jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mahdi M. Hanoon | Mahdi Fakhar
Source: AUIQ technical engineering science. (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Encapsulation of Corrosion Inhibitors: Methods, Preparation, and Emerging Applications Corrosion remains a critical global challenge, imposing substantial economic, safety, and environmental burdens across infrastructure, transportation, marine, and energy sectors. Traditional corrosion inhibitors, while effective, suffer from rapid depletion, uncontrolled release, environmental risks, and limited long-term stability. Encapsulation of corrosion inhibitors has emerged as a transformative strategy, enabling controlled and stimuli-responsive release, improved stability, and self-healing capabilities. This mini review provides a comprehensive overview of encapsulation technologies, including polymeric microcapsules, inorganic carriers, hybrid systems, and advanced nanoengineering approaches. Preparation methods, ranging from physical and chemical routes to emerging microfluidic and electrospinning techniques, are critically examined. Furthermore, the article highlights state-of-the-art characterization techniques SEM, TEM, FTIR, XPS, Raman, TGA, DSC, UV-Vis, and EIS essential for evaluating morphology, composition, stability, and release behavior. Key applications in protective coatings, marine and offshore structures, smart self-healing systems, and eco-friendly green chemistry approaches are discussed. Finally, challenges regarding scalability, cost, compatibility, and long-term stability are addressed, alongside future perspectives emphasizing multifunctional nanocontainers, bio-based inhibitors, and AI-driven design. This review underscores the promise of encapsulated corrosion inhibitors as a next-generation solution for sustainable, intelligent, and durable corrosion protection " Corrosion ; Encapsulation (networking) ; Nanoengineering ; Corrosion inhibitor ; Transformative learning Corrosion Behavior and Inhibition ; Polymer composites and self-healing ; Surface Modification and Superhydrophobicity
Authors: Xiaocang Han | Xiaoxu Zhao
Source: Nano Letters (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Stackingtronics: Programmable Interlayer Sliding in 2D Materials The stacking order in two-dimensional (2D) materials reveals a hidden quantum degree of freedom, turning multilayers into platforms for ferroelectricity, magnetism, correlated flat bands, and superconductivity─properties largely apart from monolayer counterparts. This stacking-enabled electronic/magnetic diversity defines <i>stackingtronics</i>, where the interlayer registry operates as a programmable knob for modulating quantum matter and reaching next-generation nanodevices. This review covers stacking-order-induced functionalities, multiphysics coupling, and effective strategies for their manipulations. In situ real-space techniques, capable of resolving multiple states and interlayer sliding dynamics, are highlighted for uncovering the microstructural origin of sliding ferroelectric polarization. Finally, we briefly discuss the key challenges in the synthesis, property optimization, structural dynamics and artificial-intelligence-induced growth-structure-property design frameworks that hold promise for the full potential of <i>stackingtronics</i> in 2D materials. Stacking ; Monolayer ; Multiphysics ; Ferroelectricity ; Quantum ; Molecular dynamics ; Property (philosophy) 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Graphene research and applications
Authors: Jyothirmayee Aravind Vishnusekhar
Source: Premier journal of science. (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

The Trio of Carbon Nanomaterials: Fullerene, CNT, and Graphene—A Review of Their Discovery and Development in the Last Decade Carbon, due to its versatile allotropes with exceptional structure and properties, continuously contributes to the application of nanotechnology in different sectors of electronics, energy and environment, biomedical and agricultural fields. After the discovery of 0D fullerene, 1D carbon nanotube, and 2D graphene, these carbon nanomaterials are the most fascinating and wonder materials for fundamental and applied research, as a large number of publications and patents have been reported investigating their structure, synthesis, properties, and potential applications. The timeline and discovery of these carbon nanomaterials and various synthesis techniques have been summarized in this review. It is forecasted that the world’s energy demand will double by 2050, and this calls for efficient and clean energy sources to meet the energy challenges in the future. Carbon nanomaterials, with their unique properties including superior electrical and thermal conductivities, mechanical strength, and catalytic properties, can be leveraged for various energy applications. In this context, advancements in the application of carbon nanomaterials in one of the major renewable energy technologies, i.e., fuel cells, during the last decade are presented. Various synthesis techniques for carbon nanomaterials, challenges for mass production, and the transformative potential of artificial intelligence in carbon nanomaterials science are also briefly discussed. Graphene ; Fullerene ; Carbon nanotube ; Nanotechnology ; Nanomaterials ; Materials science ; Carbon fibers ; Chemistry ; Composite material ; Composite number ; Organic chemistry Carbon Nanotubes in Composites ; Fullerene Chemistry and Applications ; Graphene research and applications
Authors: Yin Hu | Tao Hong | Sheng Zhou | Y.N. Wang | Qihang Ye | Sheng Sun | Shifang Wang | Chuang He | Haijie He | Xu Minjie
Source: Coatings (article, 2025-06-06)
Field: Materials Science — Materials Chemistry

Bibliometric Analysis of Coating Protection from 2015 to 2025 Composite protective coatings are critical for material durability but face challenges like fragmented knowledge and scalability issues. Existing research lacks the systematic integration of nanomaterial properties with macroscale performance and standardized evaluation protocols for hybrid systems. This study uses CiteSpace to analyze 18,363 publications (2015–2025) from Web of Science, visualizing collaborative networks, keyword clusters, and citation bursts. China leads global research output (8508 publications), with the USA and India following, while materials science, chemistry, and physics dominate disciplines. Key themes include nanocomposite coatings (e.g., graphene oxide, MXene), corrosion resistance mechanisms, and sustainable technologies, with citation bursts highlighting nanocomposites and surface functionalization. The study reveals interdisciplinary synergies in 2D nanomaterial-polymer systems, thereby improving barrier properties and enabling stimuli-responsive inhibitor release, yet it identifies gaps in lifecycle sustainability and industrial scalability. By constructing a holistic knowledge framework, this work bridges theory and application, quantifying interdisciplinary linkages and pinpointing frontiers like smart, multifunctional coatings. This study integrates data-driven insights to facilitate cross-sector collaboration. It delivers a strategic framework to tackle global challenges in material durability, sustainability, and practical application. Coating ; Materials science ; Nanotechnology Corrosion Behavior and Inhibition ; High-Temperature Coating Behaviors ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Milad Asadnia | Hatam Najafi Fath Dehghan | Mahsa Khorasani | Hadieh Rahbar Kafshboran | Mehdi Sadat-Shojai | Soheila Ghasemi | Amir Abdolmaleki
Source: Coordination Chemistry Reviews (article, 2025-12-13)
Field: Materials Science — Biomaterials

Cellulose/zeolitic imidazolate framework (ZIF) hybrid: Recent progress in synthesis and applications Zeolitic imidazolate framework ; Commercialization ; Imidazolate ; Metal-organic framework ; Hybrid material ; Sustainability Advanced Cellulose Research Studies ; Metal-Organic Frameworks: Synthesis and Applications ; Antimicrobial agents and applications
Authors: Pier Luigi Gentili
Source: Advanced Quantum Technologies (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Exploring New Chemical Paths in Quantum AI: Preliminary Accomplishments and Future Perspectives Abstract Quantum Artificial Intelligence (QAI) refers to the integration of AI with quantum information science, with the dual objective of accelerating AI development and advancing quantum computing, sensing, and communication technologies. In this work, two innovative strategies for the development of an energetically sustainable quantum information science are proposed. Both strategies originate from the Chemical AI (CAI) research line, which aims to emulate biological intelligence by designing engineered chemical systems in liquid solutions, the characteristic phase of life. The first strategy involves implementing chemical fuzzy sets and processing fuzzy logic at the molecular level, which corresponds to manipulating thermalized quantum mixed states. Although quantum superposition, interference, entanglement, and tunneling are excluded, noteworthy advancements in quantum sensing, communicating, and computing are still within reach. The second strategy arises from neuromorphic engineering in wetware. In this context, photochromic compounds are employed as dynamic surrogates of neurons operating in the phasic excitable regime. These compounds are proposed as molecular candidates that can simulate certain features of qubits. Finally, perspectives on these two novel strategies, highlighting their potential to reshape the future of quantum information science beyond traditional qubit‐based paradigms, are outlined. Quantum ; Quantum chemical ; Quantum computer ; Quantum information ; Fuzzy logic ; Neuromorphic engineering ; Information processing Photochromic and Fluorescence Chemistry ; Synthesis and Properties of Aromatic Compounds ; Supramolecular Chemistry and Complexes
Authors: Bowen Li | Jiachi Liao | Di Yin | He Shao | Shuai Zhang | Bao-Jie Chen | Chi Hou Chan | Johnny C. Ho
Source: Advanced Materials Technologies (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Recent Advances in Tunable/Reconfigurable Terahertz Metadevices Based on Phase‐Change Materials Abstract Emerging artificial intelligence and Internet of Things (IoT) technologies have greatly enhanced daily life, yet processing vast data volumes poses challenges for computing hardware and communication systems. Terahertz (THz) waves, positioned between microwaves and infrared light, offer significant potential for future communication and sensing applications due to their wide bandwidth, high data rate, superior resolution, and non‐destructive capabilities. However, traditional passive THz devices face adaptive control and dynamic modulation limitations. The emergence of tunable and reconfigurable metadevices, especially those using phase‐change materials, provides promising solutions through substantial and rapid modulation of electromagnetic responses. This review examines recent advancements in THz metadevices utilizing phase‐transition materials, focusing on mechanisms like insulator‐to‐metal transitions, amorphous‐to‐crystal transformations, ferroelectric behavior, and liquid‐crystal transitions. It summarizes strategies in structural design, material selection, and device fabrication. Furthermore, it discusses the latest developments in THz modulation and applications using phase‐change metadevices, highlighting key performance parameters such as frequency range, modulation depth, tunability, and switching speed. Finally, the review outlines current challenges and future prospects in advancing adaptive THz metadevices, pointing to potential pathways for dynamic, multifunctional THz systems. Terahertz radiation ; Phase change ; Phase (matter) ; Materials science ; Computer science ; Optoelectronics ; Engineering physics ; Engineering ; Physics Phase-change materials and chalcogenides ; Advanced Thermoelectric Materials and Devices ; Photonic and Optical Devices
Authors: Kevin Heng | James E. Owen | Tian Meng
Source: ArXiv.org (preprint, 2025-04-03)
Field: Chemistry — Organic Chemistry

The Gradient of Mean Molecular Weight Across the Radius Valley Photo-evaporation shapes the observed radii of small exoplanets and constrains the underlying distributions of atmospheric and core masses. However, the diversity of atmospheric chemistries corresponding to these distributions remains unelucidated. We develop a first-principles carbon-hydrogen-oxygen-sulfur-silicon (CHOSSi) outgassing model that accounts for non-ideal gas behavior (via fugacities) at high pressures, as well as the tendency for water and hydrogen to dissolve in melt (via solubility laws). We use data-driven radius valley constraints to establish the relationship between the atmospheric surface pressures and melt temperatures of sub-Neptunes. Sub-Neptunes with less massive rocky cores retain less of their primordial hydrogen envelopes, which leads to less heat retention and diminished melt temperatures at the surfaces of these cores. Lower melt temperatures lead thermodynamically to the dominance of carbon-, oxygen-, sulfur- and silicon-bearing molecules over molecular hydrogen, which naturally produce a diversity of mean molecular weights. Our geochemical outgassing calculations robustly predict a gradient of mean molecular weight across the radius valley, where the strength of this gradient is primarily driven by the oxygen fugacity of the molten cores and not by the carbon enrichment (or "metallicity") of the atmosphere. Smaller sub-Neptunes are predicted to have less hydrogen-dominated atmospheres. The precise relationship between the observed and outgassed chemistries requires an understanding of how convection near the core interacts with large-scale atmospheric circulation (driven by stellar heating) near the photosphere, as well as the influence of photochemistry. RADIUS ; Physics ; Mathematics ; Geography ; Computer science Fullerene Chemistry and Applications ; Zeolite Catalysis and Synthesis
Authors: Eleonora Romeo | Futian You | Haibin Ma | Francesc Illas | Boon Siang Yeo | Federico Calle‐Vallejo
Source: ACS Catalysis (article, 2025-10-16)
Field: Materials Science — Materials Chemistry

Improving the Description of Adsorbed Hydroxyl to Make Predictive Catalytic Activity Models Artificial intelligence may take catalytic models to a level that matches reality only when provided with high-quality data. Currently, activity plots based on the Sabatier principle are among the most extensively used tools to design materials in thermocatalysis and electrocatalysis. While salient examples attest to their usefulness, activity plots based on density functional theory calculations often fail for various reasons, one of them being the inaccurate approximation of the exchange-correlation energy for free molecules, catalysts, and adsorbed species. Here, we combine experiments and computational modeling to assess the errors in adsorbed hydroxyl (*OH), which are otherwise overly difficult to assess. Upon correcting a systematic adsorbed-phase error in *OH, our predictions match the experimental results for ethylene oxide electroreduction on transition-metal electrodes. The transferability of *OH corrections is illustrated by enhancing activity estimations of CO electrooxidation, where *OH is a key intermediate. Finally, we provide an alternative to cases in which the assessment of the adsorbed-phase errors is unfeasible. In summary, we show here how to improve electrocatalytic estimations by depurating their input data. Transferability ; Catalysis ; Density functional theory ; Adsorption ; Salient ; Key (lock) ; Ethylene oxide ; Ethylene Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Haoyang Wu | Anna C. Doner | Hao‐Wei Pang | William H. Green
Source: ChemRxiv (preprint, 2025-04-08)
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 on part 1 of this 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. Switching to DLPNO-CCSD(T)-F12d/cc-pVDZ-F12//GFN2-xTB reduces computational cost by an order of magnitude, with only a modest increase in 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 ; Enthalpy ; Standard enthalpy change of formation ; Quantum ; Thermodynamics ; Chemistry ; Physical chemistry ; Physics ; Quantum mechanics Chemical Thermodynamics and Molecular Structure ; Various Chemistry Research Topics ; Machine Learning in Materials Science
Authors: Henry Agnew | Roya Savoj | Richa Rashmi | Benjamin Savala | Francesco Paesani
Source: ChemRxiv (preprint, 2025-04-17)
Field: Chemistry — Spectroscopy

Molecular Insights into the Influence of Ions on Water Structure. II. Halide Ions in Solution Understanding how halide ions affect the structure and dynamics of water at the molecular level is essential for a wide range of chemical, biological, and environmental processes. In this study, we use molecular dynamics simulations with MB-nrg data-driven many-body potential energy functions to investigate the hydration properties of halide ions in bulk water. The results reveal distinct trends in hydration structure, residence times, dipole moment distributions, and infrared spectral signatures, reflecting variations in ion size, charge density, and polarizability. In particular, fluoride promotes uniquely strong and more directional hydrogen bonds with the surrounding water molecules, which leads to substantial spectral shifts and slower water exchange dynamics. In contrast, heavier halides induce only minimal perturbations on the water hydrogen-bond network, even within the first hydration shell. These insights provide a quantitative framework for understanding ion-specific effects in aqueous systems and set the stage for future studies of more complex environments such as aqueous interfaces and confined systems. Halide ; Ion ; Chemistry ; Chemical physics ; Inorganic chemistry ; Materials science ; Organic chemistry Mass Spectrometry Techniques and Applications ; Spectroscopy and Quantum Chemical Studies ; Various Chemistry Research Topics
Authors: Tingting Cui | Heng Liu | Xin Liu | Yonggang Min
Source: Materials (article, 2025-12-10)
Field: Materials Science — Polymers and Plastics

Interpreting Molecular Descriptors for Glass Transition Temperature Prediction and Design of Polyimides The rational design of polyimides (PIs) with targeted glass transition temperature (<i>T</i><sub>g</sub>) is crucial for advanced microelectronics applications. While data-driven approaches offer promise, there is a pressing need for models that are not only predictive but also physically interpretable, especially with limited datasets. Herein, we present a highly interpretable Quantitative Structure-Property Relationship (QSPR) model for accurate <i>T</i><sub>g</sub> prediction of PIs. Employing a Genetic Algorithm combined with Multiple Linear Regression (GA-MLR), we identified an optimal set of seven molecular descriptors from a curated dataset. The model demonstrates robust predictive performance and strong generalization ability, validated through rigorous statistical tests. Crucially, we provide a deep physicochemical interpretation of the descriptors, unifying their influence under the framework of free volume theory. We show that key descriptors govern <i>T</i><sub>g</sub> by modulating the fractional free volume through distinct mechanisms: descriptors like Chi0n increase free volume by introducing molecular branching that disrupts chain packing, while MinPartialCharge influences <i>T</i><sub>g</sub> through its effect on intermolecular interactions. This mechanistic understanding is translated into clear molecular design guidelines, distinguishing strategies for achieving high-<i>T</i><sub>g</sub> versus processable, low-<i>T</i><sub>g</sub> polymers. Our work establishes a reliable and transparent computational tool that bridges data-driven prediction with fundamental chemical insight for accelerating PIs development. Molecular descriptor ; Glass transition ; Volume (thermodynamics) ; Branching (polymer chemistry) ; Quantitative structure–activity relationship ; Intermolecular force ; Generalization ; Predictive modelling ; Set (abstract data type) Synthesis and properties of polymers ; Machine Learning in Materials Science ; Organic Electronics and Photovoltaics
Authors: Shubham Jaiswal | Chetna Dhand | Neeraj Dwivedi
Source: Small Methods (review, 2025-10-04)
Field: Materials Science — Polymers and Plastics

Shape Morphable Soft Machines and Systems Enabled by Responsive Advanced Materials Soft robotics revolutionizes robotic design by enabling adaptability, flexibility, and motion inspired by biology. Unlike traditional rigid robots, soft robotic systems interact dynamically with their environment, making them ideal for biomedical devices. A key component of these systems is the actuator. Shape memory polymers (SMPs) have emerged as a promising actuation technology due to their programmable shape transformations in response to various stimuli, reversible deformations, and tunable mechanical properties. However, actuation speed, fatigue resistance, and long-term durability remain their major bottlenecks. SMP composites, incorporating foreign materials, can enhance the functionality and durability of soft machines and systems. This work critically examines SMP-driven actuation technologies and their potential in shaping the next generation of soft robotics. The need for smart polymers such as SMPs for soft robotics, the engineering of SMPs on demand, various stimulated responses of soft systems, and finally, the applications of SMP-enabled soft machines and systems for artificial muscles, grippers, sensors (high-temperature detectors), switches, and RFID applications are discussed. Multi-stimuli-responsive SMPs and their integration with artificial intelligence to advance functionality are also highlighted. In the end, the current challenges and prospects in shape-memorable soft machines and systems are discussed. Soft robotics ; Robotics ; Robot ; Soft materials ; Key (lock) ; Component (thermodynamics) ; Shape-memory polymer ; Durability Polymer composites and self-healing ; Advanced Materials and Mechanics ; Advanced Sensor and Energy Harvesting Materials