jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gregor Pieplow | Cem Güney Torun | Charlotta Gurr | J. H. D. Munns | F. Herrmann | Andreas Thies | Tommaso Pregnolato | Tim Schröder
Source: Nature Communications (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

Quantum electrometer for time-resolved material science at the atomic lattice scale The detection of individual charges plays a crucial role in fundamental material science and the advancement of classical and quantum high-performance technologies that operate with low noise. However, resolving charges at the lattice scale in a time-resolved manner has not been achieved so far. Here, we present the development of an electrometer with 60 ns acquisition steps, leveraging on the spectroscopy of an optically-active spin defect embedded in a solid-state material with a non-linear Stark response. By applying our approach to diamond, a widely used platform for quantum technology applications, we can distinguish the distinct charge traps at the lattice scale, quantify their impact on transport dynamics and noise generation, analyze relevant material properties, and develop strategies for material optimization. Electrometer ; Lattice (music) ; Atomic units ; Quantum ; Diamond ; Physics ; Quantum computer ; Nanotechnology ; Optoelectronics ; Engineering physics ; Materials science ; Quantum mechanics Diamond and Carbon-based Materials Research ; Force Microscopy Techniques and Applications ; High-pressure geophysics and materials
Authors: Huimin Duan | Chenguang Li | Liu‐Meng Mo | Jingshuang Dang | Xiaohui Jia | Jiacheng Yu | Y. Mei | Anders Thapper | Hongyan Wang
Source: Advanced Science (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Unassisted Switchable Dual‐Photoelectrode Devices Utilizing p‐n Carbon Quantum Dots as “Semiconductor Electrolytes”: Optimization Between H <sub>2</sub> O <sub>2</sub> and Solar Electricity Production Switchable self-driven photoelectrochemical (PEC) devices are developed to boost H<sub>2</sub>O<sub>2</sub> or electricity generation under visible-light illumination, in which p-n type carbon quantum dots (N-CQDs) is applied as conceptually-new "semiconductor electrolytes". The N-CQDs contains N-dopants, and both negatively- and positively-charged surface groups. This allows N-CQDs to act as the electrolyte and to interact with both a BiVO<sub>4</sub> photoanode and a Cu<sub>2</sub>O photocathode. In a two-compartment cell with a separating membrane, N-CQDs can dynamically form p-n heterojunctions with the photoanode or the photocathode, facilitating charge separation. In this setup, the fine-tuned electronic structure of N-CQDs promotes the two-electron reactions with water or O<sub>2</sub> to produce H<sub>2</sub>O<sub>2</sub>, achieving a rate of 28 µm min<sup>-1</sup> and Faradic efficiency exceeding 80%. Switching into a one-compartment cell, N-CQDs promotes four-electron charge transfer and stabilizes the photoelectrodes, giving electricity output for over 120 h. This control over electron transfer, selectivity, and durability cannot be achieved using traditional electrolytes. Quantum dot ; Semiconductor ; Materials science ; Electrolyte ; Optoelectronics ; Dual (grammatical number) ; Carbon fibers ; Nanotechnology ; Chemical engineering ; Electrode ; Chemistry ; Engineering ; Composite material ; Physical chemistry Carbon and Quantum Dots Applications ; Advanced Photocatalysis Techniques ; Quantum Dots Synthesis And Properties
Authors: Barbora Vénosová⬡ | František Karlický
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Effects of surface functionalization and size of MXene-based quantum dots on their optical properties: the exciton confinement matters In this study, we explore the influence of surface functionalization and quantum dot size on the electronic and optical properties of MXene quantum dots (MXQDs) using time-dependent density functional theory. Our findings demonstrate that surface terminations have a significant influence on the electronic and optical properties of Ti<sub>2</sub>CT<sub>2</sub> (T = O, F, OH) MXQDs. Different functional groups induce notable shifts in both the energy gap and the absorption spectrum. The oxygen termination yields the highest stability and the largest energy gap, while hydroxyl and fluorine terminations shift absorption toward the visible and near-infrared regions. The study also reveals a pronounced blue shift in the absorption spectrum as the Ti<sub>2</sub>CO<sub>2</sub> QDs shrink. In addition, small quantum dots with a lateral size of ∼1-2 nm exhibit strong quantum coupling effects, accompanied by an increase in exciton binding energy with delocalization across the quantum dot. The binding energy of the first exciton in MXQDs can achieve up to 75% of its energy gap (in corresponding 2D materials, the typical value is around 25%), which critically influences (increases and shifts) optical absorption. Quantum dot ; Delocalized electron ; Exciton ; Surface modification ; Band gap ; Absorption (acoustics) ; Binding energy ; Density functional theory ; Potential well MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Arnaud Le Gac | Sonia Mallet‐Ladeira | Julien Roger | Jean‐Cyrille Hierso | Karinne Miqueu | Ghenwa Bouhadir | Didier Bourissou
