jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ahmad Gholizadeh | Ali Shabani
Source: Results in Chemistry (article, 2025-04-22)
Field: Materials Science — Materials Chemistry

Artificial intelligence techniques In this study, Fe₃O₄/TiO₂ nanocomposites were synthesized and optimized for enhanced photocatalytic degradation of organic pollutants. The synthesis involved a sol-gel process followed by calcination, forming a core-shell structure to improve charge separation and light absorption. Additionally, surface modifications were applied to enhance catalytic performance. The photocatalytic activity was evaluated under simulated sunlight, considering the effects of pH, initial pollutant concentration, adsorbent dosage, and reaction time on degradation efficiency. The results demonstrated that the Fe₃O₄/TiO₂ composite exhibited superior photocatalytic performance compared to pure TiO₂, achieving a high removal efficiency. Furthermore, the catalyst retained significant activity after multiple reuse cycles, indicating its stability and reusability. To optimize the process, an artificial intelligence (AI)-assisted model was developed to predict removal efficiency based on experimental data. The AI model exhibited strong predictive capabilities, providing a data-driven approach to optimize photocatalytic performance more efficiently than conventional experimental methods. This study highlights the potential of integrating AI with advanced photocatalysts for wastewater treatment and suggests a scalable strategy for industrial applications. The findings pave the way for future research on iron oxide-based composites for environmental remediation. • XRD confirmed Fe₃O₄@TiO₂ composite structure with peaks matching JCPDS reference data. • VSM analysis showed superparamagnetic behavior with reduced magnetization due to TiO₂ coating. • Fe₃O₄@TiO₂ demonstrated superior photocatalytic activity compared to pure TiO₂ under sunlight. • AI model predicted dye removal efficiency using variables like pH and ion concentration. • Composite synthesis is scalable, with potential for magnetic recovery in wastewater treatment systems. Artificial intelligence ; Computer science Advanced Nanomaterials in Catalysis ; Machine Learning and ELM
Authors: Cesar Augusto Roque‐Borda | Fernando R. Pavan | Beatriz G. de la Torre | Fernando Alberício
Source: Nature Reviews Chemistry (article, 2025-12-01)
Field: Materials Science — Biomaterials

Redefining peptide chemistry beyond accumulating analogues Peptide ; Peptide synthesis ; Covalent bond ; Molecule ; Chemical synthesis ; Peptidomimetic ; Peptide sequence Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Click Chemistry and Applications
Authors: Ayesha Yousaf | Zhihua Zhao | Muhammad Waseem Boota
Source: Journal of Solid State Electrochemistry (article, 2025-11-15)
Field: Chemistry — Inorganic Chemistry

Enhancing electrochemical detection with metal-organic frameworks: advances in functionalization, synthesis, and application Bimetallic strip ; Bridge (graph theory) ; Key (lock) ; Sustainability ; Transformative learning ; Electrochemistry Metal-Organic Frameworks: Synthesis and Applications ; Electrochemical sensors and biosensors ; Electrochemical Analysis and Applications
Authors: Mohamed Abboud | Abdelkarim Chaouiki | Maryam Chafiq | Noureddine Elboughdiri | Lei Guo | Amir Mahmoud Makin | M. Galai | Jee-Hyun Kang | Young Gun Ko
Source: Sustainable materials and technologies (article, 2025-05-31)
Field: Materials Science — Materials Chemistry

Designing covalent organic frameworks for environmental remediation: Photocatalytic strategies for water treatment Environmental remediation ; Photocatalysis ; Water treatment ; Covalent bond ; Groundwater remediation ; Environmental science ; Human decontamination ; Environmental chemistry ; Waste management ; Nanotechnology ; Chemistry ; Materials science ; Environmental engineering ; Engineering ; Catalysis ; Contamination ; Organic chemistry ; Ecology Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Jinhua Zhou | Yanzhou Ji | Yuxiang Wang | Liansong Xie | Longlu Wang
Source: Advanced Sustainable Systems (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Grain Boundary Engineering: A Shortcut to Efficient Electrocatalytic Reactions Abstract Grain boundary engineering has witnessed significant advancements in recent years, which optimizes material properties by precisely controlling the density and characteristics of grain boundaries. Various advanced fabrication methods have emerged, such as pulsed laser ablation, electrodeposition, vapor phase growth, and interfacial synthesis. These techniques enable the production of grain boundary‐enriched materials through the precise control of manufacturing parameters. Additionally, grain boundary engineering demonstrates substantial potential in diverse catalytic applications. This review thoroughly examines recent advancements in grain boundary engineering. First, it investigates key fabrication methods such as pulsed laser ablation, electrodeposition, vapor phase growth, and interfacial synthesis, explaining how they create grain boundary‐enriched materials. Second, it explores its applications in various electrocatalytic reactions. Last but not least, it delves into the integration of grain boundary engineering with artificial intelligence for structure prediction and property optimization. Looking ahead, as synthesis technologies continue to improve and characterization methods advance, grain boundary engineering is expected to achieve more breakthroughs in energy conversion, storage, and environmental protection. It is particularly promising in the development of efficient and low‐cost catalysts. Grain boundary ; Materials science ; Boundary (topology) ; Metallurgy ; Mathematics ; Microstructure ; Mathematical analysis Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science
Authors: Weiwei Fan | Zhu Sun
Source: Responsive materials (article, 2025-07-30)
Field: Materials Science — Materials Chemistry

Exsolution‐enhanced performance for energy conversion and storage Abstract Electrical energy and chemical energy play an important role in developing the emerging intelligent vehicle and artificial intelligence. Essentially, in well‐designed energy devices, they can be converted with each other and stored based on electrochemical reactions. Since the eventual performance relates closely with the physiochemical properties of the electrode catalysts, it is crucial to tune their microstructure to enhance the reaction kinetics and performance of energy devices. Benefitted from its superb spatial distribution of exsolved nanoparticles and uniquely anchored architecture, exsolution is a robust technique to improve performance for energy conversion and storage. Here, we review the characteristics and mechanisms of exsolution to provide solid knowledge on rationally designing and fabricating of novel exsolution‐derived energy products with excellent properties. Moreover, to trigger inspirations to create new types of energy devices and widen the application window, the recent advances in the exsolution application in energy areas covering fuel cells, electrolysers and batteries, and the fundamental principles of the exsolution effect on tuning their performance are comprehensively reviewed and analyzed. Lastly, the potential directions to further improve the energy devices' performance are discussed. Energy storage ; Computer science ; Environmental science ; Materials science ; Physics ; Power (physics) ; Thermodynamics Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Hydrogen Storage and Materials
Authors: Guangjun Liu | Guan Wu | Heng Dong | Yuan Wang | Linhua Zhu | Esther T. Akinlabi | Jinjin Zhu | Ben Bin Xu | Chao Yang
Source: Small (review, 2025-10-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent Progress of Electrospun Structural Nanofiber‐Based Sulfides/Selenides for Advanced Energy Storage and Conversion Flexible electrospun carbon nanofibers/chalcogenides (CNFs/sulfides and selenides) composites have emerged as promising candidates for next-generation energy storage and conversion applications. This review comprehensively discusses the recent advances of electrospun CNFs/sulfides and selenides-based energy storage and conversion technologies, with special focus on the electrospinning equipment and assembly mechanisms. Groundbreaking strategies of artificial intelligence (AI)-driven for optimization structure and properties of electrospun fibers, sulfides, and selenides are emphasized. Second, preparation methods are introduced for CNFs/sulfides and selenides, ranging from simple physical mixing to controlled surface functionalization, and extending to complex strongly coupled hybrid synthesis strategies. Additionally, from the perspective of microstructural design to atomic-level modification, it highlights the facilitation of novel structure-property relationships of CNFs/sulfides and selenides. Next, a few energy storage and conversion applications for the developed electrospun composites are shortlisted. Finally, the current challenges are summarized, and future directions are outlined to guide the application of CNFs/sulfides and selenides for energy storage and conversion. Energy storage ; Electrospinning ; Energy transformation ; 3D printing ; Nanofiber ; Efficient energy use Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; MXene and MAX Phase Materials
Authors: Yufan Chen | Ching Ting Leung | Jianwei Sun | Yong Huang | Linyan Li | Hao Chen | Hanyu Gao
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Towards large-scale chemical reaction image parsing <i>via</i> a multimodal large language model Artificial intelligence (AI) has demonstrated significant promise in advancing organic chemistry research; however, its effectiveness depends on the availability of high-quality chemical reaction data. Currently, most published chemical reactions are not available in machine-readable form, limiting the broader application of AI in this field. The extraction of published chemical reactions into structured databases still relies heavily on manual curation, and robust automatic parsing of chemical reaction images into machine-readable data remains a significant challenge. To address this, we introduce the Reaction Image Multimodal large language model (RxnIM), the first multimodal large language model specifically designed to parse chemical reaction images into machine-readable reaction data. RxnIM not only extracts key chemical components from reaction images but also interprets the textual content that describes reaction conditions. Together with a specially designed large-scale dataset generation method to support model training, our approach achieves excellent performance, with an average <i>F</i> <sub>1</sub> score of 88% on various benchmarks, surpassing state-of-the-art methods by an average of 5%. This represents a crucial step toward the automatic construction of large databases of machine-readable reaction data parsed from images in the chemistry literature, providing essential data resources for AI research in chemistry. The source code, model checkpoints, and datasets developed in this work are released under permissive licenses. Parsing ; Limiting ; Key (lock) ; Chemical database ; Image (mathematics) ; Chemical reaction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Miao Yin | Munan Qiu | Youmeng Yuan | Chuxuan Zhu | Wenqi Zou | Biao Yang
Source: European Polymer Journal (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Advanced polymer-based thermal interface materials: A review on matrix optimization and filler engineering for superior performance Thermal grease ; Filler (materials) ; Thermal conductivity ; Electronics ; Thermal ; Interface (matter) ; Heat transfer ; Matrix (chemical analysis) Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Expansion and Ionic Conductivity
Authors: Thiago F. Santos | Arvind Pereira | Vishnu Vijay Kumar | Domingos Fabiano de Santana Souza | Bhargav Akkinepally | José Heriberto Oliveira do Nascimento
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Borophene 2.0: nanoarchitecting the future from tunable properties to next-generation applications Borophene, a two-dimensional boron-based material, is a promising platform for next-generation applications due to its unique electronic, mechanical, and chemical properties. This review examines advances in nanoarchitected borophene (n-BPh) from 2016 to 2024, focusing on doping and decoration strategies that enhance stability and functionality. Techniques like surface passivation, hydrogenation, and doping with alkali metals, transition metals, and non-metals (<i>e.g.</i>, C, P, N, O) enable tunable properties, including conductivity and band gap, for applications in hydrogen storage (<i>e.g.</i>, Li-decorated BPh with 13.7 wt% capacity), supercapacitors, electrocatalysis, biosensing, and optoelectronics. Challenges such as environmental stability and scalable synthesis are addressed, with future trends emphasizing AI-driven predictive design <i>via</i> density functional theory calculations. This work underscores n-BPh's transformative potential in energy, electronics, and nanotechnology, paving the way for innovative, sustainable technologies. Borophene ; Doping ; Scalability ; Density functional theory ; Transformative learning ; Chemical stability ; Hydrogen storage Boron and Carbon Nanomaterials Research ; Boron Compounds in Chemistry ; Organoboron and organosilicon chemistry
Authors: Yongyang Song | Shutao Wang
Source: Advanced Materials (review, 2025-07-21)
Field: Materials Science — Materials Chemistry

Advances in Nature‐Inspired Particles for Bioanalytical Applications Nature has evolved sophisticated prototypes to achieve functions from efficient separation to selective capture, targeted interaction, and interactive communication. These biological blueprints provide transformative inspiration for engineering advanced particle materials tailored for bioanalytical challenges. This review comprehensively examines recent advances in nature-inspired particles from natural prototypes to preparation methods and various bioanalytical applications. The design principle of nature-inspired particles originates from the unique chemical and topological characteristics of natural prototypes including biomolecules (proteins and nucleic acids), subcellular particles (virus, extracellular vesicles (EVs), bacteria, and platelet), cells (erythrocyte, sperm cell, immune cell, and pollen), creatures (urchin and hedgehog), and minerals (zeolites). Various preparation methods have been developed to replicate the intricate features of these prototypes. These nature-inspired particles have demonstrated effectiveness in bioanalytical applications, such as i) adsorption, separation, and removal of biological molecules, ii) interaction, recognition, and capture of biological particles, iii) biological sensing, and iv) biological imaging. Some existing challenges and potential research opportunities have also been indicated in bioanalytical practice. It is anticipated that more nature-inspired particles would be created with programmable chemistry and topology, exhibiting integrated functions, and benefiting various practical bioanalytical applications with the assistance of artificial intelligence (AI) and big data processing. Materials science ; Bioanalysis ; Nanotechnology Advanced Nanomaterials in Catalysis ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Layla Haythoor Kharboot | Ataf Ali Altaf
Source: Inorganica Chimica Acta (article, 2025-08-29)
Field: Materials Science — Materials Chemistry

Quantum dot-sensitized solar cells: broader implications for renewable energy solutions from (2020–2025) Chemistry ; Quantum dot ; Renewable energy ; Nanotechnology ; Engineering physics ; Aerospace engineering ; Physics ; Electrical engineering Quantum Dots Synthesis And Properties ; TiO2 Photocatalysis and Solar Cells ; Advanced Photocatalysis Techniques
Authors: Dingchen Wang | Xipeng Tao | Xinguang Wang | Rui Zhang | Zijian Zhou | Weihong Zhang | C.H. Zhang | Xiaofeng Sun | Yahui Zhou | Chuanyong Cui
Source: Journal of Materials Science (article, 2025-12-21)
Field: Materials Science — Materials Chemistry

Progress and challenges in high-vanadium high-speed steel for shield machine cutter tools Service life ; Carbide ; Shield ; Vanadium carbide ; Powder metallurgy ; High-speed steel ; Toughness ; Solid mechanics Metal Alloys Wear and Properties ; Advanced materials and composites ; Tunneling and Rock Mechanics
Authors: Chun-Yue Shi | Geqi Chen | Min Ye | Xia Zhang | Yanna Ye | Aparna Kushwaha | Jun Wang | Abhinav Kumar | Ying Pan
Source: Dyes and Pigments (article, 2025-06-11)
Field: Chemistry — Inorganic Chemistry

Advances in AI-driven applications of metal-organic frameworks for the diagnosis and treatment of pancreatic cancer Pancreatic cancer ; Metal-organic framework ; Cancer ; Nanotechnology ; Chemistry ; Computer science ; Materials science ; Medicine ; Organic chemistry ; Internal medicine Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Nanoparticle-Based Drug Delivery
Authors: Jingnan Zhang | Zahra Teymouri | Lanqi Zhou | Mengyue Zhou | Zijian Zhi | Célio Dias Santos Júnior | Haizhou Wu
Source: Trends in Food Science & Technology (article, 2025-11-07)
Field: Materials Science — Biomaterials

Precision meat preservation via intelligent non-migratory antimicrobial packaging Food spoilage ; Active packaging ; Antimicrobial ; Meat spoilage ; Food packaging ; Food safety ; Shelf life Nanocomposite Films for Food Packaging ; Meat and Animal Product Quality ; Listeria monocytogenes in Food Safety
Authors: Xin Huang | Chen Li | Guixian Xie | Huiqiang Ma | Bo Wang
Source: Separation and Purification Technology (article, 2025-10-06)
Field: Materials Science — Materials Chemistry

What governs phosphorus adsorption on biochar-supported layered double hydroxides? A data-driven analysis of parameter interactions Adsorption ; Rational design ; Optimal design ; Work (physics) ; Key (lock) ; Set (abstract data type) ; Hydroxide ; Response surface methodology Layered Double Hydroxides Synthesis and Applications ; Phosphorus and nutrient management ; Adsorption and biosorption for pollutant removal
Authors: Kamaladdin Abedi | Mahdi Jamshidi Rastani | Kian Dana
Source: Chemical Engineering Journal Advances (article, 2025-09-23)
Field: Materials Science — Materials Chemistry

Innovations and future directions in hybrid non-thermal plasma-catalyst systems for VOC decomposition • Hybrid non-thermal plasma–catalyst systems for VOC abatement reviewed. • Maps energy–selectivity trade-offs across reactors and catalyst classes. • Identifies five reactor–catalyst “matchings” that drive performance. • Details humidity, ozone slip, and deactivation controls for durability. • Outlines future work: tunable pulsed power, advanced oxides, and AI control. Volatile organic compounds (VOCs) are challenging to abate because conventional thermal and adsorption approaches trade high energy use for incomplete mineralization and by-product formation. Hybrid non-thermal plasma–catalyst (NTP–catalyst) systems address these limits by coupling plasma-generated oxidants with surfaces that steer reaction pathways at near-ambient bulk temperatures. This review examines NTP–catalyst performance across dielectric-barrier discharge, corona, and gliding-arc reactors, and across catalysts including oxygen-vacancy-rich MnO x and CeO 2 , TiO 2 , zeolites, metal-organic frameworks (MOFs), and noble-metal dopants. We organize disparate results using five “matchings”: (i) position (in-plasma vs post-plasma placement), (ii) time-scale (microsecond plasma pulses vs millisecond–second surface kinetics), (iii) energy (specific input energy (SIE) optima beyond which carbon balance and CO 2 selectivity degrade), (iv) material (multi-metal sites and vacancy engineering), and (v) operation (temperature, humidity, and residence time). We synthesize trends on conversion, CO 2 selectivity, ozone slip, and partial-oxidation by-products, and discuss durability (coking, halogen/sulfur poisoning, plasma aging) alongside mitigation via robust supports, sacrificial oxygen, and periodic regeneration. Finally, we chart future directions: Nanosecond repetitively pulsed (NRP) and frequency-tunable power supplies; structured catalysts and reactors (honeycomb, packed/fluidized beds, and 3D-printed dielectrics) for uniform plasma–solid contact; multi-process hybrids (plasma + photocatalysis/ozonation) to lower energy intensity; in-situ/operando diagnostics and microkinetic–plasma modeling; and data-driven control (AI/ML) for real-time optimization and scale-up. This synthesis provides design guidance and research priorities toward industrially scalable, energy-efficient VOC abatement. Catalysis ; Decomposition ; Nanosecond ; Adsorption ; Thermal decomposition ; Ozone ; Thermal ; Flexibility (engineering) ; Hybrid system Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Plasma Applications and Diagnostics
Authors: Jean Furstoss | Carlos R. Salazar | Philippe Carrez | Pierre Hirel | Julien Lam
Source: Computer Physics Communications (article, 2025-01-08)
Field: Materials Science — Materials Chemistry

All-around local structure classification with supervised learning: The example of crystal phases and dislocations in complex oxides To accurately identify local structures in atomic-scale simulations of complex materials is crucial for the study of numerous physical phenomena including dynamic plasticity, crystal nucleation and glass formation. In this work, we propose a data-driven method to characterize local atomic environments, and assign them to crystal phases or lattice defects. After constructing a reference database, our approach uses descriptors based on Steinhardt's parameters and a Gaussian mixture model to identify the most probable environment. This approach is validated against several test cases : polymorph identification in alumina, and dislocation and grain boundary analysis in the olivine structure. Crystal structure ; Materials science ; Condensed matter physics ; Crystal (programming language) ; Crystallography ; Dislocation ; Artificial intelligence ; Physics ; Computer science ; Chemistry X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallization and Solubility Studies