jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dicho Stratiev | Rosen Dinkov | Ivelina Shishkova | Angel Nedelchev | Iliyan Kolev | Georgi Argirov | Sotir Sotirov | Evdokia Sotirova | Veselina Bureva | Krassimir Atanassov | Dobromir Yordanov | Svetoslav Nenov | Denis D. Stratiev
Source: Processes (article, 2025-10-30)
Field: Chemistry — Analytical Chemistry

Feed Variability Effect on Performance of a Commercial Residue Hydrocracker Feed quality has been found to be related to both reactivity and sediment formation propensity in the residue hydrocracking process defining the conversion level. In this research, unlike other investigations, which examine hydrocrackability of individual vacuum residues, 529 mixtures of 33 vacuum residues were investigated for their hydrocrackability in a commercial H-Oil ebullated bed reactor unit. Intercriteria and regression analyses, together with singular value decomposition (SVD) and deep learning neural network techniques were employed to analyze data and model the vacuum residue conversion in the H-Oil unit. It was found that SVD model provided the best fit of H-Oil conversion training data (standard error of 0.95 wt.%). However, due to overfitting, the SVD model failed to predict H-Oil conversion on unseen data (standard error of 5.1 wt.%). The deep learning neural network exhibited standard error for all data (training, validation and testing) of 1.99 wt.%, while for the test data it was 2.35 wt.%. The linear regression model showed a standard error of 3.9 wt.% for the training data and 7.5 wt.% for the test data. Eleven properties of the vacuum residue (density, microcarbon residue, sulfur, nitrogen, saturate, aromatic, resin, C5-asphaltene, C7-asphaltene, Na, and Ni+V content) seem to be sufficiently informative for the purposes of modeling and predicting H-Oil conversion, thus enabling the assessment of the suitability of a given vacuum residue to be used as a feedstock for the H-Oil process. The best predicting model was found to be the deep learning neural network, which can be used for the purpose of the crude selection process. Artificial neural network ; Residue (chemistry) ; Raw data ; Singular value decomposition ; Regression ; Linear regression ; Test data ; Raw material ; Approximation error Petroleum Processing and Analysis ; Catalysis and Hydrodesulfurization Studies ; Enhanced Oil Recovery Techniques
Authors: Yuji Kubo
Source: Molecules (review, 2025-04-14)
Field: Materials Science — Materials Chemistry

Colorimetric Visualization of Chirality: From Molecular Sensors to Hierarchical Extension The optical sensing of chirality is widely used in many fields, such as pharmaceuticals, agriculture, food, and environmental materials. In this context, the color-based cascade amplification of chirality, coupled with chiral recognition for analytes, provides a low-cost and straightforward detection method that avoids the use of expensive and sophisticated instrumentation. However, the realization of chiral detection using this approach is still challenging because the construction of a three-dimensional optical recognition site is required to easily discern differences in chirality. Therefore, considerable efforts have been dedicated to developing a hierarchical approach based on molecular organization to provide colorimetric sensors for chirality detection. This review covers function-integrated molecular sensors with colorimetric responsive sites based on absorption, fluorescence, and aggregation-induced emission enabled by molecular organization. In line with the hierarchical approach, data-driven chemometrics is a useful method for quantitative and accurate chiral pattern recognition. Finally, colorimetric nanomaterials are discussed, focusing on sensing platforms using noble-metal nanoparticles, carbon dots, and photonic crystal gels. Chirality (physics) ; Nanotechnology ; Context (archaeology) ; Molecular recognition ; Nanomaterials ; Materials science ; Computer science ; Chemistry ; Physics ; Molecule ; Organic chemistry Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Polydiacetylene-based materials and applications
Authors: Assohoun Fulgence Kraidy | Abé Simon Yapi | Jacques Yao Datté | M. Voué | M. El Marssi | Anthony Ferri | Y. Gagou
Source: Condensed Matter (article, 2025-07-14)
Field: Materials Science — Materials Chemistry

High-Performance Natural Dye-Sensitized Solar Cells Employing a New Semiconductor: Gd2Ru2O7 Pyrochlore Oxide We investigated a novel natural dye-sensitized solar cell (DSSC) utilizing gadolinium ruthenate pyrochlore oxide Gd2Ru2O7 (GRO) as a photoanode and compared its performance to the TiO2-Gd2Ru2O7 (TGRO) combined-layer configuration. The films were fabricated using the spin-coating technique, resulting in spherical grains with an estimated mean diameter of 0.2 µm, as observed via scanning electron microscopy (SEM). This innovative photoactive gadolinium ruthenate pyrochlore oxide demonstrated strong absorption in the visible range and excellent dye adhesion after just one hour of exposure to natural dye. X-ray diffraction confirmed the presence of the pyrochlore phase, where Raman spectroscopy identified various vibration modes characteristic of the pyrochlore structure. Incorporating Gd2Ru2O7 as the photoanode significantly enhanced the overall efficiency of the DSSCs. The device configuration FTO/compact-layer/Gd2Ru2O7/Hibiscus-sabdariffa/electrolyte(I−/I3−)/Pt achieved a high efficiency of 9.65%, an open-circuit voltage (Voc) of approximately 3.82 V, and a current density of 4.35 mA/cm2 for an active surface area of 0.38 cm2. A mesoporous TiO2-based DSSC was fabricated under the same conditions for comparison. Using impedance spectroscopy and cyclic voltammetry measurements, we provided evidence of the mechanism of conductivity and the charge carrier’s contribution or defect contributions in the DSSC cells to explain the obtained Voc value. Through cyclic voltammetry measurements, we highlight the redox activities of hibiscus dye and electrolyte (I−/I3−), which confirmed electrochemical processes in addition to a photovoltaic response. The high and unusual obtained Voc value was also attributed to the presence in the photoanode of active dipoles, the layer thickness, dye concentration, and the nature of the electrolyte. Dye-sensitized solar cell ; Materials science ; Cyclic voltammetry ; Dielectric spectroscopy ; Pyrochlore ; Electrolyte ; Hibiscus sabdariffa ; Chemical engineering ; Scanning electron microscope ; Non-blocking I/O ; Oxide ; Analytical Chemistry (journal) ; Electrochemistry ; Phase (matter) ; Electrode ; Chemistry ; Composite material ; Organic chemistry Nuclear materials and radiation effects ; Advanced Condensed Matter Physics ; Advanced Photocatalysis Techniques
Authors: Siti Azimatul Luthfiyyah | Sunaryono Sunaryono | Hazri Bakhtiar | Arif Hidayat | Nurul Hidayat
Source: Trends in Sciences (review, 2025-07-26)
Field: Materials Science — Materials Chemistry

Advances in Optical Fiber Sensing Using Gold/Silver Nanocomposites: A Review The past 20 years have witnessed a marked increase in research focused on optical fiber sensors for their potential to detect changes due to physical and chemical properties. Various modifications on optical fibers have been explored to improve their sensing performance and application-specific suitability. One such approach involves the coating of optical fibers with plasmonic nanomaterials, enabling the activation of Localized Surface Plasmon Resonance (LSPR) effect. Among these, gold/silver nanocomposites (Au/Ag NCs) demonstrate strong LSPR effect that can enhance the sensitivity of optical fiber sensors. Diverse functionalization techniques have been developed to integrate these plasmonic nanomaterials effectively, while the structural configuration of the optical fiber is equally critical for attaining optimal sensing performance. This review highlights the role of Au/Ag NCs in advancing optical fiber capabilities, explores relevant fiber structures, examines the limitations and challenges, and discusses potential applications in healthcare, industry, and environmental monitoring. Finally, future research directions are proposed to guide further innovation in this rapidly evolving field. HIGHLIGHTS Gold/silver nanocomposites (Au/Ag NCs) enhance the performance of optical fiber sensors. The structure and type of optical fiber significantly influence the sensor performance. Au/Ag NCs coating methods on optical fiber include self-assembly monolayer, electroless plating, and photochemical deposition. Optical fiber sensors functionalized with Au/Ag NCs show great potential for applications in healthcare, industry, and environmental monitoring. GRAPHICAL ABSTRACT Materials science ; Nanotechnology ; Colloidal gold ; Nanocomposite ; Optical fiber ; Optical sensing ; Optoelectronics ; Optics ; Nanoparticle ; Physics Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Tongwei Wu | Yao Sun | Xiaoxi Guo | Lin Tian | Yanning Zhang | Haitao Zhao | Yuen Wu
Source: ChemRxiv (preprint, 2025-06-13)
Field: Materials Science — Materials Chemistry

A large language models-guided grand canonical DFT framework for accelerating the discovery of efficient electrocatalysts Electrocatalysis plays a critical role in enabling sustainable energy conversion. However, current electrocatalysts rely on either scarce precious metals or transition metals with limited efficiency, and their design strategies lack systematic frameworks for precise optimization guidance. Here, we propose an advanced collaborative framework that integrates large language models (LLMs) with grand-canonical density functional theory (GCE-DFT) calculations, thereby accelerating the discovery of high-performance and cost-effective electrocatalysts. As a validation, this framework was applied to the promising Fe-N-C oxygen reduction reaction (ORR) catalysts, demonstrating that asymmetric FeN4 coordination with mixed pyrrolic (pyrr-N) and pyridinic (pyri-N) nitrogen environments serves as a critical determinant of catalytic activity and stability. Furthermore, the integration of GCE-DFT calculations with in situ X-ray absorption spectroscopy demonstrated that the asymmetric FeN4 coordination in Fe-N-C eletrocatalysts induces symmetric electron density redistribution at the Fe active sites, thereby significantly enhancing both catalytic activity and stability. Based on these insights, we synthesized asymmetric N-coordinated FeN4 site in Fe-N-C electrocatalysts via templating-assisted pyrolysis for acidic ORR applications, achieving superior cycle stability (>90% activity retention after 30,000 cycles) while maintaining high activity. This breakthrough successfully addresses the long-standing trade-off riddle between stability and activity in acidic ORR systems. Overall, the proposed framework significantly accelerates the discovery of high-performance and cost-effective electrocatalysts by integrating data-driven artificial intelligence with atomistic-level simulations. Computer science Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Hyeokjun Kim | Taek Seong Lee | Hyung Taek Kim | Ji‐Young Shin | Jisu Baek | Jaeyoung Jang | In Hwan Jung
Source: Journal of Polymer Science (article, 2025-01-14)
Field: Materials Science — Materials Chemistry

Highly Simplified <scp>FeCl<sub>3</sub></scp>‐Assisted Copolymerization and Doping for Organic Thermoelectrics ABSTRACT The FeCl 3 is a well‐known oxidizing agent for oxidative polymerization as well as a p‐type dopant for making conducting polymers, but no studies have yet explored the simultaneous use of FeCl 3 for both polymerization and doping of organic soluble conjugated polymers (CPs) in organic thermoelectric (OTE) applications. In this study, soluble 4 H ‐cyclopenta[2,1‐ b :3,4‐ b ′]dithiophene (CPDT)‐ co ‐3,4‐ethylenedioxythio‐phene (EDOT) polymers were successfully synthesized via a simple FeCl 3 ‐assisted oxidative coupling reaction at room temperature. In addition, FeCl 3 doping of the synthesized polymers enabled them to exhibit thermoelectric properties. The addition of 10% ~ 20% EDOT in a polymer chain gave a synergetic effect on the electrical conductivity and power factor (PF) in the OTE devices. In that ratio, the strong electron‐donating EDOT facilitates doping in the CPs, and the planar CPDT backbones stabilize the generated polaron/bipolaron species through efficient π‐electron delocalization, resulting in the highest electrical conductivity. At the optimal EDOT ratio of 20%, a more than 10‐fold enhancement in PFs was achieved reaching up to 0.182 ± 0.021 μ W m −1 K −2 . The EDOT moiety, except in PEDOT:PSS, is rarely found in conjugated copolymers, but its proper incorporation is expected to enhance thermoelectric properties of the organic soluble CPs. Copolymer ; Doping ; Materials science ; Thermoelectric materials ; Chemical engineering ; Polymer chemistry ; Polymer ; Optoelectronics ; Engineering ; Composite material Advanced Thermoelectric Materials and Devices ; Conducting polymers and applications ; Organic Electronics and Photovoltaics
Authors: Si Sun | Fangzhen Tian | Huanhuan Qiao | Qi Xin | Xinxu Zhang | Nan Song | Yuxing Yan | Ling Liu | Yili Wang | Lijie Zhang | Ke Chen | J L Yang | Shu Zhang | Jianning Zhang | Yonghui Li | Hao Wang | Xiao‐Dong Zhang
Source: Advanced Materials (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Metabolic Enzyme‐Inspired Molecular Clusters for Advanced Type 2 Diabetic Complications by Alleviating Immunosuppression ABSTRACT Type 2 diabetes mellitus is the most prevalent disease in the world, with one‐tenth of the population suffering from the disease, and the most critical challenges are its complications that induce high disability and mortality rates. The state‐of‐the‐art therapeutic agents can manage glucose but fail to prevent renal failure as well as neurodegeneration with immunosuppression. Herein, we developed a deep learning design strategy that exploits the ‘size‐fitting effect’ to engineer an atomic‐precision metal cluster for preventing diabetic complications by targeting metabolic abnormality and immunosuppression. The designed AuZn cluster achieves almost 100% α‐amylase inhibition and 88% α‐glucosidase inhibition, resulting in the normalized glycated hemoglobin and sustained glucose control. The intrinsic redox properties reduce oxidative stress damage, promoting β‐cell regeneration and metabolic stress alleviation. Consequently, the renal function, the most prevalent complications, shows that glomerular filtration can be restored to normal levels without urinary protein, while the clinical dulaglutide is not show any improvement. The key marker during early neurocognitive disorders, the amyloid precursor protein (APP) induced by complications, can be effectively suppressed, and diabetes induced organelle degeneration in neurons can be restored. Diabetes mellitus ; Oxidative stress ; Neurodegeneration ; Glycated hemoglobin ; Diabetic nephropathy ; Disease ; Population ; Immunosuppression ; Kidney disease Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Alzheimer's disease research and treatments
Authors: H.M. Abd El Salam
Source: Environmental Progress & Sustainable Energy (article, 2025-12-19)
Field: Chemistry — Inorganic Chemistry

Sustainable green synthesis and statistical optimization of Co@Cu– <scp>MOF</scp> /alginate beads for efficient antibiotic adsorption from aqueous systems Abstract The growing prevalence of antibiotic pollutants in aquatic habitats presents significant health and ecological hazards, making the creation of efficient and sustainable remediation techniques imperative. Co@Cu–MOF embedded alginate beads were manufactured in this study adopting a facile and environmentally friendly method, and they were utilized as a novel adsorbent to remove antibiotics from aqueous solutions. Alginate's inexpensive cost and biological compatibility as a natural support matrix were utilized in the manufacturing procedure in order to boost the MOF material's stability, flexibility, and recyclability. A series of characterization approaches, including x‐ray diffraction patterns, Fourier transform infrared spectroscopy, scanning electron microscopy, and energy‐dispersive x‐ray spectroscopy analyses were used to describe the synthesized material's shape and elemental composition, indicating that the in situ synthesis of Co@Cu‐based MOFs with alginic acid was achieved. Response surface methodology was used to comprehensively optimize the adsorption performance at pH (3–10), initial antibiotic concentration (10–100 mg/L), adsorbent dosage (0.02–0.35 g/L), and duration of contact (5–80 min) and validate the model. The Co@Cu–MOF/alginate beads demonstrated a high adsorption capacity of 95.75 mg/g and great affinity toward antibiotic molecules under the ideal conditions (pH 9, 45 min contact duration, and 0.17 g adsorbent dosage). Multilayered adsorption was confirmed by the equilibrium following the Freundlich isotherm and the kinetic data fitting well with the pseudo‐second‐order model. After four regeneration cycles, the beads maintained more than 90% of their efficiency, demonstrating stability and reusability and ecofriendly treatment of antibiotic‐polluted water. Adsorption ; Aqueous solution ; Fourier transform infrared spectroscopy ; Freundlich equation ; Response surface methodology ; Alginic acid ; Environmentally friendly ; Biocompatibility Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Covalent Organic Framework Applications
Authors: Xiaotong Liu | Yuhang Wang | Tao Yang | Xingchen Liu | Xiaodong Wen
Source: ChemRxiv (preprint, 2025-02-12)
Field: Materials Science — Materials Chemistry

AlchemBERT: Exploring Lightweight Language Models for Materials Informatics The emergence of large language models (LLMs) has spurred numerous applications across various domains, including material design. In this field, an increasing number of generative models focus on directly generating materials with desired properties more accurately, moving away from the traditional approach of enumerating vast numbers of candidates and relying on computationally intensive screening algorithms. However, we assert that without accurate prediction capabilities, effective material design is unattainable, as generating necessary structures becomes futile if their quality cannot be reliably evaluated. Matbench provides an excellent foundation for predictive tasks, yet prior efforts with LLMs have primarily focused on composition-related tasks using models such as GPT or LLaMA. In this study, we revisit BERT, a relatively small LLM with 110 million parameters, which is significantly smaller than GPT or LLaMA models containing billions of parameters. Remarkably, we demonstrate that BERT-base achieves comparable performance to these larger models in material property prediction tasks. Extending beyond composition tasks, we introduce BERT’s application in structure prediction using CIF (Crystallographic Information File) data and natural language descriptions of structures. Our results rival state-of-the-art composition models such as CrabNet and, in several tasks across datasets ranging from a few hundred to over a hundred thousand samples, even surpass traditional structure-based and knowledge-driven models. Additionally, on the latest Matbench test task, Matbench-Discovery, our model outperformed the Voronoi RF model and achieved MAE results comparable to other models that rely solely on energy predictions. Our findings provide a new reference point for future LLM applications in material design, offering valuable insights for leveraging language model in this domain and emphasizing natural language descriptions over conventional model-centric design. We term this application of BERT in material design AlchemBERT, signifying its novel role in bridging natural language and structural representations. Informatics ; Computer science ; Engineering ; Electrical engineering Machine Learning in Materials Science
Authors: Jakub Lach | Kamil Wróbel | Wojciech Tokarz | J. T. WROBEL | Piotr Podsadni | A. Czerwiński
Source: Energies (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Hydrogen Storage Systems Supplying Combustion Hydrogen Engines—Review The hydrogen drive is a promising zero-emission solution in transportation that can be realised through hydrogen internal combustion engines or hydrogen fuel cells. The hydrogen combustion engine’s advantage lies in the simplicity and greater maturity of the technology. At the same time, these solutions require appropriate fuel storage systems. The publication presents an overview of the currently used and developed hydrogen storage technologies. The main focus is placed on hydrogen tanks intended for vehicles powered by hydrogen internal combustion engines. The manuscript describes physical storage, including popular pressurised and cryogenic tanks. Additionally, technologies which can lead to improvements in the future, such as metallic and non-metallic hydrides and sorbents, are presented. The characteristics of the storage technologies in connection with the combustion engines are shown, as well as the outlook for the future of these solutions and their recent uses in vehicles. When focusing on vehicular and combustion applications, their specifics make physical storage methods the leading technology for now. Hydrogen storage today is still not competitive with fossil fuels; however, there are promising developments than can lead to achieving the requirements needed for its viable storage and use. Combustion ; Hydrogen storage ; Hydrogen technologies ; Hydrogen ; Hydrogen vehicle ; Hydrogen fuel enhancement ; Hydrogen fuel Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Advanced Combustion Engine Technologies
Authors: Macdenis Egbuhuzor | C. O. Akalezi | Sarah B. Ulaeto | Damian Njoku | Emeka E. Oguzie
Source: Discover Materials (article, 2025-11-27)
Field: Materials Science — Metals and Alloys

A review of recent developments on the localized corrosion of mild steel used in the oil and gas industry Localized corrosion in oil and gas facilities has been a significant challenge due to its catastrophic consequences on pipelines and installations. This is affected mainly by the heterogeneous nature of the environment in which the pipeline and other oil and gas facilities operate. The substantial economic impact and cost of localized corrosion on these facilities have shifted research interest to this area. This work reviewed holistic and recent developments on localized corrosion of mild steel structures in the petroleum and gas sectors of the economy. Some types of localized corrosion commonly encountered in the industry are discussed, including the environments that give rise to localized corrosion, the effects of grain size and grain boundaries on the microstructure of mild steel, and its resistance to localized corrosion. We extensively reviewed various corrosion assessment techniques, which depend on the type of material deployed, the corrosion mechanism, the environment, and different mitigation strategies employed to reduce localized corrosion of mild steel structures in the petroleum and allied industries. Finally, we reviewed recent advancements by integrating new mechanistic insights, molecular modelling and simulations of corrosion, innovative mitigation strategies, and advanced monitoring technologies that are pushing the boundaries of understanding and managing localized corrosion in the oil and gas industry. Corrosion ; Petroleum industry ; Pipeline transport ; Fossil fuel ; Petroleum ; Microstructure ; Work (physics) Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis