jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kristina Tolić Čop | Stjepan Jozinović | David Visentin | Dejan Milenković | Petra Vukovinski | Ramona Petko | Robert Vianello | Dragana Mutavdžić Pavlović
Source: Polymers (article, 2025-11-30)
Field: Chemistry — Analytical Chemistry

Preparation and Application of Sulfamethoxazole-Imprinted Polymer on Solid-Phase Extraction of Pharmaceuticals from Water Pharmaceutical compounds are small, invisible, and biologically powerful molecules that, due to widespread production and consumption, have become part of the environment, causing long-term adverse effects on biota even at low doses. Advances in sensitive and reliable analytical methods have made their detection possible in complex environmental matrices such as wastewater. Given the large number of synthesized pharmaceuticals with various therapeutic purposes, the occurrence of a synergistic effect is to be expected, interfering with their analysis. Therefore, the challenging analysis is often improved through the application of different sample preparation techniques. This paper includes the development of an SPE-HPLC-DAD method for the determination of eleven pharmaceuticals from water samples. To achieve better recoveries for the specified pharmaceutical (sulfamethoxazole) and possibly other components of the mixture, a sulfamethoxazole-imprinted polymer (MIP-SMETOX) was prepared and used in combination with a commercial sorbent (Oasis HLB) for MIP-SPE-HPLC-DAD. After optimization of the extraction conditions, both methods were validated. The LOD was 0.1 to 0.5 µg/L for SPE-HPLC-DAD and 0.1 to 0.25 µg/L for MIP-SPE-HPLC-DAD, depending on the pharmaceuticals. The matrix effect is different (77-196%) for both methods. A decrease in the signal for sulfamethoxazole (77%) was observed with SPE-HPLC-DAD, while MIP-SMETOX as a sorbent is not suitable for procaine (196%), and this is also the highest matrix effect. To extend the data obtained, additional in silico methods were used to gain deeper insights into the nature and strength of the binding interactions. Both methods (with and without MIP) confirmed their purpose by determining various validation performance features, and the final goal of the developed methods was tested using complex wastewater. The MIP-SMETOX produced justified its production, as the MIP-SPE-HPLC-DAD method is generally slightly better than the method using only a commercial sorbent. Sorbent ; Extraction (chemistry) ; Matrix (chemical analysis) ; Complex matrix ; Polymer ; Sample preparation Analytical chemistry methods development ; Pharmaceutical and Antibiotic Environmental Impacts ; Analytical Chemistry and Chromatography
Authors: Thomas Doucet | Jean-François Mogniotte | Raphaël Amiot | Alaa Hijazi | Pascal Venet | Minh‐Quyen Le | Pierre-Jean Cottinet
Source: Micromachines (article, 2025-11-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multiphysics Measurement Method for Supercapacitors State of Health Determination This work presents a comparative study on the ageing of supercapacitors and a method for monitoring their state of health (SoH) through mechanical deformation. This study aims to evaluate the accelerated ageing behaviours of these systems under specific cycling conditions and temperatures, allowing the establishment of a correlation between SoH and casing deformation in supercapacitors. Experimental ageing tests revealed supercapacitors displayed an initial "burning" phase followed by a linear ageing trend. Strain gauges were employed to measure the mechanical deformation of supercapacitor casings, providing real-time insights into their SoH. Capacitance fading in supercapacitors was modelled using Brunauer-Emmett-Teller (BET) theory, hypothesizing that gas adsorption during ageing significantly contributes to performance decline. Model predictions were validated against experimental data, demonstrating a clear correlation between capacitance fading, internal resistance, remaining energy, and casing deformation. This work highlights the potential of mechanical deformation monitoring as a practical and non-invasive approach for assessing the SoH of supercapacitors. Multiphysics ; Capacitance ; Supercapacitor ; Casing ; Deformation (meteorology) ; Work (physics) ; Ageing ; Condition monitoring ; Mechanical system Supercapacitor Materials and Fabrication ; Electric and Hybrid Vehicle Technologies ; Fuel Cells and Related Materials
Authors: Agnieszka Rybarczyk | Jakub Zdarta
Source: Earth Environmental Sustainability (article, 2025-10-09)
Field: Chemistry — Analytical Chemistry

Enzymes in the Removal of Harmful Substances: The Potential of Biotechnology in Environmental Protection In the face of growing environmental pressures, enzymes are emerging as powerful and versatile tools for combating pollution. With their exceptional specificity, ability to function under mild conditions, and minimal environmental impact, enzymes offer a sustainable alternative to traditional remediation methods. They can effectively break down and neutralize a wide range of pollutants—including pesticides, pharmaceuticals, heavy metals, dyes, and microplastics—without generating toxic by-products. Innovations such as enzyme immobilization, microbial consortia, and hybrid technologies have significantly enhanced their stability and performance in real-world conditions. Advances in protein engineering and the use of artificial intelligence now enable the design of tailor-made enzymes with improved resilience and substrate range. Enzymes also play a vital role in the circular economy by transforming waste into valuable secondary raw materials, biofuels, and biodegradable products. While challenges remain in scaling up these technologies and reducing costs, the potential of enzyme-based biotechnologies is immense, positioning them as a promising path toward environmentally friendly and efficient solutions for pollution control, resource recovery, climate-resilient development, and as a cornerstone of future environmental strategies. Environmental pollution ; Environmentally friendly ; Environmental biotechnology ; Emerging technologies ; Function (biology) ; Environmental remediation ; Resilience (materials science) ; Resource (disambiguation) ; Environmental restoration Dye analysis and toxicity
Authors: Arefeh Naghizadeh | Ahmadreza Jafari-Sirizi | Fahimeh Hadavimoghaddam | Saeid Atashrouz | Ali Abedi | Abdolhossein Hemmati‐Sarapardeh | Ahmad Mohaddespour
Source: Scientific Reports (article, 2025-10-08)
Field: Chemistry — Inorganic Chemistry

Toward accurate prediction of N2 uptake capacity in metal-organic frameworks The efficient and cost-effective purification of natural gas, particularly through adsorption-based processes, is critical for energy and environmental applications. This study investigates the nitrogen (N<sub>2</sub>) adsorption capacity across various Metal-Organic Frameworks (MOFs) using a comprehensive dataset comprising 3246 experimental measurements. To model and predict N<sub>2</sub> uptake behavior, four advanced machine learning algorithms-Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and Gaussian Process Regression with Rational Quadratic Kernel (GPR-RQ)-were developed and evaluated. These models incorporate key physicochemical parameters, including temperature, pressure, pore volume, and surface area. Among the developed models, XGBoost demonstrated superior predictive accuracy, achieving the lowest root mean square error (RMSE = 0.6085), the highest coefficient of determination (R<sup>2</sup> = 0.9984), and the smallest standard deviation (SD = 0.60). Model performance was rigorously validated using statistical metrics and graphical analysis. Trend consistency with experimental data confirmed that XGBoost accurately captures the effect of pressure on N₂ uptake. Additionally, SHAP (Shapley Additive Explanations) analysis identified temperature as the most influential factor in adsorption prediction. Finally, an outlier assessment using the Leverage method indicated that approximately 94% of the data points were statistically valid and within the model's applicability domain. Outlier ; Leverage (statistics) ; Boosting (machine learning) ; Data point ; Support vector machine ; Gradient boosting ; Artificial neural network ; Regression ; Mean squared error ; Multivariate statistics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Simón Hettler | Raúl Arenal
Source: Micron (article, 2025-05-22)
Field: Materials Science — Materials Chemistry

On the relationship between oxygen K edge fine structure and cobalt/nickel content in magnetite-based core-shell nanoparticles Iron oxide nanoparticles (NPs) are used in a broad range of applications and offer the possibility of fine tuning their properties by forming hybrid core-shell structures. Scanning transmission electron microscopy (STEM) combined with electron energy loss spectroscopy (EELS) is frequently used to study such NPs at the local scale. Specifically, the energy loss near edge structure (ELNES) provides rich information on the local chemistry. Here, the dependence between the ELNES signal of the oxygen K edge and the incorporation of cobalt and nickel into the shell of magnetite NPs is investigated quantitatively using an intensity ratio between specific energy windows of the O-K edge. The analysis shows that both the local and global cobalt and nickel content of a NP influence this relationship. In addition, the overlap between the Fe-L<sub>1</sub> and Ni-L<sub>2,3</sub> edges is studied, the impact of a supporting film on the ELNES analysis is discussed, and the importance of a clean sample preparation is demonstrated. Cobalt ; Nickel ; Materials science ; Magnetite ; Nanoparticle ; Shell (structure) ; Enhanced Data Rates for GSM Evolution ; Core (optical fiber) ; Metallurgy ; Chemical engineering ; Nanotechnology ; Composite material ; Computer science Magnetic Properties and Synthesis of Ferrites ; Characterization and Applications of Magnetic Nanoparticles ; Iron oxide chemistry and applications
Authors: Kaichuang Yang | Jie Zheng | Ying Lu | Ziyun Zhang | Hui Zhang | Zhi Liu | Qiyang Lu
Source: Nature Communications (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

Elucidating the role of interfacial charge transfer on the oxygen incorporation/evolution reactions for solid oxide cells The oxygen incorporation and evolution reactions (OIR/OER) at air electrodes are key challenges limiting the performance of reversible solid oxide cells (SOCs). Surface modification using binary oxides has emerged as a promising strategy to enhance OIR/OER kinetics, with PrO<sub>x</sub> as a popular choice of the modification layer. However, the mechanisms behind this improvement of reaction kinetics remain unclear. In this study, we combine insights from electrochemical measurements and operando X-ray absorption spectroscopy to reveal that interfacial charge transfer plays a pivotal role in enhancing the OIR/OER activity in La<sub>0.6</sub>Sr<sub>0.4</sub>Co<sub>0.2</sub>Fe<sub>0.8</sub>O<sub>3-δ</sub> (LSCF) with PrO<sub>x</sub> surface modification. The charge transfer increases the hole concentration in LSCF, which can be quantitatively correlated with accelerated OIR/OER kinetics (up to ~70 times enhancement) over a broad range of oxygen chemical potential. We further demonstrate this mechanism in realistic SOCs devices, showing enhanced performance in both fuel cell and electrolysis modes. Our work provides critical insights into the role of interfacial charge transfer and defect chemistry in surface-modified SOCs electrodes, offering a pathway to optimize SOCs performance through surface modifications. Oxide ; Kinetics ; Electrochemistry ; Electrolysis ; Redox ; Oxygen ; Surface modification ; Charge (physics) ; Surface charge Advancements in Solid Oxide Fuel Cells ; Electrocatalysts for Energy Conversion ; Electronic and Structural Properties of Oxides
Authors: Xueqing Yan | Hakan Akgün | Kenji Kawaguchi | N. Duane Loh | Ching Hua Lee
Source:  (preprint, 2025-06-10)
Field: Materials Science — Materials Chemistry

HSG-12M: A Large-Scale Benchmark of Spatial Multigraphs from the Energy Spectra of Non-Hermitian Crystals AI is transforming scientific research by revealing new ways to understand complex physical systems, but its impact remains constrained by the lack of large, high-quality domain-specific datasets. A rich, largely untapped resource lies in non-Hermitian quantum physics, where the energy spectra of crystals form intricate geometries on the complex plane -- termed as Hamiltonian spectral graphs. Despite their significance as fingerprints for electronic behavior, their systematic study has been intractable due to the reliance on manual extraction. To unlock this potential, we introduce Poly2Graph: a high-performance, open-source pipeline that automates the mapping of 1-D crystal Hamiltonians to spectral graphs. Using this tool, we present HSG-12M: a dataset containing 11.6 million static and 5.1 million dynamic Hamiltonian spectral graphs across 1401 characteristic-polynomial classes, distilled from 177 TB of spectral potential data. Crucially, HSG-12M is the first large-scale dataset of spatial multigraphs -- graphs embedded in a metric space where multiple geometrically distinct trajectories between two nodes are retained as separate edges. This simultaneously addresses a critical gap, as existing graph benchmarks overwhelmingly assume simple, non-spatial edges, discarding vital geometric information. Benchmarks with popular GNNs expose new challenges in learning spatial multi-edges at scale. Beyond its practical utility, we show that spectral graphs serve as universal topological fingerprints of polynomials, vectors, and matrices, forging a new algebra-to-graph link. HSG-12M lays the groundwork for data-driven scientific discovery in condensed matter physics, new opportunities in geometry-aware graph learning and beyond. Hamiltonian (control theory) ; Metric (unit) ; Graph ; Spectral line ; Quantum ; Pipeline (software) ; Benchmark (surveying) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topological and Geometric Data Analysis
Authors: Minghan Hu
Source: ChemSystemsChem (article, 2025-10-08)
Field: Materials Science — Materials Chemistry

Microcapsule‐Based Self‐Reporting Materials: Moving Beyond Detection Toward Quantification of Microscale Damage Abstract Inspired by biological systems that can visualize damage through optical signal such as bruising or bleeding, microcapsule‐based self‐reporting materials offer a promising strategy for autonomous detection of mechanical stress. These systems embed dye‐filled microcapsules in polymer matrices, which rupture under stress to release optical signals. However, most of these systems only indicate whether damage has occurred, without providing information about how much damage or force was involved. This concept paper introduces self‐reporting microcapsule systems and explores how they can be further developed to quantify microscale mechanical damage. Recent innovations enable force quantification through intensity‐based outputs, ratiometric spectral shifts, multilayer capsule architectures, and rupture threshold engineering. By overcoming the current limitation of damage detection, force‐resolved self‐reporting materials open new possibilities for smarter, safer, and more durable systems in aerospace and infrastructure. Microscale chemistry ; Aerospace ; Stress (linguistics) ; Mechanical system ; SIGNAL (programming language) ; Current (fluid) ; Structural health monitoring Luminescence and Fluorescent Materials ; Dendrimers and Hyperbranched Polymers ; Polydiacetylene-based materials and applications
Authors: S Amrutha | Beni B. Dangi | Oliver Steinbock
Source: APL Machine Learning (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Synthetic images from generative AI for compositional analysis of dried solution patterns Image-based identification of solutes from dried drop stains offers a low-cost, scalable alternative to traditional chemical analysis, enabled by artificial intelligence and annotated image libraries. While recent advances in robotic droplet imaging have facilitated the collection of such datasets, experimental acquisition remains a key bottleneck. Here, we explore whether synthetic image generation can supplement or replace real data in the context of salt classification. Using Stable Diffusion with low-rank adaptation, we generate 133 000 synthetic images from as few as N = 1–128 experimental images per salt across seven common inorganic solutes. Synthetic images become visually indistinguishable from real ones for N ≥ 6; however, quantitative analysis based on 47 image metrics reveals subtle differences that vanish around N = 128. When used to train random forest, XGBoost, and multilayer perceptron (MLP) models, synthetic data alone achieve classification accuracies of up to 90% in some low-data regimes. Synthetic augmentation offers a powerful strategy for expanding training sets and enabling image-based chemical classification in data-scarce scenarios, even if it seldom outperforms models trained on real data. The realism of synthetic images also highlights the growing need for tools that detect AI-generated scientific images to ensure data integrity. Synthetic data ; Pattern recognition (psychology) ; Context (archaeology) ; Identification (biology) ; Scalability ; Image synthesis ; Image processing ; Image (mathematics) Machine Learning in Materials Science ; Calcium Carbonate Crystallization and Inhibition ; Crystallization and Solubility Studies
Authors: Caroline M. Carter | Jan Brossette | Hendrik Zipse
Source: Chemistry - A European Journal (article, 2025-11-02)
Field: Chemistry — Organic Chemistry

Size and Polarizability as Design Principles for Stereoselective Catalysis Molecular volumes and isotropic polarizabilities of a larger number of carbocyclic π-systems ranging from benzene on the small to heptacene on the large end are provided in order to facilitate the analysis of positively size-dependent catalytic processes, where an increase in substrate or catalyst size leads to an increase in reaction rate and/or reaction stereoselectivity. The utility of this approach is demonstrated for selected stereoselective catalytic processes from the contemporary literature. Catalysis ; Stereoselectivity ; Polarizability ; Substrate (aquarium) ; Isotropy ; Benzene Synthesis and Properties of Aromatic Compounds ; Catalytic Cross-Coupling Reactions ; Cyclization and Aryne Chemistry
Authors: Stefania Riva | Fredrik O. L. Johansson | Sergei M. Butorin | Corrado Comparotto | Olivier Donzel‐Gargand | P. Thakur | Tien‐Lin Lee | Henry Nameirakpam | M. Venkata Kamalakar | Soham Mukherjee | Jonathan J. S. Scragg | Håkan Rensmo
Source: ACS Applied Energy Materials (article, 2025-08-14)
Field: Materials Science — Materials Chemistry

Surface Processing and Characterization of Stoichiometry-Varied BaZrS<sub>3</sub> Thin Films The chalcogenide perovskite BaZrS3 is a semiconductor that exhibits a high absorption coefficient and is composed of earth-abundant elements, making it a promising candidate for sustainable optoelectronic devices. To integrate BaZrS3 thin films into devices, one needs to obtain clean surfaces and characterize them thoroughly. Herein, we report a sputtering-annealing method to produce clean surfaces of prefabricated BaZrS3 thin films with varying metal ratios (Ba-rich, stoichiometric, and Zr-rich). This method combines Ar sputtering with high-temperature annealing (600 and 750 °C) in ultra-high vacuum. Depth-profiling via photoelectron spectroscopy with soft (950 eV) and hard X-rays (6.6 keV) confirms that this processing route substantially mitigates undesired surface oxidation of the films, revealing predominantly core level peaks characteristic of the BaZrS3 perovskite. As a drawback, the sputtering process also produces Zr0, which persists in the Zr-rich sample even after the annealing treatment. In contrast, the Ba-rich and stoichiometric BaZrS3 samples converge to similar surface compositions free of Zr0, and the low roughness of the Ba-rich thin film indicates its preference for device integration. While the thermal treatment modifies the surface chemistry, the bulk characteristics, e.g., nominal metal-ratio, orthorhombic structure, and crystallite sizes, remain unaffected. However, high-temperature annealing affects band realignment with respect to the Fermi level, resulting in n-type doping characteristics. By correlating the experimentally measured valence band to the density functional theory calculated molecular orbital picture, we assign the valence band features to specific elemental orbitals and their interactions. The proposed cleaning procedure has the potential to advance the application of BaZrS3 in layered devices, such as photovoltaic cells and photodetectors. Stoichiometry ; Characterization (materials science) ; Thin film ; Materials science ; Surface (topology) ; Analytical Chemistry (journal) ; Chemical engineering ; Mineralogy ; Nanotechnology ; Chemistry ; Physical chemistry ; Mathematics ; Environmental chemistry ; Geometry ; Engineering Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials ; Chalcogenide Semiconductor Thin Films
Authors: Ann Power | Cara‐Lena Nies | Stefan Schulz
Source: ArXiv.org (preprint, 2025-12-15)
Field: Materials Science — Materials Chemistry

Developing a valence force field model for wurtzite semiconductors by exploiting similarities with [111]-oriented zinc blende systems: The case of wurtzite boron nitride, III-N materials and (B,In,Ga)N alloys Controlling the crystal phase and lattice mismatch of semiconductors offers a powerful route to engineer electronic and optical properties of heterostructures. As a consequence, semiconductors in the wurtzite phase are increasingly sought after, superseding the thermodynamically favored cubic zinc blende phase. Empirical atomistic modeling, required for large scale simulations of heterostructures and their properties, relies heavily on valence force field (VFF) methods to find the equilibrium atomic positions in an alloy. For zinc blende crystals, VFF models are well-established. In the case of wurtzite, such parameters are frequently adopted without rigorous analysis, despite subtle but consequential differences from the zinc blende structure. Such an approach can compromise accuracy in describing material properties, since the structural differences between zinc blende and wurtzite directly influence electronic and optical characteristics. Based on the analytical VFF model by Tanner et al., and using structural similarities between wurtzite and [111]-oriented zinc blende, we construct a wurtzite VFF without introducing additional parameters. Our framework relies on analytic expressions and minimization routines to project zinc blende models onto wurtzite systems. Beyond elastic tensors, we train the model to reproduce bond length asymmetries and band gaps by using output of the VFF model in density functional theory calculations. Applied to wurtzite III-N compounds and BN, the model accurately reproduces targeted observables but also properties it has not been trained on, including the internal parameter u. We further validate the model on highly mismatched alloys such as (B,Ga)N and (B,In,Ga)N, exhibiting good agreement between VFF and density functional theory results when using identical supercells in these calculations. Wurtzite crystal structure ; Density functional theory ; Zinc ; Valence (chemistry) ; Semiconductor ; Heterojunction ; Crystal structure ; Band gap Machine Learning in Materials Science ; GaN-based semiconductor devices and materials ; Boron and Carbon Nanomaterials Research