jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jaco C. Knol | Mengge Lyu | Franziska Böttger | Madalena Monteiro | Thang V. Pham | Frank Rolfs | Andrea Vallés‐Martí | Tim Schelfhorst | Richard R. de Goeij‐de Haas | Irene V. Bijnsdorp | Shuaiyao Wang | Fangfei Zhang | Jun Arita | Bart A. Westerman | Barbara Sitek | Janne Lehtiö | Jan Köster | Jan N.M. IJzermans | Hanneke W. M. van Laarhoven | Maarten F. Bijlsma | Jan Paul Medema | Alex A. Henneman | Sander R. Piersma | Ruud H. Brakenhoff | Jacqueline Cloos | Valentina Cordo’ | Daphne de Jong | Geert Kazemier | Danijela Koppers‐Lalic | Mariëtte Labots | Tessa Y. S. Le Large | John W.M. Martens | Jules P.P. Meijerink | Madiha Mumtaz | Chantal Scheepbouwer | Robby E. Kibbelaar | David P. Noske | Renske D.M. Steenbergen | Nicole C.T. van Grieken | Winan J. van Houdt | Elisa Giovannetti | Geert J.L.H. van Leenders | Jos Jonkers | Tong Liu | Meisi Yan | Xiaolu Zhan | Tiannan Guo | Connie R. Jiménez
Source: Cancer Cell (article, 2025-05-29)
Field: Chemistry — Spectroscopy

The pan-cancer proteome atlas, a mass spectrometry-based landscape for discovering tumor biology, biomarkers, and therapeutic targets Most cancer proteomics studies to date have focused on a single cancer type. We report The Pan-Cancer Proteome Atlas (TPCPA) based on data-independent acquisition mass spectrometry, to better understand cancer biology and identify therapeutic targets and biomarkers. TPCPA includes 9,670 proteins derived from 999 primary tumors representing 22 cancer types. We describe pan-cancer and cancer type-enriched proteins with extensive external annotation, prioritizing candidate drug targets and biomarkers. Relevant for proteolysis-targeting chimeras, we identify E3-ubiquitin ligases highly expressed in specific tumor types, including HERC5 (esophageal cancer) and RNF5 (liver cancer). Co-expression analysis reveals 13 modules, including unexpected hub proteins as potential drug targets (e.g., GFPT1, LRPPRC, PINK1, DOCK2, and PTPN6). Analysis of 195 colorectal cancers identifies protein markers for RNA-based consensus molecular subtypes (CMSs) and two immune subtypes with prognostic value. We report a cancer type classifier for identification of cancers of unknown primary origin. All TPCPA data can be queried in a dedicated web resource. Proteome ; Computational biology ; Proteomics ; Atlas (anatomy) ; Biology ; Mass spectrometry ; Cancer ; Biomarker discovery ; Cancer research ; Bioinformatics ; Chemistry ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Ferroptosis and cancer prognosis ; Cancer Genomics and Diagnostics
Authors: Shanfan Lin | Hua Li | Peng Tian | Yingxu Wei | Mao Ye | Zhongmin Liu
Source: Journal of the American Chemical Society (review, 2025-03-26)
Field: Chemistry — Inorganic Chemistry

Methanol to Olefins (MTO): Understanding and Regulating Dynamic Complex Catalysis The research and development of methanol conversion into hydrocarbons have spanned more than 40 years. The past four decades have witnessed mutual promotion and successive breakthroughs in both fundamental research and industrial process development of methanol to olefins (MTO), demonstrating that MTO is an extremely dynamic, complex catalytic system. This Perspective summarizes the endeavors and achievements of the Dalian Institute of Chemical Physics, Chinese Academy of Sciences, in the continuous study of reaction mechanisms and process engineering of the dynamic, complex MTO reaction system. It elucidates fundamental chemical issues concerning the essence of the dynamic evolution of the MTO reaction and the cross-talk mechanisms among diffusion, reaction, and catalyst (coke modification), which are crucial for technology development and process optimization. By integrating the chemical principles, the reaction-diffusion model, and coke formation kinetics of MTO, a mechanism- and model-driven modulation of industrial processes has been achieved. The acquisition of a deepening understanding in chemistry and engineering has facilitated the continuous optimization and upgrading of MTO catalysts and processes. Chemistry ; Catalysis ; Chemical reaction engineering ; Methanol ; Biochemical engineering ; Process (computing) ; Nanotechnology ; Process development ; Process engineering ; Coke ; Chemical reaction ; Reaction mechanism ; Chemical process ; Organic chemistry ; Computer science ; Engineering ; Materials science Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Catalysis for Biomass Conversion
Authors: Changjin Guo | Lejiaqi Zhang | Qi Zhang | Congcong Ni | Ning Deng | Xin Huang
Source: Separations (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

Efficient Adsorptive Removal of Phosphonate Antiscalant HEDP by Mg-Al LDH Phosphonate-based antiscalants such as 1-hydroxyethane-1,1-diphosphonic acid (HEDP) are extensively employed in industrial water treatment but pose significant environmental challenges due to their persistence and phosphorus content. In this study, Mg-Al layered double hydroxide (Mg-Al LDH) was synthesized and evaluated for its capacity to adsorb and remove HEDP. Mg-Al LDH showed a pronounced adsorption affinity and an exceptionally high capacity of 276.0 mg g−1 at pH 7.0. The adsorption process was remarkably fast, attaining 97% of equilibrium uptake within 45 min at 298 K. The adsorption data fit well to the Elovich kinetic model and the Langmuir isotherm, indicating that the adsorption process is dominated by chemisorption. Thermodynamic analysis further confirmed its spontaneous nature. Additionally, Mg-Al LDH demonstrated strong tolerance to environmental fluctuations. Characterization techniques, including XRD, FTIR, and zeta potential measurements, confirmed that HEDP adsorption onto Mg-Al LDH primarily occurs via surface complexation with metal sites and electrostatic attraction. These findings demonstrate that Mg-Al LDH is a highly effective adsorbent for removing persistent phosphonate pollutants from wastewater streams. Adsorption ; Phosphonate ; Hydroxide ; Zeta potential ; Langmuir adsorption model ; Water treatment ; Langmuir ; Kinetics Layered Double Hydroxides Synthesis and Applications ; Phosphorus and nutrient management ; Chemical Synthesis and Characterization
Authors: Yujang Cho | Jong Won Beak | Mingyu Sagong | Seongcheol Ahn | Jong Seok Nam | Il‐Doo Kim
Source: Advanced Materials (article, 2025-07-01)
Field: Materials Science — Biomaterials

Electrospinning and Nanofiber Technology: Fundamentals, Innovations, and Applications (Adv. Mater. 28/2025) Electrospinning and Nanofiber Technology In article number 2500162, Il-Doo Kim, and co-workers provide a comprehensive overview of electrospinning technology, covering its fundamental principles, material design strategies, and emerging applications. The review highlights recent progress in the fabrication of nanofibers (NFs) from polymers, metal oxides, carbon-based materials, and composites with diverse architectures such as porous, core-shell, hollow, and aligned structures. Advanced electrospinning techniques, including coaxial, aligned, yarn-based, and roll-to-roll electrospinning, are discussed with a focus on their scalability and versatility. Moreover, the review further emphasizes the integration of electrospun NFs into functional membranes, gas sensors, energy devices, and catalytic systems. Lastly, it underscores the future direction of electrospinning through the adoption of green solvents and artificial intelligence (AI)-driven process optimization, aiming for enhanced performance, sustainability, and industrial scalability. Based on this content, the cover image illustrates the diversity of electrospun nanofiber architectures being produced from a robotic hand, symbolizing the future integration of advanced electrospinning techniques with AI-driven automation. Electrospinning ; Materials science ; Nanofiber ; Nanotechnology ; Polymer science ; Composite material ; Polymer Electrospun Nanofibers in Biomedical Applications
Authors: Qingqing Zhang | Yu Huang | Zhendong Dai | Yuan Li | Zhaohua Li | Runchen Lai | Facai Wei | Feng Shao
Source: ACS Applied Materials & Interfaces (review, 2025-04-30)
Field: Materials Science — Materials Chemistry

Covalent Organic Framework Membranes: Synthesis Strategies and Separation Applications Covalent organic frameworks (COFs) have emerged as highly promising materials for membrane separations due to their high porosity, tunable pore sizes, ordered crystalline structures, and exceptional chemical stability. With these features, COF membranes possess greater selectivity and permeability than conventional materials, making them the preferred choice in various fields, including membrane separations. Fascinating research endeavors have emerged encompassing fabrication strategies for COF-based membranes and their diverse separation applications. Hence, this review summarizes the latest advancements in COF synthesis, including COF powders and continuous COF-based membranes and their applications in separation membranes. Special consideration was given to regulation strategies for the performance optimization of COF membranes in separation applications, such as pore size, hydrophilicity/hydrophobicity, surface charge, crystallinity, and stability. Furthermore, applications of COF membranes in water treatment, metal ion separation, organic solvent nanofiltration, and gas separation are comprehensively reviewed. Finally, the research results and future prospects for the development of COF membranes are discussed. Future research may be focused on the following key directions: (1) single-crystal COF fabrication, (2) cost-effective membrane preparation, (3) subnanometer pore engineering, (4) advanced characterization techniques, and (5) AI-assisted development. Membrane ; Materials science ; Nanofiltration ; Gas separation ; Nanotechnology ; Porosity ; Chemical engineering ; Covalent bond ; Fabrication ; Membrane technology ; Covalent organic framework ; Crystallinity ; Polymer ; Organic chemistry ; Chemistry ; Composite material ; Engineering Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Sayee Srikarah Volaity | Bryan K. Aylas‐Paredes | Taihao Han | Jie Huang | Seetharaman Sridhar | Gaurav Sant | Aditya Kumar | Narayanan Neithalath
Source: npj Materials Sustainability (review, 2025-07-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Towards decarbonization of cement industry: a critical review of electrification technologies for sustainable cement production Cement production accounts for 8% of global CO2 emissions, necessitating its deep decarbonization. This paper reviews: (i) electrolysis-based methods to produce cement precursors, and (ii) electrified process heat technologies, along with heat storage approaches. We highlight scaled-up calciner and kiln designs and the associated technoeconomic factors. Addressing renewable energy intermittency, and the need for grid upgrades and strategic infrastructure investments are critical to enabling the transition to low-carbon cement manufacturing. Cement ; Electrification ; Production (economics) ; Sustainable production ; Business ; Engineering ; Waste management ; Natural resource economics ; Environmental science ; Materials science ; Electricity ; Metallurgy ; Economics ; Electrical engineering Supercapacitor Materials and Fabrication ; Advancements in Solid Oxide Fuel Cells ; Membrane-based Ion Separation Techniques
Authors: Lu Guan | Dajin Li | Shanshan Ji | Xiu‐Zhi Wei | Fancheng Meng
Source: Materials (review, 2025-01-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Structural Regulation and Performance Enhancement of Carbon-Based Supercapacitors: Insights into Electrode Material Engineering The development of carbon-based supercapacitors is pivotal for advancing high energy and power density applications. This review provides a comprehensive analysis of structural regulation and performance enhancement strategies in carbon-based supercapacitors, focusing on electrode material engineering. Key areas explored include pore structure optimization, heteroatom doping, intrinsic defect engineering, and surface/interface modifications. These strategies significantly enhance electrochemical performance through increasing surface area, improving conductivity, facilitating charge transfer, introducing additional pseudocapacitive reactions, and optimizing the density of states at the Fermi level, among other mechanisms. After introducing these fundamental concepts, the review details various preparation methods and their effects on supercapacitor performance, highlighting the interplay between material structure and electrochemical properties. Challenges in scaling advanced fabrication techniques and ensuring the long-term stability of functionalized materials are discussed. Additionally, future research directions are proposed, emphasizing the development of cost-effective, scalable methods and interdisciplinary approaches to design next-generation supercapacitors, thereby meeting the growing demand for efficient and sustainable energy storage solutions. Supercapacitor ; Nanotechnology ; Materials science ; Heteroatom ; Energy storage ; Computer science ; Electrochemistry ; Electrode ; Power (physics) Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Advanced battery technologies research
Authors: Dewei Feng | Xiao Geng | Lingling Zuo | Zhifang Li | Lei Wang
Source: Advanced Science (article, 2025-04-25)
Field: Chemistry — Organic Chemistry

