jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hongju Zhou | Xuezhong Zhang | Zhibo Luo | Xin Wei | Hua Deng
Source: Materials Horizons (review, 2025-09-30)
Field: Materials Science — Materials Chemistry

Flexible thermoelectric materials and devices for sensing applications Given the explosive advancements in artificial intelligence and the Internet of Things, flexible, portable, sustainable and maintenance-free sensors are increasingly demanded. Aligning with that, the development of efficient energy harvesting devices to power these sensors is crucial and has aroused extensive attention recently. Flexible thermoelectric devices have emerged as promising candidates because they are capable of directly converting temperature differentials into voltage while offering the advantages of flexibility, absence of vibration, long service lives, and seamless integration with various multi-functional miniaturized electronics. Additionally, flexible thermoelectric (FTE) devices can perform as self-powered sensors, displaying great application prospects. In this review, the fundamental knowledge on FTE materials and devices is summarized, highlighting recent progresses in the state-of-the-art FTE materials and devices. The representative sensing application scenarios, including temperature sensors, pressure sensors, strain sensors, airflow sensors, respiration sensors, dual-modal sensors, multi-modal sensors, and electronic skin, are systematically outlined. Finally, the current development bottlenecks, challenges and prospects towards the future development of FTE materials and devices for sensing applications are discussed with a view to drive further advances in the field of self-powered and flexible sensors. Thermoelectric effect ; Energy harvesting ; Thermoelectric cooling ; Electronics ; Thermoelectric materials ; Field (mathematics) ; Voltage Advanced Thermoelectric Materials and Devices
Authors: Chengsheng Wu | Jingtong Zhang | Wang Yin-li | Tao Qian | Chang Liu | Huiran Zhang | Jie Wang | Tao Xu
Source: Journal of Applied Physics (article, 2025-03-25)
Field: Materials Science — Materials Chemistry

Artificial intelligence-assisted multi-scale phase field simulations for ferroelectrics: Cases for solid solution Ba<i>x</i>Sr1−<i>x</i>TiO3 and 2D ferroelectric In2Se3 Although the phase field method is a robust tool for theoretical studies of ferroelectrics, determining the parameters of the Helmholtz free energy in the phase-field model, particularly the Landau coefficients, remains a highly complex and challenging task. Here, we propose a general approach to identify all phase field parameters by developing an artificial intelligence-assisted multi-scale phase field model. This model hierarchically bridges ab initio accuracy with the mesoscale phase field model, linked by the effective Hamiltonian model and deep potential molecular dynamics (DPMD) simulations, effectively overcoming the limitations inherent in relying on a single method. Specifically, Monte Carlo simulations and DPMD calculations are used from first principles to determine temperature-dependent polarization and dielectric constants, which are then fitted to thermodynamic potentials using the particle swarm optimization algorithm. In addition to the Landau–Devonshire energy function, other material properties in the phase field simulation, such as gradient coefficients, electrostriction, and elastic coefficients, are also directly calculated from first-principles calculation, establishing a multi-scale phase field model. To effectively demonstrate the proposed multi-scale model, we have chosen BaxSr1−xTiO3 (BST) solid solutions and two-dimensional (2D) ferroelectric α-In2Se3 as representative examples, showcasing its applicability to both perovskite and 2D ferroelectrics. Based on the multi-scale phase field models developed for BST and α-In2Se3, we performed phase field simulations to explore their thermodynamic properties. Ferroelectricity ; Solid solution ; Materials science ; Phase (matter) ; Scale (ratio) ; Field (mathematics) ; Condensed matter physics ; Chemical physics ; Chemistry ; Physics ; Dielectric ; Optoelectronics ; Metallurgy ; Mathematics Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; Solidification and crystal growth phenomena
Authors: Amita Kajrolkar
Source: Premier journal of science. (article, 2025-02-07)
Field: Chemistry — Spectroscopy

Advancements in Plant Proteomics and Metabolomics: Tools and Techniques Advancements in plant proteomics and metabolomics have revolutionized our understanding of plant biology by providing comprehensive insights into molecular mechanisms governing growth, development, and stress responses. Proteomics enables large-scale protein profiling through advanced extraction techniques, mass spectrometry-based approaches, and bioinformatics tools, elucidating protein interactions and functions. Similarly, metabolomics explores metabolic pathways via cutting-edge separation techniques and mass spectrometry, offering crucial insights into plant metabolism and stress adaptation. Integrating these ‘omics’ approaches has facilitated discoveries in plant stress biology, crop improvement, and plant-microbe interactions. Despite challenges in data integration, coverage, and reproducibility, future developments, including artificial intelligence and multi-omics integration, promise to enhance plant science applications in agriculture, biotechnology, and environmental sustainability. Metabolomics ; Proteomics ; Computational biology ; Computer science ; Data science ; Biology ; Bioinformatics ; Biochemistry Advanced Proteomics Techniques and Applications
Authors: Maziyar Sabet
Source: Journal of Materials Science Materials in Electronics (article, 2025-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Graphene-based hybrid composites for dual electrochemical and environmental applications Materials science ; Graphene ; Composite material ; Electrochemistry ; Dual (grammatical number) ; Composite number ; Nanotechnology ; Electrode Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Graphene research and applications
Authors: Pedro Augusto Ramos | Ireneia Melo | Victor Henrique de Mattos Medeiros | Pedro Brito
Source: Materials Today Communications (article, 2025-01-01)
Field: Materials Science — Metals and Alloys

Microstructure and mechanical properties evolution of modified Nb-alloyed A297 HH refractory austenitic stainless steel after ageing process at 1000°C Materials science ; Microstructure ; Metallurgy ; Austenite ; Ageing ; Refractory (planetary science) ; Austenitic stainless steel ; Corrosion Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; High Temperature Alloys and Creep
Authors: K.L. Zeng | Zehua Tang | Guannan Yang | Yu Zhang | Guanghan Huang | Chengqiang Cui
Source:  (article, 2025-08-05)
Field: Materials Science — General Materials Science

Surface metallization of glass substrate through an interlayer of glass-copper composite With the rapid development of 5G communication, Internet of Things and artificial intelligence technology, glass substrate has become a key material in the field of high-density electronic packaging due to its high flatness, low dielectric constant, low thermal expansion coefficient and low cost. However, the metal layer on the glass surface has poor adhesion, which becomes a key problem in the surface metallization of glass substrate. In this study, we proposed a design of interlayer structure of glass-copper composite material to realize the reliable surface metallization of glass substrate. A composite paste of glass powders and copper powders at a mass ratio of 1:1 was prepared and sintered onto the surface of glass substrate through hot press sintering. Then a circuit layer was prepared on the interlayer through hot press sintering of copper powders. Experiments showed that the interlayer had a bonding strength of 15.5 MPa. The electrical resistivity of the upper circuit layer was 4 times that of bulk copper, which meet the requirements of high-density packaging. This method provides an effective approach to achieve low-cost and reliable surface metallization of glass substrates, and could promote the applications of glass substrates in 5G RF modules and power devices. Layer (electronics) ; Sintering ; Substrate (aquarium) ; Composite number ; Copper ; Dielectric ; Thermal expansion ; Electrical resistivity and conductivity Material Properties and Applications
Authors: T. Hemalatha | D Vivekananda Reddy
Source:  (article, 2025-07-25)
Field: Chemistry — Analytical Chemistry

Novel Approaches used for Sugarcane Yield Prediction using Artificial Intelligence and Machine Learning Sugarcane is a major cash crop in India that must be closely regulated because it contributes significantly to national exchange and employs over a million people mostly through sugarcane and renewable bioenergy production. Many facets of sugarcane crop management will benefit from an accurate model for estimating sugarcane production. It is a significant crop in the Indian agricultural market. Techniques aimed at increasing raw material production and quality, such as localized management, have been used manually by farmers for many years and have significant potential. Many Sugarcane yield monitoring systems have been developed and tested over the years, but their utilization is still limited. This study proposes artificial intelligence and ML algorithms for predicting Sugarcane Yield/Production Environments using a reduced number of low-cost variables. Satellite imagery was combined with climate, soil, and elevation data, and a Sugarcane harvester was provided in the Controller Area Network. All of the factors were required for the models to forecast Sugarcane Yield for better performance. Cash crop ; Crop yield ; Crop ; Agriculture ; Yield (engineering) ; Production (economics) ; Bioenergy ; Renewable resource Spectroscopy and Chemometric Analyses ; Sugarcane Cultivation and Processing ; Smart Agriculture and AI
Authors: Jintao Shen | Guizhi Shen | Tianyu Li | Chaojuan Yang | Ruirui Xing | Xuehai Yan
Source: Advanced Functional Materials (article, 2025-11-27)
Field: Materials Science — Biomaterials

Self‐Assembling Peptide‐Based Materials for Therapy of Inflammatory Diseases Abstract Inflammatory diseases (IDs), characterized by chronic inflammation and symptoms including redness, swelling, heat, and pain, present substantial global health and economic burdens. Conventional anti‐inflammatory therapeutic strategies often offer only temporary relief, and fail to achieve targeted, sustained, and safe modulation of the inflammatory microenvironment. Peptide‐based materials (PBMs) have emerged as a highly promising platform for precision inflammation modulation, leveraging their inherent biocompatibility, tunable structures, and capacity for self‐assembly into diverse supramolecular architectures. This review systematically categorizes these PBMs into self‐assembling peptides, conjugated peptide assemblies, and coassembled systems, delineating their design principles, assembly mechanisms, inflammation reprogramming and immunomodulation functions. Recent advances in their application for IDs treatment are critically evaluated and emerging strategies are introduced, notably the integration of artificial intelligence in peptide design. Finally, current translational challenges are discussed and perspectives on future research directions and clinical potential are provided. Inflammation ; Reprogramming ; Inflammatory response ; Translational research ; Immune modulation Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Chemical Synthesis and Analysis
Authors: Erica Alves | Bannimath Gurupadayya | Prabitha Prabhakaran
Source: Current Analytical Chemistry (article, 2025-08-15)
Field: Chemistry — Spectroscopy

Advancements in Mass Spectrometry Imaging for Cancer Research: From Tumor Profiling to Personalized Medicine Introduction: Mass Spectrometry Imaging (MSI) is a label-free analytical technique that enables spatially resolved molecular profiling of biomolecules in tissue sections. By simultaneously mapping proteins, lipids, and metabolites, MSI has significantly advanced cancer research by revealing tumor heterogeneity, metabolic reprogramming, and drug distribution patterns—key factors in precision oncology. Methods: This review is based on a comprehensive literature survey using databases such as Pub- Med, Scopus, and Web of Science. Studies were selected based on their relevance to recent innovations in MSI, particularly within cancer research and translational applications. Foundational landmark studies were also included to provide historical context and technological evolution. Results: Significant technological progress has been made in Matrix-Assisted Laser Desorption/ Ionization (MALDI) and Desorption Electrospray Ionization (DESI), including the development of MALDI-2 post-ionization and improvements in ambient DESI. These advancements have enhanced spatial resolution (to sub-10 μm), sensitivity, and molecular coverage. MSI applications have enabled high-resolution mapping of tumor microenvironments, drug localization, and discovery of spatially defined biomarkers. Additionally, integrative MSI approaches support multi-omic profiling, which aids in cancer subtype classification and provides insights into drug resistance and metabolic vulnerabilities. Discussion: MALDI and DESI offer complementary advantages. MALDI excels in proteomic and biomarker studies with high spatial fidelity, while DESI operates under ambient conditions and is ideal for lipidomics and in situ drug mapping. The integration of MSI with other spatial omics platforms and data-driven techniques has enhanced its utility in understanding tumor biology and therapeutic outcomes. Conclusion: MSI is poised to become a cornerstone of precision oncology and spatial biology. While challenges remain in standardization, data complexity, and clinical adoption, continued advances in instrumentation, ambient ionization, and artificial intelligence–assisted data analysis are accelerating its translational impact. Ongoing innovations promise to bridge the gap between experimental cancer research and routine clinical applications. Profiling (computer programming) ; Personalized medicine ; Mass spectrometry imaging ; Mass spectrometry ; Medicine ; Computational biology ; Medical physics ; Computer science ; Chemistry ; Chromatography ; Bioinformatics ; Biology Mass Spectrometry Techniques and Applications ; Nuclear Physics and Applications
Authors: Bowen Shen | Fei Zhou | Isabelle Souza Luz | Laura Rodríguez | Steven J. Prior | Wagner Fontes | Péter Nemes
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-08-20)
Field: Chemistry — Spectroscopy

Rapid CE–MS with Real-Time Eco–AI Resolves Proteomic Heterogeneity Among Single Human Neutrophils Single-cell proteomics by mass spectrometry is advancing rapidly, yet throughput and sensitivity remain limiting-particularly for small, protein-poor cell types such as neutrophils. As the most abundant circulating leukocytes in humans, neutrophils are central to immune defense and inflammation, but their proteomes comprehensive single-cell level characterization has only concurrently been reported <sup>1</sup> and remains limited. Here, we introduce a rapid capillary electrophoresis-mass spectrometry (CE-MS) platform, which integrates electrophoresis-correlative real-time data acquisition with sub-7-minute separations and artificial intelligence (AI)-based data processing software to achieve deep, high-throughput profiling. Using single-cell-equivalent HeLa digests, the Rapid Eco-AI platform identified ∼1,350 proteins from 300 pg and ∼835 proteins from 75 pg of input- approaching the complexity of a mammalian cell proteome. Applied to freshly isolated human neutrophils, the workflow identified 151 proteins from ∼2 pg of material, ∼3% of the total cell proteome. Analysis of 13 individual cells revealed marked functional heterogeneity across pathways of degranulation, neutrophil extracellular trap (NET) formation, chemotaxis, and innate immunity, with hierarchical clustering resolving at least four distinct proteomic subtypes. These results establish Rapid Eco-AI as a sensitive, scalable, and broadly applicable CE-MS approach for immune-cell phenotyping at single-cell and subcellular resolution, facilitating new research opportunities in systems immunology and clinical proteomics. Computational biology ; Computer science ; Chemistry ; Biology Advanced Proteomics Techniques and Applications ; Neutrophil, Myeloperoxidase and Oxidative Mechanisms ; Single-cell and spatial transcriptomics
Authors: Kimberly Intonti | Anna Sessa | Hazel Neill | Vilas Patil | Aniello Pelella | Nadia Martucciello | Lida Ansari | Paul K. Hurley | Farzan Gity | Antonio Di Bartolomeo
Source:  (article, 2025-07-13)
Field: Materials Science — Materials Chemistry

Gate-Driven Bi-Directional Photoresponse in MoTe<sub>2</sub> Based Field Effect Transistors A flexible, multi-mode and adaptable response plays a crucial role in the design of next generation devices to keep up with the rise of neuro-artificial intelligence. A bidirectional behavior, which consists in the coexistence of positive and negative photoconductivity (PPC and NPC), is required as a first ingredient to enable multi-dimensional information expansion. In this context, transistors based on ambipolar twodimensional (2D) materials offer great opportunities for next generation nanophotonic devices. In this study we investigate the gate-driven bidirectionality of a MoTe2-based field effect transistor in high vacuum. The device shows an ambipolar transfer characteristic that evolves from a p- to an n-type dominant behavior, and we monitored the main figures of merit over time. It exhibits a tunable photoresponse under the illumination of a white supercontinuum laser that strongly depends on the operating conditions. We demonstrate that both PPC and NPC can be induced by changing the applied gate voltage. The hysteretic behavior of MoTe<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> enables further information channels, since the photocurrent sign and intensity also depend on the previous history of the material. In the end, we explore the switching properties of the device to sequences of laser pulses of increasing intensity. Transistor ; Field-effect transistor ; Optoelectronics ; Materials science ; Logic gate ; Field (mathematics) ; Computer science ; Gallium arsenide ; Electrical engineering ; Engineering ; Mathematics ; Voltage 2D Materials and Applications ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Mark E. Orazem | Burak Ülgüt
Source: Current Opinion in Electrochemistry (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Can interpretation of electrochemical impedance spectroscopy data be automated? Where do artificial intelligence algorithms stand? Automation ; Interpretation (philosophy) ; Dielectric spectroscopy ; Electrical impedance ; Quality (philosophy) ; Experimental data ; Robot ; Instrumentation (computer programming) Machine Learning in Materials Science ; Water Quality Monitoring and Analysis ; Spectroscopy and Chemometric Analyses
Authors: Jong Hyeok Han | Boseong Heo | Myeong Jin Ju | Youngjin Kim | Joon Ha Chang | Hee‐Jae Jeon
Source: Materials Science and Engineering B (article, 2025-09-25)
Field: Materials Science — Surfaces, Coatings and Films

Quantitative image-analysis framework for precise discrimination of cation mixing in high-nickel NCM cathodes Mixing (physics) ; Gaussian ; Interpretation (philosophy) ; Convolution (computer science) ; Limiting ; Coherence (philosophical gambling strategy) ; Cathode ; Image processing Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and devices ; Advancements in Photolithography Techniques
Authors: Ameen, Nihayat Hussein
Source: Kirkuk University Journal For Agricultural Sciences (article, 2025-03-15)
Field: Materials Science — Materials Chemistry

Advances in corrosion and abrasive wear resistance of agricultural machinery &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The wear and the friction in machinery components due to environmental factors, such as soil, humidity, and agricultural chemicals, lead to performance degradation and increased maintenance costs. Wear-related failures contribute to over 50% of machinery breakdowns, with key components like combine harvesters and rotary plow knives having limited lifespans. This study examines wear types in agricultural machinery, key influencing factors, and resistance strategies. Several researchers have conducted studies on various wear mechanisms in agricultural equipment and proposed advanced resistance strategies, highlighting the need for innovative materials and protective solutions to enhance durability. Advanced materials like HARDOX 450 steel and tungsten carbide coatings have improved wear resistance by up to 50%, while increasing tungsten carbide content from 50% to 60% enhances durability by 25%. Plasma transferred arc (PTA) welding and powder welding (PW) further strengthen surfaces and extend equipment lifespan. Additionally, antioxidant-enriched lubricants and anti-wear additives reduce friction and wear by 20%, improving efficiency and lowering maintenance costs. Microscopic analyses confirm structural enhancements in coated surfaces, reducing crack propagation. Future advancements in composite materials, nanocoating, and self-healing smart technologies offer promising solutions to further enhance the durability and performance of agricultural machinery. Furthermore, recent studies suggest that hybrid wear-resistant coatings, combining ceramic and metallic reinforcement, provide superior protection against abrasive and impact wear. The use of biodegradable lubricants and eco-friendly surface treatments is gaining interest due to their lower environmental impact and enhanced tribological properties. Additionally, AI-driven predictive maintenance systems and sensor-based monitoring technologies are being integrated to optimize wear detection and prevention, ensuring longer equipment lifespan and reduced operational costs. The continued advancement of material science, tribology, and smart manufacturing technologies is expected to revolutionize the efficiency and sustainability of agricultural machinery in the coming years. Several researchers have conducted studies on various wear mechanisms in agricultural equipment and proposed advanced resistance strategies, emphasizing the effectiveness of multi-layer coatings, nano-enhanced lubricants, and intelligent monitoring systems in mitigating wear-related failure Abrasive ; Corrosion ; Metallurgy ; Agriculture ; Materials science ; Resistance (ecology) ; Wear resistance ; Engineering ; Forensic engineering ; Agronomy ; History ; Biology ; Archaeology Metal Alloys Wear and Properties ; Surface Treatment and Coatings ; Surface Treatment and Residual Stress
Authors: Jing Chen | Qun Zhao | Yukui Zhang | Lihua Zhang
Source: Current Opinion in Chemical Biology (review, 2025-09-13)
Field: Chemistry — Spectroscopy

In vivo cross-linking mass spectrometry: Advances and challenges in decoding protein conformational dynamics and complex regulatory networks in living cells In vivo ; Intramolecular force ; Mass spectrometry ; Peptide ; Protein–protein interaction ; Decoding methods ; Characterization (materials science) ; Living cell Mass Spectrometry Techniques and Applications ; RNA and protein synthesis mechanisms