jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ajay Beniwal | Rahul Gond | Xenofon Karagiorgis | Brajesh Rawat | Chong Li
Source: IEEE Sensors Letters (article, 2025-01-08)
Field: Materials Science — Polymers and Plastics

Room-Temperature-Operated Fe<sub>2</sub>O<sub>3</sub>/PANI-Based Flexible and Eco-Friendly Ammonia Sensor With Sub-ppm Detectability In this work, a room temperature (RT; ∼27 °C) operated ferric oxide/polyaniline (Fe <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ;sol;PANI) composite-based flexible ammonia (NH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ) sensor with substantial sensing performance is reported. Initially, interdigitated electrodes (IDEs) were screen printed (using graphene-carbon based ink) on a bio-degradable paper substrate. Further, PANI nanofibers were electrospun on printed IDEs, followed by drop casting a layer of Fe <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> . X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR) studies were performed to confirm the composite formation followed by scanning electron microscopy (SEM) analysis to examine the sensing surface morphology. The ammonia sensing performance was examined within the range 0.5 ppm (i.e., 500 ppb) to 50 ppm, with a 1.99% response achieved even at 0.5 ppm. The response/recovery times were noted as 950 s / 250 s, towards 0.5 ppm of ammonia. Additionally, selectivity towards interference gases including carbon dioxide (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ), nitrogen dioxide (NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ), carbon monoxide (CO) and sulfur dioxide (SO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) were also investigated. The proposed sensing mechanism of the composite material towards ammonia gas detection is also presented. Graphene ; Materials science ; Ammonia ; Nuclear chemistry ; Fourier transform infrared spectroscopy ; Analytical Chemistry (journal) ; Chemical engineering ; Nanotechnology ; Chemistry ; Organic chemistry Conducting polymers and applications ; Analytical Chemistry and Sensors ; Gas Sensing Nanomaterials and Sensors
Authors: Idalina Domingos | Miguel Ferreira | José Ferreira | Bruno Esteves
Source: Sustainability (article, 2025-07-24)
Field: Chemistry — Organic Chemistry

Olive Tree (Olea europaea) Pruning: Chemical Composition and Valorization of Wastes Through Liquefaction Olive tree branches (OB) and leaves (OL) from the Viseu region (Portugal) were studied for their chemical composition and liquefaction behavior using polyalcohols. Chemical analysis revealed that OL contained higher ash content (4.08%) and extractives, indicating more bioactive compounds, while OB had greater α-cellulose (30.47%) and hemicellulose (27.88%). Lignin content was higher in OL (21.64%) than OB (16.40%). Liquefaction experiments showed that increasing the temperature from 140 °C to 180 °C improved conversion, with OB showing a larger increase (52.5% to 80.9%) compared to OL (66% to 72%). OB reached peak conversion faster, and the optimal particle size for OB was 40–60 mesh, while OL performed better at finer sizes. OL benefited more from higher solvent ratios, whereas OB achieved high conversion with less solvent. FTIR analysis confirmed that acid-catalyzed liquefaction breaks down lignocellulosic structures, depolymerizes cellulose and hemicellulose, and modifies lignin, forming hydroxyl, aliphatic, and carbonyl groups. These changes reflect progressive biomass degradation and the incorporation of polyalcohol components, converting solid biomass into a reactive, polyol-rich liquid. The study highlights the distinct chemical and processing characteristics of olive branches and leaves, informing their potential industrial applications. Olea ; Pruning ; Liquefaction ; Composition (language) ; Olive trees ; Tree (set theory) ; Pulp and paper industry ; Environmental science ; Chemistry ; Waste management ; Botany ; Mathematics ; Biology ; Engineering ; Organic chemistry ; Art Edible Oils Quality and Analysis ; Lignin and Wood Chemistry ; Botanical Research and Applications
Authors: Timo Reents | Elmar Kataev | Daniel Duarte-Ruiz | Regan G. Wilks | Raul Garcia‐Diez | Marcus Bär | Caterina Cocchi
Source: Scientific Reports (article, 2025-02-17)
Field: Materials Science — Polymers and Plastics

Combinatorial ab initio calculations and core spectroscopy unravel the electronic structure of nickel cobalt manganese oxide The rising interest in complex oxides for energy storage applications calls for the development of efficient computational schemes that enable exploring the vast configurational space of these materials to guide and complement experiments. In this work, we adopt a high-throughput screening method based on density-functional theory to investigate the electronic-structure fingerprints of a specific stoichiometry of lithiated manganese-cobalt-nickel oxide, [Formula: see text], which are relevant for the identification of the material in X-ray spectroscopy experiments. After creating the candidate structures in an automated fashion, we inspect their structural characteristics and electronic properties focusing specifically on the Ni and O contributions to the density of states. To do so, we exploit data analysis schemes that provide us with a metric to classify the considered structures according to the properties of interest, including the oxidation state. Comparison with X-ray absorption spectroscopy measurements confirms the robustness of the developed computational approach and reveals the most likely composition of the probed sample. Density functional theory ; Nickel ; Electronic structure ; Materials science ; Spectroscopy ; Manganese ; Cobalt ; Ab initio ; Ab initio quantum chemistry methods ; Stoichiometry ; Oxide ; XANES ; Crystallography ; Chemical physics ; Computational chemistry ; Chemistry ; Physical chemistry ; Physics ; Molecule ; Quantum mechanics Transition Metal Oxide Nanomaterials ; Advancements in Battery Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Daniel J. Burrill | Chang Liu | Michael G. Taylor | M. J. Cawkwell | Danny Pérez | Enrique R. Batista | Nicholas Lubbers | Ping Yang
Source: Journal of Chemical Theory and Computation (article, 2025-01-29)
Field: Materials Science — Materials Chemistry

MLTB: Enhancing Transferability and Extensibility of Density Functional Tight-Binding Theory with Many-body Interaction Corrections We present a hybrid semiempirical density functional tight-binding (DFTB) model with a machine learning neural network potential as a correction to the repulsive term. This hybrid model, termed machine learning tight-binding (MLTB), employs the standard self-consistent charge (SCC) DFTB formalism as a baseline, enhanced by the HIP-NN potential as an effective many-body correction for short-range pairwise repulsive interactions. The MLTB model demonstrates significantly improved transferability and extensibility compared to the SCC-DFTB and HIP-NN models. This work provides a practical computational framework for developing reliable SCC-DFTB models with additional many-body corrections that more closely approach the DFT level of accuracy. We illustrate this method with the development of an accurate model for the thorium-oxygen system, applied to the study of its nanocluster structures (ThO<sub>2</sub>)<sub><i>n</i></sub>. Transferability ; Extensibility ; Computer science ; Density functional theory ; Tight binding ; Statistical physics ; Physics ; Computational chemistry ; Chemistry ; Programming language ; Machine learning Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Mohammad-Reza Mohammadi | Seyyed-Mohammad-Mehdi Hosseini | Behnam Amiri-Ramsheh | Saptarshi Kar | Ali Abedi | Abdolhossein Hemmati‐Sarapardeh | Ahmad Mohaddespour
Source: Scientific Reports (article, 2025-07-19)
Field: Chemistry — Analytical Chemistry

Modeling residue formation from crude oil oxidation using tree-based machine learning approaches In-situ combustion (ISC) offers a compelling solution for enhancing oil recovery, particularly for heavy crude oils. This process involves the oxidation and pyrolysis of hydrocarbons, generating heat and depositing fuel in the combustion front. In this work, the thermo-oxidative profiles and residue formation of crude oils during thermogravimetric analysis (TGA) were modeled using 3075 experimental data points from 18 crude oils with API gravities ranging from 5 to 42. Four advanced tree-based machine learning algorithms comprising gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost) were utilized to develop accurate predictive models. The results indicated that CatBoost outperformed the other models, achieving mean absolute relative error (MARE) values of 4.95% for the entire dataset, 5.92% for the testing subset, and 4.71% for the training subset. Moreover, it recorded a determination coefficient (R<sup>2</sup>) value of 0.9993, highlighting its exceptional predictive capability. Furthermore, temperature was identified as the most influential factor affecting residual crude oil content, exhibiting a significant negative correlation, while API gravity also showed a negative impact. Conversely, asphaltene, resin, and heating rate positively correlated with residual content. Finally, the leverage method demonstrated that only 2.14% of the data were identified as suspected, with no out-of-leverage points detected, underscoring the reliability of the CatBoost model and the gathered experimental data. Effective management of fuel consumption and residue formation is crucial for maintaining the ISC process, and the CatBoost model has demonstrated strong predictive capabilities that support this objective. Gradient boosting ; Leverage (statistics) ; Random forest ; Combustion ; Computer science ; Crude oil ; Boosting (machine learning) ; Artificial intelligence ; Residual oil ; Machine learning ; Chemistry ; Petroleum engineering ; Engineering ; Organic chemistry Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Miguel Ángel Aguirre | Dominique Vidal | Mazaher Ahmadi | Antonio Canals
Source: TrAC Trends in Analytical Chemistry (article, 2025-04-14)
Field: Chemistry — Analytical Chemistry

Stimulus-responsive smart solvents and their application in analytical liquid-liquid (micro)extraction techniques Switchable-hydrophilicity solvents are a new type of solvents that could be very promising for liquid-liquid (micro)extraction. Nevertheless, they have been scarcely used in this critical step of chemical analysis. This article presents a comprehensive and critical review of fundamentals, chemical factors and stimulus, that reversibly modify the polarity of these solvents. This unique characteristic allows the reversible transition of the solvent from a hydrophobic to a hydrophilic state, favoring liquid-liquid (micro)extraction. Special attention is paid to new types of stimuli as light and voltage to extend the applicability of these exciting solvents in liquid-liquid (micro)extraction. • Switchable-hydrophilicity solvents are promising candidates for analytical liquid-liquid (micro)extraction. • The chemical properties that regulate the changes in polarity of smart solvents are discussed. • Different stimuli used with switchable-hydrophilicity solvents are presented. • Applications of smart solvents in liquid-liquid (micro)extraction techniques are outlined. • Investigation of new stimuli will drive analytical liquid-liquid (micro)extraction forward. Stimulus (psychology) ; Extraction (chemistry) ; Liquid–liquid extraction ; Chromatography ; Computer science ; Materials science ; Process engineering ; Chemistry ; Engineering ; Psychology ; Cognitive psychology Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Ionic liquids properties and applications
Authors: Yinkai Wang | Xiaohong Chen | Liping Liu | Soha Hassoun
Source: arXiv (Cornell University) (preprint, 2025-01-03)
Field: Materials Science — Surfaces, Coatings and Films

MADGEN: Mass-Spec attends to De Novo Molecular generation The annotation (assigning structural chemical identities) of MS/MS spectra remains a significant challenge due to the enormous molecular diversity in biological samples and the limited scope of reference databases. Currently, the vast majority of spectral measurements remain in the "dark chemical space" without structural annotations. To improve annotation, we propose MADGEN (Mass-spec Attends to De Novo Molecular GENeration), a scaffold-based method for de novo molecular structure generation guided by mass spectrometry data. MADGEN operates in two stages: scaffold retrieval and spectra-conditioned molecular generation starting with the scaffold. In the first stage, given an MS/MS spectrum, we formulate scaffold retrieval as a ranking problem and employ contrastive learning to align mass spectra with candidate molecular scaffolds. In the second stage, starting from the retrieved scaffold, we employ the MS/MS spectrum to guide an attention-based generative model to generate the final molecule. Our approach constrains the molecular generation search space, reducing its complexity and improving generation accuracy. We evaluate MADGEN on three datasets (NIST23, CANOPUS, and MassSpecGym) and evaluate MADGEN's performance with a predictive scaffold retriever and with an oracle retriever. We demonstrate the effectiveness of using attention to integrate spectral information throughout the generation process to achieve strong results with the oracle retriever. Spec# ; Political science ; Computer science ; Programming language Electron and X-Ray Spectroscopy Techniques ; X-ray Spectroscopy and Fluorescence Analysis ; Genetics, Bioinformatics, and Biomedical Research
Authors: Guang-Wu Sun | Liu Xiu-ming | Bin Lian | Shijie Wang
Source: Minerals (article, 2025-07-29)
Field: Materials Science — Biomaterials

Research Progress on Vaterite Mineral and Its Synthetic Analogs As the most unstable crystalline form of calcium carbonate, vaterite is rarely found in nature due to being highly prone to phase transitions. However, its high specific surface area, excellent biocompatibility, and high solubility properties have led to a research boom and the following breakthroughs in the last two decades: (1) From primitive calculations and spectroscopic analyses to modern multidimensional research methods combining calculations and experiments, the crystal structure of vaterite has turned from early identifications in orthorhombic and hexagonal crystal systems to a complex polymorphic structure within the monoclinic crystal system. (2) The formation process of vaterite not only conforms to the classical crystal growth theory but also encompasses the nanoparticle aggregation theory, which incorporates the concepts of oriented nanoparticle assembly and mesoscale transformation. (3) Regardless of the conditions, the formation of vaterite depends on an excess of CO32− relative to Ca2+, and its stability duration relates to preservation conditions. (4) Vaterite demonstrates significant value in biomedical applications—including bone repair scaffolds, targeted drug carriers, and antibacterial coating materials—leveraging its porous structure, high specific surface area, and exceptional biocompatibility. While it also shows utility in environmental pollutant adsorption and general coating technologies, the current research remains predominantly concentrated on its medical applications. Currently, the rapid transformation of vaterite presents the primary limitation for its industrial application. Future research should prioritize investigating its formation kinetics and stability. Vaterite ; Mineral ; Calcite ; Mineralogy ; Geochemistry ; Geology ; Chemistry ; Materials science ; Metallurgy ; Aragonite Calcium Carbonate Crystallization and Inhibition ; Clay minerals and soil interactions ; Bone Tissue Engineering Materials
Authors: Aleksandra Ochal | Daniel Grochała | Rafał Grzejda | Agnieszka Kochmańska
Source: Coatings (article, 2025-04-30)
Field: Materials Science — General Materials Science

Investigations on Changes in the Surface Geometrical Texture Caused by the Use of Corrosion Product Removers In addition to ensuring the functionality of objects used in the household, transport or industry at large, applied design focuses on aesthetic qualities related to the external form and condition of a surface. At the same time, there is a trend for plastic, rubber or aluminium objects made by moulding (both injection and casting) to look as if they were made of natural materials. This effect is ensured by properly designed and manufactured surface textures in the mould seats. However, the working surfaces of the moulds often corrode as a result of inadequate maintenance and storage. The aim of this study was to find out how popular agents on the market dedicated to corrosion product removal would change the surface geometrical texture. During the prepared experimental plan, it was also decided to investigate the properties in this respect of one of the popular drinks (i.e., cola) which is sometimes used in workshop practice as an alternative corrosion product removal agent. Based on the results of the study, conclusions were drawn about the short- and long-term effects of the corrosion product removal agents. Texture (cosmology) ; Corrosion ; Product (mathematics) ; Materials science ; Metallurgy ; Forensic engineering ; Computer science ; Engineering ; Mathematics ; Geometry ; Artificial intelligence Material Properties and Applications ; Surface Roughness and Optical Measurements
Authors: Esther J. Kok | Aneesh Chauhan | Michele Tufano | Edith J. M. Feskens | Guido Camps
Source: Frontiers in Nutrition (article, 2025-01-15)
Field: Chemistry — Analytical Chemistry

The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings Further analysis on non-homogeneous mixed food items, using computer vision techniques, will contribute toward a generalizable system. While there are still significant technical challenges to overcome before such a system can be routinely implemented in studies of free-living subjects, we believe it holds promise as a future tool for nutrition research and population intake monitoring. Hyperspectral imaging ; Artificial intelligence ; Artificial neural network ; Computer science ; Population ; Deep learning ; Machine learning ; Homogeneous ; Pattern recognition (psychology) ; Range (aeronautics) ; Mathematics ; Medicine ; Engineering ; Environmental health Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Nutritional Studies and Diet
Authors: Demetrius C. Levendis | Ivan Bernal
Source: Zeitschrift für Kristallographie - Crystalline Materials (article, 2025-04-17)
Field: Chemistry — Organic Chemistry

The Grubbs catalyst – dichlorido(1-(2,6-diethylphenyl)-3,5,5-trimethyl-3-phenylpyrrolidin-2-ylidene)-({5-nitro-2-[(propan-2-yl)oxy]phenyl}methylidene)ruthenium(II) – some observations on the crystallography and stereochemistry of a racemic mimic pair Abstract The title species has been studied in two crystalline forms resulting from reports describing (1) a centrosymmetric form [ P 2 1 / n , Z ′ = 1.0] and (2) a Sohncke form [ P 2 1 , Z ′ = 2.0; Flack parameter = −0.04(2)]. They are related by the fact that (a) the two have, approximately, the same cell constants, while (b) Z ′ is doubled in the latter in the course of losing the n-glide plane. The existence of species displaying such a remarkable relationship was noted approximately 75 years ago, but, ignored probably because the discoverers (see below) never completed the necessary experimental work to fully document the phenomenon, inasmuch as (1) crystallography was in its infancy then and (2) synthesizing the necessary, pure, chiral species and growing the necessary high-quality crystals was a major challenge which has, not even yet, been accomplished. Moreover, O. Hassel, the major player in this field, was distracted by his work on the chair-boat equilibrium in cyclohexane and related rings systems for which he eventually won the Nobel Prize (1969). Herein, we have attempted to contribute a chapter to this curious crystallographic phenomenon, which has been made even more fascinating by the observation that a ruthenium Grubbs catalyst belongs in the racemic mimic class. Ruthenium ; Catalysis ; Nitro ; Medicinal chemistry ; Chemistry ; Organic chemistry Synthetic Organic Chemistry Methods ; Organometallic Complex Synthesis and Catalysis ; Asymmetric Hydrogenation and Catalysis
Authors: Yueqiang Wang | Shengwei Sun
Source: Analytical and Bioanalytical Chemistry (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

Amino acid regulation of peroxidase-like activity of Cu2O nanozyme for detection of tetracycline antibiotics Tetracycline antibiotics (TCs) are widely used in medicine, agriculture, and animal husbandry. However, their overuse has led to environmental pollution, posing a significant threat to water sources, soil, and food safety. Therefore, there is an urgent need for efficient, sensitive, simple, and low-cost detection methods for environmental pollution monitoring. In this study, the catalytic activity of copper-based nanozymes was regulated by AAs. Lysine, aspartic acid, glycine, and arginine were chosen as ligands to synthesize different copper-based nanozymes. The results showed that the type of amino acid significantly influenced the particle size, morphology, and peroxidase (POD)-like catalytic activity of Cu₂O. Based on these amino acid-regulated Cu₂O nanozymes, we further developed a highly sensitive, easy-to-use, and low-cost colorimetric sensor array that can effectively distinguish TCs. This sensor array was successfully validated in binary mixtures and wastewater environments. This study not only provides important insights into the small-molecule regulation of copper-based nanozyme catalytic performance but also offers a novel approach for the detection of TCs in environmental monitoring. Chemistry ; Tetracycline ; Peroxidase ; Lysine ; Amino acid ; Tetracycline antibiotics ; Glycine ; Combinatorial chemistry ; Antibiotics ; Environmental pollution ; Copper ; Catalysis ; Pollution ; Environmental chemistry ; Biochemistry ; Enzyme ; Biology ; Organic chemistry ; Environmental science ; Ecology ; Environmental protection Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors