jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Julian Heinrich (8670390) | Karin Schwarzenberger (7483064) | Tine Marquardt (22360667) | Xuegeng Yang (1290762) | Mateusz Marek Marzec (22360670) | Johannes Manthey (22360673) | Michael Stadler (111171) | Thomas Ammann (22360676) | Kati Schatz (22360679) | Inez M. Weidinger (1426870) | Kerstin Eckert (1290771)
Source: Figshare (article, 2025-10-02)
Field: Materials Science — Surfaces, Coatings and Films

Surface Functionalizationof Additively ManufacturedPolypropylene and Stainless Steel 316L: Impact on Wettability andOxygen Nucleation Bubble nucleation, a fundamental solid–gas interaction, plays a critical role in both natural phenomena and industrial processes. In water electrolysis, it is relevant not only for the generation of H<sub>2</sub> bubbles but also for the formation of O<sub>2</sub> as a byproduct, which significantly impacts heat and mass transfer efficiency. While surface functionalization for bubble management has been actively explored for electrodes, the electrolyzer periphery has received little attention to date. Additive manufacturing enables the design of advanced geometries to optimize the multiphase flow in such systems. In this study, the wettability of additively manufactured polypropylene and stainless steel 316L substrates is modified by plasma-enhanced chemical vapor deposition based on 1H,1H,2H,2H-perfluorooctyl acrylate (PFAC-6) and hexamethyldisiloxane (HMDSO), creating hydrophobic or hydrophilic surfaces with final water contact angles ranging from 40 to 145°. Surface morphology and properties are assessed by digital microscopy, water contact angle measurements, and X-ray photoelectron spectroscopy. The coating stability and long-term wettability in contact with air or water, as a first step toward industrial conditions, were monitored over a period of up to 150 days. X-ray photoelectron spectroscopy identified distinct aging mechanisms: defluorination for PFAC-6 coatings and reorientation of polar surface groups for HMDSO. Bubble dynamics in O<sub>2</sub>-oversaturated solutions are recorded and analyzed using machine learning techniques, showing O<sub>2</sub> bubble coverage increased by up to 500% for polypropylene and 350% for stainless steel with PFAC-6, while HMDSO coatings reduced nucleation down to 30%. This work highlights the pivotal role of surface structure and chemical modification in governing solid–gas interactions and offers valuable insights for the development of optimized materials in industrial applications such as electrolysis. Wetting ; Contact angle ; Polypropylene ; Surface modification ; Nucleation ; X-ray photoelectron spectroscopy ; Bubble ; Coating Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Heat Transfer and Boiling Studies
Authors: Jiahao Wu (6877154) | Weipeng Lu (476104) | Jingwen Wu (80821) | Bing Zhang (129) | Yanchuan Guo (4329250)
Source: Figshare (article, 2025-06-16)
Field: Materials Science — Materials Chemistry

Graph TransformerModel Integrating Physical Featuresfor Projected Electronic Density of States Prediction The prediction of the projected density of states (PDOS) in materials has traditionally relied on deep learning models based on graph convolutional networks (GCN) and Graph Attention Networks (GAT). In this study, utilizing PDOS data from the Materials Project, we demonstrate that the Graph Transformer (GT)which employs multi-head scaled dot-product attention over each node and its local neighborhoodconsistently outperforms both GAT and GCN models in prediction accuracy under identical energy-level-corrected conditions. Furthermore, by incorporating the valence electron counts of the s, p, d, and f orbitals for the entire structure as an additional feature into a GT-based model, we further enhance the model’s predictive performance. The novel framework and physics-informed feature enhancements offer valuable insights into improving the prediction of PDOS and potentially other electronic properties. Graph ; Deep learning ; Computation ; Feature (linguistics) ; Atomic orbital ; Transformer ; Feature learning ; Feature extraction Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems
Authors: Davin | Edi Edi | Hendri
Source: Journal of Artificial Intelligence and Engineering Applications (JAIEA) (article, 2025-10-15)
Field: Chemistry — Analytical Chemistry

Designing A Wine Quality Identification Application Using Naïve Bayes Classifier Wine is one of the drinks with the highest number of consumers in the world, and its quality is greatly influenced by various chemical ingredients such as fixed acidity, citric acid, pH, and others. Wine quality testing is traditionally subjective and requires special expertise. Therefore, a more objective and efficient approach is needed, one of which is through the application of data mining algorithms. This study aims to design a wine quality identification application using the Naïve Bayes Classifier algorithm and analyze its accuracy in the classification of red and white wines. The dataset used was taken from the UCI Machine Learning Repository, with input attributes in the form of wine chemical content and output in the form of wine quality scores ranging from 2 to 8. The test results showed that the Naïve Bayes algorithm was able to classify the quality of wine with an accuracy rate of 86.50% for red wines and 79.80% for white wines. The developed application successfully processes input data and provides prediction results in real-time. This research shows that the use of Naïve Bayes algorithms can be a practical and effective solution in helping consumers and producers in recognizing the quality of wine and improving the production process in the wine industry. Wine ; Naive Bayes classifier ; White Wine ; Classifier (UML) ; Bayes' theorem ; Identification (biology) ; Quality (philosophy) Spectroscopy and Chemometric Analyses
Authors: Jianyong Yu | Merwa Alhadrawi | Farag M. A. Altalbawy | Ahmed Rasol Hasson | M. Mehdi Shafieezadeh
Source: Journal of Petroleum Exploration and Production Technology (article, 2025-07-23)
Field: Chemistry — Analytical Chemistry

Optimizing demulsifier selection for crude oil dehydration: a fuzzy TOPSIS-based multi-criteria decision-making approach The effective separation of water from crude oil is essential for maintaining oil quality, optimizing production efficiency, and minimizing operational challenges in the petroleum industry. However, selecting an optimal demulsifier remains a complex problem due to the need to balance separation efficiency, environmental impact, cost-effectiveness, and ease of application. This study addresses this challenge by applying the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) method, a robust multi-criteria decision-making (MCDM) approach, to evaluate and rank demulsifiers under uncertain conditions systematically. Four commercial demulsifiers, Alcopol 500, Polymer-based Demulsifier, Nalco Champion EC7135A, and Schlumberger’s ClearPhase, were assessed using fuzzy logic to quantify expert evaluations. The results indicate that Nalco Champion EC7135A achieved the highest closeness coefficient (0.751), making it the top-ranked demulsifier due to its superior separation efficiency and lower environmental impact. Alcopol 500 ranked second (0.708), followed by the Polymer-based Demulsifier (0.692) and Schlumberger’s ClearPhase (0.619). Compared to conventional selection approaches that often rely on trial-and-error or single-criterion assessments, this study demonstrates that Fuzzy TOPSIS provides a structured, quantitative, and transparent approach to demulsifier selection. The novelty of this research lies in its integration of fuzzy logic with MCDM for demulsifier evaluation, offering a more precise and adaptable framework for decision-making in oilfield operations. These findings have significant practical implications for the petroleum industry, as they enable more data-driven and sustainable selection processes, reducing operational costs and improving crude oil dehydration efficiency. Future research should focus on incorporating real-time operational data, expanding the evaluation to emerging eco-friendly demulsifiers, and integrating predictive machine learning models to enhance the accuracy of the selection process. Offshore geotechnical engineering ; Demulsifier ; Selection (genetic algorithm) ; TOPSIS ; Crude oil ; Petroleum engineering ; Dehydration ; Biochemical engineering ; Operations research ; Computer science ; Engineering ; Environmental science ; Chemistry ; Artificial intelligence ; Geotechnical engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Reservoir Engineering and Simulation Methods
Authors: Wanxing Lin (13833897) | Xiaobo Li (118828) | Yan-Ling Zhao (3617390)
Source: Figshare (article, 2025-08-28)
Field: Chemistry — Organic Chemistry

A Molecular RotorDriven by an Electric Field on Graphene We propose a scheme for driving a dipolar molecular rotor to rotate continuously by applying an external electric field: the dipolar rotor is fixed on a graphene sheet via a metal atom to facilitate the free rotation; it is in the meantime subjected to an electric field oriented parallel to the graphene sheet. We use computational modeling with density functional theory and Newtonian mechanics, similar to molecular dynamics simulations, to obtain the torque, angular velocity, and rotation period of the rotor. Our results show that the dipolar rotor designed here can rotate with a period of 2.96 ps by an alternating rectangular electric field with a strength of 0.5 V/Å. However, a cosine wave alternating electric field depending on time cannot drive the dipolar rotor to rotate regularly. Therefore, a cosine wave electric field depending on the rotation angle is suggested, as it can not only drive the rotor but also produce additional power. Machine learning molecular dynamics (MLMD) simulations further confirm that the rotor remains thermodynamically stable under an electric field. This work reveals the rotation mechanism of a dipolar molecular rotor in a transverse electric field, and we hope this work can open a new path for designing more diverse molecular machines in experiments. Electric field ; Rotor (electric) ; Dipole ; Rotation (mathematics) ; Molecular dynamics ; Field (mathematics) ; Work (physics) ; Electric dipole moment Supramolecular Chemistry and Complexes ; Molecular Junctions and Nanostructures ; Synthesis and Properties of Aromatic Compounds
Authors: Madeleine Cochrane | Sarah K. Scholten | Christopher Perrella | Anton van den Hengel | Kishan Dholakia | André N. Luiten | Zhibin Liao | Johan Verjans
Source: ACS Omega (article, 2025-10-20)
Field: Chemistry — Spectroscopy

CO <sub>2</sub> Isotopologue Quantification Using Direct Frequency Comb Spectroscopy and Machine Learning The estimation of molecular concentrations in gas mixtures has applications in healthcare, environmental monitoring, and food quality assessment. Direct frequency comb spectroscopy provides benefits as a sensing platform in these application areas due to its accuracy, species selectivity and capacity for broadband analysis and miniaturization. One challenge associated with direct frequency comb spectroscopy is the accurate estimation of the concentration levels of molecules with overlapping absorption features. Traditional analysis methods, such as curve fitting, do not scale well to mixtures containing many different molecules. Here, we use machine learning methods to estimate the concentration level of two isotopologues of carbon dioxide: <sup>12</sup>C<sup>16</sup>O<sub>2</sub> and <sup>13</sup>C<sup>16</sup>O<sub>2</sub>. We compare several machine learning methods for analyzing both transmission spectra and the image output of a virtually imaged phased array spectrometer. Additionally, we evaluate two data augmentation methods to improve the model's generalization ability. Our optimal approach, the transformer method with shifted spectra data augmentation, outperforms curve fitting estimates with a mean absolute error of 14.7 ppm for <sup>12</sup>C<sup>16</sup>O<sub>2</sub> and 0.3 ppm for <sup>13</sup>C<sup>16</sup>O<sub>2</sub>, both 3 orders of magnitude smaller than the equivalent curve fitting values. Isotopologue ; Spectroscopy ; Curve fitting ; Spectral line ; Analytical Chemistry (journal) ; Artificial neural network ; Pattern recognition (psychology) Spectroscopy and Laser Applications ; Mass Spectrometry Techniques and Applications ; Advanced Fiber Laser Technologies
Authors: Mohamadali Tofigh | Daniel J. Smith | Saeed Hanifi | Amir Reza Hanifi | Mahdi Shahbakhti
Source:  (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

A deep Conv-Atten-KAN architecture to identify solid oxide fuel cell dynamics under fuel starvation Solid Oxide Fuel Cells (SOFCs) are coupled, nonlinear systems with interactions of heat transfer, mass transport, and electrochemical reactions that can produce clean, efficient electrical power. Fuel starvation is a frequent abnormal condition during SOFC operation, leading to microstructural changes and ultimately performance degradation. Identifying SOFC performance under fuel starvation is crucial for developing control and monitoring systems to enhance efficiency and longevity. Due to the complex interplay of multi-physics in SOFCs escalated by fuel starvation effects, physics-based modeling approaches are often computationally inefficient for real-world implementation. We introduce an experimental approach that induces fuel starvation in a single SOFC to gather diverse, representative data for data-driven modeling of its dynamics. Additionally, we propose a new deep neural network combining a convolutional Self-Attention mechanism and the Kolmogorov-Arnold approximation theory, called Conv-Atten-KAN, to effectively capture the nonlinear, time-varying dynamics underlying SOFCs affected by fuel starvation. The performance of the proposed model is evaluated using experimental data and benchmarked against state-of-the-art neural networks for system identification. The results indicate an average improvement of $28 \%$ in prediction accuracy compared to recurrent, convolutional, and fully-connected architectures. Fuel cells ; Architecture ; Dynamics (music) ; Chemistry ; Computer science ; Solid oxide fuel cell ; Chemical engineering ; Biological system ; Cell biology ; Engineering ; Biology ; Physics Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Nuclear Materials and Properties
Authors: Dina Arabi | Omnia Hamdy | Zienab Abdel‐Salam | Mahmoud Mohamed | Mohamed Abdel‐Harith
Source: Microbial Pathogenesis (article, 2025-09-16)
Field: Chemistry — Analytical Chemistry

From phylogeny to photonics: A smart spectroscopic approach assisted with neural networks for rapid typing of Enterobacteriaceae isolates from chicken meat Phylogenetic tree ; Bacteria ; Food spoilage ; 16S ribosomal RNA ; Bacterial taxonomy ; Phylogenetics ; Enterobacteriaceae ; Contamination ; Polymerase chain reaction Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Shi‐Jie Chen | Xun‐Lu Li | Zheng Xu | Yanyu Zhao | Qifeng Lu | Yiming Liu | Cheng Zhang | Xinli Cheng | Shuai Yuan | Ning Wang | Shiqing Zhao | Fangchao Li | Yixiang Li | Chunlan Ma | Yang Li
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Organic-Engineered MXenes Enabling Digital-to-Analog Switching for Neuromorphic Application Neuromorphic engineering ; Memristor ; MXenes ; Stacking ; Resistive random-access memory ; Bridging (networking) ; Electrode ; Non-volatile memory MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Ish Kumar Jha | Sumit Kumar Maurya | Satinder Singh | Aditi Gupta
Source: Modelling and Simulation in Materials Science and Engineering (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Application of deep learning for identification of grain boundaries in atomistic simulation data Abstract Grain boundaries (GB) characteristics, such as tilt, twist, ∑3, ∑5, etc. play a critical role in determining the microstructural and functional properties of the materials. Due to experimental challenges, atomistic simulations have become a powerful alternative for studying these GB. However, the post-processing and interpretation of simulation data often require expert input, which can introduce subjectivity into the analysis. This study proposes a deep learning (DL)-based framework for the automated segmentation of GB atoms from bulk atoms in three-dimensional atomic configurations derived from molecular dynamics (MD) simulations. We utilize a publicly available MD simulation dataset for aluminum GB structures. Raw MD simulation snapshots from this dataset are pre-processed to extract features representative of diverse GB configurations. We employ K -means clustering to explore the structural classes within the dataset, while a 3D CNN model is later used for classifying bulk and GB atoms to identify GB. The K -means clustering analysis revealed distinct patterns in the dataset for GB and bulk atoms. The 3D CNN model demonstrates classification accuracy comparable to that of the commonly used common neighbor analysis method in identifying GB atoms across a range of misorientation angles. This work highlights the potential of DL-based approaches for consistent and accurate characterization of GB in atomistic simulations. Materials science ; Identification (biology) ; Grain boundary ; Statistical physics ; Metallurgy ; Microstructure Microstructure and mechanical properties ; Advanced Materials Characterization Techniques ; Machine Learning in Materials Science
Authors: Jonathan Rains (21494230) | Elliot Steeples (21494233) | Ben Robinson (7121621) | Andrew J. Pierce (7903925) | Danielle Sayer (21494236)
Source: Figshare (article, 2025-06-06)
Field: Chemistry — Spectroscopy

High-ThroughputHPLC Method for the Measurement ofOctanol–Water Partition Coefficients without an Organic Modifier We present a high-performance chromatographic method for the determination of octanol–water partition coefficients of organic molecules, suitable for pharmaceuticals, agrochemicals, and fine-chemicals featuring acidic and basic p<i>K</i><sub>a</sub> values. By selecting short-alkyl-columns based on length and coating them with octanol present in the mobile phase, we can measure a log<i>P</i> range of 0–5.6 without the use of an organic additive. The method has shown to be accurate for a set of 146 commercial molecules reported here, as well as over 27,000 in-house proprietary research molecules, the results of which have been used to train our own machine learning model. The method is also insensitive to analyte concentration or purity, and can be partially automated to obtain a relatively high throughput for industrial purposes. Analyte ; Partition coefficient ; Partition (number theory) ; Measure (data warehouse) ; Organic molecules ; High-performance liquid chromatography ; Set (abstract data type) Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Spectroscopy and Chemometric Analyses
Authors: Logan Gibb | Brandon Chung | Michael Simpson | Luther W. McDonald
Source: Journal of Radioanalytical and Nuclear Chemistry (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Morphological signatures of U-oxides produced from the high-temperature firing of U-metals Uranium ; Calcination ; Metal ; Uranium ore ; Convolutional neural network ; Signature (topology) ; Characterization (materials science) Nuclear Materials and Properties ; Radioactive contamination and transfer ; Radioactive element chemistry and processing
Authors: Baoqing Bai | Xiaojie Li | Siyuan Meng | Ying Zhang | Bo Tao | Jinhua Zhang | Yukun Yang
Source: Analytical Methods (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Determination of amide herbicides in food by salting-out vortex-assisted dispersive liquid–liquid microextraction coupled with gas chromatography-tandem mass spectrometry A new method, salting-out vortex-assisted dispersive liquid-liquid microextraction (SO-VA-DLLME), was developed for extracting amide herbicides (AHs) from food samples. Quantitative analysis of AHs was performed using gas chromatography-tandem mass spectrometry (GC-MS/MS). One-variable-at-a-time testing was used to investigate the impact of the experimental parameters on extraction efficiency. Based on the results of our previous research, this study analyzed the impact of various extraction factors on AH extraction efficiency and improved pre-treatment conditions using response surface methodology (RSM) and a back propagation-genetic algorithm (BP-GA) neural network model. Specifically, the BP-GA neural network exhibited exceptional stability during AH extraction, facilitating comprehensive optimization. The method demonstrated excellent linearity (<i>R</i><sup>2</sup> ≥ 0.992) within the 2.00-2000 ng kg<sup>-1</sup> range. The detection limits (LODs) were estimated to be 0.060-1.20 ng kg<sup>-1</sup>, and quantitation limits (LOQs) were 0.200-2.00 ng kg<sup>-1</sup> at S/N ratios of 3 and 10, respectively. Extraction recoveries ranged from 87% to 98%, with enrichment factors of 3480 to 3920, indicating effectiveness. Intra-day and inter-day relative standard deviations (RSDs) were below 9.60%, ensuring reliability. In line with the white analytical chemistry principles, the method confirmed environmental safety. Overall, this rapid, cost-effective method efficiently detected amide herbicides in food samples. Extraction (chemistry) ; Mass spectrometry ; Detection limit ; Amide ; Gas chromatography–mass spectrometry ; Relative standard deviation Analytical chemistry methods development ; Pesticide Residue Analysis and Safety ; Analytical Chemistry and Chromatography
Authors: Federico Albi | Marco Salucci | Giacomo Oliveri | Giorgio Gottardi | Aarón Á. Salas-Sánchez | A. Massa
Source:  (article, 2025-07-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

AI-Driven System-by-Design Synthesis of Static-Passive EM Skins Meta-Atoms In this work, pixel-based Static-Passive Electromagnetic Skins (SP-EMSs) meta-atoms are designed exploiting a novel artificial intelligence (AI)-driven technique. More specifically, the synthesis problem at hand is effectively and efficiently solved within the System-by-Design (SbD) framework, by leveraging (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$i$</tex>) a smart definition/coding of the degrees-offreedom (DoFs), (ii) a deep learning (DL) strategy for building an accurate-but-fast surrogate model (SM) of the forward solver, and (iii) an effective evolutionary-based global search engine. An illustrative example is provided to assess the effectiveness, reliability, and computational efficiency of the proposed SbD method. Surrogate model ; Deep learning ; Key (lock) ; Artificial neural network Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Wireless Communication Technologies
Authors: Jie Liang | Yi Feng | Dorina Gui | Xiangyuan Zheng | Rui Gao | Jing‐dong Zhou | Ji‐An Liang | Jing Diao | Juan He | Yulin Zhao | Jianxing He | Xuan Wu | Bo Cheng | Wenhua Liang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Spectroscopy

A Dynamic Proteomic Signature for the Prediction of Lung Cancer: A Longitudinal Analysis in the UK Biobank Cohort Mendelian randomization ; Biobank ; Proteome ; Cohort ; Lung cancer ; Disease ; Causal inference ; Proportional hazards model ; Proteomics Advanced Proteomics Techniques and Applications ; Ferroptosis and cancer prognosis ; Lung Cancer Research Studies