jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ruchi Chauhan (9181025) | Abhilash Rana (20855503) | Abhishek Santra (4623370) | Ajay K. Singh (316888)
Source: Figshare (article, 2025-11-13)
Field: Chemistry — Organic Chemistry

Machine Learningfor Efficient Substitution Controltoward Azido-Substituted l‑Sugar Synthesis via FlowChemistry Over functionalization of sugars under condition-dependent constraints without disrupting their native architecture remains a significant challenge in vaccine development. Here, we report an AI-guided, automated flow platform with variable reaction conditions that enables azide incorporation at the C2 and C2–C4 positions of l-rhamnose and l-fucose derivatives, achieving yields of up to 90–97%. This approach delivers a safe handling of NaN<sub>3</sub>, minimum human intervention, and approximately 3000-fold enhancement in space–time yield compared to conventional batch synthesis. Subsequent in-flow Cu-catalyzed azide–alkyne cycloaddition (CuAAC) affords mono- and ditriazoles, offering a scalable route to glycoconjugates for both medicinal and material applications. Substitution (logic) ; Flow chemistry ; Yield (engineering) ; Cycloaddition ; Scalability ; Flow (mathematics) ; Azide ; Glycoconjugate ; Variable (mathematics) Click Chemistry and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation ; Biochemical and Structural Characterization
Authors: Zimo Zhao | Lin Hu | Hong‐Hui Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Predicting Catalytic Properties of Transition Metal Sulfides Using Fine-Tuned Large Language Models: A High-Accuracy Approach for Band Gap and Predicted Stable Estimation Transition metal ; Band gap ; Materials science ; Catalysis ; Optoelectronics ; Computer science ; Chemistry Machine Learning in Materials Science
Authors: Ting Li | Jizhou Li
Source: Photon Science (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Agentic AI for Accelerated Materials Characterization in Synchrotron Science Materials characterization plays key roles in scientific innovation, driving the design and optimization of materials in diverse fields. Advanced techniques at synchrotron light sources have significantly accelerated this process, offering high-resolution, multimodal insights into material structures, chemistries and dynamics. While artificial intelligence (AI) is increasingly integrated into synchrotron science, its current implementations often focus on isolated tasks. This limits their capability to capture complex causal relationships and provide comprehensive scientific insights. In this Commentary, we discuss the transformative potential of Agentic AI, an emerging paradigm that automates entire research workflows to revolutionize material characterization in synchrotron science. In fact, an Agentic AI system is analogous to a synchrotron’s organizational structure: specialized agents operate around a central engine that orchestrates autonomous operation. Users define objectives, and then the system deploys suitable beamline resources, conducts experiments, analyzes data, and delivers actionable conclusions. This will bypass intermediate technical complexities. Importantly, Agentic AI continuously refines its knowledge through user feedback and accumulates experiences from past executions. As next-generation synchrotrons are coming online worldwide, embedding Agentic AI into these facilities is expected to dramatically shorten the characterization cycle and unlock new frontiers in materials research. Workflow ; Characterization (materials science) ; Synchrotron ; Transformative learning ; Focus (optics) ; Key (lock) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Ndenga Lumbu Barack
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Q-BattX Cloud™: A Quantum-AI–Driven Cloud Platform for Next-Generation Energy Storage Simulation and Optimization &lt;div&gt; &lt;br&gt;&lt;/div&gt; Battery (electricity) ; Energy storage ; Efficient energy use ; Dashboard ; Software ; Cloud computing ; Energy (signal processing) ; Distributed ledger Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced Battery Materials and Technologies
Authors: Khalid Alfaleh | Manar Alshafi | Nawaf Alharbi | Tawfik Alghamdi
Source:  (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

High-Performance Corrosion Inhibitors for Sour and HPHT Environments: A Review of Recent Advances Abstract Corrosion in sour (H2S rich) and high pressure, high temperature (HPHT) environments remain a major challenge in the oil and gas industry, contributing to equipment degradation, safety risks, and elevated operational costs. Conventional mitigation methods such as coatings, cathodic protection, and corrosion resistant alloys often underperform in these extreme conditions due to thermal instability, flow induced damage, or high implementation costs. This review highlights recent developments in corrosion control strategies tailored for HPHT sour service, with a focus on advanced corrosion inhibitors. Classes such as imidazolines, quaternary ammonium salts, polymers, and pyridine-based compounds are examined for their adsorption behavior, thermal stability, and compatibility with multiphase environments. Special attention is given to green and biodegradable formulations derived from natural sources, aligning with sustainability and regulatory trends. Emerging digital tools including AI driven design, quantum modeling, and real-time monitoring are transforming how inhibitors are developed, screened, and deployed. Corrosion ; Cathodic protection ; Compatibility (geochemistry) ; Corrosion inhibitor ; Adsorption ; Biocide Corrosion Behavior and Inhibition ; Polymer composites and self-healing ; Polymer Science and PVC
Authors: Yuanyuan Zhang | Bo Wang
Source: APL Materials (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Emerging leaders in materials science: Capturing key advances in materials innovation Materials science continues to play a crucial role in addressing critical challenges in technology and society, driving advancements that span a wide range of industries. The development of new materials, combined with advanced characterization techniques and data-driven approaches, is fundamental to unlocking new possibilities in fields such as electronics, energy, and beyond. This collection highlights the inspiring work of emerging leaders in materials science, presenting their innovative research across a wide array of topics, spanning materials synthesis, characterization, and applications in fields such as optics, magnetics, electronics, energy, and biomaterials. Materials science ; Key (lock) ; Nanotechnology ; Engineering physics ; Engineering ethics ; Systems engineering ; Computer science ; Engineering Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Lei Zhang | Kunyang An | Zhe Yang | Mei Yang | Hongshuang Guo | Yinzhao Jia | Keyin Liu | Xuezhi Liang | Caoyang Hu
Source: Chemical Communications (article, 2025-12-08)
Field: Chemistry — Spectroscopy

An AI-engineered peptidyl fluorescence probe for GRP78: advancing renal cell carcinoma diagnosis By the integration of AI and molecular engineering, we developed an activatable peptidyl fluorescent probe targeting GRP78 with high sensitivity and specificity. The probe enabled high-contrast imaging of renal cell carcinoma in patient-derived tissue samples. AI-driven molecular engineering provides a scalable platform for developing precision diagnostics. Fluorescence ; Renal cell carcinoma ; Molecular probe ; Cell ; Molecular imaging ; Kidney Molecular Sensors and Ion Detection ; Nanocluster Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Chun-Hung Lin
Source: Highlights in Science Engineering and Technology (article, 2025-09-10)
Field: Materials Science — Materials Chemistry

Research on the Applications of Novel Two-Dimensional Materials in Energy Storage and Energy Conversion The current energy structure dominated by fossil energy is not only difficult to support the continuous growth of global energy demand but also threatens ecological security due to carbon emissions, and the intermittent and regional defects of renewable energy that need to make breakthroughs in efficient energy storage and conversion technology. Faced with the dual challenges of the structural crisis of the traditional energy system and climate change, the new generation of energy technology with new two-dimensional materials as the core is becoming a key breakthrough to solve the dilemma. Two-dimensional materials show revolutionary potential in the energy field due to their properties such as atomic thickness, controllable electronic structure, and highly active surface. In terms of energy storage, through layer number regulation and heterogeneous structure construction, its conductivity, light absorption efficiency, and ion diffusion rate can be accurately optimized, which can provide an ideal ion transport channel for the energy storage system to alleviate the volume expansion problem. In terms of energy conversion, two-dimensional materials improve catalytic efficiency through the quantum confinement effect. Despite the challenges of preparation processes, environmental stability, and industry suitability, by incorporating artificial intelligence in the development process, upgrading preparation processes, and interdisciplinary collaborative innovation, two-dimensional materials are driving the leapfrog development from nanoscience to engineering applications, which is expected to reshape the energy technology landscape and accelerate the achievement of global carbon neutrality goals. Renewable energy ; Energy transformation ; Carbon neutrality ; Energy storage ; Fossil fuel ; Energy engineering ; Efficient energy use ; Energy (signal processing) ; Energy security MXene and MAX Phase Materials ; Covalent Organic Framework Applications ; Supercapacitor Materials and Fabrication
Authors: علي البدري عبد اللطيف محمد | مصطفى محمد الشيخ | يوسف السيد عبدالجيد سعد
Source: مجلة كلية التربية – جامعة كفرالشيخ (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

برنامج تدريسي قائم على تطبيقات الذكاء الاصطناعي لتنمية مهارات حل المشكلات الكيميائية لدى طلاب المرحلة الثانوية A teaching Program Based on Artificial Intelligence Applications to Develop Chemical Problems Solving Skills among Secondary Stage Students هدف البحث الحالي إلى تنمية مهارات‏ حل المشكلات‏ الكيميائية لدى طلاب المرحلة الثانوية من خلال توظيف برنامج تدريسي قائم على تطبيقات الذكاء الاصطناعي، وتكوَّنت عينة البحث من مجموعة قصدية مكونة من (62) طالباً من طلاب الصف الثاني الثانوي بمدرسة بندار الكرمانية الثانوية بإدارة سوهاج التعليمية، تم تقسيمهم إلى مجموعتين: إحداهما تجريبية (31) طالب، والأخرى ضابطة (31) طالب، واستخدم البحث المنهج التجريبي ذو التصميم شبه التجريبي القائم على استخدام المجموعتين (التجريبية – الضابطة) مع القياس (القبلي – البعدي)، وتمثلت أدوات البحث في اختبار مهارات‏ حل المشكلات‏ الكيميائية، وأسفرت نتائج البحث عن يوجد فرق ذو دلالة إحصائية عند مستوى دلالة (α ≤0.05) بين متوسطي درجات طلاب المجموعة التجريبية ودرجات طلاب المجموعة الضابطة في القياس البعدي لاختبار مهارات حل المشكلات الكيميائية لصالح طلاب المجموعة التجريبية، وقدم البحث عدة من التوصيات: توجيه معلمي الكيمياء لممارسة الإجراءات التدريسية القائمة على تطبيقات الذكاء الاصطناعي ومراعاة مهارات‏ حل المشكلات‏ الكيميائية لدى طلاب المرحلة الثانوية. Curriculum ; Process (computing) ; Component (thermodynamics) Machine Learning in Materials Science ; Innovative Teaching Methods ; Educational Technology and Pedagogy
Authors: Zhuoxuan Chen | Lu Qi | Muhammad Ahsan Iqbal | Zibo Li | Xiaogang Wu | Sichao Dai | Siliang He | Zihao Wang | Jing Ren | Chenyang Xing | Houzhi Cai | Yu-Jia Zeng
Source: Photonics Research (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Multi-state storage and optoelectronic logic gates implementation with van der Waals ferroelectric field effect transistors To overcome the von Neumann bottleneck, van der Waals ferroelectric field-effect transistors (vdW FeFETs) offer a promising route by integrating nonvolatile storage and logic computation in a single device. In this work, a vdW FeFET is fabricated to achieve multi-resistance states and optoelectronic logic gates, which are realized by integrating an interrelated ferroelectric semiconductor α - In 2 Se 3 and an out-of-plane (OOP) ferroelectric gate β - InSe . The device exhibits four distinct and stable resistance states enabled by the OOP polarization of the gate dielectric, together with partial polarization of the upper and lower surfaces of the channel semiconductor. By exploiting the OOP polarization of two ferroelectric materials, the FeFET achieves direct switching among four storage states via gate voltage modulation. Furthermore, by leveraging the bulk photovoltaic effect of α - In 2 Se 3 , the device can be utilized as an optoelectronic logic gate, capable of executing AND and OR operations. This integration of multi-state memory and optoelectronic storage-computing within a single FeFET presents a promising strategy for advancing artificial intelligence, optical communication, and edge computing technologies. Ferroelectricity ; Non-volatile memory ; Field-effect transistor ; Logic gate ; Polarization (electrochemistry) ; Transistor ; AND gate ; Von Neumann architecture ; Computer data storage 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; Multiferroics and related materials
Authors: Nicole M. Iverson | Omer Sadak
Source: The Electrochemical Society Interface (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Recent Single Walled Carbon Nanotube Sensors as Optical Transducers: Breakthroughs in the Medical Field Carbon nanotubes (CNTs) have emerged as versatile nanomaterials with unique electrical, mechanical, and optical properties that enable a wide range of applications, including in the biomedical field. Among the various carbon nanomaterials, single walled carbon nanotubes (SWCNTs) are particularly attractive for sensor design due to their intrinsic near-infrared (NIR) fluorescence, high surface area, and amenability to chemical functionalization. These properties allow for sensitive, real-time biomolecule detection and noninvasive imaging in complex physiological settings. Recent advances have leveraged SWCNTs for real-time molecular imaging, disease diagnostics, drug monitoring, wearable sensors, and AI-assisted diagnostics. SWCNTs can be used for a wide range of biomedical applications, including fluorescence modulation resulting from molecular changes and super-resolution imaging of the extracellular space in live brain tissue. Here, we highlight some of the recent breakthroughs in SWCNT sensor technology in the medical domain. Carbon nanotube ; Biomolecule ; Nanomaterials ; Biosensor ; Graphene ; Nanosensor ; Nanobiotechnology Carbon Nanotubes in Composites ; Carbon and Quantum Dots Applications ; Neuroscience and Neural Engineering
Authors: Zishuo Zhang (10510000) | Bing Yan (170259)
Source: Figshare (article, 2025-10-23)
Field: Chemistry — Inorganic Chemistry

PhotoresponsiveHOF-Based Platforms for Large LanguageModel-Assisted Multimodal Diagnosis of Metabolic Diseases Artificial intelligence (AI), particularly large language models (LLMs) such as chat generative pretrained transformer (ChatGPT), is revolutionizing various fields. Here, we present an AI-enhanced olfactory diagnosis platform that integrates a photoresponsive hydrogen-bonded organic framework (HOF)-based sensor with the multimodal GPT-4o model. Eu-FDA@HOF prepared by introducing europium ions (Eu<sup>3+</sup>) and 2,5-furandicarboxylic acid (FDA) via a host–guest coassembly strategy possesses ultralong room-temperature phosphorescence (461 ms) and characteristic fluorescence emission. The photoresponsive sensor achieves selective and rapid detection of phenylpyruvic acid (PPA) and creatinine (Cr), odor molecules of phenylketonuria and renal dysfunction, respectively, with low detection limits (1.53 μM and 1.18 μM). Body odor, as a reflection of metabolic status, offers a potential foundation for olfactory diagnosis. Leveraging PL responses, GPT-4o, guided via a human-in-the-loop strategy, performs accurate concentration prediction and generates clinically interpretable diagnoses. This work bridges photoluminescent sensing and AI-driven medical interpretation, demonstrating a versatile and accessible platform for intelligent olfactory diagnostics. Phosphorescence ; Odor ; Fluorescence ; Olfactory system ; Generative grammar ; Photoluminescence Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Luminescence and Fluorescent Materials
Authors: Haoyang Wu | Anna C. Doner | Hao‐Wei Pang | William H. Green
Source: ChemRxiv (preprint, 2025-01-28)
Field: Chemistry — Organic Chemistry

Toward Accurate Quantum Mechanical Thermochemistry: (2) Optimal Methods for Enthalpy Calculations from Comprehensive Benchmarks of 284 Model Chemistries Accurate and efficient computations of standard enthalpies of formation (Hf) for small organic molecules are crucial for diverse chemical engineering and scientific applications. Building upon our earlier work [J. Phys. Chem. A 2024, 128, 21, 4335–4352], we systematically benchmark 284 model chemistries for Hf computations. These methods span semiempirical approaches, density functional theory (DFT), wave function theory, and composite schemes. We derive Petersson- and Melius-type bond-additivity corrections (BACs) for each method using a curated database of 421 reference species. We further validate the top-performing methods using an independent test set of 500 species including ions, radicals, and other challenging cases. Across nearly all methods and species, BACs significantly improve accuracy, especially for neutral singlet species. Composite schemes coupling moderate-level DFT geometries with local coupled-cluster single-point energies strike an excellent balance between cost and accuracy, often approaching chemical accuracy ($\leq$ 1 kcal/mol). Notably, DLPNO-CCSD(T)-F12d/cc-pVTZ-F12//ωB97X-D/def2-TZVPD with Petersson BAC attains the benchmark-best mean absolute error (MAE) of 0.57 kcal/mol. Remarkably, switching to DLPNO-CCSD(T)-F12d/cc-pVDZ-F12//GFN2-xTB substantially reduces the computational cost by an order of magnitude while modestly increasing the MAE (0.96 kcal/mol). Although carefully tuned model chemistries can also benefit charged and open-shell species, the scarcity of robust reference data in these areas highlights the need for broader, high-accuracy thermochemistry datasets. Overall, this benchmark provides practical guidance on selecting optimal model chemistries to efficiently compute accurate Hf under varied computational constraints and molecular complexities, laying a foundation for large-scale, high-throughput thermochemical calculations that will support data-driven discovery and industrial applications. Thermochemistry ; Standard enthalpy change of formation ; Enthalpy ; Quantum ; Computational chemistry ; Chemistry ; Thermodynamics ; Physics ; Physical chemistry ; Quantum mechanics Chemical Thermodynamics and Molecular Structure ; Various Chemistry Research Topics ; Machine Learning in Materials Science
Authors: Dali Vilma Francis | Divakar Dahiya | Shyam Kurup | Trupti Gokhale | Poonam Singh Nigam
Source: SCI Sustainability (article, 2025-01-01)
Field: Materials Science — Biomaterials

Advancing Sustainability Through Circular Economy, Emerging Technologies and Bibliometric Insights Towards Innovative Biodegradable Packaging for Dairy Products, Contributing to Sustainable Development Goal 9, 12, 13 ABSTRACT The growing demand for sustainable solutions in the dairy industry has spurred advancements in biodegradable packaging to address environmental concerns, reduce food waste, and enhance product quality. Dairy products, characterized by their short shelf life and susceptibility to microbial contamination, present unique challenges that require innovative packaging strategies. This review explores the latest developments in biodegradable polymer films, such as polylactic acid (PLA), polyhydroxyalkanoates (PHA), and chitosan‐based materials, and their application in dairy product preservation. Emphasis is placed on valorization‐based packaging, where agro‐industrial and dairy by‐products like whey, casein, and fruit pomace are transformed into functional films with enhanced barrier, antioxidant, and antimicrobial properties. The study also explores active and intelligent packaging technologies incorporating antimicrobial agents, natural extracts, and oxygen scavengers featuring freshness indicators, time–temperature indicators (TTIs), and radio‐frequency identification (RFID) technology. The integration of circular economy principles within the dairy packaging framework underscores the potential for resource efficiency and waste minimization. By adopting biodegradable materials and closed‐loop systems, the industry aligns with eco‐conscious consumer preferences while ensuring long‐term sustainability. Furthermore, the use of precision farming and AI‐driven frameworks enhances raw material quality, supply chain efficiency, and cold chain management, complementing advancements in sustainable packaging. A bibliometric analysis of 167 publications highlights key research trends, collaboration networks, and gaps in the field, offering insights into future directions for innovation. This review underscores the need for a multidisciplinary approach, combining material science, nanotechnology, and sustainability practices, to optimize packaging solutions for the dairy industry. By addressing environmental, economic, and consumer demands, this review contributes to the development of resilient and eco‐friendly dairy packaging systems. Circular economy ; Sustainability ; Supply chain ; Product (mathematics) ; Resource efficiency ; Emerging technologies ; New product development ; Active packaging ; Life-cycle assessment ; Sustainable development Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Food Waste Reduction and Sustainability
Authors: Souvik Guha | Rémy Mariaux | Aurélien Crochet | Ludovic Gremaud
Source: ACS Sustainable Chemistry & Engineering (article, 2025-12-22)
Field: Chemistry — Organic Chemistry

Mechanochemically Accelerated <i>N</i> -Arylation: A Scalable and Solvent-Minimized Approach Bypassing Friction Sensitive Reagents We report a comprehensive mechanochemical strategy for the solvent-minimized N-arylation of sulfonamides via C–N coupling using commercially available copper powder as the catalyst and water as a green liquid-assisted grinding (LAG) additive. Inspired by the limitations of traditional solution-phase protocols, such as substrate dependency, over arylation, and poor sustainability, we developed a scalable, efficient and eco-compatible protocol under ball-milling conditions. Initial screening revealed the high thermal reactivity of the masked amine reagent, prompting a shift toward primary sulfonamides as safer and more robust nitrogen nucleophiles. The protocol tolerated a wide range of aryl, heteroaryl, and halogenated boronic acids, affording moderate to high yields, although alkyl and chelating 8-quinolinyl substrates remain challenging. Furthermore, data-driven modeling based on molecular descriptors enabled as an exploratory QSAR tool, which guided successful extension to new substrates. The process was readily scaled to gram quantities and applied to late-stage functionalization of sulfonamide-containing bioactive scaffolds, underscoring its potential as a practical and green synthetic strategy for medicinal and process chemistry applications. Reagent ; Substrate (aquarium) ; Alkyl ; Catalysis ; Amine gas treating ; Reactivity (psychology) ; Process (computing) Catalytic Cross-Coupling Reactions ; Crystallography and molecular interactions ; N-Heterocyclic Carbenes in Organic and Inorganic Chemistry