jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Da Wan | Shulin Bai | Suhua Fan | Xiao Xiang | Zhen Li | Yu Liu | Peng Kang | Lei Zheng | Li‐Dong Zhao | Huibin Xu
Source: Nature Communications (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

Strain-driven lone pair electron expression for thermal transport in BiCuSeO The stereochemical activity of lone-pair electrons critically influences lattice anharmonicity and thermal transport in crystals. However, traditional chemical substitution methods lack continuity and reversibility. We propose a strain-engineered bond angle distortion strategy in layered BiCuSeO to continuously modulate lone-pair electrons. Theoretically, tensile strain reduces the O-Bi-O bond angle, expands lone-pair electron spatial distribution, and decreases Bi-O bond charge overlap, intensifying Bi atom anharmonic vibrations. Furthermore, tensile strain induces reverse O atom vibrations and strong lattice dynamic disorder, lowering the phonon band gap and enhancing anharmonic phonon-phonon interactions and Umklapp scattering. Importantly, strain modulates lone-pair electron distribution and interaction strength without uniformly weakening long-range interatomic forces. As a result, 4% tensile strain reduces lattice thermal conductivity of BiCuSeO to 0.53 W/mK (54% decrease) at 300 K. This work establishes a multiscale framework linking strain, lone-pair electron behavior, and phonon dynamics, enabling robust and continuous control of thermal transport properties. Lone pair ; Anharmonicity ; Phonon ; Materials science ; Condensed matter physics ; Electron ; Chemistry ; Physics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Expansion and Ionic Conductivity
Authors: Zhiqiang Tian | Hong Chen | Ping Zhao
Source: Frontiers in Pharmacology (review, 2025-02-12)
Field: Materials Science — Biomaterials

Compliant immune response of silk-based biomaterials broadens application in wound treatment The unique properties of sericin and silk fibroin (SF) favor their widespread application in biopharmaceuticals, particularly in wound treatment and bone repair. The immune response directly influences wound healing cycle, and the extensive immunomodulatory functions of silk-based nanoparticles and hydrogels have attracted wide attention. However, different silk-processing methods may trigger intense immune system resistance after implantation into the body. In this review, we elaborate on the inflammation and immune responses caused by the implantation of sericin and SF and also explore their anti-inflammatory properties and immune regulatory functions. More importantly, we describe the latest research progress in enhancing the immunotherapeutic and anti-inflammatory effects of composite materials prepared from silk from a mechanistic perspective. This review will provide a useful reference for using the correct processes to exploit silk-based biomaterials in different wound treatments. SILK ; Fibroin ; Sericin ; Immune system ; Wound healing ; Self-healing hydrogels ; Inflammation ; Skin repair ; Nanotechnology ; Materials science ; Medicine ; Immunology Silk-based biomaterials and applications ; Antimicrobial Peptides and Activities ; Wound Healing and Treatments
Authors: Jianli Wang | Yongting Deng | Chengmiao Wang | Yu Lin | Yeming Han | Junchi Liu | Xiufeng Liu | Hongwen Li | Jan G. Korvink | Yongbo Deng
Source: Light Science & Applications (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Portable astronomical observation system based on large-aperture concentric-ring metalens The core advantage of metalenses over traditional bulky lenses lies in their thin volume and lightweight. Nevertheless, as the application scenarios of metalenses extend to the macro-scale optical imaging field, a contradiction arises between the increasing demand for large-aperture metalenses and the synchronous rise in design and processing costs. In response to the application requirements of metalens with diameter reaching the order of 10<sup>4</sup>λ or even 10<sup>5</sup>λ, this paper proposes a novel design method for fixed-height concentric-ring metalenses, wherein, under the constraints of the processing technology, a subwavelength 2D building unit library is constructed based on different topological structures, and the overall cross-section of the metalens is assembled. Compared to global structural optimization, this approach reduces computational resources and time consumption by several orders of magnitude while maintaining nearly identical focusing efficiency. As a result, a concentric-ring metalens with a designed wavelength of 632.8 nm and a diameter of 46.8 mm was developed, and a quasi-telecentric telescope system composed of aperture stop and metalens was constructed, achieving high-resolution detection within a 20° field of view. In the subsequent experiments, the unique weak polarization dependence and narrowband adaptability of the meta-camera are quantitatively analyzed and tested, and excellent imaging results were finally obtained. Our work not only ensures the narrowband optical performance but also promotes the simplicity and light weight of the metalens based telescopic system, which further advances the deep application of large-diameter metalenses in the field of astronomical observation. Optics ; Aperture (computer memory) ; Field of view ; Concentric ; Miniaturization ; Numerical aperture ; Physics ; Computer science ; Wavelength ; Materials science ; Nanotechnology Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Optical Coatings and Gratings
Authors: Guoce Yang | Mengyun Wang | June Sang Lee | Nikolaos Farmakidis | Joe Shields | Carlota Ruíz de Galarreta | Stuart Kendall | Jacopo Bertolotti | Alona Moskalenko | Kairan Huang | Andrea Alù | C. David Wright | Harish Bhaskaran
Source: Light Science & Applications (article, 2025-05-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nonlocal phase-change metaoptics for reconfigurable nonvolatile image processing The next generation of smart imaging and vision systems will require compact and tunable optical computing hardware to perform high-speed and low-power image processing. These requirements are driving the development of computing metasurfaces to realize efficient front-end analog optical pre-processors, especially for edge detection capability. Yet, there is still a lack of reconfigurable or programmable schemes, which may drastically enhance the impact of these devices at the system level. Here, we propose and experimentally demonstrate a reconfigurable flat optical image processor using low-loss phase-change nonlocal metasurfaces. The metasurface is configured to realize different transfer functions in spatial frequency space, when transitioning the phase-change material between its amorphous and crystalline phases. This enables edge detection and bright field imaging modes on the same device. The metasurface is compatible with a large numerical aperture of ~0.5, making it suitable for high resolution coherent optical imaging microscopy. The concept of phase-change reconfigurable nonlocal metasurfaces may enable emerging applications of artificial intelligence-assisted imaging and vision devices with switchable multitasking. Computer science ; Image processing ; Enhanced Data Rates for GSM Evolution ; Computer hardware ; Optics ; Artificial intelligence ; Physics ; Image (mathematics) Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Random lasers and scattering media
Authors: María Bueno Álvez | Sofia Bergström | Josefin Kenrick | Emil Johansson | Mikael Åberg | Murat Akyıldız | Ozlem Altay | Helén Nilsson Sköld | Κωνσταντίνος Αντωνόπουλος | Emmanouil Apostolakis | Yasin Hasan Balcıoğlu | Anna Bergström | Göran Bergström | Sophia Björkander | Søren Brage | Petter Brodin | Lynn M. Butler | Sara Cajander | Hanna Danielsson | Murat Dayangaç | Gizem Dinler Doğanay | Levent Doğanay | Gunilla Enblad | Malin Enblad | Linn Fagerberg | Sara Falck‐Jones | Anna Färnert | Mattias Forsberg | Laura González‐Aguilera | Anders Gummesson | Karin Gunnarsson | Iva Gunnarsson | Ulf Gyllensten | Göran Hesselager | Andreas Hober | Martin Höglund | Marie Holmqvist | Begum Horuluoglu | Rebecka Hultgren | María Jesús Iglesias | Helena Janols | Fredric Johansson | Anette Johnsson | Lars Klareskog | David Kotol | Inger Kull | Marika Kvarnström | Maximilian Julius Lautenbach | Ulrika Liljedahl | Henrik Lindman | Cecilia Lindskog | Miklós Lipcsey | Ingrid E. Lundberg | Adil Mardinoğlu | Erik Melén | Lingqi Meng | Anne‐Sophie Merritt | Jan Mulder | M. Nguyen | Jessica Nordlund | Anna Norrby‐Teglund | Antonella Notarnicola | Piotr Nowak | Jacob Odeberg | Per Oksvold | Tomas Olsson | Leonid Padyukov | Karlis Pauksens | Fredrik Piehl | Elisa Pin | Fredrik Pontén | Natallia Rameika | Anton Reepalu | Joy Roy | Jochen M. Schwenk | Meltem Sen | Antti Siika | Oscar E. Simonson | Åsa Sivertsson | Tobias Sjöblom | Evelina Sjöstedt | Lovisa Skoglund | Anna Smed‐Sörensen | Klara Sondén | Anders Sönnerborg | Karin Stålberg | Kristoffer Strålin | Jonas Sundén‐Cullberg | Christopher Sundling | Thanadol Sutantiwanichkul | Fernanda Costa Svedman | Mattias Svensson | Elisabet Svenungsson | Tadepally Lakshmikanth | Khue Hua Tran-Minh | Hasan Türkez | Christian Unge | Per Venge | Marie Wahren‐Herlenius | Jakob Woessmann
Source: Science (article, 2025-10-09)
Field: Chemistry — Spectroscopy

A human pan-disease blood atlas of the circulating proteome The human blood proteome provides a holistic readout of health states through the assessment of thousands of circulating proteins. In this study, we present a pan-disease resource to enable the study of diverse disease phenotypes within a harmonized proteomics dataset. By profiling protein concentrations across 59 diseases and healthy cohorts, we identified proteins associated with age, sex, and body mass index, as well as disease-specific signatures. This study highlights shared and distinct protein patterns across conditions, demonstrating the power of a unified proteomics approach to uncover biological insights. The dataset, covering 8262 individuals and up to 5416 proteins, serves as an online resource for exploring disease-specific protein profiles and advancing precision medicine research. Proteome ; Proteomics ; Human Protein Atlas ; Profiling (computer programming) ; Human proteome project ; Phenotype ; Quantitative proteomics ; Protein expression ; Systems biology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Bioinformatics and Genomic Networks
Authors: Yueyi Yuan | Wenjie Zhou | Ruogu Wang | Yuxiang Wang | Yongkang Dong | Shah Nawaz Burokur | Kuang Zhang
Source: Advanced Photonics (article, 2025-10-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Non-orthogonal metasurfaces for channel-locked spin-orbital transitions Spin-orbital interaction characterizes the momentum coupling between polarization and spatial behaviors of electromagnetic waves. The corresponding transition phenomena have been recently manifested by geometric phase metasurfaces, which provide compact and intuitive platforms to replace traditional optical waveplate cascading systems. Nevertheless, metasurfaces based on geometric phase can only impose two opposite modes of orbital angular momentum (OAM) in conjugate spin channels. Although cross-talks among adjacent channels can be exhaustedly minimized, the purity and efficiency of spin-orbital transitions would still be impacted by the coexisting conjugate coupling. Here, we exploit a metasurface with non-orthogonal eigen formalism to achieve the channel-locked spin-orbital transitions, where the output spin state can be constantly fixed while at the same time imposed with versatile modes of OAM with high purity and efficiency. Besides, the robustness of the channel-locked spin-orbital transition is also demonstrated under full polarization illuminations. Such results provide a scheme for full-Stokes beam generation and high-performance meta-polarizer design, which shows the potential of reconfigurable polarization control in wireless communication systems. Angular momentum ; Polarization (electrochemistry) ; Formalism (music) ; Robustness (evolution) ; Geometric phase ; Phase conjugation ; Circular polarization ; Topology (electrical circuits) Metamaterials and Metasurfaces Applications ; Topological Materials and Phenomena ; Quantum Mechanics and Non-Hermitian Physics
Authors: Hellen C. T. Firmino | Emanuel Pereira do Nascimento | Keila Costa | Luis C. C. Arzuza | Rondinele N. Araujo | Bianca Viana de Sousa | Gelmires de Araújo Neves | Marco A. Morales | Romualdo Rodrigues Menezes
Source: Materials (article, 2025-02-08)
Field: Materials Science — Materials Chemistry

High-Efficiency Adsorption Removal of Congo Red Dye from Water Using Magnetic NiFe2O4 Nanofibers: An Efficient Adsorbent The pollution caused by organic dyes in water bodies has become a major environmental issue, and removing such pernicious dyes presents an immense challenge for the scientific community and governments. In this study, a sorbent based on nickel ferrite (NiFe<sub>2</sub>O<sub>4</sub>) fibers was fabricated by the solution blow spinning (SBS) method for the adsorptive removal of anionic Cong red (CR) dye. The cubic-spinel structure and the magnetic and porous nature of NiFe<sub>2</sub>O<sub>4</sub> were confirmed by XRD, magnetometry, BET, and SEM analyses. The saturation magnetization confirmed the magnetic nature of the fibers, which favorably respond to an external magnetic field, facilitating separation from a treated solution. The sorption kinetics of CR on NiFe<sub>2</sub>O<sub>4</sub> were best described by the pseudo-second-order model, while sorption equilibrium agreed best with the Freundlich, Langmuir, Sips, and Temkin isotherm models, suggesting a complex mechanism involving chemisorption, monolayer coverage, and heterogeneous adsorption. The NiFe<sub>2</sub>O<sub>4</sub> fibers annealed at 500 °C showed a high CR removal efficiency of ~97% after only 30 min. The sorbent's porous structure and high specific surface area were responsible for the improved removal efficiency. Finally, the results indicated the potential of the NiFe<sub>2</sub>O<sub>4</sub> fibers in the remediation of water contaminated with Congo red dye. Sorbent ; Congo red ; Adsorption ; Freundlich equation ; Sorption ; Materials science ; Langmuir adsorption model ; Chemical engineering ; Langmuir ; Red mud ; Malachite green ; Rhodamine B ; Chemisorption ; Chemistry ; Metallurgy ; Organic chemistry ; Catalysis Magnetic Properties and Synthesis of Ferrites ; Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal
Authors: Shuhan Liu | Bin Tan | Feng Wang | Ying Yu
Source: Frontiers in Pharmacology (review, 2025-01-06)
Field: Materials Science — Biomaterials

Applications of polymeric nanoparticles in drug delivery for glioblastoma Glioblastoma (GBM) remains one of the most aggressive and treatment-resistant brain tumors, necessitating innovative therapeutic approaches. Polymer-based nanotechnology has emerged as a promising solution, offering precise drug delivery, enhanced blood-brain barrier (BBB) penetration, and adaptability to the tumor microenvironment (TME). This review explores the diverse applications of polymeric nanoparticles (NPs) in GBM treatment, including delivery of chemotherapeutics, targeted therapeutics, immunotherapeutics, and other agents for radiosensitization and photodynamic therapy. Recent advances in targeted delivery and multifunctional polymer highlight their potential to overcome the challenges that GBM brought, such as heterogeneity of the tumor, BBB limitation, immunosuppressive TME, and consideration of biocompatibility and safety. Meanwhile, the future directions to address these challenges are also proposed. By addressing these obstacles, polymer-based nanotechnology represents a transformative strategy for improving GBM treatment outcomes, paving the way for more effective and patient-specific therapies. Drug delivery ; Glioblastoma ; Nanotechnology ; Tumor microenvironment ; Blood–brain barrier ; Medicine ; Materials science ; Cancer research ; Tumor cells ; Internal medicine ; Central nervous system Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Glioma Diagnosis and Treatment
Authors: Sandra Berber | Mathea Brückner | Nikolai Maurer | Johannes Huwer
Source: Journal of Chemical Education (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Chemistry Research─Implications for Teaching and Learning Artificial intelligence (AI) has become an important tool in modern scientific research, particularly in chemistry and related disciplines. Despite its growing relevance, the discussion of AI as a tool in research in (secondary) chemistry education remains limited. To address this gap, a review of AI applications in chemical and chemistry-related research was first carried out. According to this, AI is being used most frequently in the fields of pharmacology and biochemistry and is mainly used to make predictions and design molecules. Based on this review a workshop for pre- and in-service chemistry teachers was conducted to provide an initial overview of AI applications in scientific research. The workshop introduced key areas such as retrosynthesis, protein structure prediction, image processing, and autonomous laboratories, fostering awareness of AI’s role in scientific innovation. Participating secondary education science teachers reported an increased familiarity with AI applications and expressed intentions to incorporate these topics into their teaching. The findings highlight the importance of targeted teacher training programs to strengthen Technological Content Knowledge (TCK) in this emerging field. In addition, insights were gained into how such teacher training could be structured in the future to enable teachers to discuss “AI in science” in chemistry lessons. This research emphasizes the need to empower educators to bridge the gap between scientific advancements and chemistry education, preparing students for new career opportunities in AI-driven sciences. Although this work provides initial indications of possible approaches for the integration of AI applications in chemistry teacher education and chemistry lessons, further research is still needed in this area. Chemistry education ; Mathematics education ; Chemistry ; Science education ; Computer science ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Maryam Abbasi | Paulo Váz | José Silva | Pedro Martins
Source: Sustainability (article, 2025-01-02)
Field: Chemistry — Analytical Chemistry

Machine Learning Approaches for Predicting Maize Biomass Yield: Leveraging Feature Engineering and Comprehensive Data Integration The efficient prediction of corn biomass yield is critical for optimizing crop production and addressing global challenges in sustainable agriculture and renewable energy. This study employs advanced machine learning techniques, including Gradient Boosting Machines (GBMs), Random Forests (RFs), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs), integrated with comprehensive environmental, soil, and crop management data from key agricultural regions in the United States. A novel framework combines feature engineering, such as the creation of a Soil Fertility Index (SFI) and Growing Degree Days (GDDs), and the incorporation of interaction terms to address complex non-linear relationships between input variables and biomass yield. We conduct extensive sensitivity analysis and employ SHAP (SHapley Additive exPlanations) values to enhance model interpretability, identifying SFI, GDDs, and cumulative rainfall as the most influential features driving yield outcomes. Our findings highlight significant synergies among these variables, emphasizing their critical role in rural environmental governance and precision agriculture. Furthermore, an ensemble approach combining GBMs, RFs, and ANNs outperformed individual models, achieving an RMSE of 0.80 t/ha and R2 of 0.89. These results underscore the potential of hybrid modeling for real-world applications in sustainable farming practices. Addressing the concerns of passive farmer participation, we propose targeted incentives, education, and institutional support mechanisms to enhance stakeholder collaboration in rural environmental governance. While the models assume rational decision-making, the inclusion of cultural and political factors warrants further investigation to improve the robustness of the framework. Additionally, a map of the study region and improved visualizations of feature importance enhance the clarity and relevance of our findings. This research contributes to the growing body of knowledge on predictive modeling in agriculture, combining theoretical rigor with practical insights to support policymakers and stakeholders in optimizing resource use and addressing environmental challenges. By improving the interpretability and applicability of machine learning models, this study provides actionable strategies for enhancing crop yield predictions and advancing rural environmental governance. Feature engineering ; Yield (engineering) ; Feature (linguistics) ; Biomass (ecology) ; Machine learning ; Data integration ; Computer science ; Artificial intelligence ; Agricultural engineering ; Data mining ; Engineering ; Agronomy ; Deep learning ; Materials science ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Jothi Arunachalam Solairaju | R. Saravanan | Nashwan Adnan Othman | T. Sathish | Jayant Giri | Muzhda Azizi | Taoufik Saidani
Source: Engineering Reports (article, 2025-04-01)
Field: Materials Science — Polymers and Plastics

Artificial Intelligence‐Driven Prediction and Optimization of Tensile and Impact Strength in Natural Fiber/Aluminum Oxide Polymer Nanocomposites ABSTRACT This study investigates the mechanical properties of hybrid composites reinforced with jute, kenaf, and glass fibers, incorporating Aluminum Oxide (Al 2 O 3 ) as a nanoparticle filler. The effects of three key parameters—fiber orientation, fiber sequence, and weight percentage of Al 2 O 3 on—the tensile and impact strength of the composites were examined. Three levels for each factor were considered: fiber orientation (0°, 45°, and 90°), fiber sequence (1, 2, and 3 layers), and varying Al 2 O 3 content (3%, 4%, and 5%). The response surface methodology (RSM) was employed to optimize the parameters, providing insights into the interactions between these factors and their influence on the composite's mechanical performance. Additionally, artificial neural networks (ANN) were used for prediction modeling. The outcome presented that the ANN model outpaced RSM in terms of accuracy, with a higher correlation between predicted and experimental values. The optimal parameters for achieving the highest tensile and impact strength were determined, with fiber orientation at 90°, fiber sequence at 3, and Al 2 O 3 content at 5%. This study demonstrates the effectiveness of ANN in predicting the mechanical properties of the laminated composite and highlights the significant role of fiber orientation, sequence, and nanoparticle reinforcement in enhancing composite performance. Ultimate tensile strength ; Materials science ; Composite material ; Nanocomposite ; Fiber ; Natural fiber ; Polymer ; Aluminum oxide ; Aluminium Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Polymer Nanocomposites and Properties