jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexandria L. Sohn | John G. Witherspoon | Robert C. Smart | David C. Muddiman
Source: Analytical and Bioanalytical Chemistry (article, 2025-02-03)
Field: Chemistry — Spectroscopy

Three-dimensional mass spectrometry imaging (3D MSI): incorporating top-hat IR-MALDESI and automatic z-axis correction Leveraging a depth profiling approach expands the chemical elucidation of mass spectrometry imaging techniques to another dimension. Three-dimensional MSI (3D MSI) reveals the distribution of analytes with greater anatomical detail to add another level of information in a biological study. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) has demonstrated utility for an ablation-based approach, enabling simplified sample preparation workflows and streamlined data processing pipelines compared to a serial-sectioning strategy. To improve 3D MSI on the IR-MALDESI platform, two technologies have been characterized in tandem for the intention of minimizing sampling bias: (1) a top-hat optical train and (2) a chromatic confocal probe (CA probe). While the modified optical train creates a square spot size to avoid a Gaussian ablation crater after the analysis of subsequent layers, the CA probe enables automatic z-axis correction (AzC) to maintain the laser's focus on the surface of the sample. The work herein demonstrates the integration and optimization of these technologies on mouse skin, motivated by the clear biological skin layers that result in differential lipid expression and subsequent detection. Results support that a laser energy of 1.3 mJ/burst with the top-hat optical train and a 120 µm step size in the X and Y dimensions presented a comparable depth resolution to previous studies at under 7 µm. Further, the optimized parameters were utilized on two biological replicates to evaluate method reproducibility where lipid annotations and their abundance were considered. Mass spectrometry ; Mass spectrometry imaging ; Chemistry ; Laser ; Analytical Chemistry (journal) ; Resolution (logic) ; Optics ; Computer science ; Artificial intelligence ; Chromatography Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Edan Lerner | Eran Bouchbinder
Source: Physical review. E (article, 2025-01-10)
Field: Materials Science — Ceramics and Composites

Testing the heterogeneous-elasticity theory for low-energy excitations in structural glasses Understanding the statistical mechanics of low-energy excitations in structural glasses has been the focus of extensive research efforts in the past decades due to their key roles in determining the low-temperature mechanical and transport properties of these intrinsically nonequilibrium materials. While it is established that glasses feature low-energy nonphononic excitations that follow a non-Debye vibrational density of states, we currently lack a well-founded theory of these fundamental objects and their vibrational spectra. A recent theory-that builds on the so-called heterogeneous-elasticity theory (HET) and its extensions-provides explicit predictions for the scaling of the low-frequency tail of the nonphononic spectrum of glasses, the localization properties of the vibrational modes that populate this tail, and its connections to glass formation history and to the form of the distribution of weak microscopic (interatomic) stresses. Here, we employ computer models of structural glasses to quantitatively test these predictions. Our findings do not support the HET's predictions regarding the nature and statistics of low-energy excitations in glasses, highlighting the need for additional theoretical developments. Elasticity (physics) ; Materials science ; Physics ; Statistical physics ; Composite material Glass properties and applications ; Material Dynamics and Properties ; Material Science and Thermodynamics
Authors: Wael Joudi | Reiner Windisch | Alberto Trentino | Diana Propst | Jacob Madsen | Toma Susi | Clemens Mangler | Kimmo Mustonen | Florian Libisch | Jani Kotakoski
Source: Physical Review Letters (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

