jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matej Kučera | Miroslav Gutten | Daniel Korenčiak | Jozef Kúdelčík
Source: Sensors (article, 2025-07-05)
Field: Materials Science — Materials Chemistry

Using the Spark Plug as a Sensor for Analyzing the State of the Combustion System This article presents a method that uses a spark plug as a sensor to monitor an internal combustion engine. In addition, the voltage sensors measured the high voltage at the spark plugs using a non-contact method. Monitoring can now be performed in a simple way in real time, along with data processing. This method can be effectively used for the monitoring of all cylinders in an internal combustion engine as well as supplementing other measurement methods to optimize engine maintenance and enable correct diagnostic decisions to be made. Experimental analysis focused on the effect of the spark plug gap on the arc duration, flashover voltage, and high-voltage waveforms. It was found that with an increasing gap, the arc duration is shortened, and the breakdown voltage increases linearly, indicating wear of the spark gap. With increasing temperature, the breakdown voltage value decreased. Non-contact measurements at different frequencies showed a relationship between the magnitude of the electric field and the spark plug gap. Spark plug ; Spark gap ; Waveform ; Voltage ; Arc flash ; Arc (geometry) ; SPARK (programming language) ; Combustion ; Automotive engineering ; Internal combustion engine ; High voltage ; Materials science ; Electric arc ; Electric spark ; Electrical engineering ; Engineering ; Mechanical engineering ; Computer science ; Electrode ; Chemistry High voltage insulation and dielectric phenomena ; Embedded Systems and FPGA Design ; Power Transformer Diagnostics and Insulation
Authors: Jihanne El Haouari | Jean‐Michel Gaucel | Christelle Pittet | Jean‐Yves Tourneret | Herwig Wendt
Source: Atmospheric measurement techniques (article, 2025-06-18)
Field: Chemistry — Analytical Chemistry

In-flight estimation of instrument spectral response functions using sparse representations Abstract. High-resolution spectrometers are composed of different optical elements and detectors that must be modeled as accurately as possible. Specifically, accurate estimates of instrument spectral response functions (ISRFs) are critical in order not to compromise the retrieval of trace gas concentrations from spectral measurements. Currently, parametric models are used to estimate these response functions. However, these models cannot always take into account the diversity of ISRF shapes that are encountered in practical applications. This paper studies a new ISRF estimation method based on a sparse representation of the ISRF in a dictionary. The proposed method is shown to be very competitive when compared to parametric models, yielding up to 1 order of magnitude smaller normalized ISRF estimation errors. The method is applied to different high-resolution spectrometers, demonstrating its reproducibility for multiple remote sensing missions. Computer science ; Parametric statistics ; TRACE (psycholinguistics) ; Representation (politics) ; Biological system ; Remote sensing ; Environmental science ; Algorithm ; Mathematics ; Statistics ; Geology ; Biology Spectroscopy and Chemometric Analyses ; Calibration and Measurement Techniques ; Remote-Sensing Image Classification
Authors: Lei Zhang | Qiang Jiang | Xuedian Zhang | Songlin Zhuang
Source: Photonics (article, 2025-05-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Generation of Longitudinal Bessel Beam Based on Complex Amplitude Metasurface Bessel beams occupy an important position in optical research due to their characteristics of long focal depth, self-healing ability, and diffraction-free propagation. Traditional methods for generating Bessel beams suffer from complexity, a large size, low uniformity, and limited NA. Metasurfaces are considered to be a new technology for the miniaturization of optical devices due to their ability to regulate optical fields at subwavelength scales flexibly. Here, we generated Bessel beams by a complex-amplitude (CA) metasurface. The polarization conversion efficiency was controlled by the geometric size, while the phase value from 0 to 2π was manipulated based on the Pancharatnam–Berry (PB) phase. This approach enabled precise control over the axial intensity distribution of the optical field, which facilitated the generation of sub-millimeter-scale Bessel beams. Axial light field control based on CA metasurfaces has great potential for applications in a variety of fields, such as particle manipulation, large-depth-of-field imaging, and laser processing. Optics ; Amplitude ; Bessel function ; Beam (structure) ; Bessel beam ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Orbital Angular Momentum in Optics
Authors: Fenglong Jiao | Merav Braitbard | Clinton Yu | Ben Shor | Bjorn-Erik Wulff | Dina Schneidman‐Duhovny | Lan Huang
Source: Nature Communications (article, 2025-12-12)
Field: Chemistry — Spectroscopy

