jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tianliang Li | Lifei Chen | Bin Cao | Siyuan Liu | Lixing Lin | Zeyu Li | Yingying Chen | Zhenzhen Li | Tong‐Yi Zhang | Lingyan Feng
Source: Materials Genome Engineering Advances (article, 2025-08-03)
Field: Materials Science — Materials Chemistry

Optimize the quantum yield of G‐quartet‐based circularly polarized luminescence materials via active learning strategy‐BgoFace Abstract G‐quartet (G4)‐based circularly polarized luminescence (CPL) materials within CPL engineering have attracted substantial attention in optoelectronics and photonics owing to their excellent chiral properties and promising applications in advanced optical devices. However, their practical use is limited by relatively low quantum yield (QY), which reduces emission efficiency. Addressing this challenge, we present BgoFace, an integrated active learning (AL)‐based software, to optimize G4‐based CPL materials with high QY. Starting with an initial dataset of 54 experimentally validated samples, the system executed six AL cycles encompassing 24 targeted experimental groups. Through this closed‐loop workflow, BgoFace successfully identified G4 complexes exhibiting a near doubling of QY (37.25%). This achievement significantly advances the previously low QY values typically reported for G4‐based CPL materials. The optimized materials demonstrate enhanced stability and processability, attributable to the AL algorithm's simultaneous consideration of multiple physicochemical parameters. This study not only advances the field of G4‐based CPL materials for optical and photonic applications, but also establishes a generalizable AL framework suitable for optimizing functional nanomaterials in optoelectronic device design. By bridging data‐driven design and experimental validation, BgoFace offers a transformative strategy for accelerating the development of functional nanomaterial engineering. Luminescence ; Quantum yield ; Yield (engineering) ; Quantum ; Optoelectronics ; Materials science ; Physics ; Optics ; Quantum mechanics ; Composite material Luminescence and Fluorescent Materials ; Synthesis and Properties of Aromatic Compounds ; Perovskite Materials and Applications
Authors: Zhen Xu | Chun Zhu | Hongjian Tang | Lunbo Duan
Source: Electrochimica Acta (article, 2025-07-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Feature-enhanced machine learning prediction of supercapacitor cycle life from limited early-cycle data Supercapacitor ; Feature (linguistics) ; Artificial intelligence ; Computer science ; Machine learning ; Pattern recognition (psychology) ; Chemistry ; Electrochemistry ; Electrode Supercapacitor Materials and Fabrication ; Advanced Battery Technologies Research ; Electric and Hybrid Vehicle Technologies
Authors: Afshin Tatar | Abbas Zeinijahromi | Manouchehr Haghighi
Source: Separation and Purification Technology (article, 2025-01-22)
Field: Materials Science — Materials Chemistry

Explainable Artificial Intelligence in modelling hydrogen gas solubility in n-Alkanes • Explained AI model predicts hydrogen solubility in n-alkanes accurately. • XAI techniques optimize inputs and show data anomalies. • Linear relationship found between solubility and key variables. • Anomalies in C1 require specialized modelling considerations. This research aims to enhance the predictive modelling of hydrogen gas (H 2 ) solubility in n-alkanes using Explainable Artificial Intelligence (XAI) techniques. The focus is on elucidating the impact of key variables on solubility, optimizing model inputs, and ensuring data integrity. The study employed the Extra Trees (ET) regression model complemented by XAI approaches, including Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE) plots, and Friedman H-statistics for assessing feature interactions. Feature importance (FI) was quantified using Permutation Feature Importance (PFI), Tree-based Feature Importance (TFI), and Partial Dependence Feature Importance (PDFI), which facilitated informed feature selection and model refinement. Analysis revealed that pressure ( P ), dimensionless P ( PD ), and dimensionless temperature ( TD ) significantly influence H 2 solubility, demonstrating a near-linear relationship. The application of XAI not only optimized model inputs but also played a critical role in identifying and correcting data anomalies, enhancing overall data quality. The refined model, ET2_4, demonstrated improved accuracy, achieving a Root Mean Squared Error ( RMSE ) of 0.0085 on testing data, with H-statistics confirming strong interactions, particularly between P , PD , and TD . Notable deviations were observed for C1, suggesting specialized modelling considerations for atypical n-alkanes. The integration of XAI techniques provided profound insights into variable interactions and solubility dynamics, significantly advancing the accuracy of predictive models for H 2 solubility ( x ) in n-alkanes. The findings emphasize the necessity of incorporating advanced analytical methods in chemical process simulations to ensure data reliability and model efficacy. Future research should explore alternative characterization methods to extend these insights across diverse chemical systems, especially for compounds exhibiting deviated behaviours. Solubility ; Hydrogen ; Chemistry ; Thermodynamics ; Chemical engineering ; Materials science ; Organic chemistry ; Engineering ; Physics Machine Learning in Materials Science ; Advanced Data Processing Techniques ; Reservoir Engineering and Simulation Methods
Authors: Makungu M. Madirisha | Lenganji Simwanda | Regina P. Mtei
Source: International Journal of Hydrogen Energy (article, 2025-03-28)
Field: Materials Science — Materials Chemistry

Predicting the hydrogen storage capacity of alumina pillared interlayer clays using interpretable ensemble machine learning Hydrogen storage ; Ensemble learning ; Materials science ; Chemical engineering ; Computer science ; Environmental science ; Machine learning ; Composite material ; Engineering Graphite, nuclear technology, radiation studies ; Hydrogen Storage and Materials
Authors: Anrong Chen | Zhenghong Zhou | Yang Han | Bo Yang | Yingzi Li | Feng Zhu
Source: Angewandte Chemie International Edition (article, 2025-08-18)
Field: Chemistry — Organic Chemistry

Pd‐Catalyzed Stereospecific and Ligand‐Controlled Regiodivergent Suzuki–Miyaura Cross‐Coupling for the Synthesis of 3‐<i>C</i>‐Glycals and 2,3‐Unsaturated <i>C</i>‐Glycosides The selective modification of carbohydrates to achieve structural complexity has emerged as a crucial strategy in carbohydrate-based drug development. However, the intricate stereochemistry and densely packed functional groups of carbohydrates pose significant challenges for precise stereoselectivity and regioselectivity control. Herein, we report a palladium-catalyzed, stereospecific, and ligand-controlled regiodivergent glycosyl Suzuki-Miyaura coupling of 3-boryl-glycals, establishing a robust platform for glycoside diversification with versatile C1 and C3 modification. Notably, stable 3-boryl-glycals with 1,1,2,2-tetraethylethylene glycol protection were first synthesized and fully characterized. Operating under mild conditions, this reaction enables the efficient synthesis of diverse 2,3-unsaturated aryl/alkenyl C-glycosides with exceptional regio- and stereoselectivity. Importantly, this ligand-controlled regiodivergent C-glycosylation offers a concise and efficient strategy for accessing 3-C-glycals (C3-arylated/alkenylated glycals), circumventing time-consuming and labor-intensive multi-step sequences. To elucidate the origins of regioselectivity control, we employ a data-driven approach integrating DFT-assisted multivariate regression analysis, identifying key ligand parameters that govern regioselectivity. Leveraging this predictive model, we pursued rational ligand design to enhance C3-glycosylation selectivity and yield. Additionally, density functional theory calculations (DFT) provide critical insights into the fundamental principles dictating regioselectivity and tunability. These findings establish a solid foundation for advancing ligand-controlled regiodivergent glycosylation reactions and significantly expand the synthetic toolkit for glycochemical synthesis. Regioselectivity ; Chemistry ; Stereoselectivity ; Ligand (biochemistry) ; Glycosylation ; Combinatorial chemistry ; Stereospecificity ; Aryl ; Rational design ; Stereochemistry ; Catalysis ; Organic chemistry ; Nanotechnology Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Chemical Synthesis and Analysis
Authors: Dimitra Xenitopoulou | Nikolaos Tsakiridis | Achilleas Panagiotis Zalidis | George Zalidis
Source: Future Foods (article, 2025-07-08)
Field: Chemistry — Analytical Chemistry

Real-time detection of turmeric adulteration with metanil yellow using a miniaturized NIR sensor and AI techniques Spices have been among the most targeted foods in the European Union (EU) for fraudsters, given that the spice market exemplifies complex and globalized supply chains. Turmeric is a widely used spice famous for its vivid color, flavor, and purported health advantages. Its medicinal properties in addressing various health issues have sparked a surge in global demand, raising concerns about the spice industry’s integrity. The most common and hazardous adulterants of turmeric, added for financial gain, are synthetic, non-authorized azo dyes, particularly Metanil Yellow (MY). To tackle malpractices in the turmeric supply chain, this study proposes a scalable method utilizing a miniaturized near-infrared (NIR) (1350–2500 nm) sensor coupled with advanced Artificial Intelligence (AI) techniques to detect the presence of MY in turmeric. A dataset comprising 202 samples, including pure turmeric, MY, and their admixtures (5%–40% w/w), was analyzed using both the portable device and a high-resolution benchtop visible and near-infrared–short-wave infrared (VNIR–SWIR) spectroradiometer. Multiple spectral pre-treatments were applied, and classification was performed using Random Forest (RF), XGBoost, and Support Vector Machines (SVM). The best performance was achieved by the RF model on raw reflectance spectra collected with the miniaturized sensor (98% accuracy, κ = 0.97). Key diagnostic wavelengths (e.g., 1495, 1640, 1675, 2155, 2475 nm) linked to MY’s chemical structure were pinpointed through feature importance analysis. This work highlights the potential of portable, low-cost NIR sensors as tools for broadening access to food authentication, enabling rapid, non-destructive testing with a practical limit of detection of 5% w/w for MY adulteration. • Metanil Yellow (MY) detected in turmeric at 5%–40% w/w using a miniaturized NIR sensor. • ML classifiers reach 98% accuracy and a κ of 0.97 in identifying MY in turmeric. • Classification models assessed utilizing nested cross-validation approach. • The portable NIR sensor performed comparably to the benchtop VNIR–SWIR device. • Key diagnostic bands linked to MY identified within NIR sensor’s range. ALIZARIN RED ; Chromatography ; Food science ; Chemistry ; Biology Dye analysis and toxicity ; Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food
Authors: Ritika Sharma | Dilbag Singh Rana
Source: ChemistrySelect (article, 2025-04-01)
Field: Chemistry — Analytical Chemistry

Catalytic Engineering of Waste Biomass‐Derived Nano‐Carbon for Ultra‐Low Detection of Lead and Mercury Ions Abstract Sensor‐based technologies are driving a revolution in environmental monitoring, enabling precise measurement and effective pollutant management. Accurate quantification of environmental pollutants is the foundation upon which remediation, protection, and policy enforcement solutions are built. These technologies ensure that environmental management strategies are data‐driven, precise, and responsive to real‐time conditions. This study has explored the sustainable yet effective development of sensor from parthenium hysterophorus waste biomass for detecting lead and mercury ions. The catalytic graphitization process has effectively converted plant biomass into graphitized carbon. A metal catalyst and specific functional groups are introduced to enhance the conductivity and sensitivity of the nanocomposite. The porous network of graphene‐like nanostructure, along with high crystallinity, is successfully developed, where the presence of silver, nitrogen, sulfur, and oxygen is confirmed through EDS and XPS. FE‐SEM images represent the layered structure of nanocomposite, while TEM has indicated the network of carbon structure with the distribution of silver nanoparticles on it. XRD spectra have shown a clear peak at 26°, which indicates the layered architecture with high crystallinity. CV study was also conducted to study the redox behavior of lead and mercury ions. DPASV study was conducted to study the sensitivity of lead and mercury ions. Nanocomposite also shows high selectivity in the mixed metal solution. It also shows high sensitivity with detection limits of 600 and 300 nM for lead and mercury ions. At the same time, a real sample study also showed a good response, where a detection limit of 400 nM was obtained for lead in tap water. The RSD calculated after studying the response with three different electrodes is 3.2%. The nanocomposite also represent high stability even after 60 days of storage. This study paves the way for developing sustainable nanomaterials from waste biomass for sensing heavy metal ions. Mercury (programming language) ; Catalysis ; Waste management ; Ion ; Environmental science ; Biomass (ecology) ; Lead (geology) ; Nano- ; Nanotechnology ; Environmental chemistry ; Chemistry ; Materials science ; Organic chemistry ; Engineering ; Computer science ; Composite material Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Adsorption and biosorption for pollutant removal
Authors: Xiao‐Lei Wang | Lingtao Wang | Hao Gong | Yifei Chen
Source: ChemCatChem (article, 2025-01-25)
Field: Chemistry — Organic Chemistry

Research Progress of Heterogeneous Co‐Based Catalysts for Olefin Hydroformylation Abstract The hydroformylation reaction is a large‐scale industrial process for the synthesis of aldehydes. At present, the most widely used are Rh‐based and Co‐based catalysts in homogeneous systems. However, the catalyst separation is a critical issue leading to high cost because of the metal leaching and ligand loss. Co‐based heterogeneous catalysts have more application prospects. This review introduces the research progress of Co‐based heterogeneous catalysts in recent years from different heterogeneous strategies. It can be found that increasing the dispersion of active sites, adjusting the interaction between metal and metal or support, and changing the reaction conditions can change the reactivity and stability of the catalyst. At the end of this review, we put forward suggestions and prospects for the future research of cobalt‐based heterogeneous catalysts. It is expected to develop a catalyst with high catalytic activity, selectivity, and stability that can adapt to industrial production. Hydroformylation ; Olefin fiber ; Catalysis ; Heterogeneous catalysis ; Chemistry ; Rhodium ; Organic chemistry Organometallic Complex Synthesis and Catalysis ; Catalysis and Hydrodesulfurization Studies ; Carbon dioxide utilization in catalysis
Authors: Oresegun Olakunle Ibrahim | Josiah Oluwadunsin | Moses Antwi | Francis Mekunye | Michael Oluwatobi | Abiodun Olorunfemi | O.R. Obanla | Mohammed Issa Abdul-Rahman | Kehinde Oladayo BABALOLA | Oladipupo Tolulope BABALOLA
Source: American Journal of Nanosciences (review, 2025-01-23)
Field: Materials Science — Polymers and Plastics

The Evolution of Self-Healing Electrodes: A Critical Review of Nanomaterial Contributions The ability of self-healing electrodes to withstand electrical breakdown at high electric fields has drawn a lot of interest to them in recent decades. Applications include electronic skins, sensors, supercapacitors, and lithium-ion batteries have resulted from the integration of conductive nanoparticles in flexible self-healing electrodes. Prior self-healing electrodes based on hydrogels and polymers had low strengths and conductivities. However, nanomaterials offer vast surface area, abundant functional groups, and special qualities that speed up the healing process. Self-healing electrodes, capable of autonomously repairing damage and extending their operational lifespan, represent a paradigm shift in material science and electronic device design. This review paper charts the remarkable evolution of self-healing electrodes, with a particular focus on the pivotal role of nanomaterials in driving this progress. The emergence of self-healing concepts is then discussed, encompassing both intrinsic mechanisms inherent to specific materials and extrinsic approaches that rely on the integration of healing agents. We explore how the distinct physicochemical properties of nanomaterials, such as their high surface area, adjustable conductivity, and catalytic activity, have been used to give electrodes the ability to cure themselves. Specific examples showcasing the successful incorporation of nanomaterials like carbon nanotubes, graphene, MXenes, and metallic nanoparticles into various electrode architectures are presented. The underlying self-healing mechanisms, ranging from reversible chemical bonding to dynamic supramolecular interactions, are elucidated. Furthermore, we critically assess the performance enhancements achieved through nanomaterial integration, including improved mechanical robustness, enhanced electrical conductivity, and extended cycling stability. Nanomaterials ; Nanotechnology ; Materials science Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Fuel Cells and Related Materials
Authors: Jessy John
Source: Journal of Nanotechnology and Nanomaterials (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Cutting-Edge Nanoparticle Innovations in Biomedical Science: Synthesis, Applications, Challenges, and Future Prospects Nanoparticles (NPs), identified as particles measuring between 1 and 100 nanometers, exhibit distinct physical and chemical properties that set them apart from larger materials. These attributes, which encompass a substantial surface area relative to volume and quantum phenomena, render them highly across various sectors. With their ground-breaking approaches to medication distribution, therapeutic treatments, and diagnostics, nanoparticles play a crucial role in biomedical innovations. This paper presents the extensive use of nanoparticles in biomedical field, the techniques for nanoparticles, encompassing both top-down and bottom-up methodologies, and assesses their wide-ranging applications in healthcare, environmental studies, and the industrial realm. Despite the vast potential of nanoparticles, they also pose notable challenges like potential toxicity, ecological consequences, and regulatory obstacles. Tackling these hurdles is imperative for nanoparticle technology’s responsible and sustainable advancement. But in recent years, there have been a number of attempts to develop eco-friendly technology that creates nanoparticles from natural materials rather than hazardous chemicals. Biological methods are used in green synthesis to create nanoparticles because they are easy to use, affordable, safe, clean, and very productive. The paper concludes by delving into future opportunities and ongoing studies dedicated to overcoming current restrictions, ensuring that nanoparticles can realize their revolutionary capabilities in various fields. Nanotechnology ; Nanoparticle ; Computer science ; Biochemical engineering ; Engineering ; Materials science Nanoparticles: synthesis and applications
Authors: Wael A. Mahdi | Adel Alhowyan | Ahmad J. Obaidullah
Source: Scientific Reports (article, 2025-04-21)
Field: Chemistry — Organic Chemistry

Computational study of carboplatin interaction with PEG-functionalized C60 fullerene as a drug carrier using DFT and molecular dynamics simulations In this research, the interaction of carboplatin with polyethylene glycol (PEG) functionalized iron-encapsulated fullerene (Fe@C<sub>60</sub>) molecule was investigated using Density Functional Theory (DFT) and molecular dynamics simulations (MD). Our results indicate that the inclusion of PEG enhances the stability of the Fe@C<sub>60</sub> molecule, leading to a shift in the formation energy of the structures from approximately - 3.4 to - 4.77 eV/atom in correlation with the quantity of surface polyethylene glycols. Additionally, the electric dipole moment of the Fe@C<sub>60</sub> structure increases following the surface modification with PEG molecules, fostering a more efficient interaction with carboplatin. The optical absorption spectrum reveals several peaks within the 200-600 nm range for Fe@C<sub>60</sub>:PEG. Particularly noteworthy is the impact of the interaction with carboplatin on the optical properties of the structure, providing valuable insights into the assessment of drug adsorption behavior. Furthermore, the adsorption energy computations demonstrate that the complexes formed between Fe@C<sub>60</sub> and carboplatin exhibit stability, with physical adsorption energies falling within a range conducive for the loading and release of carboplatin. Detailed analyses, including IR frequencies and molecular dynamics simulations, provide further insights into the structural and dynamic properties of this complex system, shedding light on its potential applications in drug delivery and related fields. Molecular dynamics ; Carboplatin ; Fullerene ; PEG ratio ; Drug ; Computational chemistry ; Bioinformatics ; Chemistry ; Pharmacology ; Medicine ; Biology ; Cisplatin ; Genetics ; Organic chemistry Fullerene Chemistry and Applications ; Boron and Carbon Nanomaterials Research ; Graphene research and applications
Authors: Teng Zhu | Tianwei Zhang | Lei Xiao | Cunwei Zhang | Yuming Li
Source: Catalysts (article, 2025-09-01)
Field: Chemistry — Inorganic Chemistry

Synthesis and Applications of Zeolite-Encapsulated Metal Catalysts Supported metal catalysts are extensively applied in the heterogeneous catalysis field. However, metal species are prone to migration and aggregation during catalytic reactions due to their high surface energy, which leads to deactivation. In recent years, the use of porous materials, particularly zeolites, to anchor metal species has gained significant attention. By confining metal single atoms, subnanometer metal clusters, and nanoparticles within the pores or nanocavities of these materials, the dispersion and stability of the metal species can be greatly enhanced, thereby improving the catalytic performance. This review systematically discussed the synthesis principles and diverse methodologies to fabricate zeolite-encapsulated metal catalysts. It further outlined their catalytic applications across various catalysis fields, emphasizing enhanced stability and selectivity enabled by confinement effects. Finally, the review provided critical perspectives on future developments, addressing challenges in precise structural control and scalability for industrial implementation. Zeolite ; Catalysis ; Materials science ; Chemical engineering ; Chemistry ; Organic chemistry ; Engineering Zeolite Catalysis and Synthesis ; Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies