical properties revealed that all inhibitor compounds possess favorable pharmacological characteristics. Docking (animal) ; VEGF receptors ; Chemistry ; Pharmacology ; Computer science ; Computational biology ; Medicine ; Biology ; Cancer research Synthesis and biological activity ; Angiogenesis and VEGF in Cancer ; HER2/EGFR in Cancer Research
Authors: Mehedi Hasan | Balachandran Baheerathan | Shrikanta Sutradhar | Ronak Shahbandinejad | Sudip Kumar Rakshit | Janusz A. Koziński | Dongbing Li | Yulin Hu | Kang Kang
Source: Carbon Research (article, 2025-06-21)
Field: Materials Science — Materials Chemistry

Microwave-assisted synthesis of biomass-derived N-doped carbon dots for metal ion sensing Abstract Biomass-derived carbon dots (CDs) have gained significant research interest for environmental monitoring applications thanks to their cost-effectiveness and sustainability. Using eco-friendly biowastes as precursors for CDs production offers an alternative to expensive and unsustainable inorganic and chemically synthesized CDs. This study presents the findings regarding the successful synthesis of biomass-based nitrogen-doped carbon dots (N-CDs) via a rapid, cost-effective, and environmentally friendly microwave-assisted method. Carboxymethyl cellulose (CMC) and glycine were used as carbon precursors and nitrogen dopants for the first time. The N-CDs exhibited a moderately high quantum yield of 31.6 ± 1.5% with an optimal fluorescence excitation wavelength of 400 nm. FTIR, CHNS, and SEM–EDX analyses characterized the N-CDs' surface functional groups and elemental composition. The optical stability of the N-CDs was validated across varying pH levels and NaCl concentrations. The N-CDs displayed notable selectivity and sensitivity for Fe 3 ⁺, Cu 2 ⁺, and Hg 2 ⁺ ions. The primary quenching mechanisms involve electrostatic interactions, π–π interactions, inner filter effects, and energy transfer. Stern–Volmer analysis revealed strong linear quenching for Fe 3 ⁺, Cu 2 ⁺, and Hg 2 ⁺ ions within the 0–10 µM range concentrations, with detection limits (LOD) of 6.0 µM, 1.41 µM and 1.36 µM for Fe 3 ⁺, Cu 2 ⁺, and Hg 2 ⁺, respectively. The fluorescence quenching for Fe 3 ⁺ ions enhanced sensitivity at higher concentrations, while selectivity decreased at lower concentrations. These findings highlight the potential of these N-CDs as a cost-effective and sustainable tool for environmental monitoring, offering a promising approach to addressing critical water contamination issues. Graphical Abstract Materials science ; Microwave ; Doping ; Metal ; Carbon fibers ; Biomass (ecology) ; Metal ions in aqueous solution ; Ion ; Optoelectronics ; Nanotechnology ; Chemistry ; Metallurgy ; Computer science ; Telecommunications ; Composite number ; Organic chemistry ; Composite material Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques ; Nanocluster Synthesis and Applications
Authors: Wei Li | Zhiyuan Xu | Yu Yan | Qianfeng Gao | Yaya Song | Taiqiang Wang | Hongyu Dun | Mingyu Yang | Qian Huang | Xiaodan Zhang | Ying Zhao | Guofu Hou
Source: Small (review, 2025-01-16)
Field: Materials Science — Materials Chemistry

2D MXenes: Synthesis, Properties, and Applications in Silicon‐Based Optoelectronic Devices MXenes, a rapidly emerging class of 2D transition metal carbides, nitrides, and carbonitrides, have attracted significant attention for their outstanding properties, including high electrical conductivity, tunable work function, and solution processability. These characteristics have made MXenes highly versatile and widely adopted in the next generation of optoelectronic devices, such as perovskite and organic solar cells. However, their integration into silicon-based optoelectronic devices remains relatively underexplored, despite silicon's dominance in the semiconductor industry. In this review, a timely summary of the recent progress in utilizing Ti-based MXenes, particularly Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>, in silicon-based optoelectronic devices is provided. The composition, synthesis methods, and key properties of MXenes that contribute to their potential for enhanced device performance are focused on. Furthermore, the latest advancements in MXene applications in silicon-based solar cells and photodetectors are discussed from fundamental and applied perspectives. Finally, the key challenges and future opportunities for the integration of MXenes in silicon-based optoelectronic devices are outlined. MXenes ; Materials science ; Silicon ; Silicon carbide ; Optoelectronics ; Nanotechnology ; Photodetector ; Semiconductor ; Engineering physics ; Engineering MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Memory and Neural Computing
Authors: Pei Zhao | Haiyan Lu | Chuan‐Feng Chen
Source: Chemical Society Reviews (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Recent advances in preparation and applications of white circularly polarized luminescent materials White circularly polarized luminescence (WCPL) integrates the characteristics of circular polarization luminescence and broadband white emission, enabling chiroptical luminescence under photoexcitation or electroluminescence conditions. Recently, WCPL materials have attracted increasing attention from both academic and industrial communities due to their potential applications in a wide range of fields, including optical anti-counterfeiting, information storage, biomedical diagnostics, optical sensing, and next-generation optoelectronic devices. In this review, we systematically summarize recent advances in the design, synthesis, and application of WCPL-active materials. Particular emphasis is placed on three representative designing strategies of WCPL materials: polymer-based systems, multicomponent-doped systems and single-component systems. These approaches collectively highlight the diverse molecular design principles and structure-property relationships underlying efficient WCPL behavior. We believe that this review will provide valuable insights for researchers across various disciplines and inspire further exploration and innovation in this rapidly evolving field of chiral luminescent materials. Luminescence ; White light ; White (mutation) ; Materials science ; Nanotechnology ; Doping ; Polymer ; Circular polarization ; Optoelectronics ; Optics ; Chemistry ; Physics ; Composite material Synthesis and Properties of Aromatic Compounds ; Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research
Authors: Yi Yang | Hui Li | Zhen Xu | Siyi Luo | Lidong Chen
Source: Moore and More (article, 2025-02-17)
Field: Materials Science — Materials Chemistry

Wearable self-powered devices based on polymer thermoelectric materials Abstract Driven by rapid advances in the thermoelectric (TE) performance of organic materials, conjugated polymer thermoelectric (PTE) materials are considered ideal candidates for flexible self-powered devices because of their intrinsic flexibility, tailored molecular structure, large-area solution processability, and low thermal conductivity. One promising application is the flexible and wearable TE devices used on the human body to convert human energy (human motion or body heat) into electricity. The self-powered character with extended functions allows PTE devices to monitor human activity or health status. In this review, we first introduce existing high-performance PTE materials and the architectures of PTE devices. Then, we focus on the progress of research on flexible self-powered devices based on PTE materials, including TE generators, TE sensors, and Peltier coolers. Finally, possible challenges in the development of PTE devices are discussed. Wearable computer ; Thermoelectric effect ; Wearable technology ; Materials science ; Nanotechnology ; Polymer ; Engineering physics ; Computer science ; Engineering ; Embedded system ; Physics ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Advanced Sensor and Energy Harvesting Materials
Authors: Antonio Pulcinella | Stefano Bonciolini | Robin Stuhr | Damiano Diprima | Minh Tran | Magnus J. Johansson | Axel Jacobi von Wangelin | Timothy Noël
Source: Nature Communications (article, 2025-01-22)
Field: Chemistry — Organic Chemistry

Deoxygenative photochemical alkylation of secondary amides enables a streamlined synthesis of substituted amines Secondary amines are vital functional groups in pharmaceuticals, agrochemicals, and natural products, necessitating efficient synthetic methods. Traditional approaches, including N-monoalkylation and reductive amination, suffer from limitations such as poor chemoselectivity and complexity. Herein, we present a streamlined deoxygenative photochemical alkylation of secondary amides, enabling the efficient synthesis of α-branched secondary amines. Our method leverages triflic anhydride-mediated semi-reduction of amides to imines, followed by a photochemical radical alkylation step. This approach broadens the synthetic utility of amides, facilitating late-stage modifications of drug-like molecules and the synthesis of saturated N-substituted heterocycles. The pivotal role of flow technology in developing a scalable and robust process underscores the practicality of this method, significantly expanding the organic chemist's toolbox for complex amine synthesis. Alkylation ; Chemoselectivity ; Chemistry ; Combinatorial chemistry ; Amination ; Amine gas treating ; Reductive amination ; Primary (astronomy) ; Amide ; Organic chemistry ; Organic synthesis ; Catalysis Advanced Synthetic Organic Chemistry ; Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis
Authors: Mukil Madhusudanan | Jian Zhang | Santosh Pandit | Priyanka Singh | Geum‐Jae Jeong | Fazlurrahman Khan | Ivan Mijakovic̀
Source: Advanced NanoBiomed Research (review, 2025-09-04)
Field: Materials Science — Materials Chemistry

Green Synthesis of Silver Nanoparticles: A Review of Polymer and Antimicrobial Drug Combinations for Enhanced Antimicrobial Applications Silver nanoparticles (AgNPs) have emerged as a pivotal class of nanomaterials due to their potent medicinal properties, offering promising solutions for combating microbial resistance, which is a growing global health concern. Traditional methods of synthesizing AgNPs often involve toxic chemicals and energy‐intensive processes, raising environmental and safety concerns. Green synthesis approaches have gained considerable attention utilizing plant and microbial extracts, natural polymers, and other eco‐friendly reducing agents. These methods mitigate the environmental impact and enable the production of AgNPs with enhanced biocompatibility and tailored physicochemical properties. The synergistic effects of combining AgNPs with polymers result in improved stability, biocompatibility, and targeted delivery capabilities, while the incorporation of antimicrobial drugs generates composite materials with multifaceted modes of action against a wide range of microbial pathogens. This review delves into the green synthesis of AgNPs, focusing on the integration of natural and synthetic polymers, as well as antimicrobial drugs, to boost their antimicrobial efficacy. In addition, it is further explored that how these green‐synthesized nanocomposites can be applied in areas such as wound healing and drug delivery, highlighting their potential in various biomedical fields. Moreover, the review critically examines the challenges and prospects of green synthesis, including scalability, cytotoxicity, biocompatibility, and stability hurdles. Antimicrobial ; Antimicrobial drug ; Drug ; Silver nanoparticle ; Nanotechnology ; Nanoparticle ; Materials science ; Chemistry ; Medicine ; Pharmacology ; Organic chemistry Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Graphene and Nanomaterials Applications
Authors: Alexandru Madalin Hasan | Simona Cavalu | Ahmed Y. Kira | Rabab S. Hamad | Mustafa Ahmed Abdel‐Reheim | Elsayed A. Elmorsy | Attalla F. El‐kott | Kareem Morsy | Ali S. Alsheri | Sally Negm | Sameh Saber
Source: International Journal of Nanomedicine (review, 2025-01-01)
Field: Materials Science — Biomaterials

Localized Drug Delivery in Different Gastrointestinal Cancers: Navigating Challenges and Advancing Nanotechnological Solutions Different types of cancers affect the gastrointestinal tract (GIT), starting from the oral cavity and extending to the colon. In general, most of the current research focuses on the systemic delivery of the therapeutic agents, which leads to undesired side effects and a limited enhancement in the therapeutic outcomes. As a result, localized delivery within gastrointestinal (GI) cancers is favorable in overcoming these limitations. However, the localized delivery via oral administration faces many challenges related to the complex structure of GIT (varied pH levels and transit times) as well as the harsh environment within tumor cells (hypoxia, efflux pumps, and acidity). To overcome these obstacles, nano-drug delivery systems (NDDs) have been designed and proved their potential by exploiting these challenges in favor of offering a specific delivery to the desired target. The current review begins with an overview of different GI cancers and their impact globally. Then, it discusses the current treatment approaches and their corresponding limitations. Additionally, the different challenges associated with localized drug delivery for GI cancers are summarized. Finally, the review discusses in detail the recent therapeutic and diagnostic applications of NDDs that have been conducted in oral, esophageal, gastric, colon, and liver cancers, aiming to offer valuable insights into the current and future state of utilizing NDDs for the local treatment of GI cancers. Drug delivery ; Drug ; Targeted drug delivery ; Medicine ; Drug carrier ; Nanotechnology ; Materials science ; Pharmacology Nanoparticle-Based Drug Delivery ; Pancreatic and Hepatic Oncology Research ; Cancer Research and Treatments
Authors: Yue‐Jian Yang | Denis Ari | Zhe-Hong Yu | Kelvine Lettelier | Olivier Jeannin | Qi Zheng | Aziz Khan | Cassandre Quinton | Dong‐Ying Zhou | Jiang Zuo-quan | Cyril Poriel
Source: Angewandte Chemie International Edition (article, 2025-03-11)
Field: Materials Science — Materials Chemistry

Pure Hydrocarbon Hosts Enabling Efficient Multi‐Resonance TADF Blue‐Emitting Organic Light‐Emitting Diodes Pure hydrocarbon (PHC) materials are a class of highly efficient and stable host materials for organic light-emitting diodes (OLEDs), composed solely of carbon and hydrogen atoms. Despite recent great advancements in PHC research, their applications are still mainly limited to phosphorescent OLEDs (PHOLEDs). High-performance blue OLEDs still pose a considerable challenge. Thus, expanding PHC materials into other types of OLEDs is critical for advancing organic electronic technologies. In this study, we designed a series of original high-triplet PHC materials based on a multi-substitution approach of the 9,9'-spirobifluorene (SBF) backbone and used them, for the first time, as a host in phosphorescence-sensitized multi-resonance thermally activated delayed fluorescence (MR-TADF) OLEDs. Devices based on the 2,6-bis(3,6-di-tert-butyl-9H-carbazol-9-yl)boron (DtBuCzB) emitter, using FIrpic or fac-Ir(tpz)<sub>3</sub> as the sensitizer, achieved high maximum external quantum efficiency (EQE<sub>max</sub>) values ranging from 29.1 % to 33.9 %. Additionally, blue MR-TADF OLED devices based on v-DABNA with a phosphorescent sensitizer (CN-Ir), demonstrated outstanding electroluminescent performance, with an EQE<sub>max</sub> of approximately 31 % due to an excellent molecular orientation induced by the PHC hosts. All devices exhibited narrow full-width at half-maximum spectra and minimal efficiency roll-off. This study marks the first application of PHC materials as hosts in phosphorescence-sensitized MR-TADF OLEDs, highlighting their potential as promising candidates for next-generation blue OLEDs and offering a viable pathway to achieve high-performance devices. OLED ; Hydrocarbon ; Optoelectronics ; Materials science ; Diode ; Light-emitting diode ; Resonance (particle physics) ; Blue light ; Photochemistry ; Chemistry ; Nanotechnology ; Organic chemistry ; Physics ; Particle physics ; Layer (electronics) Luminescence and Fluorescent Materials ; Fluorine in Organic Chemistry ; Supramolecular Chemistry and Complexes
Authors: Jieyu Lü | Zhengshuai Song | Qiyuan Zhao | Yuanqi Du | Yirui Cao | Haojun Jia | Chenru Duan
Source: Journal of the American Chemical Society (article, 2025-07-03)
Field: Materials Science — Materials Chemistry

Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge and Reasoning Capability of Large Language Models Large language models (LLMs) have shown promise in science, such as structure-property prediction and acting as AI agents, yet their intrinsic knowledge and reasoning capability for scientific discovery remains underexplored. We introduce LLM-EO, an integration of LLMs into evolutionary optimization, and demonstrate its success in optimizing transition metal complexes (TMCs). LLM-EO demonstrates advantages in few-sample learning due to the intrinsic chemical knowledge embedded within LLMs and their ability to leverage entire historical data collected during optimizations. Through natural language instructions, LLM-EO offers enhanced accessibility for multiobjective optimizations, potentially lowering barriers for experimental chemists without extensive programming expertise. As generative models, LLM-EO possesses the capability to propose novel ligands and TMCs with unique chemical properties by amalgamating both internal knowledge and external chemistry data, thus combining the benefits of efficient optimization and generation. With advancements in LLMs, both in their capacity as pretrained foundational models and new strategies in post-training inference, we anticipate broad applications of LLM-EO in chemistry and materials design. Chemistry ; Generative grammar ; Artificial intelligence ; Natural language processing ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Nawaf Alampara | Mara Schilling-Wilhelmi | Martiño Ríos-García | Indrajeet Mandal | Pranav Khetarpal | Hargun Singh Grover | N. M. Anoop Krishnan | Kevin Maik Jablonka
Source: Nature Computational Science (article, 2025-08-11)
Field: Materials Science — Materials Chemistry

Probing the limitations of multimodal language models for chemistry and materials research Recent advancements in artificial intelligence have sparked interest in scientific assistants that could support researchers across the full spectrum of scientific workflows, from literature review to experimental design and data analysis. A key capability for such systems is the ability to process and reason about scientific information in both visual and textual forms-from interpreting spectroscopic data to understanding laboratory set-ups. Here we introduce MaCBench, a comprehensive benchmark for evaluating how vision language models handle real-world chemistry and materials science tasks across three core aspects: data extraction, experimental execution and results interpretation. Through a systematic evaluation of leading models, we find that although these systems show promising capabilities in basic perception tasks-achieving near-perfect performance in equipment identification and standardized data extraction-they exhibit fundamental limitations in spatial reasoning, cross-modal information synthesis and multi-step logical inference. Our insights have implications beyond chemistry and materials science, suggesting that developing reliable multimodal AI scientific assistants may require advances in curating suitable training data and approaches to training those models. Computer science ; Workflow ; Data science ; Identification (biology) ; Inference ; Set (abstract data type) ; Artificial intelligence ; Management science ; Engineering ; Programming language Machine Learning in Materials Science ; Semantic Web and Ontologies
Authors: Junkil Park | Youhan Lee | Jihan Kim
Source: Nature Communications (article, 2025-01-02)
Field: Materials Science — Materials Chemistry

Multi-modal conditional diffusion model using signed distance functions for metal-organic frameworks generation The design of porous materials with user-desired properties has been a great interest for the last few decades. However, the flexibility of target properties has been highly limited, and targeting multiple properties of diverse modalities simultaneously has been scarcely explored. Furthermore, although deep generative models have opened a new paradigm in materials generation, their incorporation into porous materials such as metal-organic frameworks (MOFs) has not been satisfactory due to their structural complexity. In this work, we introduce MOFFUSION, a latent diffusion model that addresses the aforementioned challenges. Signed distance functions (SDFs) are employed for the input representation of MOFs, marking their first usage in representing porous materials for generative models. Using the suitability of SDFs in describing complicated pore structures, MOFFUSION exhibits exceptional generation performance, and demonstrates its versatile capability of conditional generation with handling diverse modalities of data, including numeric, categorical, text data, and their combinations. Computer science ; Flexibility (engineering) ; Generative model ; Modal ; Categorical variable ; Generative grammar ; Representation (politics) ; Modalities ; Artificial intelligence ; Machine learning ; Materials science ; Mathematics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Block Copolymer Self-Assembly