Source: Angewandte Chemie International Edition (article, 2025-03-11)
Field: Chemistry — Inorganic Chemistry

Phosphine→Borane‐Functionalized Pyrenes and Anthracenes The functionalization of polycyclic aromatic hydrocarbons (PAHs) with N→B Lewis pairs, so-called borylative fusion, has recently emerged as a simple and powerful means to modulate their electronic and photophysical properties thanks to the extension of the π system. Herein, we considered a new class of PAHs appended with phosphine→borane Lewis pairs and investigated pyrene as well as anthracene derivatives. In these compounds, strong P→B interactions are enforced geometrically, but the π-system is not extended. Nevertheless, such P→B functionalization was found to significantly impact the optical and electrochemical properties. The P,B-functionalized PAHs display noticeably reduced HOMO-LUMO gaps and enhanced fluorescence. Both the number and position of P→B units turned out to play a significant role. Borane ; Phosphine ; Chemistry ; Organic chemistry ; Catalysis Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis ; Chemical Reactions and Isotopes
Authors: Elena Blundo | Federico Tuzi | Marzia Cuccu | Michele Re Fiorentin | Giorgio Pettinari | Atanu Patra | Salvatore Cianci | Z. R. Kudrynskyi | Marco Felici | Takashi Taniguchi | Kenji Watanabe | A. Patanè | Maurizia Palummo | A. Polimeni
Source: Nano Letters (article, 2025-02-05)
Field: Materials Science — Materials Chemistry

Giant Light Emission Enhancement in Strain-Engineered InSe/MS<sub>2</sub> (M = Mo or W) van der Waals Heterostructures Two-dimensional (2D) heterostructures (HSs) offer unlimited possibilities for playing with layer number, order, and twist angle. The realization of high-performance optoelectronic devices, however, requires the achievement of specific band alignments, <i>k</i>-space matching between conduction and valence band extrema, and efficient charge transfer between the constituent layers. Fine-tuning mechanisms to design ideal HSs are lacking. Here, we show that layer-selective strain engineering can be exploited as an extra degree of freedom to tailor the band alignment and optical properties of 2D HSs. To that end, strain is selectively applied to MS<sub>2</sub> (M = Mo or W) monolayers in InSe/MS<sub>2</sub> HSs, triggering a giant photoluminescence enhancement of the highly tunable but weakly emitting InSe of up to >2 orders of magnitude. Resonant excitation measurements, supported by first-principles calculations, provide evidence of a strain-activated charge transfer from the MS<sub>2</sub> monolayers toward InSe. The huge emission enhancement of InSe widens its range of applications for optoelectronics. Heterojunction ; Materials science ; Optoelectronics ; Photoluminescence ; Strain engineering ; Monolayer ; Band gap ; Nanotechnology 2D Materials and Applications ; MXene and MAX Phase Materials ; Perovskite Materials and Applications
Authors: Qingyuan Bu | Jiaxin Zhang | Huiru Zhang | Ran Li | Tao Zhang | Lin Liu | Peng Yu | Hai‐Bin Yu
Source: Advanced Science (article, 2025-04-07)
Field: Materials Science — Materials Chemistry

Separating the Role of Mixing‐Entropy on the Dynamics of Glass‐Forming Liquids While the concept of high entropy has gained prominence in materials design, disentangling specific impacts of entropy on material properties from the enthalpy of mixing remains elusive. It is demonstrated that the role of entropy can be distinguished from the dynamics of glass-forming liquids through micro-alloying. Based on experiment analysis of 79 compositions, liquid fragility is found to consistently decreases under two conditions: i) when the alloying content x is minimal, irrespective of the elements used; or ii) when increasing the diversity of alloying elements at a constant x, namely the high-entropy micro-alloying. These observations are consistent with thermodynamic principles that favor an entropy-dominated regime over enthalpy. These findings elucidate the subtle impact of mixing entropy on material properties and provide evidence of the entropy nature of glass transition. Fragility ; Enthalpy ; Thermodynamics ; Entropy (arrow of time) ; Materials science ; Configuration entropy ; Entropy of mixing ; Physics Phase-change materials and chalcogenides ; Material Dynamics and Properties
Authors: Abhinav Priyadarshi | Paul Prentice | Dmitry Eskin | Peter Lee | Iakovos Tzanakis
Source: Ultrasonics Sonochemistry (article, 2025-01-18)
Field: Materials Science — Materials Chemistry

Synchronized acoustic emission and high-speed imaging of cavitation-induced atomization: The role of shock waves This study experimentally investigates the role of cavitation-induced shock waves in initiating and destabilizing capillary (surface) waves on a droplet surface, preceding atomization. Acoustic emissions and interfacial wave dynamics were simultaneously monitored in droplets of different liquids (water, isopropyl alcohol and glycerol), using a calibrated fiber-optic hydrophone and high-speed imaging. Spectral analysis of the hydrophone data revealed distinct subharmonic frequency peaks in the acoustic spectrum correlated with the wavelength of capillary waves, which were optically captured during the onset of atomization from the repetitive imploding bubbles. This finding provides the first direct evidence that the wavelength of the growing surface waves, which governs capillary instability resulting in droplet breakup, is linked to the periodicity of shock waves responsible for the onset of the subharmonic frequencies detected in the acoustic spectra. This work contributes to a deeper understanding of ultrasonic atomization, signifying the role of cavitation and shock waves in the atomization process. Shock wave ; Cavitation ; Acoustics ; Shock (circulatory) ; Acoustic emission ; Sonochemistry ; Materials science ; Speed of sound ; Mechanics ; Physics Ultrasound and Cavitation Phenomena ; Electrohydrodynamics and Fluid Dynamics ; Fluid Dynamics and Heat Transfer
Authors: Ayşegül Tepe
Source: Journal of Engineering Mathematics (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Vibration analysis of SWCNTs resting on an elastic foundation using the initial values method Abstract Single-walled carbon nanotubes (SWCNTs) possess exceptional mechanical and dynamic properties, making them indispensable for diverse applications such as nanoelectromechanical systems, sensor technologies, energy storage devices and composite material design. However, a more accurate, efficient and systematic analysis of their vibrational behaviour under conditions involving elastic foundation stiffness and nanoscale effects remains essential for tailoring their applicability to specific purposes. This study introduces a novel methodological approach to analyse the vibrational behaviour of SWCNTs resting on an elastic foundation, leveraging nonlocal elasticity theory to incorporate small-scale effects. The proposed framework combines the initial value method with the approximate transfer matrix technique to efficiently compute natural frequencies. Vibrational analyses were performed under various boundary conditions, including simply supported, clamped–clamped and clamped–simply supported. Parametric studies revealed that increases in the nonlocal parameter significantly reduce natural frequencies, whereas a higher elastic foundation stiffness enhances the system rigidity and increases vibrational frequencies. This framework offers substantial computational efficiency and accuracy, presenting a robust tool for modelling SWCNT dynamics. The results provide valuable insights for designing effective SWCNT-based structures, contributing to their applications in nano-engineering and other engineering domains requiring precise vibrational analysis. Foundation (evidence) ; Vibration ; Mathematical analysis ; Mathematics ; Structural engineering ; Physics ; Acoustics ; Engineering ; Law Nonlocal and gradient elasticity in micro/nano structures ; Numerical methods in engineering ; Composite Structure Analysis and Optimization
Authors: Jonathan Tersur Orasugh | Ankita Mohanty | Amit Malakar | Suryasarathi Bose | Suprakas Sinha Ray
Source: Advanced Functional Materials (article, 2025-08-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design and Applications of Multi‐Frequency Programmable Metamaterials for Adaptive Stealth Abstract Metamaterials (MMs) are precisely designed composites with electromagnetic properties not found in natural materials, emerging as a groundbreaking technology for advanced stealth applications. This review offers a thorough synthesis of recent advancements in MM design, highlighting their exceptional capability to manipulate electromagnetic waves across microwave, infrared, and visible spectral ranges. The core principles behind MM‐enabled stealth, such as negative refractive index, cloaking, and wavefront shaping are explored, showcasing their effectiveness in significantly lowering radar cross‐section and thermal signatures, thus improving concealment. A detailed evaluation of nanoscale synthesis techniques, using both inorganic and organic materials, underscores the crucial importance of precise structural control to achieve these sophisticated functionalities. This work provides a comprehensive analysis of MM applications within military and aerospace stealth contexts, while also addressing contemporary challenges related to scalability, cost‐effectiveness, and environmental stability. Additionally, it presents a balanced evaluation of the technology's current maturity and its prospects for near‐future deployment. Beyond strategic defense uses, the transformative potential of MMs in civilian fields like transportation and communication is examined, highlighting their extensive influence on the progress of next‐generation technologies. This review outlines a clear path for future research, highlighting the crucial role of MMs in advancing electromagnetic control and stealth. Materials science ; Metamaterial ; Acoustic metamaterials ; Nanotechnology ; Optoelectronics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Q Li | Rui Jiao | Liming Wu | Tiannian Zhu | Wenbing Huang | Shifeng Jin | Junyang Liu | Hongming Weng | Xiaolong Chen
Source: Nature Communications (article, 2025-08-11)
Field: Materials Science — Materials Chemistry

Powder diffraction crystal structure determination using generative models Accurate crystal structure determination is critical across all scientific disciplines involving crystalline materials. However, solving and refining crystal structures from powder X-ray diffraction (PXRD) data is traditionally a labor-intensive process that demands substantial expertise. Here we introduce PXRDGen, an end-to-end neural network that determines crystal structures by learning joint structural distributions from experimentally stable crystals and their PXRD, producing atomically accurate structures refined through PXRD data. PXRDGen integrates a pretrained XRD encoder, a diffusion/flow-based structure generator, and a Rietveld refinement module, solving structures with unparalleled accuracy in seconds. Evaluation on MP-20 dataset reveals a record high matching rate of 82% (1-sample) and 96% (20-samples) for valid compounds, with Root Mean Square Error (RMSE) approaching the precision limits of Rietveld refinement. PXRDGen effectively tackles key challenges in PXRD, such as the resolution of overlapping peaks, localization of light atoms, and differentiation of neighboring elements. Powder diffraction ; Rietveld refinement ; Diffraction ; Crystal structure ; Materials science ; Crystal (programming language) ; Crystallography ; Computer science ; Chemistry ; Physics ; Optics X-ray Diffraction in Crystallography ; Crystallography and molecular interactions ; Crystallization and Solubility Studies
Authors: Yuehui Xian | Cheng Li | Yangyang Xu | Yumei Zhou | Dezhen Xue
Source: SusMat (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

AI‐Driven Advances in Sustainable Materials for Green Energy: From Innovation to Lifecycle Management ABSTRACT Artificial intelligence (AI) is revolutionizing sustainable materials science, yet a comprehensive and timely evaluation of the rapidly evolving AI techniques applied across the entire materials lifecycle remains lacking. This work reviews AI‐driven advances in sustainable materials, specifically focusing on battery materials, thermal management materials, energy conversion materials, and catalysts. The key patterns, capabilities, and limitations of AI are identified across three interconnected phases: sustainable materials design (leveraging predictive and generative models for accelerated discovery), green processing (integrating adaptive synthesis optimization and autonomous experimentation), and extending to lifecycle management (encompassing real‐time monitoring, predictive maintenance, and intelligent recycling). Then, the persistent challenges, including data sparsity, domain‐specific knowledge integration, and limited model generalizability, are investigated, followed by an exploration of emerging solutions such as federated learning for privacy‐preserving data sharing, physics‐informed neural networks for knowledge integration, and multimodal AI for cross‐modal knowledge transfer. Finally, the computational sustainability challenges of AI methods themselves are also discussed. This review highlights key bottlenecks impeding scalable adoption and discuss pathways for realizing the full potential of AI in sustainable materials development. Sustainable energy ; Business ; Application lifecycle management ; Engineering ; Computer science ; Renewable energy Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Advancements in Battery Materials
Authors: Xiang Liu | Penglei Sun | Shuyan Chen | Longhan Zhang | Peijie Dong | Hoydoo You | Yongqi Zhang | Chang Yan | Xiaowen Chu | Tong‐Yi Zhang
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research Xiang Liu, Penglei Sun, Shuyan Chen, Longhan Zhang, Peijie Dong, Huajie You, Yongqi Zhang, Chang Yan, Xiaowen Chu, Tong-yi Zhang. Findings of the Association for Computational Linguistics: EMNLP 2025. 2025. Perovskite (structure) ; Solar cell ; Photovoltaic system ; Coupling (piping) ; Computational model Machine Learning in Materials Science ; Perovskite Materials and Applications ; Computational and Text Analysis Methods
Authors: Bogala Madhu
Source: Journal of Food Science (article, 2025-11-29)
Field: Materials Science — Biomaterials

AI‐Driven Food Packaging Systems: A New Frontier in Intelligent Food Safety and Shelf‐Life Management The integration of artificial intelligence (AI) into food packaging systems is transforming traditional packaging from passive containment into an intelligent, adaptive component of food quality management. This review provides a critical and systematic evaluation of AI-driven packaging technologies published between 2015 and 2025, retrieved from Scopus, Web of Science, ScienceDirect, and IEEE Xplore databases using defined keywords related to AI, machine learning, and smart packaging. It examines how algorithms such as convolutional neural networks, recurrent neural networks, and support vector machines are integrated with embedded sensors, computer vision, and edge analytics to enable real-time spoilage prediction, microbial risk detection, and supply-chain traceability. The analysis incorporates model performance indicators precision, recall, and coefficient of determination (R<sup>2</sup>) and assesses limitations associated with dataset imbalance, energy consumption, and model transferability. Regulatory frameworks from European Food Safety Authority (EFSA), Food and Drug Administration (FDA), Food Safety and Standards Authority of India (FSSAI), and Codex Alimentarius are reviewed alongside sustainability aspects concerning life-cycle impacts, e-waste, and biodegradable AI components. Industrial implementations, including SpoilerAlert, Aryballe, and Tetra Pak's connected systems, demonstrate the transition toward commercial adoption. The review concludes by defining future directions for developing scalable, ethical, and resource-efficient AI-enabled packaging systems aligned with global food safety and circular economy objectives. Food safety ; Food packaging ; Food safety management ; Food spoilage ; Active packaging ; Sustainability ; Analytics ; Interoperability ; Food industry Nanocomposite Films for Food Packaging ; Postharvest Quality and Shelf Life Management ; Food Waste Reduction and Sustainability
Authors: Markus J. Buehler
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Agentic deep graph reasoning yields self-organizing knowledge networks Abstract We present an agentic, autonomous graph expansion framework that iteratively structures and refines knowledge in situ. Unlike conventional knowledge graph construction methods relying on static extraction or single-pass learning, our approach couples a reasoning-native large language model with a continually updated graph representation. At each step, the system actively generates new concepts and relationships, merges them into a global graph, and formulates subsequent prompts based on its evolving structure. Through this feedback-driven loop, the model organizes information into a scale-free network characterized by hub formation, stable modularity, and bridging nodes that link disparate knowledge clusters. Over hundreds of iterations, new nodes and edges continue to appear without saturating, while centrality measures and shortest path distributions evolve to yield increasingly distributed connectivity. Applied to materials design problems, we present compositional reasoning experiments to foster knowledge synthesis, yielding cross-domain ideas that transcend rote summarization. Graphic abstract Computer science ; Artificial intelligence ; Cognitive science ; Psychology Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling