jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fang Zheng | Yanjun Zhang | Xiaoxia Feng | Na Li | Liang Chen | Xianquan Zhan
Source: Journal of Proteomics (article, 2025-08-19)
Field: Chemistry — Spectroscopy

Proteoformics: Current status and future perspectives Proteoforms represent the ultimate structural/functional forms of a gene product, defined by multiple factors, including amino acid sequences, post-translational modifications, spatial conformations, and interactions with other molecules. The human proteoform diversity significantly exceeds the number of human genes/transcripts, emphasizing the need for advanced analytical methods to characterize this complexity. Two-dimensional gel electrophoresis-liquid chromatography/mass spectrometry (2DE-LC/MS) and top-down MS (TD-MS) are complementary to detect, identify, and quantify the large-scale proteoforms. The emerging AI tools for structural biology such as AlphaFold 3 and D-I-TASSER will enable proteoformics to be high-throughput and precisely predict spatial conformations and molecular interactions. Integrating the large-scale experimental data derived from 2DE-LC/MS and TD-MS with AI-driven high-throughput structural analysis paves the way to deeply understand proteoform diversity and functionality. The combination of advanced 2DE-LC/MS, TD-MS, and AI-driven structural analysis represents a pivotal advancement in proteoformics. This integrated approach enables the comprehensive profiling of proteoforms, providing critical insights into their roles in health care. Such advancements hold promise for predictive, preventive, and personalized medicine, particularly through biomarker discovery and therapeutic target identification. Future developments in high-throughput capabilities and dynamic modeling are expected to address current challenges and further expand the applicability of proteoformics in biological and clinical research. SIGNIFICANCE: Proteoformics is the future of proteomics, whose two main complementary analytical approaches are 2DE-LC/MS and TD-MS. The AI-driven large-cale structural analysis enables to high-throughput and precisely analyze spatial conformations and molecular interactions of proteoforms, which helps to deeply understand proteoform diversity and functionality. Proteoformics holds transformative potential to uncover biomarkers, guide targeted therapies, and advance predictive diagnosis in the context of personalized medicine. Current (fluid) ; Computer science ; Engineering ; Electrical engineering Advanced Proteomics Techniques and Applications
Authors: Sang‐Won Lee | Seungtae Lee | Solhee Lee | Ji-Seong Hwang | Wonwook Oh | Kirsten T. Winther | Dong Un Lee | Dong-Hwan Kim | Adam C. Nielander | Thomas F. Jaramillo | Yoonmook Kang | Hae‐Seok Lee
Source: Cell Reports Physical Science (article, 2025-10-11)
Field: Materials Science — Materials Chemistry

Bridging artificial intelligence with photovoltaics Turning the significant possibilities presented by artificial intelligence (AI) into practical outcomes for sustainable-energy technology advancement remains challenging, as it requires bridging the independently developed energy and AI domains. This review leverages insights from the photovoltaics (PV) field to highlight the opportunities and challenges presented by AI while simultaneously providing an organized overview to AI researchers on the status of AI application in the PV domain. The current AI application landscape is classified according to the tasks and features required at each stage of PV development. The five major domains identified are material development, process development, fault diagnosis, PV power forecasting and planning, and operation and management. The status and challenges of AI applications are provided, highlighting imbalance in research fields, data dependency, the relationships between components of PV devices, and energy consumption of AI. Further advancements will hinge on our ability to enable AI to operate safely beyond existing datasets so that we can unlock its potential to explore uncharted territories in sustainable technologies and achieve a transformative impact. Lee et al. review how artificial intelligence (AI) connects with photovoltaics (PV). They identify five key domains—materials discovery, process optimization, fault diagnosis, power forecasting, and system management—and highlight challenges such as dependence on human-made data while outlining how AI can move beyond existing datasets to unlock future opportunities. Bridging (networking) ; Applications of artificial intelligence ; Photovoltaics ; Process (computing) ; Photovoltaic system ; Transformative learning Machine Learning in Materials Science ; Photovoltaic System Optimization Techniques
Authors: Jihong Deng | Mingxing Zhao | Hui Jiang
Source: Foods (review, 2025-07-30)
Field: Chemistry — Analytical Chemistry

Research Progress in the Detection of Mycotoxins in Cereals and Their Products by Vibrational Spectroscopy Grains and their derivatives play a crucial role as staple foods for the global population. Identifying grains in the food chain that are free from mycotoxin contamination is essential. Researchers have explored various traditional detection methods to address this concern. However, as grain consumption becomes increasingly time-sensitive and dynamic, traditional approaches face growing limitations. In recent years, emerging techniques-particularly molecular-based vibrational spectroscopy methods such as visible-near-infrared (Vis-NIR), near-infrared (NIR), Raman, mid-infrared (MIR) spectroscopy, and hyperspectral imaging (HSI)-have been applied to assess fungal contamination in grains and their products. This review summarizes research advances and applications of vibrational spectroscopy in detecting mycotoxins in grains from 2019 to 2025. The fundamentals of their work, information acquisition characteristics and their applicability in food matrices were outlined. The findings indicate that vibrational spectroscopy techniques can serve as valuable tools for identifying fungal contamination risks during the production, transportation, and storage of grains and related products, with each technique suited to specific applications. Given the close link between grain-based foods and humans, future efforts should further enhance the practicality of vibrational spectroscopy by simultaneously optimizing spectral analysis strategies across multiple aspects, including chemometrics, model transfer, and data-driven artificial intelligence. Chemometrics ; Hyperspectral imaging ; Molecular spectroscopy ; Spectroscopy ; Infrared spectroscopy ; Environmental science ; Analytical technique ; Near-infrared spectroscopy ; Contamination ; Mycotoxin ; Biochemical engineering ; Computer science ; Materials science ; Nanotechnology ; Chemistry ; Food science ; Artificial intelligence ; Biology ; Physics ; Optics ; Machine learning ; Engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Listeria monocytogenes in Food Safety
Authors: Aditya Pandey | Shreya Sharma | Peeyush Phogat
Source: Sustainable materials and technologies (article, 2025-07-17)
Field: Materials Science — Materials Chemistry

MXene as high-performance 2D materials for next-generation photovoltaic cells Photovoltaic system ; Materials science ; Engineering physics ; Optoelectronics ; Photovoltaics ; Automotive engineering ; Engineering ; Electrical engineering MXene and MAX Phase Materials ; Energy Harvesting in Wireless Networks ; 2D Materials and Applications
Authors: Junwei Li | Tao Yang | Shipeng Rao | Menghong Li | Zhihong Zhang | Qinghui Jiang | Yubo Luo | Xin Li | Junyou Yang
Source: Renewables (article, 2025-08-21)
Field: Materials Science — Materials Chemistry

Oriented Structural Design of Thermal Interface Materials: Methods, Properties, and Challenges With the rapid development of artificial intelligence (AI) and big data technologies, electronic devices are evolving toward higher integration and power density, posing severe challenges for thermal dissipation.High-performance thermal interface materials (TIMs) used for minimizing the interfacial thermal resistance between the heater and heat sink are key to achieving efficient thermal management of electronic devices.To date, significant progress has been made in achieving high thermal conductivity by constructing oriented structures in TIMs.In this review, we systematically summarize and discuss the preparation methods for the design of TIMs with oriented structure.Furthermore, we particularly review the recent advancements in improving thermal contact resistance in oriented TIMs and emphasize that when the intrinsic thermal conductivity of the TIM is sufficiently high, thermal contact resistance becomes the primary bottleneck in heat dissipation.Finally, we outline the fundamentals of TIMs, the challenges, and potential directions associated with orientation structural design and practical applications, aiming to provide insights into the development of highperformance TIMs. Interface (matter) ; Materials science ; Thermal ; Thermal grease ; Composite material ; Thermal conductivity ; Physics ; Thermodynamics Thermal properties of materials ; Advanced ceramic materials synthesis
Authors: Renjie Dong‬ | Fang Wang | Shiyin Zhao | Jianfang Wang | Tao Cheng
Source: Review of Materials Research (article, 2025-04-01)
Field: Chemistry — Inorganic Chemistry

Photocatalytic degradation of organic pollutants by porphyrin metal-organic framework: Structural engineering, design strategies, and environmental applications As the economy and society evolve, there is a marked increase in pollutant production. This escalation coincides with heightened environmental quality expectations from the populace, necessitating urgent solutions to numerous environmental issues related to organic pollutants. Photocatalysis emerges as a sustainable and eco-friendly approach with significant potential for organic pollutant degradation. Porphyrin metal-organic frameworks (PMOFs) stand out as photocatalysts due to their modifiable structures, expansive specific surface areas, optimal band gaps, and commendable catalytic performance, positioning them as promising agents for the efficient photocatalytic degradation of organic contaminants. This paper commences by delineating the fundamental structure of PMOF materials and their photochemical mechanisms in catalysing organic pollutant degradation. Subsequently, recent advancements in the synthesis strategies for porous PMOFs have been reviewed, focusing on approaches that enhance photocatalytic efficiency and expand the range of their applications. These strategies encompass the construction of heterojunctions, introduction of mixed ligands, establishment of photo-Fenton (like) catalytic systems, implementation of defect engineering, regulation of two-dimensional material formation, loading of PMOF, and doping with magnetic materials. This review also highlights the key challenges that must be addressed to facilitate the successful implementation of PMOFs in environmental engineering applications. • The structure and photochemical mechanisms of PMOFs in degrading organic pollutants are explained. • Recent progress in synthesizing porous PMOFs is reviewed. • The relationship between PMOF structure, mechanisms, preparation and performance is emphasized. • The research path of PMOFs and their role in photocatalytic degradation of organic pollutants are explored. • The key challenges (scalability, environmental risks, and AI - guided design) are emphasized. Porphyrin ; Pollutant ; Degradation (telecommunications) ; Photocatalysis ; Environmental science ; Environmental chemistry ; Materials science ; Photochemistry ; Chemistry ; Engineering ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Porphyrin and Phthalocyanine Chemistry
Authors: Julian Traciak | Krzysztof Kozioł | Magdalena Małecka | Anna Blacha | Sławomir Boncel | Gaweł Żyła
Source: International Journal of Thermophysics (article, 2025-05-10)
Field: Materials Science — Materials Chemistry

The First Step into Material Table Dataset for Surface Tension of Nanofluids: Insights from the Case Study of Ethylene Glycol-Based Graphene Nanofluids Nanofluid ; Ethylene glycol ; Materials science ; Surface tension ; Thermodynamics ; Graphene ; Surface (topology) ; Nanoparticle ; Table (database) ; Nanotechnology ; Mechanics ; Chemical engineering ; Computer science ; Physics ; Data mining Graphene research and applications ; Diamond and Carbon-based Materials Research ; Thermal properties of materials
Authors: Huiyuan Cheng | Li Liu | Hongyan Zhang | Yu Yang | Qiang Cai | Shang Wang | Mo Yang | Wenxu Lu | Jie Cheng | Yan Jin
Source: Trends in Food Science & Technology (article, 2025-08-05)
Field: Chemistry — Analytical Chemistry

Artificial intelligence-enhanced bioavailability studies: Advancing the quality of food Bioavailability ; Quality (philosophy) ; Computer science ; Business ; Biology ; Pharmacology ; Philosophy Spectroscopy and Chemometric Analyses ; Consumer Attitudes and Food Labeling ; Heavy Metals in Plants
Authors: A. Priyadharshini | Janquiel Xavier
Source: Journal of Analytical and Applied Pyrolysis (article, 2025-11-06)
Field: Materials Science — Polymers and Plastics

A review of recent trends in graphene-based nanocomposite coatings for enhanced flame retardancy and corrosion protection Graphene ; Nanocomposite ; Oxide ; Coating ; Corrosion ; Aerospace ; Thermal stability ; Surface modification Flame retardant materials and properties ; Graphene research and applications ; Electromagnetic wave absorption materials
Authors: Zhenbin Wang | Zikai Meng | Siyi Mo | L.D. Zhang | Peiyuan Cheng | Xinyu Wang | Zhidong Ma
Source: Journal of Solid State Chemistry (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

A universal strategy for multicolor mechanoluminescence by cation substitution in Ca9Tb1-xEux(PO4)7 phosphor Phosphor ; Mechanoluminescence ; Substitution (logic) ; Photoluminescence ; Chemistry ; Materials science ; Inorganic chemistry ; Mineralogy ; Nuclear chemistry ; Optoelectronics ; Computer science Luminescence Properties of Advanced Materials ; Luminescence and Fluorescent Materials ; Radiation Detection and Scintillator Technologies
Authors: Linfeng Chen | Yongxin Wang | Xiaowen Jiang | Jing Ding | Hui Yan | Lei Guo | Junjie Dai | Abdulaziz Al‐Anazi | Fengxia Deng | Jizhou Jiang
Source: Journal of Industrial and Engineering Chemistry (article, 2025-02-28)
Field: Materials Science — Materials Chemistry

Analysis of MXene in water treatment: Data sources and trends Environmental chemistry ; Chemistry ; Environmental science MXene and MAX Phase Materials ; Energy Harvesting in Wireless Networks ; Graphene and Nanomaterials Applications
Authors: Yijian Zhao | Wenhan Yang | Xinru Lin | Qinzhi Wang
Source: Food Chemistry (review, 2025-08-19)
Field: Materials Science — Materials Chemistry

MXene-based intelligent sensors in food safety monitoring: Synthesis strategies, on-site applications and novel insights into prospects Environmental monitoring ; Nanotechnology ; Computer science ; Biochemical engineering ; Environmental science ; Engineering ; Materials science ; Environmental engineering MXene and MAX Phase Materials ; Energy Harvesting in Wireless Networks ; Advanced biosensing and bioanalysis techniques
Authors: S. K. Chakarvarti | Devender Gehlawat | A.S. Manocha
Source: Journal of Thermal Analysis and Calorimetry (review, 2025-05-20)
Field: Materials Science — Materials Chemistry

Thermal interface materials for thermal management of microelectronic devices: a review Microelectronics ; Interface (matter) ; Thermal grease ; Thermal ; Thermal management of electronic devices and systems ; Materials science ; Thermal analysis ; Nanotechnology ; Composite material ; Mechanical engineering ; Engineering ; Thermal conductivity ; Physics ; Thermodynamics Thermal properties of materials ; Heat Transfer and Optimization ; Advanced ceramic materials synthesis
Authors: Ziyi Chen | Yuan Yang | Liang Shen | Meng Wan | Kai Li | Weiqi Zhou | Yangang Wang | Zongguo Wang
Source: Computational Materials Science (article, 2025-02-24)
Field: Materials Science — Materials Chemistry

An automatic scientific data collection framework for materials science Data science ; Data collection ; Computer science ; Nanotechnology ; Materials science ; Sociology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Youjian Song | Zhidong Pan | Cheng‐Ming Luo | Yue Wang | Tao Zheng | Yuan Pan | Nabuqi Bu | Ruiyang Xu | Nengjie Huo
Source: ACS Applied Materials & Interfaces (article, 2025-04-23)
Field: Materials Science — Materials Chemistry

Ferroelectric α-In<sub>2</sub>Se<sub>3</sub> Semi-floating Gate Transistors for Multilevel Memory and Optoelectronic Logic Gate Progress in artificial intelligence (AI) demands efficient data storage and high-speed processing. Traditional von Neumann architecture, with space separation of memory and computing units, struggles with increased data transmission, causing power inefficiency and date latency. To address this challenge, we designed a semi-floating gate transistor (SFGT) that integrates data storage and logical operation into a single device by employing a ferroelectric semiconductor α-In<sub>2</sub>Se<sub>3</sub> as a semi-floating gate layer. Leveraging the ferroelectric polarization of α-In<sub>2</sub>Se<sub>3</sub>, the device exhibits improved non-volatile memory performance with a high program/erase ratio of 1 × 10<sup>6</sup> and reliable durability over 1000 cycles. Through the dual-gate modulation, the SFGT achieves multilevel storage function with at least seven controllable programming states and performs three types of digital logic gate operations ("AND", "NOR", and "OR") at an ultralow bias of 10 mV. Compared to traditional FGT architectures, the α-In<sub>2</sub>Se<sub>3</sub>-based semi-floating gate structure achieves multifunctional integration of data storage and logic computing, effectively addressing energy consumption and time delay issues in data transmission, making it highly significant for applications in data-intensive and low-power integrated circuits. Materials science ; Optoelectronics ; Transistor ; Ferroelectricity ; Non-volatile memory ; Logic gate ; NOR gate ; NAND gate ; Electrical engineering ; Voltage ; Dielectric Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Semiconductor materials and interfaces
Authors: Jinhui Hu | Lulu Jiang | Lanqing Li | Wen‐Hua Chen
Source: European Journal of Medicinal Chemistry (article, 2025-09-13)
Field: Chemistry — Organic Chemistry

Environmentally sensitive fluorescent probes for the visualization of selective proteins: Recent progress and biomedical applications Fluorophore ; Fluorescence ; Modular design ; Bioorthogonal chemistry ; Linker Click Chemistry and Applications
Authors: Zhixian Lv | Jiahao Huang | Chengyang Yue | Junseok Kim | Yibao Li
Source: Computers & Mathematics with Applications (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Efficient prediction of phase-field crystal dynamics via β-variational autoencoders and time-series transformers on coupled physical fields Autoencoder ; Regularization (linguistics) ; Dimensionality reduction ; Inference ; Curse of dimensionality ; Artificial neural network ; Transformer ; Discretization ; Physical system Solidification and crystal growth phenomena ; Model Reduction and Neural Networks ; Generative Adversarial Networks and Image Synthesis
Authors: Maximilian Wolf | Georg K. H. Madsen | Théodoros Dimopoulos
Source: ACS Applied Energy Materials (article, 2025-03-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Bayesian Optimization of Spray Parameters for the Deposition of Ga<sub>2</sub>O<sub>3</sub>–Cu<sub>2</sub>O Heterojunctions The accelerated discovery and optimization of materials relies on the integration of advanced experimental techniques with data-driven methodologies. In this work, Bayesian optimization (BO) is applied to optimize the ultrasonic spray pyrolysis (USP) process for the deposition of copper oxides, targeting high-quality Ga<sub>2</sub>O<sub>3</sub>-Cu<sub>2</sub>O heterojunctions for optoelectronic applications. By employing BO with an initial data set of 12 samples and conducting 4 USP parameter optimization cycles, significant improvements in device performance are achieved, with the open-circuit voltage increasing from 288 to 804 mV. During the optimization process, the performance of the model declines, necessitating the identification of a reliable subset of samples from the full data set. Through the application of BO, the cross-validation error of the model is minimized based on the sample selection, whereby accuracy is restored and generalizability is achieved. The subsequent model evaluation reveals two distinct deposition regimes, each characterized by unique process conditions, leading to specific material properties and device performances. These findings not only demonstrate the application of a data-driven experimental workflow in the context of thin film deposition but also highlight the importance of robust data validation and model evaluation. Deposition (geology) ; Bayesian optimization ; Materials science ; Context (archaeology) ; Heterojunction ; Process optimization ; Workflow ; Computer science ; Experimental data ; Optoelectronics ; Biological system ; Artificial intelligence ; Mathematics ; Engineering ; Chemical engineering Ga2O3 and related materials ; ZnO doping and properties
Authors: Faiz Ahmad Mohammed | Mohammed A. Mohammed | Parween R. Kareem | Mushtaq Abed Al-Jubbori
Source: Russian Physics Journal (review, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Revolutionizing scanning electron microscopy with compound lenses: a comprehensive review Scanning electron microscope ; Materials science ; Scanning confocal electron microscopy ; Optics ; Electron microscope ; Microscopy ; Nanotechnology ; Physics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advancements in Photolithography Techniques