Corrugation-Dominated Mechanical Softening of Defect-Engineered Graphene We measure the two-dimensional elastic modulus E^{2D} of atomically clean defect-engineered graphene with a known vacancy distribution and density in correlated ultrahigh vacuum experiments. The vacancies are introduced via low-energy (<200 eV) Ar ion irradiation, and the atomic structure is obtained via semiautonomous scanning transmission electron microscopy and image analysis. Based on atomic force microscopy nanoindentation measurements, a decrease of E^{2D} from 286 to 158 N/m is observed when measuring the same graphene membrane before and after introducing vacancies at a density of 1.0×10^{13} cm^{-2}. This decrease is significantly greater than what is predicted by most theoretical studies and in stark contrast to some measurements presented in the literature. With the assistance of atomistic simulations, we show that this softening is mostly due to corrugations caused by local strain at vacancies with two or more missing atoms, while the influence of single vacancies is negligible. We further demonstrate that the opposite effect can be measured when surface contamination is not removed before defect engineering. Graphene ; Softening ; Materials science ; Nanotechnology ; Condensed matter physics ; Composite material ; Physics Graphene research and applications ; Graphene and Nanomaterials Applications ; Carbon Nanotubes in Composites
Authors: Lincan Fang | Ru‐Wen Peng | Xia Liu | Gui‐Lin Zhuang
Source: Advanced Science (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Active Learning‐Assisted Exploration of [PO <sub>40</sub> Mo <sub>12</sub> ] <sup>3−</sup> for Alzheimer's Therapy Insights Alzheimer's disease (AD), involving amyloid-β (Aβ) aggregation, has potential therapeutic modulators in polyoxometalates (POMs) like [PMo<sub>12</sub>O<sub>40</sub>]<sup>3-</sup>. To clarify their inhibitory mechanisms, a multiscale computational strategy integrating active-learning Bayesian Optimization (BO) and density functional theory (DFT) is employed to explore low-energy configurations of isolated amino acids, [PMo<sub>12</sub>O<sub>40</sub>]<sup>3</sup> <sup>-</sup>-amino acid complexes, and [PMo<sub>12</sub>O<sub>40</sub>]<sup>3</sup> <sup>-</sup>-peptide systems. Hydrogen bonding and Coulombic repulsion dominate adsorption stability. Crucially, oxygen atoms in the [PMo<sub>12</sub>O<sub>40</sub>]<sup>3</sup> <sup>-</sup> cluster form multiple weak interactions (e.g., van der Waals, hydrophobic) with alkyl side-chain hydrogens in Aβ peptides. The synergistic effect of these weak interactions induces robust binding between the POM and peptide chains, stabilizing a tightly bound complex that sterically hinders Aβ self-assembly. Notably, simulations predict that the cluster preferentially targets hydrophobic amino acids with alkyl chains (valine, lysine, leucine, isoleucine) located in Aβ regions critical for aggregation-specifically, namely Aβ12, Aβ16-18, Aβ24, Aβ28, Aβ31-32, Aβ34-36, and Aβ39-41. These insights highlight the role of multivalent weak interactions in POM-mediated inhibition and identify key interfacial residues for therapeutic targeting. Chemistry ; van der Waals force ; Amino acid ; Steric effects ; Hydrogen bond ; Peptide ; Alkyl ; Lysine ; Hydrophobic effect ; Side chain ; Density functional theory ; Stereochemistry ; Combinatorial chemistry ; Computational chemistry ; Molecule ; Organic chemistry ; Biochemistry Polyoxometalates: Synthesis and Applications ; Nanocluster Synthesis and Applications ; Alzheimer's disease research and treatments
Authors: Zhong He | Dong Fang | Yougen Yi
Source: Photonics (article, 2025-02-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design of a Tunable Metamaterial Absorption Device with an Absorption Band Covering the Mid-Infrared Atmospheric Window We propose a highly efficient broadband tunable metamaterial infrared absorption device. The design is modeled using the three-dimensional finite element method for the absorption device. The results show that the absorption device captures over 90% of the light in the wavelength range from 6.10 μm to 17.42 μm. We utilize VO2’s phase change property to adjust the absorption device, allowing the average absorption level to vary between 20.61% and 94.88%. In this study, we analyze the electromagnetic field distribution of the absorption device at its peak absorption point and find that the high absorption is achieved through both surface plasmon resonance and Fabry–Perot cavity resonance. The structural parameters of the absorption device are fine-tuned through parameter scanning. By comparing our work with previous studies, we demonstrate the superior performance of our design. Additionally, we investigate the polarization angle and incident angle of the absorption device and show that it is highly insensitive to these factors. Importantly, the simple structure of our absorption device broadens its potential uses in photodetection, electromagnetic stealth, and sensing. Infrared window ; Infrared ; Absorption (acoustics) ; Materials science ; Window (computing) ; Metamaterial ; Optics ; Optoelectronics ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Radio Wave Propagation Studies
Authors: Eric Zhang | Didong Li
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2025-04-11)
Field: Chemistry — Analytical Chemistry

Contrastive Functional Principal Component Analysis As functional data assumes a central role in contemporary data analysis, the search for meaningful dimension reduction becomes critical due to its inherent infinite-dimensional structure. Traditional methods, such as Functional Principal Component Analysis (FPCA), adeptly explore the overarching structures within the functional data. However, these methods may not sufficiently identify low-dimensional representations that are specific or enriched in a foreground dataset (case or treatment group) relative to a background dataset (control group). This limitation becomes critical in scenarios where the foreground dataset, such as a specific treatment group in biomedical applications, contains unique patterns or trends that are not as pronounced in the background dataset. Addressing this gap, we propose Contrastive Functional Principal Component Analysis (CFPCA), a method designed to spotlight low-dimensional structures unique to or enriched in the foreground dataset relative to the background counterpart. We supplement our method with theoretical guarantees on CFPCA estimates supported by multiple simulations. Through a series of applications, CFPCA successfully identifies these foreground-specific structures, thereby revealing distinct patterns and trends that traditional FPCA overlooks. Principal component analysis ; Component (thermodynamics) ; Computer science ; Functional principal component analysis ; Linguistics ; Natural language processing ; Psychology ; Artificial intelligence ; Philosophy ; Physics Spectroscopy and Chemometric Analyses
Authors: E.A. Hodille | D. Piccinelli | M. Bertoglio | T. Loarer | Javier Dufour | J. Denis | E. Lascar | Guido Ciraolo | P. Tamain | Y. Ferro | E. Geulin | A. Gallo | P. Moreau | S. Vartanian | R. Bisson | B. Pégouriè | Y. Anquetin | J. Gaspar | Y. Corre | K. Dunnell | T. Wauters
Source: Nuclear Materials and Energy (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Modelling fuel retention in the W divertor during the D/H/D changeover experiment in WEST A fuelling changeover experiment from Deuterium (D) to Hydrogen (H), and back has been performed in WEST to study the retention and removal in metallic devices with actively cooled ITER-grade tungsten divertor. The present study reports trapping-diffusion modelling of H and D in the W divertor during this session. In this model approach, the divertor is represented by multiple (radially distributed) 1D simulations. The plasma exposure conditions are calculated with soledge3x-eirene , delivering the heat and particle flux for each of the divertor bins. The material model is parametrized by reproducing a thermal desorption spectrometry experiment and an isotope exchange experiment on polycristalline tungsten. The simulated outgassing flux during the post-pulse phase of the changeover pulses are converted to H 2 , HD and D 2 partial pressure in the WEST vacuum vessel. The calculated pressure is one order of magnitude below the experimental one as only the contribution from the divertor is taken into account in this analysis. An additional source of H 2 outgassing should be taken into account to recover the dynamics of the H 2 partial pressure, especially during D plasma phases. However, the dynamics of HD and D 2 pressure drop is similar in the simulations and experiments. Finally, the analysis of the divertor H/D inventory shows that the isotope exchange is efficient near the plasma exposed surface but is limited by the D/H migration towards the bulk. • Calculating a plasma background for the D/H/D changeover experiment in WEST. • Parametrization of the W wall model by simulating TDS and isotope exchange experiment. • Simulating H/D retention and outgassing from the W ITER-like WEST divertor. • Simulating the inter-pulses pressure drop and comparing with the experimental data Divertor ; Outgassing ; Tungsten ; Changeover ; Plasma ; Flux (metallurgy) ; Deuterium ; Heat flux ; Hydrogen Fusion materials and technologies ; Magnetic confinement fusion research ; Nuclear reactor physics and engineering
Authors: Chuanxian Luo | Ye Zhu | Zhuangzhuang Li | Peng Yu | Zhengqin Zhou | Yang Xu | Minfu Liao
Source: Energies (article, 2025-01-20)
Field: Chemistry — Analytical Chemistry

Diffusion Characteristics of Dissolved Gases in Oil Under Different Oil Flow Circulations The prediction of dissolved gas concentrations in oil can provide crucial data for the assessment of power transformer conditions and early fault diagnosis. Current simulations mainly focus on the generation and accumulation of characteristic gases, lacking a global perspective on gas diffusion and dissolution. This study simulates the characteristic gases produced by typical faults at different flow rates. Using ANSYS 2022 R1 simulation software, a gas–liquid two-phase model is established to simulate the flow and diffusion of characteristic gases under fault conditions. Additionally, a fault-simulation gas production test platform was built based on a ±400 kV actual converter transformer. The experimental data show good consistency with the simulation trends. The results indicate that the diffusion of dissolved gases in oil is significantly affected by the oil flow velocity. At higher flow rates, the characteristic gases primarily move within the oil tank along with the oil circulation, leading to a faster rate of gas dissolution in oil and a shorter time to reach equilibrium within the tank. At lower flow rates, the diffusion of characteristic gases depends not only on oil flow circulation but also on self-diffusion driven by concentration gradients, resulting in a nonlinear change in gas concentration across various monitoring points. Diffusion ; Petroleum engineering ; Environmental science ; Flow (mathematics) ; Fossil fuel ; Environmental chemistry ; Chemistry ; Geology ; Thermodynamics ; Mechanics ; Organic chemistry ; Physics Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Arne Elofsson | Rachael C. Kretsch | Marcin Magnus | G.T. Montelione
Source: Proteins Structure Function and Bioinformatics (article, 2025-04-30)
Field: Materials Science — Materials Chemistry

Engaging the Community: CASP Special Interest Groups The Critical Assessment of Structure Prediction (CASP) brings together a diverse group of scientists, from deep learning experts to NMR specialists, all aimed at developing accurate prediction algorithms that can effectively characterize the structural aspects of biomolecules relevant to their functions. Engagement within the CASP community has traditionally been limited to the prediction season and the conference, with limited discourse in the 1.5 years between CASP seasons. CASP special interest groups (SIGs) were established in 2023 to encourage continuous dialogue within the community. The online seminar series has drawn global participation from across disciplines and career stages. This has facilitated cross-disciplinary discussions fostering collaborations. The archives of these seminars have become a vital learning tool for newcomers to the field, lowering the barrier to entry. CASP ; Data science ; Engineering ethics ; Field (mathematics) ; Computer science ; Protein structure prediction ; Engineering ; Biology ; Protein structure Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Genetics, Bioinformatics, and Biomedical Research
Authors: Simona Vasile | J L Arellano | Cosmin Copot | Ahmad Osman | Alexandra De Raeve
Source: Materials (article, 2025-04-18)
Field: Materials Science — Polymers and Plastics

Thermal and Moisture Management Properties of Knitted Fabrics for Skin-Contact Workwear Thermal and moisture properties of the textile materials worn in close contact with the skin greatly contribute to the comfort of the workwear and of the personal protective clothing (PPC) assemblies they are part of. This study examines in depth the thermoregulatory properties of eighteen knitted fabrics used in polo shirts and T-shirts, which function as thermal underwear, standard workwear compliant with various regulations, or as base layers in PPC systems. Most of the fabrics specifically engineered for heat protection demonstrated superior air permeability (ranging from 700 to 1200 mm/s) and efficient moisture management (OMMC 0.5-0.7). Their drying time varied between 12 and 18 min, comparable to most commodity fibre blend fabrics investigated. Generally, the heat-protective fabrics were heavier and exhibited greater thermal and vapour resistance. However, despite minor variations in predicted thermal comfort, seventeen of the fabrics were classified in the same cluster. These findings offer valuable insights into the thermal and moisture management properties of knitted fabrics with various levels of protection, and the correlations found between their thermoregulatory and physical properties, such as mass and thickness, provide guidance for the development of innovative knitted materials for workwear that enhance wearer comfort. Materials science ; Composite material ; Moisture ; Thermal Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Ergonomics and Musculoskeletal Disorders
Authors: Muhammad Shoaib Arif | Ateeq Ur Rehman | Asad Ejaz
Source: Emerging Science Journal (article, 2025-04-01)
Field: Chemistry — Analytical Chemistry

Analysis of Four-Species Diffusive and Non-Diffusive Food Chains Using Artificial Neural Networking This study uncovers the findings of a four-species food chain model, focusing on its equilibrium points, global stability, and population dynamics. Through rigorous mathematical analysis, we identify the equilibrium points of the model and investigate the global stability of the coexistence equilibrium point. We present the existence conditions for all equilibrium points and assess the stability characteristics of the coexistence fixed point. Time series solutions offer a captivating perspective on the dynamic behavior of a system. Our investigation into the effects of parameters provides the fluctuations in population density, with specific parameters exerting significant influence as a result of the random movement of linked species. Understanding the need for taking account of diffusion-dominated situations, the diffusive version of the model is developed and analyzed. By constructing a numerical system with three-time levels (n-1, n, and n+1), its stability can potentially be tested thoroughly using the Von Neumann stability criterion. Numerical simulations and graphs depict the system's dynamic interaction. We also examine how diffusion coefficients affect population density, creating remarkable charts that show interactive species relationships. We also identify exciting bifurcation occurrences in the system, which helps us comprehend its complex dynamics. Predator-prey systems can be studied using Artificial Neural Networks (ANNs) to handle complexity, discover patterns, and predict future dynamics. ANNs can predict population dynamics and assess various parameters by analyzing prior data. Their adaptability lets them improve forecasts over time, improving management methods and ecosystem balance. We use ANN methods to see how specific parameters affect interacting species population dynamics. Doi: 10.28991/ESJ-2025-09-02-011 Full Text: PDF Artificial neural network ; Food chain ; Biological system ; Statistical physics ; Artificial intelligence ; Computer science ; Biology ; Physics ; Ecology Dye analysis and toxicity ; Global Trade and Competitiveness
Authors: Ludwig Sinn | Z. Wang | Claudia Alvarez | Anjali Chelur | Ihor Batruch | Patrick Pribil | Daniela Ludwig | Stephen Tate | José Castro‐Perez | Christoph B. Messner | Vadim Demichev | Markus Ralser
Source: PROTEOMICS (article, 2025-12-26)
Field: Chemistry — Spectroscopy

Performance Characteristics of Zeno Trap Scanning DIA for Sensitive and Quantitative Proteomics at High Throughput Proteomic experiments, particularly those addressing dynamic proteome properties, time series, or genetic diversity, require the analysis of large sample numbers. Despite significant advancements in proteomic technologies in recent years, further improvements are needed to accelerate measurement and enhance proteome coverage and quantitative performance. Previously, we demonstrated that incorporating a scanning MS2 dimension into data-independent acquisition (DIA) methods (Scanning SWATH, or more generally scanning DIA), but also ion trapping, improves analytical depth and quantitative performance, especially in proteomic methods using fast chromatography. Here, we evaluate the scanning DIA approach combined with ion trapping via the Zeno trap in a method termed ZT Scan DIA, using a ZenoTOF 7600+ instrument (SCIEX). Applying this method to established proteome standards across various analytical setups, enabling intermediate to high sample throughput, we observed a 30%-40% increase in identified precursors. This enhancement extended to overall protein identification and precise quantification. Furthermore, ZT Scan DIA effectively eliminated quantitative bias, as demonstrated by its ability to deconvolute proteomes in multi-species mixtures. We propose that ZT Scan DIA can be used for a broad range of applications in proteomics, particularly in studies requiring high quantitative precision with low sample input and high-throughput workflows. Proteome ; Quantitative proteomics ; Ion trap ; Proteomics ; Sample (material) ; Mass spectrometry ; Throughput ; Ion trapping ; Sample preparation Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies