jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junaid Ahmed | Mohan Ganesh G
Source: Results in Engineering (article, 2025-06-24)
Field: Materials Science — Materials Chemistry

Investigation of the anti-corrosion effects of Azadirachta indica leaf extract on various reinforcement steels in 1 M HCl: Results from electrochemical, DFT and simulation studies • Neem extract is non-toxic and economical compared to synthetic inhibitors. • Compounds of flavonoids, phenols, terpenoids are key for corrosion inhibition. • Analysed ion flow, mechanism, and electrolyte current density variations via COMSOL. • Activation-controlled charge transfer process was confirmed via Tafel and EIS. • In Tafel, the inhibition efficiency decreases in the order of TMX > SD > TMT > CRS. The research explores the efficacy of Azadirachta indica (neem) leaf extract as a green corrosion inhibitor to mitigate corrosion of different steels in 1 M HCl using electrochemical tests - Tafel, Electrochemical impedance spectroscopy (EIS), Linear polarization resistance and Artificial Neural Network (ANN). Furthermore, cyclic voltammetry (CV), Electrochemical Noise, surface studies and COMSOL simulations were performed to evaluate the corrosion effect on TMT. The interaction of bioactive phytochemical compounds towards the metal surface was studied via DFT. Through electrochemical results, the leaf extract in increasing concentrations up to optimum (65 ml) was highly effective in reducing corrosion rate (mm/year) for all - CRS (20.192), TMT (23.03), TMX (32.056) and SD (66.357). From Tafel and EIS, the highest inhibition efficiency of 85.1% and 75.74% was achieved. Addition of neem reduced the current noise fluctuations along with a stable baseline formation, indicating reduced corrosion. Through CV, corrosion occurred at a slower rate due to the formation of an oxide layer in the passivation region, and the corresponding current density was found to be much lesser with addition of neem. Compounds having greater reactivity and less stability were primarily responsible for higher inhibition efficiency. Corrosion mechanism, ion movement, and corrosion rate was predicted using COMSOL Multiphysics and ANN. Through the obtained results it can be concluded that, the non-toxic and economical neem extract was suggested to be used as green corrosion inhibitor as a better replacement to traditional inhibitors. Azadirachta ; Corrosion ; Electrochemistry ; Reinforcement ; Materials science ; Metallurgy ; Nuclear chemistry ; Chemistry ; Composite material ; Botany ; Biology ; Electrode ; Physical chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Wei‐Hsin Chen | Yen-Kuan Lin | Ding Luo | Liwen Jin | Argel A. Bandala
Source: Energy (article, 2025-01-09)
Field: Materials Science — Materials Chemistry

Optimization of a segmented thermoelectric generator with various doping amounts using central composite design, multi-objective genetic algorithm, and artificial neural network Artificial neural network ; Composite number ; Genetic algorithm ; Thermoelectric generator ; Thermoelectric effect ; Generator (circuit theory) ; Doping ; Materials science ; Optimization algorithm ; Central composite design ; Algorithm ; Computer science ; Engineering ; Mathematical optimization ; Mathematics ; Artificial intelligence ; Response surface methodology ; Physics ; Optoelectronics ; Machine learning ; Power (physics) Advanced Thermoelectric Materials and Devices ; Heat Transfer and Optimization ; Building Energy and Comfort Optimization
Authors: Shuo Li | Daming Nie | Yu Zhang | Li Li
Source: International Journal of Mechanical Sciences (article, 2025-09-09)
Field: Materials Science — Materials Chemistry

Physics-informed neural network-based homogenization for architected lattice structures Homogenization (climate) ; Solver ; Lattice (music) ; Lattice constant ; Finite element method ; Artificial neural network ; Finite strain theory Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Acoustic Wave Phenomena Research
Authors: Yujing Ding | Shanshan Zhang | Xian Kong | Henry Hess | Yifei Zhang
Source: Advanced Science (article, 2025-03-16)
Field: Chemistry — Organic Chemistry

Replicating PET Hydrolytic Activity by Positioning Active Sites with Smaller Synthetic Protein Scaffolds Evolutionary constraints significantly limit the diversity of naturally occurring enzymes, thereby reducing the sequence repertoire available for enzyme discovery and engineering. Recent breakthroughs in protein structure prediction and de novo design, powered by artificial intelligence, now enable to create enzymes with desired functions without solely relying on traditional genome mining. Here, a computational strategy is demonstrated for creating new-to-nature polyethylene terephthalate hydrolases (PET hydrolases) by leveraging the known catalytic mechanisms and implementing multiple deep learning algorithms and molecular computations. This strategy includes the extraction of functional motifs from a template enzyme (here leaf-branch compost cutinase, LCC, is used), regeneration of new protein sequences, computational screening, experimental validation, and sequence refinement. PET hydrolytic activity is successfully replicated with designer enzymes that are at least 30% shorter in sequence length than LCC. Among them, RsPETase1 stands out due to its robust expressibility. It exhibits comparable catalytic efficiency (k<sub>cat</sub>/K<sub>m</sub>) to LCC and considerable thermostability with a melting temperature of 56 °C, despite sharing only 34% sequence similarity with LCC. This work suggests that enzyme diversity can be expanded by recapitulating functional motifs with computationally built protein scaffolds, thus generating opportunities to acquire highly active and robust enzymes that do not exist in nature. Hydrolysis ; Chemistry ; Synthetic biology ; Pet imaging ; Nanotechnology ; Materials science ; Positron emission tomography ; Biochemistry ; Computational biology ; Biology ; Neuroscience Click Chemistry and Applications ; Graphene and Nanomaterials Applications
Authors: Siyang Cao | Yingchen Pang | Yihao Wei | Deli Wang | Ao Xiong | Jun Yang | Hui Zeng
Source: International Journal of Biological Macromolecules (review, 2025-04-04)
Field: Materials Science — Materials Chemistry

Nanozymes in biomedicine: Unraveling trends, research foci, and future trajectories via bibliometric insights (from 2007 to 2024) Nanozymes, a new generation of artificial enzymes, have attracted significant attention in biomedical applications due to their multifunctional properties, multi-enzyme mimicking abilities, cost-effectiveness, and high stability. Leveraging these diverse catalytic activities, an increasing number of nanozyme-based therapeutic strategies have been developed for the treatment of various diseases. Despite substantial research efforts, a significant gap remains in comprehensive studies examining the progression, key areas, current trends, and future directions in this field. This study provides a comprehensive overview of nanozyme applications in biomedical research over the past 17 years, utilizing data from the Web of Science Core Collection, covering the period from January 1, 2007, to October 8, 2024. Advanced bibliometric and visualization tools were employed to facilitate a comprehensive analysis. The results highlight China's dominant role in this field, accounting for 76.83 % of total publications, significantly influencing the evolution of research in this area. Key contributions were made by institutions such as the Chinese Academy of Sciences, the University of Chinese Academy of Sciences, and the University of Science and Technology of China, with Qu Xiaogang as the leading author. The journal ACS Applied Materials & Interfaces has become the most prolific publisher in this field. Keyword analysis indicates that since 2022, research hotspots in this field have increasingly focused on areas such as photothermal therapy, chemodynamic therapy, and ferroptosis. Challenges such as obstacles to clinical translation, limitations in recyclability, and insufficient targeting ability were addressed. The potential applications of emerging interdisciplinary technologies, such as artificial intelligence, machine learning, and organoids, in advancing nanozyme development were explored. This study offers a data-driven roadmap for researchers to navigate the evolving landscape of nanozyme innovation, emphasizing interdisciplinary collaboration in impactful biomedical applications. Biomedicine ; Data science ; Nanotechnology ; Computer science ; Management science ; Engineering ; Materials science ; Biology ; Bioinformatics Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Wuyi Ming | Yingjie Xu | Haozhe Chang | Guojun Zhang | Yu Wang | Zhen Zhang
Source: Renewable and Sustainable Energy Reviews (review, 2025-07-15)
Field: Materials Science — Surfaces, Coatings and Films

Bio-inspired designing and fabricating hydrophobic functional surface for advanced fuel cell applications: A review Biomimetic hydrophobic surfaces (HSs) play a vital role in enhancing fuel cell (FC) performance by addressing flooding issues and improving water and gas transport. This leads to better water management, increased catalytic activity, enhanced thermal stability, and greater corrosion resistance of electrodes. This review classifies various biological HSs and introduces wetting models alongside different types of FCs. It places particular emphasis on the role of HSs within key FC components: like reinforcing the corrosion resistance of bipolar plates, improving mass transfer in gas diffusion and catalyst layers, and enhancing the conductivity of exchange membranes. The improvement of the working performance is specifically manifested in the increase in peak power density, limit current density and conversion rate. Additionally, reductions in interfacial contact resistance and corrosion current contribute to enhanced corrosion resistance. In proton exchange membrane fuel cells (PEMFCs), surface coatings have reduced interfacial resistance from 608 to 9.2 mΩ cm 2 , while adding carbon nanotubes to the GDL increased peak power density from 0.76 to 1.03 W/cm 2 . This paper summarizes diverse fabrication methods for biomimetic hydrophobic structures, including additive manufacturing, subtractive manufacturing, coating modification, highlighting the latest advancements in their application to fuel cells. The review also delves into the application of advanced manufacturing approaches such as ultra-precision machining and atomic-level fabrication for FC functional surfaces. Furthermore, it explores the emerging role of machine learning in biomimetic surface design and manufacturing, offering new opportunities to boost FC efficiency. Finally, future research directions for functional surfaces in FC applications are discussed. • Biomimetic hydrophobic surfaces enhance water management, activity, and durability. • Classify hydrophobic structures of biological surfaces and introduce wetting models. • The role of hydrophobic surfaces in each fuel cell component is summarized. • Methods for fabricating biomimetic hydrophobic structures are summarized. • Hydrophobic surface fabrication has broad advancements and poses challenges. Nanotechnology ; Fuel cells ; Materials science ; Surface (topology) ; Computer science ; Computer architecture ; Engineering ; Chemical engineering ; Mathematics Surface Modification and Superhydrophobicity ; Electrospun Nanofibers in Biomedical Applications ; Supercapacitor Materials and Fabrication
Authors: Yuan Wang | Bei Cao | Qianqian Wang | Shuyi Zhong | X. Fang | Junjian Wang | Albert S. C. Chan | Xiaolin Xiong | Taotao Zou
Source: Nature Communications (article, 2025-08-09)
Field: Chemistry — Organic Chemistry

Ligand supplementation restores the cancer therapy efficacy of the antirheumatic drug auranofin from serum inactivation Auranofin, an FDA-approved antirheumatic gold drug, has gained ongoing interest in clinical studies for treating advanced or recurrent tumors. However, gold ion's dynamic thiol exchange nature strongly attenuates its bioactivity due to the fast formation of covalent albumin-gold adducts. Here we report that newly-added thiols can modulate the dynamic albumin-gold binding and recover the therapeutic efficacy. Initially, we find that auranofin supplemented with its own thiol ligand, TGTA (1-thio-β-D-glucose tetraacetate), significantly restores the anticancer activities in cells and patient-derived xenograft models. Then, screening a collection of ligand fragments followed by machine learning evaluation unveils diverse synergizing thiols, including pantethine, that effectuate auranofin at a low dosage for rheumatoid arthritis. Interestingly, the thiol exchange inside cells accounts for a cuproptosis-like phenotype that auranofin induces. Together, we believe the ligand-enabled dynamic modulation strategy is of value to researchers and clinicians contemplating metallodrugs and ligand-like molecules in cancer therapy. Auranofin ; Thiol ; Ligand (biochemistry) ; Pharmacology ; Chemistry ; Albumin ; Drug ; Medicine ; Human serum albumin ; Biochemistry ; Rheumatoid arthritis ; Internal medicine ; Receptor Synthesis and biological activity ; Biochemical effects in animals ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Seungah Lee | Nayra A. M. Moussa | Seong Ho Kang
Source: Biosensors (review, 2025-09-01)
Field: Materials Science — Materials Chemistry

Deep Learning-Enhanced Nanozyme-Based Biosensors for Next-Generation Medical Diagnostics The integration of deep learning (DL) and nanozyme-based biosensing has emerged as a transformative strategy for next-generation medical diagnostics. This review explores how DL architectures enhance nanozyme design, functional optimization, and predictive modeling by elucidating catalytic mechanisms such as dual-atom active sites and substrate-surface interactions. Key applications include disease biomarker detection, medical imaging enhancement, and point-of-care diagnostics aligned with the ASSURED criteria. In clinical contexts, advances such as wearable biosensors and smart diagnostic platforms leverage DL for real-time signal processing, pattern recognition, and adaptive decision-making. Despite significant progress, challenges remain-particularly the need for standardized biomedical datasets, improved model robustness across diverse populations, and the clinical translation of artificial intelligence (AI)-enhanced nanozyme systems. Future directions include integration with the Internet of Medical Things, personalized medicine frameworks, and sustainable sensor development. The convergence of nanozymes and DL offers unprecedented opportunities to advance intelligent biosensing and reshape precision diagnostics in healthcare. Biosensor ; Computer science ; Intensive care medicine ; Medicine ; Nanotechnology ; Materials science Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Nanocluster Synthesis and Applications
Authors: Ehsan Kianfar
Source: Trends in Sciences (review, 2025-05-20)
Field: Materials Science — Polymers and Plastics

Advances in Nanoelectronics: Carbon Nanotubes, Graphene, and Smart Polymers: A review In recent years, in order to miniaturize electronic devices, reduce manufacturing costs and increase the efficiency of electronic and optical systems, a new field called “nanoelectronics” has been developed. In this field, silicon is no longer used as the main component of systems, and this unique material has given its place to biological, organic and mineral materials. For this reason, the general principles of design in nanoelectronics devices are subject to new parameters, each of which can lead to the development of new properties, characteristics and applications. Nanoelectronics science seeks to miniaturize traditional electronic devices and increase their efficiency. So far, a comprehensive classification for the main sub-branches of nanoelectronics that all scientific authorities follow has not been provided. However, nanoelectronics can be considered in several broad areas: 1) Nano sensors; 2) lab-on-a-chip; 3) artificial organs; 4) bionics and biomimetic; 5) imaging; 6) integrated circuits and 7) molecular nanoelectronics. Organic materials are those materials that have a continuous structure of carbon-hydrogen bonds. So far, more than 16 thousand types of organic substances with different molecular and chemical structures have been produced. One of the main applications of this category of materials is their use in the manufacture of nanoelectronic devices. In this way, a field in electronics called “organic nanoelectronics” has been developed. But so far, only a limited number of these materials have been able to be used in organic-based nanoelectronics devices. These materials include: organic charge transfer complexes, conductive or conjugated polymers with π bonds, conductive gels, and dielectric polymers. The reason for the importance of each of these types of materials is their electrical properties and unique structure. Carbon nanotube is used in many nanoelectronics devices due to its crystallographic structure and unique properties. Due to its significant surface area, hollow structure, high mechanical strength, excellent electrical properties, and unique structures in electron bonds, this material is widely used in energy storage, nanocomposite materials, and making nanoelectronics devices such as Nano sensors. HIGHLIGHTS Investigating Electrical properties of carbon nanotubes. Investigating Applications of nanoelectronics in the structure of carbon nanotubes. Investigating Electrical properties of graphene. Investigating Applications of graphene in nanoelectronics. Investigating Chemical composition of organic Materials. Investigating Advantages of organic materials in nanoelectronics. Investigating Electro biochemical principles governing nanoelectronics. Investigating The main sub-branches in the field of nanoelectronics. GRAPHICAL ABSTRACT Nanoelectronics ; Graphene ; Carbon nanotube ; Nanotechnology ; Materials science Conducting polymers and applications
Authors: L. Kempen | Mie Andersen
Source: npj Computational Materials (article, 2025-01-10)
Field: Materials Science — Materials Chemistry

Inverse catalysts: tuning the composition and structure of oxide clusters through the metal support Computational modeling of metal–oxide interfaces is challenging due to the large search space of compositions and structures and the complexity of catalyst materials under operating conditions in general. In this work, we develop an efficient structure search workflow to discover chemically unique and relevant nanocluster geometries of inverse catalysts and apply it to ZnyOx and InyOx on Cu(111), Pd(111), and Au(111). We show that the workflow is successful in obtaining a large range of chemically distinct structures. Structural geometry trends are identified, including stable motifs such as tripod, rhombus, and pyramidal motifs. Using ab initio thermodynamics, we explore the in situ stability of the structures, including single-atom alloys, at a range of oxygen availabilities. This approach allows us to find trends such as the susceptibility to oxidation of the different systems and the range of stability of different cluster motifs. Our analysis highlights the importance of taking the diversity of sites exposed by metal–oxide interfaces into account in catalyst design studies. Catalysis ; Ab initio ; Oxide ; Inverse ; Materials science ; Metal ; Tripod (photography) ; Chemical physics ; Workflow ; Nanotechnology ; Computational chemistry ; Chemistry ; Computer science ; Physics ; Database ; Geometry ; Mathematics ; Metallurgy Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Anish Prasad Lohani | Mohamed Elosta | M. I. A. Abdel Maksoud | Nimer Murshid
Source: Molecules (review, 2025-05-29)
Field: Materials Science — Materials Chemistry

Functionalized Carbon Nanotubes: Emerging Nanomaterials for Enhanced Cancer Diagnosis and Imaging Cancer remains a leading global cause of mortality, highlighting the critical need for effective early diagnosis. Despite advancements in treatment, early detection and imaging continue to pose significant challenges. Functionalized carbon nanotubes (CNTs) have emerged as promising nanomaterials due to their unique structural properties and versatile functionalization strategies. This review explores the role of both covalent (e.g., fluorination, hydrogenation, cycloadditions, aryldiazonium salt reduction, organometallic ion attachment, carboxylation, amidation, esterification, and metallic nanoparticle attachments) and non-covalent functionalization methods (e.g., surfactant coating, polymer wrapping, biomolecule attachment, and polymer encapsulation) in enhancing CNT biocompatibility and diagnostic efficiency. Functionalized CNTs are extensively applied in cancer detection through highly sensitive biosensors, including electrochemical, optical, and field-effect transistor-based systems, capable of detecting various cancer biomarkers with exceptional sensitivity. Additionally, they offer significant advantages in cancer imaging modalities such as fluorescence imaging, magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound imaging, improving contrast, resolution, and specificity. This review also discusses the challenges and future directions in the development of CNT-based diagnostic platforms, emphasizing the need for continued research to advance their clinical translation and integration into routine cancer diagnostics. Nanotechnology ; Materials science ; Surface modification ; Carbon nanotube ; Nanomaterials ; Biomolecule ; Biosensor ; Biocompatibility ; Nanoparticle ; Covalent bond ; Medical imaging ; Chemistry ; Computer science ; Organic chemistry Carbon Nanotubes in Composites ; Graphene and Nanomaterials Applications ; Molecular Communication and Nanonetworks
Authors: Yaxin Tang | Minjie Hou | Qian He | Guangfu Luo
Source: Nano Letters (article, 2025-02-03)
Field: Materials Science — Materials Chemistry

Designing Bimetallic Nanoparticle Catalysts via Tailored Surface Segregation Bimetallic nanoparticles serve as a vital class of catalysts with tunable properties suitable for diverse catalytic reactions, yet a comprehensive understanding of their structural evolution under operational conditions as well as their optimal design principles remains elusive. In this study, we unveil a prevalent surface segregation phenomenon in approximately 100 platinum-group-element-based bimetallic nanoparticles through molecular dynamics simulations and derive a thermodynamic descriptor to predict this behavior. Building on the generality and predictability of surface segregation, we propose leveraging this phenomenon to intentionally enrich the nanoparticle surface with noble-metal atoms, thereby significantly reducing their usage while maintaining high catalytic activity and stability. To validate this strategy, we investigate dozens of platinum-based bimetallic nanoparticles for propane dehydrogenation catalysis using first-principles calculations. Through a systematic examination of the catalytic sites on nanoparticle surfaces, we eventually identify several candidates featuring a stable Pt-enriched surface and superior catalytic activity, confirming the feasibility of this approach. Bimetallic strip ; Catalysis ; Nanoparticle ; Materials science ; Nanotechnology ; Platinum ; Platinum nanoparticles ; Dehydrogenation ; Noble metal ; Chemical engineering ; Chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; nanoparticles nucleation surface interactions