jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mingwei Ma | E. S. D. Chen | Huijuan Yue | Ge Tian | Shouhua Feng
Source: Nature Communications (article, 2025-01-03)
Field: Chemistry — Inorganic Chemistry

Entropy engineering activation of UiO-66 for boosting catalytic transfer hydrogenation High-entropy metal-organic frameworks (HE-MOFs) hold promise as versatile materials, yet current rare examples are confined to low-valence elements in the fourth period, constraining their design and optimization for diverse applications. Here, a novel high-entropy, defect-rich and small-sized (32 nm) UiO-66 (ZrHfCeSnTi HE-UiO-66) has been synthesized for the first time, leveraging increased configurational entropy to achieve high tolerance to doping with diverse metal ions. The lattice distortion of HE-UiO-66 induces high exposure of metal nodes to create coordination unsaturated metal sites with a concentration of 322.4 μmol/g, which increases the abundance of Lewis acid-base sites, thereby achieving a significant improvement in the performance of the catalytic transfer hydrogenation (CTH) reaction. Systematic investigation manifests that the special electronic structure of HE-UiO-66 enhances the interaction and bonding with substrate molecules and reduces the energy barrier of the hydrogen transfer process. Our approach offers a new strategy for constructing coordination unsaturated metal sites in MOFs. Metal-organic framework ; Catalysis ; Materials science ; Valence (chemistry) ; Metal ; Molecule ; Entropy (arrow of time) ; Electron transfer ; Nanotechnology ; Chemical physics ; Combinatorial chemistry ; Chemistry ; Photochemistry ; Physical chemistry ; Thermodynamics ; Physics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; MXene and MAX Phase Materials ; Catalytic Processes in Materials Science
Authors: Laura Morbiato | Lucía Cardo | Elisa Sturabotti | Pierangelo Gobbo | Giacomo Filippini | Maurizio Prato
Source: ACS Nano (article, 2025-01-22)
Field: Materials Science — Materials Chemistry

Structure Matters: Tailored Graphitization of Carbon Dots Enhances Photocatalytic Performance The chemical structure and photoredox properties of carbon dots (<b>CDs</b>) are not yet fully understood. However, it has been reported that, by carefully choosing the starting materials and tuning their synthesis conditions, it is possible to obtain <b>CDs</b> with different chemical structures and therefore different photocatalytic performance. For this work, a family of different <b>CDs</b> was synthesized in Milli-Q water <i>via</i> a microwave-assisted protocol, using citric acid and urea as precursors. The syntheses were carried out at different times and temperatures to assess the impact of the synthetic parameters on the photocatalytic properties of the final materials. After extensive and accurate purification, the photocatalytic abilities of a selected subset of <b>CDs</b> were tested by performing a photocatalyzed atom transfer radical addition reaction. Among the tested <b>CDs</b>, the best performing ones were found to be those synthesized at the highest temperature, which were the most graphitic. A number of different characterization techniques were then used to evaluate the degree of graphitization of <b>CDs</b> and to elucidate the origin of their different photocatalytic performance. Photocatalysis ; Materials science ; Nanotechnology ; Carbon fibers ; Quantum dot ; Carbon quantum dots ; Chemical engineering ; Composite material ; Catalysis ; Composite number ; Chemistry Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Fatih Çiftçi | Ali Can Özarslan | İmran Cagri Kantarci | Aslıhan Yelkenci | Özlem Tavukçuoğlu | Mansour Ghorbanpour
Source: Pharmaceutics (review, 2025-01-16)
Field: Materials Science — Biomaterials

Advances in Drug Targeting, Drug Delivery, and Nanotechnology Applications: Therapeutic Significance in Cancer Treatment In the 21st century, thanks to advances in biotechnology and developing pharmaceutical technology, significant progress is being made in effective drug design. Drug targeting aims to ensure that the drug acts only in the pathological area; it is defined as the ability to accumulate selectively and quantitatively in the target tissue or organ, regardless of the chemical structure of the active drug substance and the method of administration. With drug targeting, conventional, biotechnological and gene-derived drugs target the body's organs, tissues, and cells that can be selectively transported to specific regions. These systems serve as drug carriers and regulate the timing of release. Despite having many advantageous features, these systems have limitations in thoroughly treating complex diseases such as cancer. Therefore, combining these systems with nanoparticle technologies is imperative to treat cancer at both local and systemic levels effectively. The nanocarrier-based drug delivery method involves encapsulating target-specific drug molecules into polymeric or vesicular systems. Various drug delivery systems (DDS) were investigated and discussed in this review article. The first part discussed active and passive delivery systems, hydrogels, thermoplastics, microdevices and transdermal-based drug delivery systems. The second part discussed drug carrier systems in nanobiotechnology (carbon nanotubes, nanoparticles, coated, pegylated, solid lipid nanoparticles and smart polymeric nanogels). In the third part, drug targeting advantages were discussed, and finally, market research of commercial drugs used in cancer nanotechnological approaches was included. Nanocarriers ; Drug delivery ; Drug ; Nanotechnology ; Transdermal ; Targeted drug delivery ; Nanobiotechnology ; Drug carrier ; Nanomedicine ; Pharmacology ; Medicine ; Materials science ; Nanoparticle Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; RNA Interference and Gene Delivery
Authors: Hayeon Bae | Hyunjin Ji | Konstantin Konstantinov | Ronald Sluyter | Katsuhiko Ariga | Yong Ho Kim | Jung Ho Kim
Source: Advanced Materials (article, 2025-08-07)
Field: Materials Science — Materials Chemistry

Artificial Intelligence‐Driven Nanoarchitectonics for Smart Targeted Drug Delivery The development of data-driven and targeted drug delivery systems is essential for advancing precision therapeutics. Despite substantial progress in nanocarrier development, conventional platforms continue to face major challenges in clinical translation due to biological complexity, off-target accumulation, and limited adaptability to dynamic physiological environments. The integration of nanoarchitectonics and artificial intelligence (AI) offers an advanced strategy for engineering delivery systems that are structurally programmable, stimuli-responsive, and autonomously optimized. Nanoarchitectonics enables the construction of hierarchical nanostructures with precise spatial and temporal control, while AI facilitates modeling, prediction, and iterative optimization throughout the development pipeline. In this perspective, an AI-driven nanoarchitectonics framework is introduced for targeted drug delivery, structured around three key phases: 1) molecular target identification through bioinformatic profiling, 2) machine learning (ML)-guided surface engineering to enhance targeting specificity, and 3) in silico modeling of delivery dynamics and systemic distribution. Drawing on recent advances and representative case studies, how AI tools are illustrated, from generative design algorithms to predictive pharmacokinetic models, are transforming the field from empirical formulation toward mechanism-informed and AI-driven intelligent design. By highlighting current limitations and outlining future directions for the integration of AI and nanoarchitectonics, are concluded with a focus on enabling clinically translatable nanomedicine platforms. Materials science ; Nanotechnology ; Drug delivery ; Targeted drug delivery ; Drug ; Systems engineering ; Engineering ; Medicine ; Pharmacology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Zhilong Song | Linfeng Fan | Shuaihua Lu | Chongyi Ling | Qionghua Zhou | Jinlan Wang
Source: Nature Communications (article, 2025-01-26)
Field: Materials Science — Materials Chemistry

Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm Directly generating material structures with optimal properties is a long-standing goal in material design. Traditional generative models often struggle to efficiently explore the global chemical space, limiting their utility to localized space. Here, we present a framework named Material Generation with Efficient Global Chemical Space Search (MAGECS) that addresses this challenge by integrating the bird swarm algorithm and supervised graph neural networks, enabling effective navigation of generative models in the immense chemical space towards materials with target properties. Applied to the design of alloy electrocatalysts for CO<sub>2</sub> reduction (CO<sub>2</sub>RR), MAGECS generates over 250,000 structures, achieving a 2.5-fold increase in high-activity structures (35%) compared to random generation. Five predicted alloys- CuAl, AlPd, Sn<sub>2</sub>Pd<sub>5</sub>, Sn<sub>9</sub>Pd<sub>7</sub>, and CuAlSe<sub>2</sub> are synthesized and characterized, with two showing around 90% Faraday efficiency for CO<sub>2</sub>RR. This work highlights the potential of MAGECS to revolutionize functional material development, paving the way for fully automated, artificial intelligence-driven material design. Swarm behaviour ; Reduction (mathematics) ; Computer science ; Inverse ; Algorithm ; Generative grammar ; Swarm intelligence ; Mathematical optimization ; Artificial intelligence ; Particle swarm optimization ; Mathematics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Pravin D. Patil | Niharika Gargate | Manishkumar S. Tiwari | Shamraja S. Nadar
Source: Coordination Chemistry Reviews (article, 2025-02-16)
Field: Chemistry — Inorganic Chemistry

Defect metal-organic frameworks (D-MOFs): An engineered nanomaterial for enzyme immobilization The field of metal-organic frameworks (MOFs) for enzyme immobilization has gained significant traction due to their versatile structural properties and exceptional stability. There are certain constraints after enzyme-MOF composite formation, such as (i) reduction in enzyme activity , (ii) mass transfer limitation between substrate-enzyme, and (iii) unfavorable micro-environment for immobilized enzyme within MOF. Therefore, the defects in MOFs have emerged as a powerful tool to enhance the enzyme catalytic properties and their loading. This review delves into the recent advancements in defect engineering of MOFs, elucidating the role of structural defects in creating mesoporous environments that facilitate superior enzyme immobilization and activity. The synthesis strategies, such as post-synthetic and de-novo methods for controlled defect formation, are comprehensively discussed. Moreover, the integration of machine learning approaches for predicting and optimizing D-MOFs is highlighted, showcasing their transformative impact on biocatalytic applications. The challenges related to synthesis, characterization, enzyme microenvironment, separation, and recycling are critically examined, focusing on the future scope of defect MOFs in sustainable and scalable biocatalysis. This review underscores the untapped potential of defect engineering in MOFs to revolutionize enzyme immobilization, paving the way for innovative biocatalytic processes and applications. The defect-rich metal-organic frameworks coupled with enzymes significantly enhance the catalytic properties in terms of activity and stability. • The defect MOF can be synthesized by de novo and post-treatment synthetic methodologies. • The defect MOF-enzyme composite exhibits superior catalytic activity and conformational stability. • The defect MOF provides a carrier matrix and improves mass transfer between enzyme-substrate. • Understanding defective MOFs will open fresh discoveries that extend MOF applications. Chemistry ; Metal-organic framework ; Nanomaterials ; Nanotechnology ; Immobilized enzyme ; Combinatorial chemistry ; Enzyme ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors
Authors: Guohui Yang | Yangyang Zhang | Qiu‐Hong Zhu | Xue Xia | Ning Pan | Chunyan Ma | Jiale Liu | Yang Liu | Yilin Qin | Qingdong Zhang | Faqin Dong | Jun Li | Xiaoqin Nie
Source: Advanced Functional Materials (article, 2025-01-20)
Field: Chemistry — Inorganic Chemistry

Endeavoring a High Amidoxime Utilization Ratio and Adsorption Capacity for Uranium Extraction From Seawater: A Hydrogen Bonding Reconstruction Strategy Abstract Functionalized amidoxime [AO, R−C(NH 2 )═N(OH)] is widely used for uranium extraction from seawater, but increasing the effective functional sites for high AO utilization in adsorbents remains challenging. This study introduces a hydrogen bond reconstruction strategy to enhance uranium extraction efficiency of AO‐modified polyacrylonitrile (PAN) fibers by blending with polyvinyl alcohol (PVA). The PAN/PVA fibers are synthesized via electrospinning, followed by amidoximation on PAN to prepare PAN‐AO/PVA adsorbents. These materials can extract uranyl ions from the uranyl‐carbonate complex in seawater, achieving an adsorption capacity of 21.0 mg g⁻¹ under simulated seawater conditions with 330 ppb uranium concentration over 24 h. PVA blending increases AO utilization efficiency by 4 times (from 1/291 to 1/69), exceeding most reported values. The adsorbent also shows excellent reusability, retaining over 95% capacity after seven adsorption–desorption cycles. Quantum‐chemical studies based on density functional theory reveal that PVA modulates the hydrogen bonding network by shifting intramolecular bonds in PAN‐AO to intermolecular ones with PVA, increasing permeability and exposing more active N and O sites. Additionally, PVA's hydroxyl groups coordinate with UO₂ 2 ⁺, enhancing uranyl adsorption through an enthalpy‐entropy synergy relationship. This strategy offers a practical approach to improving uranium extraction from seawater. Seawater ; Uranium ; Adsorption ; Materials science ; Extraction (chemistry) ; Hydrogen bond ; Chemical engineering ; Chromatography ; Molecule ; Metallurgy ; Physical chemistry ; Organic chemistry ; Chemistry ; Oceanography Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Extraction and Separation Processes
Authors: Hai‐Ling Wang | Yun‐Lan Li | Hua‐Hong Zou | Fu‐Pei Liang | Zhong‐Hong Zhu
Source: Advanced Materials (article, 2025-05-08)
Field: Chemistry — Inorganic Chemistry

Smart Lanthanide Metal–Organic Frameworks with Multicolor Luminescence Switching Induced by the Dynamic Adaptive Antenna Effect of Molecular Rotors In this work, dynamic molecular rotors are used to construct smart lanthanide metal-organic frameworks (Ln-MOFs) emitters with adaptive antenna effects for the first time. The movement or distortion of the molecular rotors can be easily regulated by temperature changes, thereby inducing a dynamically changing antenna effect that can automatically match different lanthanide ions, achieving cyclic multicolor luminescence switching behavior and extremely complex multiple encryption anti-counterfeiting technology. In addition, by regulating the doping ratios of Gd(III) and Tb(III) with Eu(III) within the Ln-MOFs, differentiated energy transfer pathways are discovered, and red light emission very close to the BT.2020 color gamut standard is obtained. Gd<sub>0.99</sub>Eu<sub>0.01</sub>-MOF containing only 1% Eu(III) can show bright red luminescence, and in the range of 1-9% Eu(III) content, the characteristic emission intensity of Eu(III) ions and the content show an excellent linear relationship with a slope k as high as 2299. This can be used to identify the content of Eu(III) ions impurities in gadolinium salts from different manufacturers. Eu/Tb-MOF showed highly sensitive and visualized smart photoresponse behaviors to specific antibiotics and amino acids, respectively, with detection limits of 3.2/2.7 nM (tetracycline), 1.7/15.5 nM (oxytetracycline), 0.13/0.97 nM (aspartic acid), and 0.26/1.16 nM (glutamic acid). Luminescence ; Lanthanide ; Materials science ; Antenna effect ; Ion ; Metal-organic framework ; Doping ; Gadolinium ; Antenna (radio) ; Metal ions in aqueous solution ; Optoelectronics ; Metal ; Physical chemistry ; Chemistry ; Organic chemistry ; Computer science ; Telecommunications Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials ; Lanthanide and Transition Metal Complexes
Authors: Sijia Niu | Xiaoming Liu | Chenchong Wang | Wangzhong Mu | Wei Xu | Qiang Wang
Source: Small (article, 2025-04-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Breaking the Trade‐Off Between Complexity and Absorbing Performance in Metamaterials Through Intelligent Design Spectrally selective absorbers garner significant attention across diverse domains owing to their pivotal roles in electromagnetic stealth technologies, solar-thermal photovoltaics, and related applications. However, enhancing the absorption properties frequently necessitates the augmentation of the metamaterial patterned layer complexity. This introduces a paradox in application, where the increased intricacy of structural patterning adversely intersects with fabrication processes, thereby exacerbating the practical applicability challenges due to manufacturing constraints. Therefore, this study leverages a design methodology that combines artificial intelligence (AI) with finite element simulation. This approach propels the realization of broadband selective absorption based on a simple biomimetic metamaterial structure, achieving broadband absorption without increasing structural complexity or reducing fabrication efficiency. The spectrally selective absorbing metamaterial designed with AI achieves broadband absorption unaffected by polarization in the 5-8 µm range. With electromagnetic waves impinging perpendicularly, the average absorptance exceeds 0.9, proving valuable for radiation cooling compatible with infrared stealth. Furthermore, the design method elucidated in this study exhibits remarkable robustness and transferability, significantly improving the design efficiency of complex spectral metamaterials. This innovative approach heralds a design paradigm shift, facilitating the creation of stealth-compatible and other advanced multiband spectrally selective absorbing materials. Metamaterial ; Broadband ; Fabrication ; Materials science ; Absorption (acoustics) ; Metamaterial absorber ; Photovoltaics ; Robustness (evolution) ; Computer science ; Optoelectronics ; Material Design ; Nanotechnology ; Photovoltaic system ; Telecommunications ; Tunable metamaterials ; Engineering ; Electrical engineering ; Chemistry Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Advanced Antenna and Metasurface Technologies
Authors: Mohamed Abdellatief | Leong Sing Wong | Norashidah Md Din | Ali Najah Ahmed | Abba Musa Hassan | Zainah Ibrahim | G. Murali | Kim Hung Mo | Ahmed El‐Shafie
Source: Results in Engineering (article, 2025-01-20)
Field: Chemistry — Inorganic Chemistry

Sustainable foam glass property prediction using machine learning: A comprehensive comparison of predictive methods and techniques • GPR achieved the highest correlation coefficients and lowest error metrics, excelling in capturing complex relationships. • While GB and RF models performed well, they fell short of GPR in prediction accuracy and error reduction. • Critical variables affecting FG properties include density, foam agent content, and sintering temperature. Foam glass (FG) is characterized by its lightweight structure and exceptional insulating properties, making it a highly suitable material for a wide range of applications. It is produced by foaming molten glass, resulting in a cellular structure that enhances its insulation and impact-resistant properties. Due to its sustainability and durability, FG is increasingly used in the construction, automotive, and packaging sectors. In this context, the current study proposes a novel approach by developing a thoughtful system for assessing performance and intelligent design utilizing ML models such as Gradient Boosting (GB), Random Forest (RF), Gaussian Process Regression (GPR), and Linear Regression (LR) to predict porosity and compressive strength (CS) of FG. The dataset comprises 214 data points, encompassing input variables such as glass particle diameter, foam agent content, heating rate, holding time, sintering temperature, and dry density, with output parameters of porosity and CS. Data preprocessing involved Pearson correlation analysis to address multicollinearity and reveal nonlinear relationships among variables. Model performance was evaluated through R-values, mean absolute error, and root mean square error metrics, demonstrating that the GPR model achieved superior prediction accuracy with R-values of 0.91 and 0.82 for porosity and CS, respectively. The GB model followed closely, while the RF and LR models showed lower accuracy. Partial dependence plots and global feature importance analyses highlighted density and foam agent content as critical factors influencing FG properties. By achieving the most precise predictions with minimal error distributions, the GPR model offers actionable insights into FG design. These findings enable the optimization of FG production by providing reliable tools for predicting and controlling porosity and CS, reducing material waste, enhancing product quality, and streamlining manufacturing processes. This study demonstrates the potential of advanced ML techniques to bridge the gap between predictive modeling and practical applications in the digital design of FG. Property (philosophy) ; Artificial intelligence ; Machine learning ; Computer science ; Materials science ; Philosophy Pigment Synthesis and Properties
Authors: Qaiser Mahmood | Zexu Hu | Geng Ren | Wen‐Hua Sun
Source: Coordination Chemistry Reviews (article, 2025-05-30)
Field: Chemistry — Organic Chemistry

Recent advances in nickel Catalysis for thermoplastic polyethylene elastomers: Synthesis strategies, properties, and future perspectives Chemistry ; Thermoplastic elastomer ; Nickel ; Elastomer ; Catalysis ; Polyethylene ; Polymer science ; Thermoplastic ; Nanotechnology ; Polymer chemistry ; Organic chemistry ; Polymer ; Copolymer Organometallic Complex Synthesis and Catalysis ; Carbon dioxide utilization in catalysis ; biodegradable polymer synthesis and properties