jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Walter A. Henne | Morgan Krause
Source: OPUS - Open Portal to University Scholarship (Governors State University) (article, 2026-04-10)
Field: Chemistry — Physical and Theoretical Chemistry

Development of an AI-Coded Protein-Dye Labeling Calculator and Protocol Generator for Research and Instruction Fluorescent labeling of proteins is a critical technique in biochemistry and biology laboratories for use in immunofluorescence, flow cytometry, and dye-based assays, yet accessible computational tools for optimizing dye conjugation remain limited. To address this gap, we developed an interactive, browser-based protein-dye labeling calculator using AI-assisted coding (Claude, Anthropic) that will run on any operating system. The calculator was constructed iteratively using large language model-assisted, natural language prompts, and is freely accessible as a web application through the Henne Lab at Governors State University. This tool allows users to adjust a comprehensive set of experimentally relevant parameters, including protein molecular weight, molar extinction coefficient, protein concentration, sample volume, and desired degree of labeling (DOL). Dye parameters include the dye-to-protein molar ratio, dye extinction coefficient, and correction factor (CF) to account for dye absorbance at 280 nm. Labeling efficiency is calculated using Beer-Lambert Law-based absorbance corrections, yielding the moles of dye per mole of protein and estimated labeling yield. Default configurations include common fluorescent dyes such as NHS-fluorescein, Alexa Fluor 488, and TRITC, with the option to input custom dyes. As an added feature, the application generates downloadable protocols and data files for use in future experiments. This tool has been successfully integrated into undergraduate/graduate biochemistry courses and research, demonstrating the utility of AI-assisted development in rapidly producing accessible laboratory scientific software. Calculator ; Fluorescence ; Generator (circuit theory) ; Absorbance ; Analyte ; Protocol (science) Various Chemistry Research Topics ; Biological Stains and Phytochemicals ; Cell Image Analysis Techniques
Authors: Ahmar Rauf | Yara Mashal | Afeefa Rao | Nasar Ata | Farhan Shah | Arun Iyer
Source:  (article, 2026-02-24)
Field: Materials Science — Biomaterials

Nanomedicine Applications in Lymphoma: Advancing Precision Diagnostics, Targeted Therapeutics, and Prospective Developments Lymphomas represent a diverse array of B- and T-cell malignancies, where obstacles such as drug resistance, treatment-related toxicity, and disease heterogeneity continue to hinder cure rates, even with advancements in immunotherapy and targeted therapies. Nanomedicine offers potential solutions to these challenges by enhancing tumor-selective delivery, enabling real-time disease monitoring, and facilitating strategic combination therapy. Liposomes, gold nanoparticles, and polymeric nanocarriers improve lymphoma-specific accumulation of chemotherapeutics and biologics, counteract efflux-mediated chemoresistance, and minimize off-target exposure to these agents. Simultaneously, quantum dots, plasmonic gold nanostructures, and superparamagnetic iron oxide nanoparticles have enhanced diagnostic capabilities for highly sensitive fluorescence, optical, and MRI-based detection and separation of malignant lymphocytes, paving the way for multifunctional theranostic platforms for leukemia. Innovative strategies include pretargeted two-step nanoparticle systems for precise tumor ablation, mRNA-loaded nanoparticles for in situ vaccination using immune-stimulatory cytokines, and non-viral lipid nanoparticle technologies for in vivo CAR T engineering and CRISPR-mediated genome editing of lymphoma or stromal cells to modify the tumor microenvironment. Artificial intelligence is increasingly being used to optimize nanoparticle composition and targeting, accelerating the development of personalized nanomedicines tailored to the molecular and microenvironmental characteristics of individual lymphomas. These advancements collectively contribute to integrated nanotheranostic techniques that enhance diagnostic precision, facilitate accurate image-guided interventions, and promote safer, more effective, and tailored therapeutic approaches for patients with lymphoma. Nanomedicine ; Precision medicine ; Immunotherapy ; Disease ; Targeted drug delivery ; Drug Nanoparticle-Based Drug Delivery ; Lymphoma Diagnosis and Treatment ; CAR-T cell therapy research
Authors: Yingjie Sun | Yong Lian | Rongrong Chen | Piao Qian | Jin Zhang | Yinghu Wang | Qiang He | Dadi Zhou | Hengcan Yang
Source: npj Computational Materials (article, 2026-04-13)
Field: Materials Science — Materials Chemistry

Genetic algorithm coupled with thermodynamic calculations for high-strength nickel-based corrosion-resistant alloys design Abstract As oil and gas exploration expands to deep-sea, ultra-deep, and unconventional reservoirs, demands for nickel-based corrosion-resistant alloys' mechanical performance grow stricter. Traditional trial-and-error alloy development is time-consuming and costly, making them inadequate for rapid alloy design. This study developed an interpretable composition–microstructure–property optimization model by integrating genetic algorithm, machine learning, and thermodynamic calculations to enable fast design of ultra-high-strength age-hardened nickel-based corrosion-resistant alloys. Thermo-Calc (via TC-Python) calculated γ′/γ″ phase volume fractions and precipitation driving forces under different compositions, used as input for a machine learning-based yield strength prediction model. An AdaBoost regressor was trained and embedded into the genetic algorithm as the fitness function to perform constrained composition optimization. The optimized alloy, ML1, exhibited excellent mechanical performance under various heat treatment conditions. Notably, after solution treatment at 1030 °C followed by aging, it achieved a yield strength of 1365 MPa and an ultimate tensile strength of 1539 MPa—significantly outperforming most commercial precipitation-hardened nickel-based corrosion-resistant alloys. The proposed optimization framework effectively reduces development time and cost compared to conventional methods and is extendable to other precipitation-hardened alloy systems. It offers a new strategy for data-driven alloy design with both scientific value and engineering applicability. Alloy ; Genetic algorithm ; Yield (engineering) ; Ultimate tensile strength ; Function (biology) ; Fitness function ; Precipitation ; Volume (thermodynamics) ; Benchmark (surveying) Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; High Entropy Alloys Studies
Authors: Jia You | Yue-Ting Deng | Luo Chen | Linbo Wang | Yu Guo | Ze-Yu Li | Jianfeng Feng | Yang Yu | Wei Cheng
Source: Science Bulletin (article, 2026-03-01)
Field: Chemistry — Spectroscopy

The emerging role of high-throughput proteomics in advancing precision medicine Proteins serve as the fundamental executors of cellular function and the key mediators linking genome to phenome, offering unique insights into the molecular mechanisms underlying human diseases. Recent technological breakthroughs have enabled the quantification of thousands of proteins, opening an unprecedented avenue for exploring human health at the proteome level. As proteomic data generation expands in scale and depth, new opportunities are emerging to translate proteomic signatures into clinical practice, paving the way toward precision medicine. In this review, we summarize current progress using proteomics for biomarker discovery, disease diagnosis and prediction, and drug development. We further highlight innovative artificial intelligence strategies that have revolutionized proteomics analysis and interpretation. Finally, we discuss the key challenges, limitations, and future perspectives essential for realizing the full potential of proteomics in precision medicine. Precision medicine ; Proteomics ; MEDLINE ; Biomedicine Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; vaccines and immunoinformatics approaches
Authors: Alexander Wlodawer | Paweł Rubach | Zbigniew Dauter | Wojciech Dec | Dariusz Brzeziński | Mariusz Jaskólski | Wladek Minor
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Lysozyme Revisited: Evaluating Models of a Reference Protein in Structural Biology Lysozyme ; Protein Data Bank ; Protein Data Bank (RCSB PDB) ; Protein crystallization ; Muramidase ; Structural biology ; Raw data ; Measure (data warehouse) Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications
Authors: N. Zeiri | Ayoub Ed-Dahmouny | P.A. Mantashyan | D.B. Hayrapetyan | M. Jaouane | M.E. El Sayed | A. Samir | C.A. Duque
Source: Journal of Luminescence (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Strain-Driven Modulation of Nonlinear Optical Rectification in CdSe/ZnSe Core/Shell Quantum Dots: FEM Analysis and Machine Learning Prediction Finite element method ; Quantum dot ; Rectification ; Boosting (machine learning) ; Electric field ; Nonlinear system ; Quantum ; Photonics Machine Learning in Materials Science ; Strong Light-Matter Interactions ; Neural Networks and Reservoir Computing
Authors: Brij Mohan | Bakhtiyar Najafov | Pawan K. Sharma | Amalendu Pal | Ismayil M. Garazade | Armando J. L. Pombeiro | M. Fátima C. Guedes da Silva | Wei Sun | Pisith Singjai
Source: Small (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Electrochemiluminescent COFs and HOFs as Porous Material Engineering Systems for Bioanalysis and Environmental Monitoring Electrochemiluminescence (ECL) has emerged as a powerful biosensing technique owing to its high sensitivity and low background signal; however, limitations in stability, efficiency, and material diversity remain. This review summarizes recent advances in porous crystalline materials, with a focus on covalent organic frameworks (COFs) and hydrogen-bonded organic frameworks (HOFs), as emerging platforms for ECL sensing. COFs offer structural robustness, high surface area, and extended π-conjugated networks, enabling efficient charge transfer and ultrasensitive detection of biomolecules, toxins, and pesticides. In contrast, HOFs, constructed through reversible hydrogen bonding, provide superior processability, structural adaptability, and biocompatibility, making them attractive for detecting pathogens and heavy metal ions. Recent progress in luminophore engineering, nanostructure design, and co-reactant modulation has significantly improved ECL signal amplification, selectivity, and operational stability, leading to lower detection limits. Despite remaining challenges in large-scale synthesis, cost control, and the development of environmentally benign co-reactants, COF- and HOF-based systems show strong potential for integration with nanomaterials, biomolecular interfaces, and data-driven design strategies. These advances position porous organic frameworks as promising materials for next-generation, sustainable, and point-of-care ECL sensing technologies. Bioanalysis ; Luminophore ; Electrochemiluminescence ; Biosensor ; Porosity ; Aptamer ; Metal-organic framework Covalent Organic Framework Applications ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Haowei Liu | Ruiqi Wang | Hui Zhang | Zheng Wang | Yuhua Wu | Jianbo Wu | Hongcun Bai
Source: Applied Physics Reviews (article, 2026-02-10)
Field: Chemistry — Inorganic Chemistry

Metal–organic frameworks for high-efficiency solid-state hydrogen storage: Design, synthesis, regulation, and prospects Developing high-efficiency solid-state hydrogen storage materials becomes crucial as hydrogen energy's strategic role in achieving carbon neutrality grows. Metal–organic frameworks (MOFs) with advantages such as high specific surface area, good stability, diverse and customizable structures, which were awarded the 2025 Nobel Prize, have become a research focus in international hydrogen storage. Although MOFs show good hydrogen storage at low temperatures, their ambient-temperature performance is unsatisfactory, mainly due to the weak interaction between hydrogen gas and MOFs. Thus, it is crucial to design and construct efficient hydrogen storage MOFs and regulate their performance based on a deep understanding of their microstructures and hydrogen molecule interactions. This paper reviews the current research on MOFs' hydrogen storage design, synthesis, and performance regulation, and prospects their future development. It focuses on metal centers, organic ligands, MOFs' topological structures, and hydrogen storage research progress and applications at low and ambient temperatures. Analysis shows that MOFs' pore structures are the core of hydrogen storage. The electronic properties of metal centers and organic ligands significantly affect hydrogen adsorption. Precisely controlling pore structures, designing pore sizes, and regulating metal centers and organic ligands are key to efficient hydrogen storage in MOFs. Enhancement of the interaction between MOFs and hydrogen molecules can be achieved by introducing open metal sites, functionalized organic ligands, or nanoparticles so as to improve storage performance. For the application prospects and future of MOFs' hydrogen storage research, it is suggested to continuously conduct fundamental and technological research in AI-driven hydrogen-storage MOFs' design, performance improvement, large-scale low-cost preparation, and expanded applications. This review offers theoretical support for efficient hydrogen storage MOFs construction and promotes efficient and safe hydrogen energy storage and transportation. Hydrogen storage ; Hydrogen ; Metal-organic framework ; Hydrogen production ; Hydrogen economy ; Carbon fibers ; Hydrogen molecule ; Cryo-adsorption Metal-Organic Frameworks: Synthesis and Applications ; Hydrogen Storage and Materials ; Covalent Organic Framework Applications
Authors: Yanyu Zhang | Xiaojian Zhu | Runsheng Gao | Ziyi Zhang | Run-Wei Li
Source: The Innovation Materials (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Nanoions tuned two-dimensional van der Waals materials for information devices &lt;p&gt;The rapid advancement of artificial intelligence (AI) and the Internet of Things (IoT) has triggered an escalating demand for high-performance, miniaturized, and multifunctional electronic information devices. Silicon-based electronics, which have laid the foundation for the electronics industry over the past few decades, are now approaching the physical limitations with the extension of Moore’s law. Two-dimensional (2D) van der Waals (vdW) materials have emerged as promising candidates for next-generation electronic devices, due to their atomic-scale thickness, wide range of material choices, and excellent physical property tunability. Notably, it has been demonstrated that 2D vdW materials can facilitate ion migration under an electric field, enabling the reversible and efficient modulation of electrical resistance. Such a characteristic unlocks new possibilities for the design and construction of multifunctional and reconfigurable information devices. This review delves into the generation and transport of nanoionics in 2D vdW materials and their impact on modifying electrical properties. It explores strategies for device architecture design and their applications in advanced information processing. The latest progress in ion-driven reconfigurable devices for memory, logic-in-memory, neuromorphic computing, and data encryption is systematically summarized. Challenges in this field and future research directions are also discussed.&lt;/p&gt; Electronics ; Neuromorphic engineering ; van der Waals force ; Field (mathematics) ; Electronic materials ; Electronic systems ; Wearable technology ; Range (aeronautics) 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Manoharan Janani | Anooj Es
Source: Archives of Microbiology (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Unveiling the antibacterial efficacy of gold nanoparticles: a critical review Antibacterial activity ; Antibiotics ; Bacteria ; Colloidal gold ; Drug ; Surface modification ; Antibacterial agent ; Drug delivery Nanoparticles: synthesis and applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Binwei Chen | Huachao Leng | Chi Yeung Mang | Tsz Wai Cheung | Yanhua Chen | Wai Keung Anthony Loh | Chi Ho Wong | Chak Yin Tang
Source: ArXiv.org (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Evaluating Synthetic Images as Effective Substitutes for Experimental Data in Surface Roughness Classification Hard coatings play a critical role in industry, with ceramic materials offering outstanding hardness and thermal stability for applications that demand superior mechanical performance. However, deploying artificial intelligence (AI) for surface roughness classification is often constrained by the need for large labeled datasets and costly high-resolution imaging equipment. In this study, we explore the use of synthetic images, generated with Stable Diffusion XL, as an efficient alternative or supplement to experimentally acquired data for classifying ceramic surface roughness. We show that augmenting authentic datasets with generative images yields test accuracies comparable to those obtained using exclusively experimental images, demonstrating that synthetic images effectively reproduce the structural features necessary for classification. We further assess method robustness by systematically varying key training hyperparameters (epoch count, batch size, and learning rate), and identify configurations that preserve performance while reducing data requirements. Our results indicate that generative AI can substantially improve data efficiency and reliability in materials-image classification workflows, offering a practical route to lower experimental cost, accelerate model development, and expand AI applicability in materials engineering. Experimental data ; Robustness (evolution) ; Surface roughness ; Synthetic data ; Reliability (semiconductor) ; Surface finish ; Ceramic ; Hyperparameter Machine Learning in Materials Science ; Surface Roughness and Optical Measurements ; High-Temperature Coating Behaviors
Authors: Binwei Chen | Huachao Leng | Chi Yeung Mang | Tsz Wai Cheung | Yanhua Chen | Wai Keung Anthony Loh | Chi Ho Wong | Chak Yin Tang
Source: arXiv (Cornell University) (preprint, 2026-03-26)
Field: Materials Science — Materials Chemistry

Evaluating Synthetic Images as Effective Substitutes for Experimental Data in Surface Roughness Classification Hard coatings play a critical role in industry, with ceramic materials offering outstanding hardness and thermal stability for applications that demand superior mechanical performance. However, deploying artificial intelligence (AI) for surface roughness classification is often constrained by the need for large labeled datasets and costly high-resolution imaging equipment. In this study, we explore the use of synthetic images, generated with Stable Diffusion XL, as an efficient alternative or supplement to experimentally acquired data for classifying ceramic surface roughness. We show that augmenting authentic datasets with generative images yields test accuracies comparable to those obtained using exclusively experimental images, demonstrating that synthetic images effectively reproduce the structural features necessary for classification. We further assess method robustness by systematically varying key training hyperparameters (epoch count, batch size, and learning rate), and identify configurations that preserve performance while reducing data requirements. Our results indicate that generative AI can substantially improve data efficiency and reliability in materials-image classification workflows, offering a practical route to lower experimental cost, accelerate model development, and expand AI applicability in materials engineering. Experimental data ; Robustness (evolution) ; Surface roughness ; Synthetic data ; Reliability (semiconductor) ; Surface finish ; Ceramic ; Hyperparameter Machine Learning in Materials Science ; Surface Roughness and Optical Measurements ; High-Temperature Coating Behaviors
Authors: Tamer Y.A. Fahmy | Ahmed M. Khalil | Samir Kamel
Source: Carbohydrate Research (article, 2026-02-17)
Field: Materials Science — Biomaterials

Sustainable nanogold-cellulose composites: Green synthesis, emerging applications and future aspects Nanocellulose ; Cellulose ; Bacterial cellulose ; Papermaking ; Nanofiber ; Nanoparticle ; Raw material ; Biorefinery ; Leaching (pedology) Advanced Cellulose Research Studies ; Nanomaterials for catalytic reactions ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Lieen Ma | Ning Wang | Jingjing Zhu | Lingjie Wu | Shan He | bin zhang
Source: Acta Pharmaceutica Sinica B (article, 2026-03-01)
Field: Chemistry — Organic Chemistry

Molecular glue degraders: Rational design, specificity engineering, and advanced delivery Molecular glue degraders (MGDs) have emerged as a transformative modality in the field of targeted protein degradation (TPD), enabling the selective elimination of disease-relevant proteins, including those traditionally considered undruggable. Unlike bifunctional proteolysis-targeting chimeras (PROTACs), MGDs operate through monovalent architectures that induce protein–protein interactions (PPIs) between E3 ligases and neosubstrates, offering advantages in chemical simplicity, cell permeability, and target scope. However, MGD discovery remains serendipitously, and a translational framework that links rational design to predictable selectivity and tissue exposure is still lacking. In this review, we present an integrated framework for advancing next-generation MGDs through three critical dimensions: rational design, specificity optimization, and delivery systems. First, we examined cutting-edge strategies in MGD design, including covalent handle-based reprogramming, PPI-driven stabilization, and multi-site, multi-functional constructs. Second, we explored structure-guided engineering and chemoinformatic models, such as cereblon degron motifs, zone-based design and multiparameter optimization, to improve neosubstrate selectivity while minimizing off-target liabilities. Third, we summarized delivery platforms, including antibody‒drug conjugates, nanoparticle-enabled systems, and folate-mediated targeting, which are primarily intended to improve tissue selectivity and targeted distribution, thereby promoting local tissue accumulation. Finally, we discussed emerging opportunities at the intersection of artificial intelligence, structural biology, and systems pharmacology for accelerating MGD discovery and clinical translation. Collectively, these interdisciplinary insights underscore the therapeutic promise of MGDs and lay the groundwork for their next-generation evolution in precision medicine. This review summarizes emerging strategies for molecular glue degraders, highlighting rational design principles, specificity engineering, and advanced delivery platforms that collectively accelerate selective degradation of undruggable proteins for precision medicine. Rational design ; Drug discovery ; Degron ; Bifunctional ; Protein engineering ; Drug delivery Advanced Polymer Synthesis and Characterization ; Polymer Surface Interaction Studies ; Hydrogels: synthesis, properties, applications
Authors: Michele Massa | Pierpaolo Borrelli | Tommaso Astarita | Renato Tognaccini
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Spectroscopy

Fast infrared signature computation of engine plumes with LBL accuracy by Machine Learning Computation ; Signature (topology) ; Infrared ; Plume ; Radiative transfer ; Computational model ; Reduction (mathematics) ; Spectral signature Spectroscopy and Laser Applications ; Combustion and flame dynamics ; Radiative Heat Transfer Studies