Cysteine-enabled cleavability to advance cross-linking mass spectrometry for global analysis of endogenous protein-protein interactions Cross-linking mass spectrometry (XL-MS) is a powerful technology for probing protein-protein interactions (PPIs) and elucidating architectures of protein complexes at the systems level. While successful, the proteome coverage remains limited. To expand the scope of global PPI profiling, we introduce an innovative cysteine-based cleavable XL-MS platform using non-cleavable heterobifunctional lysine-cysteine (K-C) cross-linkers. The oxidation-induced transformation of cysteine cleavability enables unambiguous identification of cross-linked peptides. This strategy has been successfully applied to proteome-wide XL-MS analysis of intact cells, and its broad applicability has been demonstrated using three heterobifunctional cross-linkers. A total of 25,401 unique linkages from 2007 proteins have been identified, significantly expanding the existing XL-PPI map and increasing the interconnectivity of the human interactome. The XL-data generated here has been coupled with AlphaFold-based predictions and integrative modeling to reveal the structural characteristics of cellular networks, offering insights into the organization of native protein complexes in cells including the SERBP1-ribosome and eEF1A1-eEF1B complexes. Due to the effectiveness in modulating cysteine cleavability, our work presents an avenue for developing bifunctional/multifunctional cross-linking reagents to further advance XL-MS technologies. Additionally, the same strategy can be easily adapted to facilitate the characterization of cysteine modifications, reactivity and interactions to benefit chemical proteomics. Proteome ; Interconnectivity ; Cysteine ; Identification (biology) ; Proteomics ; Mass spectrometry ; Human proteome project ; Scope (computer science) Advanced Proteomics Techniques and Applications ; Biotin and Related Studies ; Mass Spectrometry Techniques and Applications
Authors: S. Mehdi Vaez Allaei | Mehriar Amininasab | Hrachya Ishkhanyan | Armen H. Poghosyan
Source: Scientific Reports (article, 2025-04-04)
Field: Materials Science — Materials Chemistry

Potential functional changes in native lysozyme induced by carbon nanotubes studied by molecular dynamics simulations A carbon nanotube (CNT) can affect biological systems, ranging from toxicity to changes in functionality. Here, a series of long-scale (1-2 µs) molecular dynamics simulations were conducted to investigate the adsorption and interaction of lysozyme with the CNT, a possible mechanism for altering protein flexibility and function. Four systems were examined: native lysozyme/CNT, denatured lysozyme/CNT, and both systems post-docking. Our results indicate that native lysozyme does not undergo conformational changes when initially captured by a CNT. However, after docking, the native lysozyme/CNT complex exhibits conformational changes. In contrast, the denatured lysozyme binds more effectively to the CNT in both pre- and post-docking scenarios. Key amino acid residues, arginine and tryptophan, have been identified as crucial for lysozyme/CNT interactions. The surface of the CNT adsorbs lysozyme through π-π stacking and van der Waals interactions, with these multimodal interactions serving as the main driving force for protein anchoring to the nanotube. These results also underscore the significance of docking in the simulation of protein/nanoparticle interactions, which can lead to entirely different conclusions regarding, for example, the toxicity or functionality of a given nanoparticle life. Carbon nanotube ; Lysozyme ; Molecular dynamics ; Dynamics (music) ; Biophysics ; Chemical physics ; Chemistry ; Nanotechnology ; Materials science ; Biology ; Computational chemistry ; Physics ; Biochemistry Enzyme Structure and Function ; Nanoparticle-Based Drug Delivery ; Protein Structure and Dynamics
Authors: Maimuna U. Zarewa | Tawfik A. Saleh
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Experimental and artificial intelligence molecular models to predict quenching behavior of carbon materials from petroleum waste for sustainable corrosion monitoring Quantum dots have attracted a lot of interest because of their special optical characteristics and potential for a variety of uses, such as in inks and sensors. Additionally, petroleum coke recycling is crucial for resource efficiency, economic growth, and environmental sustainability. It solves problems linked to carbon emissions and landfill waste. Industries can reduce their environmental impact while generating new opportunities for innovation and expansion. Petroleum coke quantum dots (PCQDs) were synthesized using the reflux method at 120 °C for 12 h, then characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and electron pair resonance spectroscopy (EPR), and were utilized to differentiate Fe<sup>2+</sup> and Fe<sup>3+</sup> for early-stage corrosion detection in real samples. LOD values of 0.39 µM and 0.36 µM for Fe<sup>2+</sup> and Fe<sup>3+</sup> were obtained using a Stern-Volmer plot. PCQDs demonstrate outstanding selectivity for iron in the presence of diverse cationic and anionic interferents, as well as remarkable stability under harsh continuous optical and thermal conditions. In addition, due to the intense blue emission of PCQDs, they have been utilized as a fluorescent security invisible ink for documenting sensitive information. The PCQDs exhibit around 70 wt% yield, 50% quantum yield, and a half-life of 9.5 ns. Due to their excellent efficiency and simplicity in synthesis, PCQDs can be utilized for industrial-scale production. Fourier transform infrared spectroscopy ; Quenching (fluorescence) ; Petroleum ; Carbon fibers ; Corrosion ; Spectroscopy ; Graphene ; Scanning electron microscope ; Quantum dot Carbon and Quantum Dots Applications ; Recycling and Waste Management Techniques ; Graphene research and applications
Authors: Xin Jing | Abhiraj Sharma | John E. Pask | Phanish Suryanarayana
Source: ArXiv.org (preprint, 2025-01-27)
Field: Materials Science — Materials Chemistry

GPU acceleration of hybrid functional calculations in the SPARC electronic structure code We present a GPU-accelerated version of the real-space SPARC electronic structure code for performing hybrid functional calculations in generalized Kohn-Sham density functional theory. In particular, we develop a batch variant of the recently formulated Kronecker product-based linear solver for the simultaneous solution of multiple linear systems. We then develop a modular, math kernel based implementation for hybrid functionals on NVIDIA architectures, where computationally intensive operations are offloaded to the GPUs while the remaining workload is handled by the CPUs. Considering bulk and slab examples, we demonstrate that GPUs enable up to 8x speedup in node-hours and 80x in core-hours compared to CPU-only execution, reducing the time to solution on V100 GPUs to around 300 seconds for a metallic system with over 6,000 electrons, and significantly reducing the computational resources required for a given wall time. Acceleration ; Code (set theory) ; Computer science ; Physics ; Programming language ; Classical mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Joseph Sartini | Xinkai Zhou | Liz Selvin | Scott L. Zeger | Ciprian Crainiceanu
Source: Journal of Computational and Graphical Statistics (preprint, 2025-12-08)
Field: Chemistry — Analytical Chemistry

Fast Bayesian Functional Principal Components Analysis Functional Principal Components Analysis (FPCA) is a widely used analytic tool for dimension reduction of functional data. Traditional implementations of FPCA estimate the principal components from the data, then treat these estimates as fixed in subsequent analyses. To account for the uncertainty of PC estimates, we propose FAST, a fully-Bayesian FPCA with three core components: (1) projection of eigenfunctions onto an orthonormal spline basis; (2) efficient sampling of the orthonormal spline coefficient matrix using a parameter expansion scheme based on polar decomposition; and (3) ordering eigenvalues during sampling. Extensive simulation studies show that FAST is very stable and performs better compared to existing methods. FAST is motivated by and applied to a study of the variability in mealtime glucose from the Dietary Approaches to Stop Hypertension for Diabetes Continuous Glucose Monitoring (DASH4D CGM) study. All relevant STAN code and simulation routines are available as supplementary material. Principal component analysis ; Bayesian probability ; Computer science ; Principal (computer security) ; Artificial intelligence Spectroscopy and Chemometric Analyses
Authors: Matthias Brosz | Johanna Buck | Fabian Grünewald | Debora Monego | Jaewoon Jung | Yuji Sugita | Camilo Aponte‐Santamaría | Frauke Gräter
Source: Biophysical Journal (article, 2025-10-10)
Field: Materials Science — Biomaterials

Coarse-grained Martini 3 model for collagen fibrils Collagen is a prevalent protein in the Animalia kingdom, especially in mammals. It is abundant in all connective tissue such as bone or ligaments, and thus, it is subjected to substantial mechanical forces. Cross-links play an essential role for the structural and mechanical integrity of collagen, determining its stiffness and rigidity. Until now, studies on collagen including cross-links have either been confined to fully atomistic simulations, which are computationally intensive and restrict the accessible time and length scales, or to coarse-grained descriptions that do not resolve the force response on a residue level and therefore do not consider the triple helical structure and the connectivity of cross-links. To bridge this gap, we report on the development and validation of a computational model based on the Martini 3 coarse-grained force field, in which we parametrized the fibrillar collagen structure including cross-links. We validated the model, through extensive equilibrium and nonequilibrium molecular dynamics simulations, against experimental properties and all-atom simulations. Because the type and distribution of cross-links vary with aging, we expect that this collagen model can be employed to provide insights into age-related changes in tissue mechanics and guide the development of biomimetic materials. Stiffness ; Collagen fibril ; Connective tissue ; Fibril ; Molecular dynamics ; Structural protein ; Type I collagen ; Biomimetic materials Collagen: Extraction and Characterization ; Advanced Numerical Analysis Techniques ; Elasticity and Material Modeling
Authors: Shihang Xu | S. N. G. Chu | Rami Mrad | Z. Q. Zhang | Zhelin Li | Runxian Jiao | Yuanping Chen
Source: The Journal of Physical Chemistry C (article, 2025-03-26)
Field: Materials Science — Materials Chemistry

Discovery of 2D Materials via Symmetry-Constrained Diffusion Model Generative models for 2D materials have shown significant promise in accelerating the material discovery process. The stability and performance of these materials are strongly influenced by their underlying symmetry. However, existing generative models for 2D materials often neglect symmetry constraints, leading to a tendency to generate overly complex, low-symmetry structures. Here, we introduce a symmetry-constrained diffusion model (SCDM) that integrates space group symmetry into the generative process. By incorporating Wyckoff positions, the model ensures adherence to symmetry principles, leading to the generation of 2,000 candidate structures. DFT calculations were conducted to evaluate the energy above the convex hull (Ehull) of these structures after structural relaxation. From the generated samples, 843 materials that met the energy stability criteria (Ehull < 0.6 eV/atom) were identified. Among these, six candidates were selected for further stability analysis, including phonon band structure evaluations and electronic property investigations, all of which exhibited phonon spectrum stability. To benchmark the performance of SCDM, a symmetry-unconstrained diffusion model was also evaluated via crystal structure prediction, with detailed comparisons revealing key advantages of symmetry constraints. The results highlight that incorporating symmetry constraints enhances the effectiveness of generated 2D materials, contributing to the discovery of 2D materials through generative modeling. Symmetry (geometry) ; Diffusion ; Statistical physics ; Theoretical physics ; Physics ; Materials science ; Mathematics ; Thermodynamics ; Geometry Machine Learning in Materials Science ; Graphene research and applications ; 2D Materials and Applications
Authors: Kiran K. Yalamanchi | Pinaki Pal | Balaji Mohan | Abdullah S. AlRamadan | Jihad Badra | Yuanjiang Pei
Source: Journal of energy resources technology. (article, 2025-04-18)
Field: Chemistry — Analytical Chemistry

A Variational Autoencoder Model Toward Molecular Structure Representation Learning of Fuels Abstract In this work, a Variational Autoencoder (VAE)-based data-driven modeling framework is developed with the overarching goal of enabling fuel design. The VAE model is trained on a large dataset with several chemical species to learn a compressed latent space molecular representation. Chemical structure in the form of Simplified Molecular Input Line Entry System (SMILES) string is fed as input, encoded into the VAE latent space, and decoded back to the SMILES string using Long Short-Term Memory (LSTM) networks. Complexities of the VAE training loss function are thoroughly examined by varying the weightage (beta (𝜷) parameter) of the latent space regularization term, thereby assessing the balance between reconstruction accuracy and validity, and focusing on both accurate molecular structure reconstruction and latent space consistency. Two different strategies for 𝜷 variation are evaluated: linear annealing and cyclic annealing. In addition, the impact of total correlation adjustment and hierarchical priors is also studied with regard to the balance between reconstruction fidelity and latent space regularization, and potential issues such as posterior collapse, over-regularization, and poor disentanglement of latent variables. Overall, the best performance of the model is achieved with hierarchical priors and incrementally increasing 𝜷 from 0 to a threshold value of 0.25 over 75 epochs. The generative VAE model can be readily coupled with Quantitative Structure–Property Relationship (QSPR) analysis to develop an integrated end-to-end framework for fuel-property prediction and molecular design of novel promising fuels. Autoencoder ; Representation (politics) ; Artificial intelligence ; Computer science ; Feature learning ; Deep learning ; Political science Petroleum Processing and Analysis
Authors: Qingchun Yang | Dongwen Rong | Zhao Wang | Qiwen Guo | Jian Guan | Yichun Dong | Huairong Zhou
Source: Industrial & Engineering Chemistry Research (article, 2025-10-06)
Field: Materials Science — Materials Chemistry

Generative Adversarial Network-Enhanced Heterogeneous Ensemble Learning for Interpretable Prediction of CO<sub>2</sub>-to-Methanol Catalyst Performance Accurate prediction of catalyst performance in CO2-to-methanol (CTM) conversion remains challenging due to the scarcity of experimental data and the complexity of feature interactions. To address these issues, this study proposes an interpretable gene-adversarial network-enhanced heterogeneous ensemble modeling (GAN-HEM) framework. It integrates four synergistic key functional modules: data augmentation, feature interaction analysis, ensemble modeling, and interpretable analysis. Comparing the variational autoencoder-based data augmentation method demonstrates that the GAN has significant advantages in maintaining the global consistency and local geometric features of the data manifold structure. After the multivariate evaluation of feature association within the hybrid data set, it was found that retaining all the identified CTM catalyst features is beneficial for enhancing the predictive accuracy and generalization ability of the prediction model. Therefore, this data set is further applied to develop various homogeneous and heterogeneous ensemble learning models of the CTM process. The hyperparameters of these models are automatically optimized using the Bayesian algorithm-based Optuna approach. Results indicated that the optimized heterogeneous ensemble learning architecture has the highest prediction accuracy (R2 = 0.9314 and RMSE = 0.2636), significantly outperforming homogeneous models through its capacity to capture complex nonlinear feature interactions. The Shapley additive explanation-based interpretability analysis identifies reaction temperature as the dominant feature (>39% contribution). Partial dependence plots reveal competitive selectivity: higher temperature favors CO (thermodynamic constraints), while increased pressure and heating rate enhance methanol selectivity (kinetic promotion). This framework accelerates CTM catalyst discovery and optimization, providing high-fidelity prediction and actionable design insights. Interpretability ; Ensemble learning ; Feature (linguistics) ; Ensemble forecasting ; Hyperparameter ; Generalization ; Bayesian probability ; Curse of dimensionality ; Pattern recognition (psychology) Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: Cong Zhang | Wei Gao | Chuanzhen Ma | Ruijie Zhang | Haiqing Yin | Yongchang Du
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Diffusion-Based Text-to-Image Generative Model for Predicting Materials Microstructure Evolutions Generative grammar ; Diffusion ; Microstructure ; Generative model ; Image (mathematics) ; Materials science ; Computer science ; Artificial intelligence ; Statistical physics ; Metallurgy ; Thermodynamics ; Physics Machine Learning in Materials Science
Authors: Jun Dong | Ruina Si | Menghan Zhang | Xuhai Pan | Ziqi Shang | Wen Duan Yan
Source: Fuel (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Characteristics of constant volume hydrogenation and construction of linear diffusion model for solid hydrogen storage materials: A case study of LaNi5 alloy Hydrogen storage ; Diffusion ; Volume (thermodynamics) ; Hydrogen ; Constant (computer programming) ; Reaction rate constant Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Catalysis and Hydrodesulfurization Studies