Radical‐Polar Crossover Bicyclization Enables a Modular Synthesis of Saturated Bicyclic Amines The rapid assembly of diverse cyclic amines from simple precursors is now considered as an ideal platform with respect to efficiency and sustainability. To date, numerous synthetic methods have been successfully developed however, most of them are limited to a narrow subset of cyclic amines, with variations in ring size often requiring different substrates and distinct synthetic strategies. Furthermore, the "escape-from-Flatland" concept has led chemists to focus on the synthesis of C(sp<sup>3</sup>)-rich small molecules for potential drug candidates. Herein, the successful realization of a radical-polar crossover bicyclization reaction is reported from easily available cyclopropylamines and substituted alkenes through photoredox catalysis. This approach introduces an innovative methodology for the de novo synthesis of a diverse collection of 4/5-, 5/5-, 6/5-,7/5-, and 5/6-fused saturated bicyclic amines in a systematic and modular manner with excellent diastereoselectivity. This work highlights the efficiency and utility of the photoinduced radical-polar crossover bicyclization, the applicability of which is showcased by excellent functional group tolerance, wide substrate scopes, and simple derivatization reactions. Derivatization ; Bicyclic molecule ; Combinatorial chemistry ; Modular design ; Polar ; Substrate (aquarium) ; Crossover ; Chemistry ; Nanotechnology ; Materials science ; Computer science ; Organic chemistry ; Machine learning ; High-performance liquid chromatography ; Physics Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions ; Sulfur-Based Synthesis Techniques
Authors: Xueer Chen | Sixu Xin | Qing Liu | Yufei Meng | Daquan Yu | Ming Lun Tseng | Longfang Ye
Source: Nanophotonics (review, 2025-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dielectric metasurface‐assisted terahertz sensing: mechanism, fabrication, and multiscenario applications Terahertz (THz) technology has attracted significant global interest, particularly in sensing applications, due to its nonionizing feature and sensitivity to weak interactions. Recently, owing to the advantages of low optical loss and the capability to support both electric and magnetic high-quality factor (high-Q) resonances, dielectric metasurfaces have emerged as a powerful platform for multiscenario terahertz sensing applications. This review summarizes recent advancements in dielectric metasurface-assisted THz sensing. We begin with an overview of the mechanisms and properties of dielectric metasurfaces with high-Q factors. Next, we discuss typical fabrication techniques for these terahertz dielectric metasurfaces. We then explore the diverse terahertz sensing applications across various scenarios, including biomolecule sensing, biomedical detection, environmental monitoring, and chiral sensing. Finally, we provide perspectives on the future development of this promising research field. Terahertz radiation ; Dielectric ; Fabrication ; Materials science ; Optoelectronics ; Nanotechnology Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Plasmonic and Surface Plasmon Research
Authors: Yidi Liu | Eun Kyo Ko | Yaoju Tarn | Lopa Bhatt | Jiarui Li | Vivek Thampy | Berit H. Goodge | David A. Muller | Srinivas Raghu | Yijun Yu | Harold Y. Hwang
Source: Nature Materials (preprint, 2025-05-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Superconductivity and normal-state transport in compressively strained La2PrNi2O7 thin films Superconductivity ; Condensed matter physics ; Thin film ; Materials science ; Physics ; Nanotechnology Magnetic and transport properties of perovskites and related materials ; High-pressure geophysics and materials ; Atomic and Subatomic Physics Research
Authors: Jason E. Johnson | Ishat Raihan Jamil | Liang Pan | Guang Lin | Xianfan Xu
Source: Light Science & Applications (article, 2025-01-20)
Field: Materials Science — Materials Chemistry

Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing Multi-photon polymerization is a well-established, yet actively developing, additive manufacturing technique for 3D printing on the micro/nanoscale. Like all additive manufacturing techniques, determining the process parameters necessary to achieve dimensional accuracy for a structure 3D printed using this method is not always straightforward and can require time-consuming experimentation. In this work, an active machine learning based framework is presented for determining optimal process parameters for the recently developed, high-speed, layer-by-layer continuous projection 3D printing process. The proposed active learning framework uses Bayesian optimization to inform optimal experimentation in order to adaptively collect the most informative data for effective training of a Gaussian-process-regression-based machine learning model. This model then serves as a surrogate for the manufacturing process: predicting optimal process parameters for achieving a target geometry, e.g., the 2D geometry of each printed layer. Three representative 2D shapes at three different scales are used as test cases. In each case, the active learning framework improves the geometric accuracy, with drastic reductions of the errors to within the measurement accuracy in just four iterations of the Bayesian optimization using only a few hundred of total training data. The case studies indicate that the active learning framework developed in this work can be broadly applied to other additive manufacturing processes to increase accuracy with significantly reduced experimental data collection effort for optimization. Bayesian optimization ; Computer science ; Kriging ; Projection (relational algebra) ; Gaussian process ; Process (computing) ; Machine learning ; Active learning (machine learning) ; 3D printing ; Artificial intelligence ; Surrogate model ; Gaussian ; Algorithm ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Nonlinear Optical Materials Studies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Mohammad Madani | Valentina Lacivita | Yongwoo Shin | Anna Tarakanova
Source: npj Computational Materials (article, 2025-01-18)
Field: Materials Science — Materials Chemistry

Accelerating materials property prediction via a hybrid Transformer Graph framework that leverages four body interactions Machine learning has advanced the rapid prediction of inorganic materials properties, yet data scarcity for specific properties and capturing thermodynamic stability remains challenging. We propose a framework utilizing a Graph Neural Network with composition-based and crystal structure-based architectures, combined with a transfer learning scheme. This approach accurately predicts energy-related properties (e.g., total energy, energy above the convex hull, energy band gap) and data-scarce mechanical properties (e.g., bulk and shear modulus). Our model incorporates four-body interactions, capturing periodicity and structural characteristics. It outperforms state-of-the-art models in 8 materials property regression tasks. Also, this model predicts local atomic environments and global structural features better than several models. Transfer learning addresses mechanical property data scarcity, while separate architecture analysis allows application to materials lacking crystal structure information. Our framework's interpretability aids in understanding elemental contributions, enhancing material design and discovery. Continuous advancements promise further performance improvements, driving efficient and accurate materials property prediction. Interpretability ; Computer science ; Property (philosophy) ; Crystal structure prediction ; Graph ; Machine learning ; Artificial intelligence ; Convex hull ; Theoretical computer science ; Regular polygon ; Crystal structure ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods