jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Benjamin Bohigues | Sergio Rojas‐Buzo | Davide Salusso | Yu Xia | Avelino Corma | Silvia Bordiga | Mercedes Boronat | Tom Willhammar | Manuel Moliner | Pedro Serna
Source: Nature Communications (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Overcoming activity/stability tradeoffs in CO oxidation catalysis by Pt/CeO2 The use of redox active metal oxides to support noble metals is critical in the design of highly-active CO oxidation catalysts for gas emissions control. Unfortunately, supports promoting the activity, such as CeO<sub>2</sub>, tend also to promote acute catalyst deactivation by turning highly-active metallic Pt clusters into less-active PtO<sub>x</sub> species, under practical reaction conditions (high-temperature and/or the excess of O<sub>2</sub>). This leads to a problematic activity/stability tradeoff where Pt/CeO<sub>2</sub> catalysts, highly-active, and Pt on non-reducible supports, highly stable, are bookends. Herein, we report a method to trap Pt at V-shaped pockets/stepped sites of CeO<sub>2</sub> that break this undesired correlation by showing both high activity and stability in the CO oxidation reaction. XAS, CO-DRIFT, XPS, HAADF-STEM, and DFT are used to infer that the generation of low order metallic Pt clusters connected to two crystallographic planes of the support is key to inhibit (deactivating) re-oxidation paths of the metal, as a result of the high-energy required to form disordered/distorted PtO<sub>x</sub> ensembles at these positions. This new material allows, thus, to operate outside the commonly observed, limiting, activity/stability tradeoff. Catalysis ; Redox ; Metal ; Limiting ; X-ray photoelectron spectroscopy ; Materials science ; Noble metal ; Active site ; Chemical engineering ; X-ray absorption spectroscopy ; Transition metal ; Chemistry ; Chemical physics ; Inorganic chemistry ; Absorption spectroscopy ; Physics ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Carla Martins | Catarina Rolo | Vanessa R. G. Cacho | Laura C. J. Pereira | João Paulo Borges | Jorge Silva | Tânia Vieira | Paula I. P. Soares
Source: Materials Advances (article, 2025-01-01)
Field: Materials Science — Biomaterials

Enhancing the magnetic properties of superparamagnetic iron oxide nanoparticles using hydrothermal treatment for magnetic hyperthermia application Superparamagnetic iron oxide nanoparticles with enhanced magnetic properties were produced by combining a chemical process and heat treatment, resulting in improved heating effects and reduced cancer cell survival during magnetic hyperthermia assays. Superparamagnetism ; Hydrothermal circulation ; Magnetic hyperthermia ; Nanoparticle ; Magnetic nanoparticles ; Materials science ; Hyperthermia ; Iron oxide ; Iron oxide nanoparticles ; Chemical engineering ; Nanotechnology ; Metallurgy ; Magnetization ; Medicine ; Magnetic field ; Engineering ; Physics ; Internal medicine Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Iron oxide chemistry and applications
Authors: Myriam González | María Paulina Romero
Source: International Journal of Nanomedicine (review, 2025-06-01)
Field: Materials Science — Materials Chemistry

Surface-Modified Carbon Dots for Cancer Therapy: Integrating Diagnostic and Therapeutic Applications Carbon dots (CDs) have become versatile nanomaterials that have found practical applications in cancer therapy due to their small size, tunable photoluminescence, and high biocompatibility. Modified CDs have shown remarkable potential in targeted drug delivery systems, enhancing solubility and specificity in tumor sites while minimizing systemic toxicity. Gene therapy applications take advantage of the ability of CDs to condense and protect genetic material from degradation, thereby facilitating efficient cellular uptake. Furthermore, metal-doped CDs can function as fluorophores and enhance imaging capabilities for tumor detection through fluorescence and MRI. Besides, in phototherapy applications, when combining photodynamic (PDT) and photothermal therapy (PTT), CDs exhibit synergistic effects wherein therapeutic efficacy is increased by the generation of reactive oxygen species (ROS) and heat. This review summarizes recent developments in surface-modified and doped CDs for in vitro and in vivo applications, particularly in drug delivery, gene therapy, multimodal imaging, photodynamic therapy (PDT), photothermal therapy (PTT), chemodynamic therapy (CDT), sonodynamic therapy (SDT) and gas therapy, for cancer therapies. Advances in modalities of surface modification that include ligand binding and metal doping have significantly improved CDs' biocompatibility and targeting precision. However, limitations such as low drug-loading capacity, complex synthesis processes, and the challenges created by hypoxic tumor environments need to be opened for further research. Future directions will focus on enhancing drug-loading efficiency, establishing long-term biocompatibility, and optimizing multifunctional nanocomposite designs for integrated cancer therapies. Cancer therapy ; Materials science ; Nanotechnology ; Cancer ; Medicine ; Internal medicine Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Daksh Shelly | Seul‐Yi Lee | Soo‐Jin Park
Source: Advanced Composites and Hybrid Materials (review, 2025-05-26)
Field: Materials Science — Polymers and Plastics

Hemp fiber and its bio-composites: a comprehensive review part I—characteristics and processing Bio-based materials are becoming pivotal in creating a more sustainable future by mitigating the adverse effects of climate change, primarily motivated by fossil fuel consumption. Among these materials, natural fiber composites have seen a resurgence over the past two decades due to the demand for environmentally friendly alternatives that offer biodegradability and recyclability. These composites are widely used in industries such as automotive, packaging, sports, construction, and consumer products. Hemp fiber, in particular, is valued for its exceptional mechanical properties, including strength and stiffness comparable to glass fibers. This review consolidates research on hemp fiber–reinforced bio-composites, focusing on their structural characteristics, and performance enhancement techniques. Both chemical and physical modification strategies are employed to optimize fiber properties and improve compatibility with polymer matrices. Chemical methods involve using reagents to reduce fiber hydrophilicity, enhancing compatibility and coupling efficiency. Physical methods adjust fiber structure and surface properties to improve interfacial bonding, offering cleaner solutions. The paper examines recent advancements in these methods and discusses the mechanical properties of hemp fiber–reinforced bio-composites. It concludes with reflections on the future prospects for hemp fiber–reinforced polymer bio-composites. Composite material ; Materials science ; Fiber Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging
Authors: Samuele Giuli | Carlos Mejuto-Zaera | Massimo Capone
Source: Physical review. B./Physical review. B (article, 2025-01-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Altermagnetism from interaction-driven itinerant magnetism Altermagnetism is a new collinear magnetic phase with zero net magnetization and bands with nonrelativistic spin splitting. The search for intrinsic altermagnets has been focused so far on the role of anisotropy in the crystallographic symmetries. The authors present here a mechanism based on electronic correlations that leverages the itinerant magnetism driven by Hubbard repulsion and van Hove singularities, and orbital anisotropies. They discuss the fingerprints of correlations in spectral properties and transport, and demonstrate the tunability of spin currents via doping. Magnetism ; Psychology ; Condensed matter physics ; Physics Magnetic and transport properties of perovskites and related materials ; Magnetic properties of thin films ; Geomagnetism and Paleomagnetism Studies
Authors: Khalid Ferji
Source: Polymer Chemistry (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Basic concepts and tools of artificial intelligence in polymer science AI-driven polymer science: a structured perspective on integrating machine learning for data analysis, property prediction, and automated research workflows. Computer science ; Polymer ; Artificial intelligence ; Management science ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Alexey O. Sanin | Jackson K. Flowers | Tobias H. Piotrowiak | Frederic Felsen | Leon Merker | Alfred Ludwig | Dominic Bresser | Helge S. Stein
Source: Advanced Energy Materials (article, 2025-01-26)
Field: Materials Science — Materials Chemistry

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes Abstract High‐performance batteries need accelerated discovery and optimization of new anode materials. Herein, we explore the Si─Ge─Sn ternary alloy system as a candidate fast‐charging anode materials system by utilizing a scanning droplet cell (SDC) as an autonomous electrochemical characterization tool with the goal of subsequent upscaling. As the SDC is performing experiments sequentially, an exploration of the entire ternary space is unfeasible due to time constraints. Thus, closed‐loop optimization, guided by real‐time data analysis and sequential learning algorithms, is utilized to direct experiments. The lead material identified is scaled up to a coin cell to validate the findings from the autonomous millimeter‐scale thin‐film electrochemical experimentation. Explainable machine learning (ML) models incorporating data from high‐throughput Raman spectroscopy and X‐ray diffraction (XRD) are used to elucidate the effect of short and long‐range ordering on material performance. Materials science ; Characterization (materials science) ; Electrochemistry ; Anode ; Throughput ; Lithium (medication) ; Battery (electricity) ; Lithium battery ; Thin film ; Nanotechnology ; Optoelectronics ; Chemical engineering ; Electrode ; Computer science ; Physical chemistry ; Ion ; Organic chemistry ; Power (physics) ; Engineering Machine Learning in Materials Science ; Advancements in Battery Materials ; Semiconductor materials and interfaces
Authors: Peter Chidiebere Obodo | Kingsley Onyebuchi Obodo | Uyiosa Osagie Aigbe | Victor Sunday Aigbodion
Source: ChemistrySelect (review, 2025-04-01)
Field: Materials Science — Materials Chemistry

Recent Advances in Molybdenum Disulfide (MoS <sub>2</sub> ) and MXene‐based Heterostructures for Photovoltaic and Water Splitting Applications: A Review Abstract Significant attention is currently being given to two‐dimensional (2D) materials due to their various attractive features, making them useful for gas scrubbing, photovoltaics, electrocatalysts, photocatalysts, electronic applications, and so forth. This review focuses on molybdenum disulfide (MoS 2) , MXenes, and MoS 2 /MXene heterostructures for photovoltaic and water splitting applications. The current state‐of‐the‐art on hydrogen evolution reaction (HER), water splitting and electrolysis, and the application of 2D materials based on MoS 2 and MXene, as well as their heterostructures for water splitting and photovoltaics, are discussed. The review article addresses and highlights knowledge gaps as well as notable limitations toward the creation and application of novel 2D materials and heterostructures based on MoS 2 and MXene as possible candidate materials in photovoltaic and water splitting applications. Thus, MoS 2 and MXene‐based heterostructure catalysts can offer a solution because they are affordable, earth‐abundant, stable, and present an opportunity for cost reduction. Molybdenum disulfide ; Heterojunction ; Photovoltaic system ; Materials science ; Water splitting ; Nanotechnology ; Molybdenum ; Optoelectronics ; Engineering physics ; Chemistry ; Physics ; Engineering ; Electrical engineering ; Photocatalysis ; Metallurgy MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Chad A. Mirkin | Sarah Hurst Petrosko | Natalie Artzi | Koray Aydın | Austin Biaggne | C. Jeffrey Brinker | Katherine E. Bujold | Y. Charles Cao | Rachel R. Chan | Chaojian Chen | Pengcheng Chen | Xiaodong Chen | Olivier J. G. L. Chevalier | Chung Hang Jonathan Choi | Richard M. Crooks | Vinayak P. Dravid | Jingshan S. Du | Sasha B. Ebrahimi | Hongyou Fan | Omar K. Farha | C. Adrian Figg | Tanner D. Fink | Connor M. Forsyth | Harald Fuchs | Franz M. Geiger | Nathan C. Gianneschi | Kyle J. Gibson | David S. Ginger | Sishi Guo | Justin Hanes | Liang Hao | Jin Huang | Bryan M. Hunter | Fengwei Huo | Jeongmin Hwang | Rongchao Jin | Shana O. Kelley | Thomas J. Kempa | Youngeun Kim | Sergej Kudruk | Sneha Kumari | Kaitlin M. Landy | Ki‐Bum Lee | Noel J. Leon | Jun Li | Yuanwei Li | Zhiwei Li | Bin Liu | Guoliang Liu | Xiaogang Liu | Luis M. Liz‐Marzán | Jochen H. Lorch | Taokun Luo | Robert J. Macfarlane | Jill E. Millstone | Milan Mrksich | Catherine J. Murphy | Rajesh R. Naik | André E. Nel | Christopher Oetheimer | Jenny Orbeck | So‐Jung Park | Benjamin E. Partridge | Nicholas A. Peppas | Michelle L. Personick | Arindam Raj | Namrata Ramani | Michael B. Ross | Susan E. Ross | Edward H. Sargent | Tanushri Sengupta | George C. Schatz | Dwight S. Seferos | Tamar Seideman | Soyoung E. Seo | Bo Shen | Wooyoung Shim | Donghoon Shin | Ulrich Simon | Andrew J. Sinegra | Peter T. Smith | Alexander M. Spokoyny | Peter J. Stang | Alexander H. Stegh | J. Fraser Stoddart | Dayne F. Swearer | Weihong Tan | Michelle H. Teplensky | C. Shad Thaxton | David R. Walt | Mary Wang | Zhe Wang | Wei David Wei | Paul S. Weiss | Peter H. Winegar | Younan Xia | Yi Xie | Xiaoyang Xu | Peidong Yang | Yiming Yang
Source: ACS Nano (article, 2025-09-04)
Field: Materials Science — Materials Chemistry

33 Unresolved Questions in Nanoscience and Nanotechnology Significant advances in science and engineering often emerge at the intersections of disciplines. Nanoscience and nanotechnology are inherently interdisciplinary, uniting researchers from chemistry, physics, biology, medicine, materials science, and engineering. This convergence has fostered novel ways of thinking and enabled the development of materials, tools, and technologies that have transformed both basic and applied research, as well as how we address critical societal challenges. In this Nano Focus, we pose and explore 33 questions whose answers could profoundly impact fields such as energy, electronics, the environment, optics, and medicine. These questions highlight the need for deeper foundational understanding, improved tools and techniques, and innovative applications─each with significant societal relevance. Together, they represent a global call-to-action for the scientific community. Nanotechnology ; Applications of nanotechnology ; Materials science Electronic and Structural Properties of Oxides ; Nanopore and Nanochannel Transport Studies ; Graphene and Nanomaterials Applications
Authors: Steven M. Quarin | Der Vang | Ruxandra I. Dima | George Stan | Pietro Strobbia
Source: npj Biosensing (review, 2025-02-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

AI in SERS sensing moving from discriminative to generative This perspective discusses the present and future role of artificial intelligence (AI) and machine learning (ML) in surface-enhanced Raman scattering (SERS) sensing. Our goal is to guide the reader through current applications, mainly focused on discriminative approaches aimed at developing new and improved SERS diagnostic capabilities, towards the future role of AI in SERS sensing, with the use of generative approaches to design new materials and biomaterials. Discriminative model ; Generative grammar ; Perspective (graphical) ; Computer science ; Artificial intelligence ; Nanotechnology ; Raman scattering ; Machine learning ; Materials science ; Raman spectroscopy ; Physics ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Jingbo Liu | Fan Jiang | Shinichi Tashiro | Shujun Chen | Masaki Tanaka
Source: Nature Communications (article, 2025-05-23)
Field: Materials Science — Materials Chemistry

A physics-informed and data-driven framework for robotic welding in manufacturing The development of artificial intelligence (AI)-based industrial data-driven models is the driving force behind the digital transformation of manufacturing processes and the application of smart manufacturing. However, in real-world industrial applications, the intricate interplay among data quality, model accuracy, and generalizability poses significant challenges, hindering the effective deployment and scalability of data-driven models in complex manufacturing environments. To address this challenge, this paper proposes a universal Physics-informed Hybrid Optimization framework for Efficient Neural Intelligence (PHOENIX) in manufacturing, demonstrating its applicability in robotic welding scenarios. This framework systematically integrates physical principles into its input, model structure, and dynamic optimization processes, enabling proactive, real-time detection and predictive of welding instability. It achieves an accuracy of up to 98% for predictions within the next 50 ms and maintains an accuracy of 86% even for forecasts up to 1 s in advance. Through physics-informed data-driven modeling, the framework significantly reduces the dependence on high-cost data while maintaining the performance of the original model. By leveraging cloud-based optimization modules that integrate new data with historical experience, the framework enables autonomous model parameter optimization, ensuring its continuous adaptation to the complex and dynamic demands of industrial scenarios. Welding ; Computer science ; Data science ; Manufacturing engineering ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Welding Techniques and Residual Stresses
Authors: Κωνσταντίνος Λιάκος | Vassilis Athanasiadis | Eleni Bozinou | Stavros I. Lalas
Source: Foods (review, 2025-10-04)
Field: Chemistry — Analytical Chemistry

Machine Learning for Quality Control in the Food Industry: A Review The increasing complexity of modern food production demands advanced solutions for quality control (QC), safety monitoring, and process optimization. This review systematically explores recent advancements in machine learning (ML) for QC across six domains: Food Quality Applications; Defect Detection and Visual Inspection Systems; Ingredient Optimization and Nutritional Assessment; Packaging-Sensors and Predictive QC; Supply Chain-Traceability and Transparency and Food Industry Efficiency; and Industry 4.0 Models. Following a PRISMA-based methodology, a structured search of the Scopus database using thematic Boolean keywords identified 124 peer-reviewed publications (2005-2025), from which 25 studies were selected based on predefined inclusion and exclusion criteria, methodological rigor, and innovation. Neural networks dominated the reviewed approaches, with ensemble learning as a secondary method, and supervised learning prevailing across tasks. Emerging trends include hyperspectral imaging, sensor fusion, explainable AI, and blockchain-enabled traceability. Limitations in current research include domain coverage biases, data scarcity, and underexplored unsupervised and hybrid methods. Real-world implementation challenges involve integration with legacy systems, regulatory compliance, scalability, and cost-benefit trade-offs. The novelty of this review lies in combining a transparent PRISMA approach, a six-domain thematic framework, and Industry 4.0/5.0 integration, providing cross-domain insights and a roadmap for robust, transparent, and adaptive QC systems in the food industry. Quality (philosophy) ; Transparency (behavior) ; Artificial neural network ; Ensemble learning ; Food industry ; Control (management) ; Reinforcement learning ; Supervised learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Food Supply Chain Traceability
Authors: Xingyue Shi | Linming Zhou | Yuhui Huang | Chaohui Wu | Yongjun Wu | Juan Li | Zijian Hong
Source: Journal of Energy Chemistry (article, 2025-01-05)
Field: Materials Science — Materials Chemistry

Machine learning assisted screening of binary alloys for metal-based anode materials Anode ; Materials science ; Binary number ; Metal ; Metallurgy ; Chemistry ; Mathematics ; Electrode ; Physical chemistry ; Arithmetic Machine Learning in Materials Science ; Advancements in Battery Materials ; Molten salt chemistry and electrochemical processes
Authors: Hong Wang | Liang Yang | Deying Leng | Yanhai Du | Ning Hao
Source: Advances in Colloid and Interface Science (review, 2025-09-10)
Field: Chemistry — Inorganic Chemistry

Accelerating the discovery and optimization of metal-organic framework materials via machine learning Reinforcement learning ; Cluster analysis ; Perspective (graphical) ; Class (philosophy) ; Development (topology) ; Process (computing) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yue Hao | Chuyang Liu | Rapkatjan Keram | Huaxin Song | Yujing Zhang | Guangbin Ji
Source: Nano Today (article, 2025-02-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-learning assisted novel insulation layer stripping technology for upgrading the transparent EMI shielding materials EMI ; Stripping (fiber) ; Electromagnetic shielding ; Materials science ; Layer (electronics) ; Electromagnetic interference ; Composite material ; Electrical engineering ; Engineering Electromagnetic wave absorption materials ; Electromagnetic Compatibility and Noise Suppression ; RFID technology advancements
Authors: Shaofeng Xu | Jing‐Yuan Wu | Ying Guo | Qing Zhang | Xiaoxia Zhong | Jinjin Li | Wei Ren
Source: Chemical Physics Reviews (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Applications of machine learning in surfaces and interfaces Surfaces and interfaces play key roles in chemical and material science. Understanding physical and chemical processes at complex surfaces and interfaces is a challenging task. Machine learning provides a powerful tool to help analyze and accelerate simulations. This comprehensive review affords an overview of the applications of machine learning in the study of surfaces and interfaces of chemical systems and materials. We categorize surfaces and interfaces into the following broad categories: solid–solid interface, solid–liquid interface, liquid–liquid interface, surface of solid, surface of liquid, and three-phase interfaces. High-throughput screening, combined machine learning and first-principles calculations, and machine learning force field accelerated molecular dynamics simulations are used to rational design and study physical and chemical processes of surfaces and interfaces in systems such as all-solid-state batteries, solar cells, and heterogeneous catalysis. This review provides detailed and comprehensive information on the applications of machine learning on surfaces and interfaces for chemical and material science. Computer science ; Human–computer interaction ; Artificial intelligence Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Memory and Neural Computing